HIStalk Interviews Charlie Harp, CEO, Clinical Architecture

Charlie Harp is CEO of Clinical Architecture of Carmel, IN.

Tell me about yourself and the company.

I’m a developer by training. I’ve been building systems in healthcare for about 35 years. Back in 2007, after working for a bunch of different companies, I started Clinical Architecture to focus on the plumbing of healthcare, such as semantic normalization, data quality, and gaining insights by looking at patient data.

The industry has more technical pipes available to exchange data, but have we equally advanced terminology and semantic issues?

In the last few years, people have become a little bit more sophisticated in how they share data. USCDI has driven some folks, through the Cures Act, to at least try to share more data. The guidelines we have are still a little fuzzy in terms of being more guidelines than rules. We have made some progress, but we are still dealing with people that might have access to the data through something like data exchange. I think TEFCA  is going to continue this, but I still think there’s a lot of hesitancy to accept that data when you get it.

The last time we talked, you said that providers don’t trust each other’s data, and that one provider doesn’t have much incentive to clean up their own data that they have already used for someone else’s benefit. Has that situation improved?

A little bit. We started doing a data quality survey last February. People generally did not think very much of their own data quality. Most of them — depending upon the domain, whether it’s drugs or labs — had some level of confidence, but they didn’t have high confidence in the quality of that data. The only thing they had less confidence in was the quality of other people’s data, which I thought was interesting.

The problem we have in healthcare today is that we gather information as a byproduct of the process of providing care. Providers rely heavily on their notes to go from one patient encounter to another. They fill in the structured data because they have to.

We have this illusion on the analytics side of healthcare that the structured data is of high quality. When we go to share the data, most of these systems — whether it’s Epic, Cerner, Meditech, or whatever — are still using dictionaries that were developed for that EMR, with code systems that are specific to the EMR. They still have to be normalized on the outbound.

The challenge with people sharing data out, especially if it’s a regulatory requirement, is that it’s a “letter of the law” as opposed to “the spirit of the law” type of engagement. The data is leaving, and people tend not to care as much about the data that’s leaving as they do about the data that’s coming in. The problem with the data coming in is that, to the people who sent it, it was data leaving, so it wasn’t as important to them.

Do those clinicians who don’t trust their own data at least have confidence in the subset that they need to treat patients, or do they create their own notes or records?

It’s a combination of the time famine that providers have. They don’t have a lot of time. A handful are aware and plugged into what’s happening with health informatics and interoperability, but a lot of them in the trenches are just focused on how to provide the best care while complying with the things they are being asked to do by their organization. A lot of them, at least the ones that I talk to, tend to still rely heavily on their unstructured notes to go from encounter to encounter.

A few years ago, we looked at the structured data and did inferencing to find patients who were undocumented diabetics, patients who had no mention of it in the structured medical record. We looked for other indicators, like the fact that they had a hemoglobin A1C that was out of whack, or they were taking something like insulin or metformin. In six months, we found 3,600 undocumented diabetics. When the folks we were working with presented that finding to providers, the feedback was pretty universal — I know those patients are diabetic, that’s why I gave them metformin.

The problem is that there’s a disconnect between the provider, who is legitimately just trying to take care of people, and the unintended consequence of not having the structured data in the system. That means that your quality measures are out of whack, your patient management software is not scheduling foot exams. There’s still a disconnect between why you put in the structured data in the first place and all the downstream systems that consume that. Analytics, machine learning, and AI, all these things that we want to embrace and leverage in healthcare, depend on the structured data being there and being correct. We are pretty far off from that, unfortunately.

Does AI offer opportunities to structure that data using free text notes or audio recordings of encounters?

We have done a lot with NLP and also evaluated what’s going on with large language models. The problem in healthcare is that when it comes to data, it always falls back to trust.

If I could wave my magic wand and fix healthcare, I think what I would change is the way that we collect the data, so that we are collecting structured data without turning the provider into a terminologist to make that work. The problem we have is that providers don’t want to go to a list and pick something. They want to be able to articulate something in a way that is natural and organic for them, and then get it back in a way that’s natural or organic. We’ve had two worlds, one where you create a note and the other where you put things into a list.

I think the real answer is finding a way where the provider gets what they want. They say something in a way that’s granular and organic. We capture in a way that preserves the granularity of that information in high resolution, and can leverage that from an analytics perspective. When the provider wants to see the data, we can deliver it in a way that’s organic to them instead of them looking at a list and reconciling things. We’re a little bit off from that.

The problem with what we are doing now is that we are trying to find an easy way out. We’re saying, let’s just take the note and use NLP, a large language model, or something else to read the note and turn the note into something structured. You can do that, and we have had some success when it comes to high-certainty type data like pulling ejection fraction out of a procedure note or looking across a complete patient record and coming back with a sense of the patient’s diabetes because I found all these references that I can correlate to that. But you still run into the problem of, how can I trust that?

When you look at all the things that are happening in the industry now with AI, large language models, and NLP, there’s a lot of talk about transparency. In the past, when people have tried to do things in healthcare with these types of approaches using NLP or AI, it hasn’t been successful. The machine works great 60% of the time, and then 40% of the time it does something horrifically wrong. That comes back to this idea of trust. If you are taking care of somebody and their life is in your hands and the machine just happens to pick that day to follow the wrong probabilistic pathway, that’s challenging in healthcare.

Thinking back to providers not trusting their own data, is that a vague impression or is it measurable? What could they measure to assess or improve data quality?

When I’m working with clients, I sometimes ask them this question, so I’ll ask you. When it comes to healthcare data for an individual patient, who is responsible for the quality, accuracy, and integrity of that one patient’s data, regardless of where it is?

Some would say the patient, although that’s not a reasonable expectation for all types of data.

The problem is that patients aren’t really trusted to do that. You can fill out a form, hand it to somebody, and they’ll type it in, but rarely is a patient trusted to own and articulate, “Here’s my health situation.” It usually has to be vetted by some kind of clinical person.

That’s fine, but it goes back to this root problem that nobody is responsible. There is no data steward for an individual patient’s health record. When you talk about how you trust the data, the fact that I can take one patient and look across multiple venues of care and see different information. They don’t really trust each other and where their data is coming from. They don’t know whether that ICD-10 code was added for billing purposes or added for clinical purposes.

The problem we have in healthcare is that we don’t have a mechanism that allows us to objectively and quantitatively look at the data and say that the quality is good or bad. We are working with other organizations to do this taxonomy for healthcare data quality, because I think that we should be able to look at patient data in an abstract way and say, is the quality of this data good? Is there duplicate stuff? Is there old stuff? Is there stuff that’s clinically impossible? Are there things in the medical record that contradict themselves?

How can we automate the evaluation of that semantic interoperability so that you don’t need a sweatshop full of clinical people looking at 5 million patient records? How do you build something that can objectively, with some type of deterministic AI, evaluate an individual patient and any data that comes in for them to say, yes, this all makes sense. It looks real, and I just noticed that there’s no mention of this patient’s diabetes, whether you’re looking at unstructured notes and pulling it out.

At the very least, you should pull the data, check it against the integrity of the rest of the medical record, and say, yes, the fact that the note says they are diabetic resonates with the fact that they’ve got a funky fasting blood sugar and they’re taking these three medications that are indicated for diabetes. Let’s go ahead and suggest that they add diabetes to their official structured medical record so that we can take advantage of that. All these things that only look at the structured medical record and retain the evidence of where that came from. Those are some things that we could do to improve the level of trust and the reliability of the data.

My big fear is that we start to roll out some of these more sophisticated things that could be beneficial, but because the data quality is bad, we fumble the results early on and these things fail, and because we applied them before the data quality was ready, people don’t have confidence. You only have one opportunity to be credible. You come in with this new technology and say, “This is going to save lives. This is going to do great things.” But because the data that we are feeding it is bad, it is very possible and probable that the results of what it comes up with will be likewise bad. We will flip the bozo bit, as they used to say, on that thing. Then later, when we fix the data quality, we say, “No, we tried that and it didn’t work.” But maybe it would work if we fed good quality data.

What is the oversight structure and mechanism of reviewing the longitudinal patient record from multiple providers and identifying missing or conflicting data? Then, going back to the data source and either asking them to fix their problem or perhaps excluding their data as being unreliable?

The first place is the pipes. Look at what’s happening with TEFCA and QHINs. Let’s say the QHINs turn on their pipes and people start streaming data from Point A to Point B for every patient. The first thing we need to do is, somewhere in that pipe, we need to have something that looks at the message. Is the message right? Does the data look fundamentally correct? Not clinically correct, but is a valid code in the value set? Let’s say it’s an RXNorm code. Does the name match what RxNorm says that code stands for? So the first thing you do is evaluate someone in the network to determine whether they are a good data provider.

If they’re not a good data provider, you can’t really remediate data quality in flight. You have to go back to the source and say, you’re not a good data provider. This is what our taxonomy is focused on. By identifying the nature of the quality failure, you can go back and say, you’re putting the decimal place in the wrong spot on your lab results. You are not using a valid RXNorm code set. Your maps are bad. Whatever the feedback is.

The first thing we need to do is to make sure that the people that are sharing data from their systems are good members of society who care about the data they are sending out and are making sure that the quality is good. QHINs are going to be in a great position to evaluate the data in flight at a basic level and say, OK, the data that you are sending looks clean, looks good, and has good intrinsic quality. That’s the first step, because that’s where you stop bad data from getting out.

We also need to do a better job of knowing where data’s coming from originally so that we can stop duplication. We worked with a partner who gave me a bunch of data to evaluate, data that was coming from a bunch of different sources. In a couple of million records, there were about 750,000 duplicates, the exact same lab result done at the exact same time. Because of the way the data was shared in some of these older formats, you had no idea that that was the same data. It just looked like this patient had 64 lab results on the same day at the same time.

That’s the other thing, if we want to trust data, we need to know where it originally came from, especially as we start sharing data across an entire network of participants.

The last thing is you need is a way where we are landing it or looking at the data in our own system, saying, does it look right for every condition that I have? Do I have a treatment for every drug that’s in their profile? Do I know why they are taking that drug? This goes back to what you are talking about when it comes to oversight. Within any repository of patient data, perhaps a large IDN doing analytics or population health on your patients, we need to have mechanisms that can identify issues in the patient. Data can alert a human operator. Let’s call them a data steward. The data steward can inform the systems that they are connected with on how to remediate the data.

There needs to be oversight. The trick is, how do we have enough automation in place so that instead of a human looking at 5 million patients, automation is looking at 5 million patients for things that are a concern, and streamlining the resolution of those things? Because it’s easy for a human to be presented with something and say, “Yeah, that looks right,” as opposed to humans poring over data looking for something. That’s why when we do semantic normalization, our software does like 85% of the work, where it tries to search for the right target and it suggests the target. A human can take two seconds to look at the target and say, “That’s right.” We need to get to the same place when it comes to patient data.

It’s one of those things where the idea of having people whose job it is to review issues that come up with patient data and resolve it at a patient level might seem a little daunting, but the problem is, that’s the only way we can fix it. You have to fix it at the atomic level to have the entire ecosystem be of high quality. There’s no way to do it at a macro level. You have to do it at an individual patient level.

What factors will be important to the company and the industry in the next few years?

For us to use artificial intelligence and some of these other things that we are coming up with in a meaningful way, we are going to have to move away from pre-coordinated terminologies as how we collect data for patients. We’re going to store patient information in a much more granular graph style, so that both software and people can make better use of it. Right now, everything we do with the terminologies and practices that we use today create these big pixels of information that limit our ability to do sophisticated reasoning over that data, whether it’s for research purposes or for decision support purposes. We’re going to have to dial up the resolution to get to where we want to be in terms of software providing meaningful assistance to people that are providing care.

HIStalk Interviews Sean Cassidy, CEO, Lucem Health

Sean Cassidy is co-founder and CEO of Lucem Health of Raleigh, NC.

Tell me about yourself and the company.

I’m an old enterprise software guy, going back to the early 1990s. I’ve been in digital health since about 2004. I’ve worked mostly on big platform type products sold to healthcare providers, such as data integration, data management, and analytics.

The idea for Lucem Health originated within Mayo Clinic a little over three years ago, Mayo Clinic, like a lot of academically oriented healthcare providers that were doing research in AI and machine learning, was struggling to figure out how to deploy AI at scale so that they could deliver real value and impact in the clinical workflow. They went looking for platforms that could be Swiss army knives, for lack of a better term, for the deployment of a broad set of clinical AI type solutions. They didn’t really find anything, so they made the decision to fund the start of a company which later became Lucem Health.

How do health system executives decide when to jump in or experiment with AI among the daily flurry of AI announcements or research results?

It’s important to have a perspective on what the potential value and impact of AI could be to your organization. But primarily, I would orient yourself — as health system leaders are doing these days – to the real problems that are vexing you; for which solutions exist in the market that are novel, unique, and different from what you have seen before; and that can be deployed against those problems and be force multipliers. When you are inquisitive about that, you will  find that there is AI at the center of a lot of those solutions.

However, it’s important to note, and this is our perspective, that AI is not a panacea. An algorithm is not a solution. It may deliver a strong and accurate predictive output, but if it can’t be delivered to stakeholders in the right context, right place, and right time, then it’s all for naught. It can’t deliver any meaningful value and it can’t solve for those problems that a healthcare provider may be facing.

Are health systems looking for a turnkey solution to address one specific problem or do they want tools and assistance that can help them develop their own expertise?

I actually think that they are looking for both. It depends on the context.

There is a certain class of provider, large providers that do a lot of research and development in a variety of areas, who are struggling with the bench-to-bedside problem. Their problem is not necessarily a technology-oriented problem, but it turns out that they need technology to solve for that.

But let’s take a provider organization that doesn’t have a data science team and is not doing research and development. They see, as they are exploring the market, that there is value in opportunity and solutions that may have AI at the center. They are telling us that they would prefer not to invest in point solutions or a fragmented set of underlying technology platforms, but would rather buy or deploy on a consistent and uniform infrastructure.

How hard is it for connect those external AI systems to their underlying data and work around issues with interoperability and terminology?

As your readers know, in healthcare, that’s a pervasive, it’s always an issue that there is heterogeneity in the operating environment, but the data can be represented in different ways and can be semantically different. AI solutions are no different from any other solution that is trying to leverage data that already exists, whether it comes from an EMR or some other modality. There is a curation process that has to occur in order to optimize the data so it can be served to the AI and provide the appropriate context to a broader AI solution so that it can be delivered effectively.

What are the steps involved in talking to a health system that has defined a problem and thinks AI can help solve it?

Leaving aside the business case for deploying it, what we find today is that providers are looking for a clinical and financial yield, an ROI, that is significant. Otherwise, it’s difficult to gin up yet another project to try to optimize data infrastructure that they have already invested in. But in terms of connecting into infrastructure, we deploy what I would define as narrow AI. These are not large language models or generative AI. These are very specific hammers for very specific nails. Their data requirements are not terribly broad. They are fairly narrow. There are, for example, plenty of ECG-based models that are powerful in terms of being able to detect cardiovascular disease. Generally speaking, virtually no optimization and curation is required on that signals data to be able to feed it into most of the models.

There are a number of really interesting models that are using EMR data. Fairly simple stuff, like demographic data plus usually one or two lab values that can identify risk of certain kinds of diseases. Again, there’s a little bit of work that has to be done to do some curation on that data, but because the dataset is relatively narrow, it’s straightforward.

If they have FHIR connectivity available in their  EMR, which they should have, we can get those EMR-based models up and running quickly without a lot of effort. FHIR as a standard is starting to take hold, and it’s incredibly useful. It’s a bigger lift if we have to go into their PACS and pull CT images. It’s a bigger lift if we have to go to Philips or GE to get enterprise class 12-lead ECGs, but it’s doable. We are finding that health system integration teams have sophisticated integration tools and are really good at being able to tap into the data that’s needed to make AI sing and dance in the real world.

A new survey found that health system executives believe that AI is ready to address some of their issues, yet few of them have developed an AI plan. Is that because they are looking at specific solutions rather than AI in general?

There is an impedance mismatch. I’ve seen those surveys too. When you poll forward-thinking CIOs, CMIOs, and clinical leaders, they are familiar with the opportunity for AI. Of course they to say that they believe that there’s value here, but when it comes down to brass tacks to actually investing in deploying these solutions, their mindset shifts away from the notion of AI as a technical capability to finding solutions that they can deploy. The tip of the spear for them is the solution and the value of the solution over the technology. We think a very high percentage of people feel that way.

How do you position an AI company in a constantly changing environment? 

We talk about that a lot. We have to be solution oriented and solution focused. When we package and position what we are taking to market, we are trying to confront real-world problems. The fact that AI is part of the equation is, to a large degree, incidental. We are in conversations with healthcare providers where AI barely comes up. We’re talking about how to identify undiagnosed diabetics, how to get people into the clinic for overdue screening colonoscopies, and how you deal with undiagnosed or undetected breast cancer from mammographies.

The other thing that we talk about a lot is that the solution matters. What we mean by solution is not just the ability to connect with data and to deliver a novel, powerful insight into a clinical workflow. How do you set up infrastructure? How are you capturing telemetry or instrumenting the process so that you can understand whether the thing is delivering the value and impact you expect? Do you have facilities in place to actually make improvements to that over time?

Every provider organization is different. They are different culturally. They are different in terms of the patients they see. They are different in terms of their affinity for technology and their ability to change or not change workflows. All of those things matter. A solution that can be highly optimal at XYZ health system may not be working very well at ABC. Why is that? How do we detect and understand that? How do we make the necessary adjustments to ensure that it does ultimately deliver value? Everything that I just said puts the solution at the front of the conversation and puts the technology in the background.

How are health systems involving physicians as they consider the potential of AI?

I have two thoughts on that. One is that we are trying to frame the conversation in such a way — and this is not a bromide, this is truth — that clinicians feel at the end of it that it’s going to help them practice better medicine. We think it is important that they are left with that impression, and that is their ultimate reality.

The other thing is that we believe that changing clinical workflows, trying to change how clinicians are using EMRs when they are already frustrated by their EMRs, is not the way to go. We are trying to bring solutions into clinical workflows that deliver impact that don’t require any modifications to workflows. They may make those workflows more efficient, but certainly won’t make them inefficient. They won’t pop up more alerts or fill their inboxes with more junk. We help them practice better medicine and practice it the way they have been doing it in the past without requiring radical change.

Your website says that you don’t make algorithms or applications. What technologies to bring to the table?

I want to be clear that our solutions have AI at the center, so we work with AI innovators. The best analogy that I can come up with is that we are car makers, not engine makers. We work with engine makers, and these engines are immensely powerful. They have a lot of horsepower. They can spin really fast. They can deliver a lot of impact. But if they are not dropped into a car, they can’t get very far down the road and actually do any good in the world. We have optimized our assembly line to make it easy for us to build a lot of different kinds of cars. We can build an ECG-based solution, an EMR-based solution, an image-based solution, and everything in between. That’s core to what we do, and it’s like breathing to us.

We use the phrase “AI solution ops” to distinguish from MLOps, which is a term that is fairly well established in the industry. What we do is relatively straightforward, and I’ve hinted at it already. So what is the car? The car is the ability to connect to a broad set of data sources, specifically to support the needs of AI. We are not a general purpose integration platform. That’s not what we are trying to build. To provide mechanisms to support the deployment of many kinds of AI, a broad set of capabilities to interpret the output of the AI in a way that’s human interpretable, human readable, and human understandable, and then to provide robust options, including an application framework for delivering AI insights to clinicians, clinical staff, or to supporting staff so they can benefit from those insights. Underneath the covers, all of that measurement of value and impact, continuous improvement stuff that I mentioned. All of that is the car for us. 

What are your goals for the company over the next few years?

Not surprisingly, like a lot of companies at our stage, we are focused on growth. We think that we have established a product-market fit. We think that we have established that what we are offering to the market is something that the market actually wants. We know who our buying personas are and we know what market segments we should be engaging with. We are focused on trying to get more and more customers to hear that story and to sign up with us and to deploy our solutions.

When we look three to four years out, our goal is to have a broad portfolio of powerful, impactful, practical, and responsible AI solutions that are confronting the hard clinical problems that are facing providers. Today we are talking about cancers, chronic diseases, and areas where there are existing solutions that have not necessarily delivered the goods that providers expected when they made original investments.

The other piece to the business is that we want to be working with a significant number of large provider organizations who are trying to do AI research and deliver AI impact to the front lines of care within their organizations and potentially even beyond, but are struggling with the bench-to-bedside problem. That’s where we intend to be.

HIStalk Interviews Patty Riskind, CEO, Orbita

Patty Riskind, MBA is CEO of Orbita of Boston, MA.

Tell me about yourself and the company.

I’ve worked in healthcare data analytics tech for over 30 years, predominantly in the patient engagement and employee engagement side of the industry. I founded a company that was the first electronic survey company in healthcare. At the same time SurveyMonkey was starting, I started a company called PatientImpact, which I ended up selling to Press Ganey, which is the largest patient satisfaction survey company in the US. From there I worked for Qualtrics, which is an experience management company, as the global head of healthcare. I created their healthcare division. 

From there, I joined Orbita. Orbita is a conversational AI company. We help use technology to automate workflows. We strive to be the human side, and use conversational AI and generative AI to make it easy for patients to navigate through the healthcare system and alleviate the administrative burden on employees and clinicians. So I moved from measurement of the patient experience now to actually trying to impact the patient experience by making it easier to do business in healthcare.

What components of the digital front door have provided the strongest return on investment, and how do you expect to see that change in the next few years?

The biggest return on investment using a digital front door is, in many cases, twofold. One, if someone goes to a website, they are looking for something. If you make it easy for them to find it, then you have improved that patient experience. Often what people are looking for is ways to schedule an appointment. If you can automate the process of identifying where to go, who to see, and then actually help them execute on that in terms of scheduling, then you are contributing to a better patient experience. You are also driving revenue. That’s an easy measurement  to understand the impact that a digital front door can have.

In addition, digital front doors can reduce call volume to call centers. Many health system and medical groups are still dependent on a patient making a phone call. If you can reduce the number of calls that are coming in, call center and front desk staff are less inundated with handling basic questions related to scheduling, where to park, or or how to prepare for the visit.

I see return on investment coming from both a cost reduction as well as revenue generation and reducing the administrative burden of staff. The cost of labor continues to be high in healthcare, and the number of the people that are needed to work in things like call centers as well as administrative roles is still a challenge for many health systems and medical groups.

Search engine companies are trying to figure out when users prefer traditional search versus AI chat. What are the use cases for provider website search and the tools that support it?

We have actually married search with a chat experience. The most common search of hospital and medical group websites are keyword searches. We have married the search capabilities with a conversation. If someone types in, “I’m looking for a foot doctor near me,” we can pick that up and then ask related questions. Are you referring to a podiatrist? When you say “near me,” what is your ZIP code or address? How old are you, so we know if this is specific to pediatric or a geriatric? What kind of physician are you looking for?

We know that about 43% of people who go to a website start with the search bar. We narrow that search by walking folks through the steps to help them find what they are looking for. There’s a real opportunity to take the chatbot out of the bottom right hand corner and instead place it anywhere on a website, including the search bar.

In the pre-cellphone days, consumers would do anything to avoid navigating a phone tree and would instead press random buttons hoping to be transferred to a human. Now they will expend equal energy to avoid talking to a human in favor of pressing phone buttons. How do health systems address those consumer preferences?

You are absolutely right. The Gen Z population doesn’t want to talk on the phone. I’m a Gen Xer and I don’t want to talk on the phone either. People are more comfortable interacting with a chatbot these days, especially with the rise of ChatGPT. There’s a greater understanding and a greater tolerance for interacting with an automated attendant.

But you always need an escape hatch. You always need the ability to escalate to a live person. You can start a digital conversation, but the bot should be smart enough to identify when someone needs to talk to a human being. Either they’re getting frustrated or they are asking questions that require a more hands-on human who can answer the questions or can help that individual.

We build in an escalation to a live agent as part of everything that we do. That’s our recommendation for customers. There’s still the need for human beings. Ideally we remove the mundane or the repetitive type questions that someone s in a call center might get, and instead they get the more complex questions or can talk to those patients who really need to talk to a human being.

One of the advantages we bring, in addition to a digital front door or a Q&A chatbot, is that we have a communications hub that allows a call center agent to have a  digital conversation. They can manage up to six conversations at the same time. A consumer might start with a chatbot type of experience and then escalate to a live agent. That live agent gets the transcript of what has been discussed up to that point, and then they can take it on. The content or the knowledge bases that we use to power our automated assistance can be used by the call center agent to answer questions. So when it comes to onboarding new staff and training them, we provide an elegant way to get folks up to speed fast so that they can start taking phone calls. They have the best of both worlds in being able to use technology to deflect those routine phone calls, but also allowing those agents to leverage the technology so that they can answer questions when they are engaging with a person.

To what extent are providers using, or planning to use, AI-powered technologies to triage calls?

They are planning on using it more, because call centers are inundated and there’s not enough staff. They can analyze the types of calls they are getting and deflect the routine questions. We’ve heard that 80% of the calls that come in involve where to park or how to schedule an appointment. It’s the same questions over and over. More providers are going to take advantage of automating the routine questions so that they can leverage the staff that they have in a more effective way.

How are customers using your CallDeflectAI product?

CallDeflectAI does exactly what we have been talking about. Patients or consumers can find information by going to the website and interacting with a chatbot versus having to talk to a human by making that phone call. CallDeflect AI uses generative AI to scrape everything that is on the customer’s website or in manuals. Whatever data that they can provide that will be relevant to what someone will call about or want to talk about. We can ingest that incredibly quickly. 

Within a couple of hours, we can stand up a Q&A type chatbot that our client places on their website. It then directs patients, or their call center or automated attendant can say, “Can I transfer you to our website or our digital assistant to answer whatever questions you may have?” It drives folks to find answers in a more convenient and helpful way versus staying on hold or taking up the time of an agent that could be spent differently. 

CallDeflectAI has been exciting for us because we have been using generative AI for some time, but this allows us to put it to work really, really quickly. When your call center is inundated, it provides an elegant way to deflect those phone calls.

As health systems expand into multiple states with dozens of hospitals and hundreds of locations, how do they use technology to help patients find the nearest location or first available appointment while enhancing the corporate brand?

You would think they would be taking advantage of it, but there’s relatively slow adoption, partly because healthcare doesn’t always move quickly in terms of adopting new tech. There is fear and concern as it relates to security, and especially with ChatGPT’s hallucinations, there’s a lot of paranoia.

We host on a private cloud. We only reference content that has been validated and authenticated. We are HIPAA and SOC 2 in terms of privacy and security. We can reassure that the content that is referenced is correct and that everything is hosted in a secure and private way.

They then can take advantage of their content that they trust and we can customize to reflect their local environment while maintaining the brand, both from look and feel as well as the content itself. We can reinforce the brand that that organization represents, but also allow for access to local doctors, the local urgent care, or the local resources in a specific community that relate to that individual location.

How has the digital health market changed and what is coming next?

We saw things slow down pretty significantly in 2022, in large part because providers had negative operating margins and the mantra was cut costs and don’t invest. But I’m seeing that loosen up. I’m seeing more curiosity about new tech, as well as more of an appetite to make specific investments. I think we are turning a corner and the market is going to continue to improve over the course of 2023 and into 2024.

It probably will take until 2025 before we start to see anything coming even close to the investment environment that we saw in 2021. We may never get to the go-go days of 2021, where companies that were not making any money got 20 times revenue type valuations. I am not sure we will see that for some time.

Orbita’s goal is to continue to leverage technology, but focus on the problems that we are solving for our customers. We will create solutions and use cases that help address the needs that our clients have, whether that relates to growing revenue or managing costs, leveraging ways to extend the capabilities of their workforce by leveraging automation and technology. We are focused on growing and listening to our customers to meet their needs. Hopefully the market will respond in kind.

HIStalk Interviews Lyle Berkowitz, MD, CEO, KeyCare

Lyle Berkowitz, MD is CEO of KeyCare of Chicago, IL.

Tell me about yourself and the company.

I’m a primary care physician. I spent 20-plus years at Northwestern Medicine as a practicing primary care doctor and as a system executive for a decade and in the classic IT informatics area in the next decade. I set up one of the earlier innovation programs. The whole time, I often had some involvement with telehealth, population health, and digital health in a variety of ways. I also often did some side hustles. I was working in entrepreneurial areas in a variety of ways as medical director and chief medical officer of a variety of companies . I eventually started creating and founding some companies, including Healthfinch. I left in 2017 and joined MDLive as a executive, overseeing operations and product strategy. I spent a few years helping them scale up and then exited that when the company sold.

I wound up meeting with my friends at Epic in deciding that the world needed a virtual care company that uses Epic as its base platform to more easily supply third-party virtualists to health systems that are using Epic in a way that is truly coordinated. That’s how we started KeyCare.

Why was it important that the virtual providers and the health system customers use Epic?

I’ve been involved in dozens and dozens of digital health companies. One of the biggest struggles has always been, how do you work with the big EMRs? With Healthfinch, we focused on looking at Epic and other EMRs as a platform that we would build on top of and within IT to support it. We were successful with that.

But the idea is understanding what being an Epic client is and everything that goes with that. I recognized that one of the ways to cut through the clutter — particularly in virtual care – was to say, what if we use the same underlying technology that 60-plus percent of the health systems are using and take away the interoperability issues? Epic has profound interoperability that allows us not only to share data, but to do cross-instance scheduling, messaging, ordering, referrals, et cetera.

I knew that they had built this technology and that we could take advantage of it to create a more seamless system. Much like we use Microsoft Word and Office, where we use those systems to create unique things that can then be more easily shared.

How did the conversation go with Epic when you approached them about becoming a customer and using that fact as a selling point?

As you can imagine, you don’t just go buy Epic off the shelf. I’ve had a long relationship working with Epic, from helping with our implementation at Northwestern, navigating Healthfinch, and being one of the early apps on Epic’s App Orchard.

In talking to a variety of folks at Epic, executives at telehealth, and others, we started out with general discussions about what’s going on in the telehealth industry. I then said, I have an idea I’d like to propose based on some of the things that you’ve been talking about, and we mutually came up with this concept. This is something that they really encouraged. 

They of course approve who they are selling to and who they are working with. It was very much a mutual discussion and decision point that it made sense that we not only would become a new client, but we would also be doing it in service to patients and health systems out there using Epic. We can theoretically support non-Epic EHRs, but it just works so beautifully when we are connected to another Epic site.

Describe a typical use case of how a health system might use your services.

Our first use case is a classic one — on-demand virtual urgent care, 24×7, 50-state access. Most health systems fall in a couple of categories. If they don’t do anything, we become this extra option that they can offer. Patients who can’t get in with their doctor, or it’s after hours, or they’re traveling out of state, can go to the health system’s front door, its MyChart. As they request an on-demand visit, KeyCare shows up as an option that they can choose in a seamless manner. It’s handed off to our providers to handle that patient. 

Whether the health system has been doing nothing, whether they do something and we’re supplementing it, whether they’re using another third-party provider and they prefer that they work with us, that workflow is seamless for the patient, and it’s through the health system’s own front door.

What’s your business model?

When we partner with health systems, we have some general maintenance fees, but the majority of our revenue is coming from doing visits. We are essentially getting paid like other physician services and provider service type companies. We’re getting paid to take care of patients. It can be done in a variety of ways. It can be per visit, hourly, or per-member per-month. But at the end of the day, we are providing access to healthcare services and we are getting paid in a variety of ways by the patient, their insurance, or some other sponsor who is at risk for the patient to pay for that type of care.

Do you contract or hire doctors directly or do you outsource your physician coverage to medical staffing companies?

We have two models. For urgent and primary care, we’ve set up a 50-state medical group, and we enroll doctors into that. We’re able to do that via either by contracting with large groups who provide the virtualists as well as being able to employ them directly if needed. We also are able to partner with other virtual care groups, so that they can put their providers onto our instance and make those available more easily to other Epic-based health systems.

How are you addressing somewhat restrictive state licensing requirements now that the public health emergency and its telehealth waivers  have ended?

We have always stuck with the state licensing requirements so that we are able to make sure that we can connect a patient who is in a certain state with a provider who is licensed to work in that state. That hasn’t changed. There’s a lot of discussion and fanfare over how liberal those rules were. Most large telehealth companies stuck with state licensure to be on the safe side.

Do you white-label your service on MyChart or do patients see the KeyCare brand?

Unlike some other third parties, we are truly of service to the brand that we are working with. That said, legally patients have to be told that they may be seeing a provider from the KeyCare medical group. But it’s as white labeled as it can be. They come through the front door of the health system, which explains that this is our partner, but the patient doesn’t need to create a new username or password. They don’t need to re-enter their medical data for it to be available to the provider. 

It’s a very seamless experience. Very white labeled. We minimize our branding. We are not looking to create our own brand. We very much are of service to the health system brand. Part of our philosophy is that we want to increase access to healthcare, but do it in coordination with our health systems, not in competition with them. We feel this creates a powerful hybrid approach, because when patients need to escalate their care, they will have a office-based option to go to, and they will know what happened in any virtual visit.

Is that less threatening to a health system that might not be comfortable sending patients off to a provider that wants to cultivate their own brand identity and customer loyalty?

That is certainly why we exist. That’s why we’ve gotten so much traction with health systems. Third-party vendors, in many cases, have come right out and said, we want to own the front door. No, the health systems want to own the front door. Why would they send it to a competitor? Why would they send patients to a company that has a completely different technology and a completely different brand? 

We are very much in line with health systems. I’m a health system guy. I grew up in health systems. I believe in the importance and power and strength of health systems. Our job is to help health systems provide some of the online convenient care that they traditionally haven’t been great at, do it in a way that feels coordinated, and allow them to focus on the stuff that they are great at — complex care, heart attacks, cancer, broken bones, and major emergencies.

We want them to be able to tell their patients, look, come to us. We will be able to provide a full variety of care. You don’t need to go anywhere else for that. We can do it in a way that feels coordinated, which in the end, means higher quality for you.

How do you make the handoff to a higher level of care as compared to the typical urgent care center?

At a high level, on-demand, virtual urgent care is supposed to be able to handle everything. That doesn’t mean that we treat everything. Sometimes we have to redirect a patient. Most of the time, hopefully 90% of the time, we are able to take care of the patient and they don’t need follow up. But five or 10% of the time, maybe they need to go to an ER and or urgent care center, and our job is to redirect them.

Part of it is helping the patient understand. You cut your hand, we can’t do anything online, you need stitches. But sometimes they need reassurance and understanding. Sometimes they have to understand what time it is. Could I wait until tomorrow morning and go to an urgent care center, do I have to go to an emergency room tonight? One of the important things to understand is that virtual urgent care is not meant to take care of everything or cure everything, but it can certainly give you good advice and triage you appropriately.

One of the issues is third-party vendors that just say, go to the ER or to urgent care. We have a little leg up, in that when we tell the patient this, they are part of a health system. They are able to go to their health system, which has access to any of the notes that we have. We also are able to look at their past history and see their medications and problems, and that can help us better understand and let patients know if they really need to go in and see someone.

Over time, as we move into more primary care support, we will be able to send messages more directly into the health system, and maybe even pick up the phone and alert them, if appropriate, in the on-demand urgent care space.

I should note that when we sign a note, the note not only goes to the health system in the appropriate place, but a message can get sent to the PCP that the patient was seen and alert them to review the note to see if they want to do any follow up. It could also be sent to a general in-basket message that can be monitored to decide if they want to follow up with the patient as well. Those are unique things that we are able to do.

How has the use of technology and support staff changed for virtual visits as compared to the early COVID days, when unprepared doctors had to wing it alone using Skype or FaceTime?

It’s important to understand that virtual care should not be looked at as simply an online version of an office-based visit. Similar to how we defined hospital care and hospitalists in the 1990s,  we are clearly moving into an era when we have to differentiate virtualists from “office-ologists” in terms of how they provide care and what the focus of patients should be. I believe that office-ologists, the folks in the office, can and should be working at the height of their license to see the more complex patients that need longer, more intense visits in the office, or need some type of task or procedure that has to be done in the office.

Virtualists can focus on what I call the triple-R threat that overwhelms our health system — routine, repeatable, rules-based care, the type of common commoditized care that right now clogs up our offices. What if we can shift those to online that is more convenient for patients? It is routine enough that it can be handled, and we have virtualists who are trained, who are specialized, in handling things online. They understand more of the nuances of being good doctors online, of what type of physical exam you can do online, because you can do certain things to and provide some level of physical exam. We are looking at a variety of tools to capture vital signs, to analyze parts of a video and picture, et cetera.

We are starting to see this differentiation, where virtualists are taking advantage of being able to do something online that, instead of looking at it as a disadvantage, we have to think about what the advantages are, such as more timely access to care. We believe that over time, we will use certain technologies online that we won’t be able to use as easily in the office.

It’s going to be a fascinating era as we continue to differentiate what should be done in the office, what could be done online, and how we can help solve this whole burnout crisis by having virtualists who don’t simply see three or four patients an hour, but really scale up. How can a virtualist manage 10, 20, or 100 patients an hour, not by doing 100 video visits, but by using asynchronous care automation, delegation to other staff, et cetera? The virtualist should be taking care of the bulk of common stuff so that the office-ologist can take care of the more complex things that truly need to be seen in the office.

How will AI change healthcare, especially virtual care?

Unlike some folks, I do not look at AI as being important for diagnosing particularly common things. Where it’s really going to shine is in communication. We’ve seen that AI can often be more empathetic and more overall informational than a busy doctor, and that’s OK  and that’s great. We are already looking how we use AI, chatbots, and other ways to communicate with patients to let them know what is going to be happening in their visit.

Maybe we’ll be able to capture information ahead of time. Maybe after the visit, we’ll be able to use AI to help explain things. Maybe AI can also be really good at detecting subtle things in a patient who looks like they have a simple cold, COVID, or UTI. Maybe there’s something else going on, and AI can surface that.

We are exploring a number of use cases to make the virtualists more efficient by helping automate pre- and post-, but also more effective in identifying things and communicating in better ways with the patient. It will be absolutely important to get us to a world where we can truly scale up virtual care to a big population.

What factors will influence the company over the next few years?

We are in growth mode now. We have signed 10 health systems in the past year, representing over 90 hospitals and 30,000 physicians. That’s a pretty quick product market fit. We are going to continue to grow that and expand the number of health systems that we can serve.

The next stage is, how do we make it as efficient as possible? That’s where we bring in technology. Our mission is to bring this tech-empowered virtual care team to be of service to health systems in a coordinated way. If our purpose is to improve healthcare access for all, and our vision is to be the best at virtual care, our mission of what we are really doing is not just bringing providers and staff, but tech-empowering them to make them more efficient and effective. Doing that at scale than any one health system can do, so that we can help health systems transform how they manage this population and do it at scale.

I often say that we don’t have a shortage of physicians as much as we have a shortage of using them efficiently. What we’re trying to do over time is help health systems rethink how they manage that population, how they split up who’s online versus who’s in the office, and how they pay their doctors. For this to work, we need them to think about compensation redesign and embrace team-based care and all that has to offer. 

We are in growth mode and laying technology on top of that to make that as efficient and effective as possible. We are also expanding well beyond urgent care to primary care, behavioral health, and specialty care. Part of our job is to set up sort of a virtual care marketplace for health systems, where they know they can come to us and find a wide variety of virtual care options, but in a way that because we are on Epic, allows it to be done in a coordinated way. Whether they might need help with cardiology, rheumatology, GI, maternal care, or dieticians, the idea is that they can come to us and we’ll have them all available in a tech-enabled way, sitting on an Epic instance and being able to scale with them.

How can we do this in a way where it doesn’t feel threatening to the physicians in the offices? Part of what we do the end of the day, my personal dream, is that a health system could go in to their physicians, primary care physicians in particular, and say, what if we could increase your salary, but decrease how many patients you have to see in the office? How would you feel about that? Of course they are going to ask, how are you going to do that? We’re going to say that we will give you this virtual care team, you’re going to be really connected to them, and together you’re going to be able to double your panel size. 

This is an opportunity truly to fix all those Quadruple Aim issues. We’re going to make it easier for patients to get care. Their experience gets better. We’re going to improve quality, mainly by improving access and making sure they can get in. We’re going to decrease costs, because we can do this at scale. We’re going to make life easier for doctors.

This isn’t going to happen overnight. This is a strategic transformation. It’s going to involve a combination of what I call the three Cs. One is having a care team that is connected and coordinated. KeyCare will provide that team to health systems. Second is compensation redesign. We have to rethink how we pay physicians and how might we pay them to manage a population, not simply be on an RVU treadmill, because that is deadly for physicians. Third is cultural and change management, educating and teaching our patients, our providers, and our staff that team-based care is not only effective, but is actually better in many ways to maintain a more consistent approach to monitoring patients in a coordinated way.

We didn’t invent the concept of population health or team-based care, but we believe that we can execute on it in a way that makes sense, is coordinated, is scalable, and makes life easier and better for providers, patients, and the health system as a whole.

HIStalk Interviews Dave Hodgson, CEO, Project Ronin

Dave Hodgson is co-founder and CEO of Project Ronin of San Mateo, CA.

Tell me about yourself and the company.

I’m a molecular biologist by training. I started my first job on the genome project in Cambridge in the UK. From there, I moved to the United States and worked for a small biotech that was selling genome data to pharmaceutical companies. Then I was part of bioinformatics  for several years for Pfizer, and then knowledge management for Pfizer. Then to  Roche, where I ran scientific computing  for a few years before I decided to completely pivot the career out of pharmaceutical executive land and became part of startups in the Bay area. I lived near Palo Alto and I worked as chief technology officer for a diagnostics company and for a telehealth company. Then I was the first chief technology officer at One Medical, which is now part of Amazon.

After all of that — having been in pharma, the genome project, telehealth, and primary care — it was time to become a consultant. I did a lot of healthcare consulting for a while, and during that was introduced to Dr. David Agus and to Larry Ellison, who were enthusiastic about how we might apply data science and thoughtful clinical interface design to something that could be embedded inside the medical record system to assist decision-making in complicated diseases such as cancer. Project Ronin was the founding of that idea. That was several years ago, and we’ve been working on that challenge ever since.

What drives Larry Elllison’s interest in healthcare and how does he see healthcare and technology merging?

A lot of us have seen the devastation that terrible diseases like cancer can do to the individual, to loved ones, to families, and to friends. It’s a truly horrible situation to be in. So many of us have seen that, and him, too. Then you think about how might you really improve the quality of care —  not just in the United States, but everywhere — and the quality of decision making. 

Cancer is complicated. It’s actually multiple different diseases. Patients have different goals. They have different desires. I want to live as long as possible. I want to stay as healthy as possible, I want to optimize my wellness for now, or I want to do whatever it takes to survive. The diversity continues to their genotype, their other clinical situations, and other things that are going on with them.

Patients are very, very individual. When you are thinking about that quality of decision-making, you have to take into all of that into account. But ideally, you could leverage the entire corpus of knowledge that we have about the disease, from everything published to everything that has happened in the past, and also everything that has happened to a patient like this, from their social situation to their clinical history to their genotype. If you were in the horrible situation of having some kind of tumor diagnosis, you would really love it if your doctor had at their fingertips every single piece of information possible. 

Given that desire and that need, you can start to look at the process of clinical decision-making — diagnosis, selection of treatment, management of treatment, management of survivorship — as a data problem. How might we bring all the world’s clinical knowledge into the space between patient and provider for their most optimized decision-making? Larry, the other founders, all of the team at Project Ronin, many of the clinicians that we work with, and the patients that are on the platform are all quite aligned on that desire to have all the world’s data be in a place where clinicians and patients can use it to make the best decisions for them possible.

What psychology is involved with trying to put all of that together using technology?

It’s a complicated problem. You have many different types of variables to consider — the desires and the situation of the patient, the experience of the oncologist, and the clinical biology of the particular disease that the patient has. Treating late-stage lung cancer is extremely different from early-stage prostate cancer, and very different from mid-stage breast cancer. Very, very different situations. You have all these very different variables. 

The assembly of the data and the processing of those data is extremely complex. That is why this has been a personal mission. Let’s do something very difficult that will have such a broad benefit. There is not only a data assembly complexity, but then there is a psychology that you need to present those data to the clinical team in a way that they can use it, digest it, and take action on it.

That means that you have to present it inside the medical record system. No clinician ever, anywhere, wants to step out of their patient record, charting what happened in the encounter with a patient and then logging into another portal to go look up information or anything else. Although they have to do that in certain cases, they don’t want to do it, because it is clumsy and inefficient. They are already seeing many patients, their day is very busy, and it’s complicated. 

The first source of psychology is to serve up data that is relevant to this particular patient in this particular situation inside the medical record system, so that the clinician has an efficient access to very rich information that they can use to assist or validate or even qualify some of the decisions that they are making.

Similarly, the patient is always wanting to know, what can I expect to happen next? Am I on the right treatment for my situation? If I have questions, can I reach my clinical team? We think very carefully about how we surface the answers to those questions, again under the direction of the clinical team and doing things the right way. But there is some human-centric design very much involved in this that is paired up with all of that data assembly and data rendering that we do. 

You’re probably getting a sense that it’s a pretty hard problem. We don’t believe that anyone has really solved it yet. That’s why we’re working very, very hard to show that it can be done.

A cancer patient with means and knowledge will often seek out the best available expert at an organization such as Sloan Kettering or MD Anderson. Can the scale of technology democratize that for for patients who lack connections, the ability to travel, or insurance coverage to seek out a super-specialist?

Very much, and that was one of our founding desires, to take the expertise that is known by the very best, highly specialized oncologists in the very best academic medical centers and make that knowledge available to practicing oncologists. That’s essentially what we are doing.

One of our best and biggest partners is one of the largest academic medical centers. We have published with them and we are developing the platform with that very goal. How do we package the expertise and the data so that a community oncologist can take advantage of it? We are working with a community practice where when they pull up the patient chart, they see the reference data and the data insights that we add to that. It is supercharging their knowledge in a particular specialty.

Typically in academic medical centers or some of the larger cancer centers, you have practice oncologists that specialize in a particular tumor type, kidney cancer or whatever. Then in the community, you tend to have a little bit more generalist oncologists who are seeing a breast cancer case in one appointment and then a prostate cancer in another. You want to be able to equip them to know, what are all my choices in the right way of treating this particular situation? What has historically been done? What do the reference guidelines suggest? What does the literature suggest? That can take a long time if you do it by hand. We automate that and then present that in an integrated way.

Are providers and pharma connecting in new ways around real-world evidence, clinical trials enrollment, and post-marketing surveillance?

We are seeing that, too. There’s definitely a desire for those worlds to be less separate than they were.

There are a few dimensions where that makes a lot of sense in the priorities of both parties, and that is to enroll the right patients for all clinical trials. There’s a lot of new medicines in the pipelines of pharmaceutical companies that are oncology drugs. There is, and has been for several years, a desire to find the right qualified patients to be enrolled in a trial and the patient’s attributes that would qualify them in or out of any particular trial. A lot of that data is in the patient chart, sitting in the medical record system. There’s an obvious place there to look for eligibility and enrollment by integrating those two systems. 

Then the other part that you mentioned of real-world evidence. There’s clearly a desire to have some kind of companion for the patient through parts of their journey, such as managing their wellness and managing their general interaction with their primary care doc. If they are in a situation where they are diagnosed with cancer, that there would be some companion that would take them through that, including if they found themselves as part of a clinical trial. You would want that companion app, let’s call it, to be with them through that, where it’s collecting the appropriate data of how they are experiencing treatment. 

Then not only have that be an input into how the treatment is performing, but also help the patient manage their side effects and symptoms, which is part of the Ronin platform as well. We do that symptom monitoring and capture of patient-reported outcomes and patient experience.

How much data is needed to make the “patients like me” concept clinically useful, especially for uncommon conditions?

We have done a lot of work in how to acquire the right patient records and then structure them, because clinical data is very much dominated by clinical notes, encounter notes that are all text. They are written in a certain clinical language that is a little bit difficult to manage. There’s a lot of work to do with the data, cleaning up and mapping to a central model. We do a lot of that.

The good news is that over time, we have become pretty good at not requiring an enormous data set or enormously high quality data set. Over our experience in the last few years, we are getting better at doing not only the cleanup, but also requiring less voluminous amounts of data. With a few hundred records, we can do quite a lot of trending and analytics on those data sets to be in a position to serve up insights in a qualified, thoughtful, and high-integrity way. We have a lot of standards around data quality. Our QA and QC processes are robust and strict, so that anything that we put before a clinician or a patient is rigorously tested and validated first.

The early days of precision medicine had limited applicability since few correlations existed between genomic data and condition management options. Will advances make precision or personalized medicine more of a standard?

Very much so. We see that certainly every day. In that data view that we have built with our collaborators that we serve up inside the medical record in the patient’s chart, we show all of the known genomic biomarkers that the patient has been tested for, and then the literature that shows any particular consideration of those. If the patient has lung cancer and is their EGFR is positive, there’s good literature around which treatments may or may not be effective because of the presence or absence of that biomarker. 

In oncology, we spend most of our time in those correlations between biomarker presence or absence and which treatments that information suggests that you should use. Those are becoming quite well published, and therefore, we want to be able to have those reference data be available to the clinician. We are seeing that progress as the science progresses and as the the clinical evidence progresses. We are serving that up when it has been published and validated in all the right ways.

What will the company’s next few years look like?

I want two things to happen, and I would have to speculate greatly on whether they will happen. Obviously there’s a lot of hullabaloo about what will AI in medicine will really look like. We are obviously very invested in that, and we have developed some pretty effective large language models for in the generative AI space. We have some really exciting prototypes that we are taking through some validation, some research processes. In the next couple of years, we are going to begin to identify the most appropriate, safe, and effective uses of AI algorithms, machine learning, and deep learning, including large language models, to the practice of medicine. I hope that we will see the safe and effective demonstrations of those, and the company is heavily invested in that.

The second, which is my dream, honestly, is a greater ubiquity of value-based care reimbursement, where the incentives for practicing medicine in the US are driven by getting paid when the quality outcome for the patient is met rather than getting paid on the volume of procedures that are performed. Value-based care has been conceptually around for decades and has made slow progress, but my dream is that that progress would go faster and that there would be more and more reimbursement using value-based care structures. Technology has a role to play in enabling that. That brings the aligned incentives that we really crave that will really drive a lot better outcomes and a lot better economics.

HIStalk Interviews Helen Waters, COO, Meditech

Helen Waters is EVP/COO of Meditech of Canton, MA.

Tell me about yourself and the company.

I’ve been with Meditech for a long time in a variety of roles. I serve today as the chief operating officer of the company. I’m responsible for a number of functions.

Meditech has been in business since 1969 as an originating founder of healthcare IT, in the sense of programming language and operating systems. We have enjoyed a long and fruitful life in healthcare IT. We are excited about where we sit today in terms of the future of the industry, the innovation going on, and the Expanse platform in particular, which is our most recent introduction as a cloud-hosted web platform.

What were the key developments over the past few years that improved the company’s position in market share and product rankings?

I would say the contributing factors were the decision to write the platform to begin with, to take the step beyond the hospital walls into the ambulatory environment, which we did with Expanse, having built an integrated and comprehensive system to get to a single electronic health record. I believe that the openness of the platform has made a difference.

We have made a conscious decision to lean into the changes that are happening for the marketplace. Innovation is coming of age, and while there’s a lot of hype that goes with that, there’s clearly an opportunity for good augmentation of electronic health records. We wanted Expanse to be a platform that would be open and capable of plug and play with other tool sets that our customers would have the choice to want to acquire and use. SMART on FHIR and open APIs are a key aspect of Expanse. That has driven a lot of interest in the platform in comparison to others who believe that they should be all things to all people. Choice will be good for healthcare.

A great example of that is our work with Google. Before the announcement in November of what GPT would do to bring AI conversations around the kitchen table, we had a vision for what artificial intelligence and large language models could do for healthcare. Our work in 2022 and the delivery of a solution today to a customer embeds the native Google search into our platform. But it goes one step further in leveraging Google’s large language model called BERT, which is a predecessor to many of those that are being released today in terms of Med-PaLM and PaLM itself. Meditech is leveraging that extensively to surface conditions from historical visits in Meditech, legacy platform, or other vendor platforms so that the physician gets a really quick search and summarization of data, scanned documents, and handwritten notes, and uses extensive LLM capabilities and learnings to do that. We are excited about where innovation sits. 

The Google project was a great example of us walking the walk about the platform and the openness and making sure that we solve problems that exist in healthcare today. Certainly the density of records and the difficulty finding data is known across all the EHRs, causing physician and nurse burnout that we are intent on addressing.

How can technology be applied to address burnout?

The first swipe at that was finding the data really easily, not depending on how a vendor stored it or what category they were under. That’s why picking Google search and being able to search on words, misspelled words, medical terms, and do that fast and easy, was the first priority.

The second was to make sure that any information that would be relevant could be found with ease, so that a clinical decision could be made with confidence. That’s the embedded utilization of machine learning and in the LLM in that solution that we call Google search and summarization.

Then fast forward and we have the introduction of ambient listening capabilities for both the physician and the patient, to be able to discern from human conversation what has been said, to begin the generation of a note and to be able to summarize for a patient what happened in that office and what the exchange was like.

Our customers are already using artificial intelligence and the learnings from all of that information. The technology is very present in solutions like Suki, Nuance DAX, or Augmedix. The next stage of that is to take the benefits of generative AI, which is significant learning over large data sets, and improve the experience and the accuracy. AI for us today, beyond what we have delivered for search and summarization, will make some of the monotonous, redundant tasks that irritate physicians and nurses much easier. That’s the set of projects that we are working on today. But in terms of ambient listening and connecting to those solutions, that’s already happening, and we are optimistic. 

The irony of all of this is that having been in the industry this long, we started out fighting with the pen and pencil in terms of handwritten orders. We went to keyboards and struggled through that. Meaningful Use mandated the changing of the way physicians and nurses delivered care and documented it. We’re going full circle all the way back around to the voice being the most powerful tool. The technology is caught up in those regards and will continue to get better and stronger as time goes on.

What are your talking points when you are in competitive situations with Epic and Cerner?

The market is really interesting right now. The acquisition of Cerner by Oracle put a whole new inflection point into the industry. Cerner has historically been a combination of acquired solutions. Cerner certainly built a lot of solutions, but they bought a lot of solutions. Now Oracle has to sort that out as it is developing something brand new to replace Millennium, which is quite dated, as indicated by the name. 

The acquisition history of this industry is challenged by what we’ve seen in the past. Really, really big companies come in and then make a decision to walk away. I would count Siemens, McKesson, and others in that bucket. We are watching Oracle and Cerner and we have an idea of where we think they will head, but that puts some uncertainty in the marketplace for customers and prospects.

Epic has been the beneficiary of a very strong market consolidation trend of the larger academic medical systems that make a decision for them, and then those academic medical centers and larger systems expand and buy a lot of hospitals. We have certainly felt that, but we have been told, and actually have some validation, that our platforms are comparable. In fact, ours might be a little bit more technologically advanced in terms of the native cloud nature of it. Applications being written for the cloud, not just running in the cloud. 

We feel quite confident in our ability to compete against any other company. Integration is a hallmark of Meditech, and it’s decidedly evident when you look at Expanse. The concept of partnering with the industry to solve problems — even if we don’t solve them all, but we bring solutions to the table — will be attractive to the market.

We are not out designing CRM systems. We’re not out designing tools that will be considered fully competitive with an innovation sector that is breathing and living. We are out there to participate and co-exist in the marketplace for the benefit of healthcare and the customer. We think that we continue to deliver something that all of a sudden is being talked about more, which is a strong, value-driven, sustainable investment in electronic health records. 

This industry is under significant distress from a financial perspective, driven by the pandemic, labor cost increases, supply chain increases, and some decline in volumes. I think we demonstrate the absolute best solution in terms of the sustainability of a modernized, contemporary, yet sophisticated platform that allows our customers to make that investment to establish the foundational pieces of a digital ecosystem, but that also leaves room for them to continue to invest as well in other solutions that are being designed and implemented with great energy in the industry by new players and innovators.

Our allocation of operating expense budget or percentage of revenue is far more moderated than the Epic system. That is well documented and proven. That has probably been a contributing factor to the financial challenges that exist in the market. Technology generally should level off or go down in cost over time. We’re an important part of the health ecosystem, but we’re not the be-all, end-all of it. We want to participate, we want to co-exist, we want to modernize, and we want to continue to thrive, but we want to be a partner to this industry and to the real challenges that exist for it, which are cost containment, high quality care, and a better user experience, even if that’s in partnership with a important player like Google and Meditech delivering that.

Epic hasn’t lost many of their direct clients, but Community Connect sites have more variable satisfaction and lower switching costs. When Cerner loses clients, it is usually to Epic. Where is Meditech’s opportunity and what is the strategy to get hospitals to switch?

The prospects come from our competitor installations for sure. I’ll be honest to say that when you make a $100 million, $1 billion, or $2 billion investment in an EHR, which is somewhat the price tag that you typically find in Epic deployments, it’s not easy to replace. The replacement concept and cost is daunting. 

The Cerner market, the Allscripts market, and certainly some of the hybrid vendors that are out there is where we are drawing customers. Meditech as a Service has been incredibly attractive and well received. We have replaced all of the vendors that you just mentioned in terms of Community Connect, CommunityWorks from Cerner, CPSI, Allscripts, and the old Paragon solutions.

We go to this market with a recognition that it’s competitive. Customers are looking to make investments for the long term. But they are looking more than ever to talk about the financial implications in the long term in these investments. We see the market as open. We see, in particular the changes in landscape with Allscripts departure and with Oracle coming in, opportunity where customers will want to have conversations. We are at those tables. We are quite active in international markets and doing very well there, in terms of the Irish UK, Canada, Australia, Africa, and other parts of the world. We are expanding our customer base in just about every English-speaking market that we are in. We are pleased with all that.

The talking points are the fact that this company has played an important role in healthcare, has a widespread impact on the delivery of healthcare, and has been an incredibly stable partner to the industry in helping to solve real problems. It has been a company that sought to do well among all that in the capitalistic society, but not add to the burdens of delivering healthcare. Our sustainability and cost model has made a difference.

Ironically, when you look at organizations like HCA, they have Meditech, Cerner, and Epic. They wrote a check to Epic about 10 or 12 years ago for a key market. They have Meditech Magic and a couple of Expanse customers. They had many Cerner sites by virtue of acquisition. We were fortunate to win their confidence and trust for the future. Part of that was undoubtedly driven on partnership and confidence. It was driven on value and in the fact that they don’t chase shiny objects. They had all three platforms to comb through over a decade and make their own determination on how big the variation and difference was. Not just go off of folklore, but actually dig deep. 

I think they were attracted to our commitment to being a platform company, to be sure that they had the freedom and flexibility to work with our system as foundational and as the main tool in the delivery of care. But also the freedom to invest and innovate on their own and with other partnerships, with Expanse enabling that and not halting the concept. Other vendors’ EHRs are more in tune with wanting to be all things to all people and to control a lot more. 

That is one of the biggest testimonials. If you look at some of the truthfully bigger not-for-profit systems that have grown from three hospitals to 20, 40,  80, or 140, they are figuring out scale. HCA figured out scale and is well managed and operated. They are incredibly invested in technology and innovation as a transformative driver to care, and we are delighted to be their partner in that journey.

Due diligence doesn’t seem to be as big of a topic in the industry. There’s a lot of folklore about systems and about physician preferences. Ironically, when you get really into the weeds and talk to doctors who are using systems, the nation and the world have a problem with electronic health records. It’s not a Meditech driven problem, it’s an industry-driven problem. It would point to the evidence that no matter how much you paid or how pristine you thought you were getting a system, we’re still on the journey to solve problems. Due diligence about investigating the depths of what a vendor can offer, and not making assumptions based on which brand-name healthcare marquee organization purchases a system, is important. I hope that boards and CFOs get back to the table talking about fiscal sustainability and the reasonableness of investments in foundational tools like EHRs.

I have seen that decline in a number of times where organizations just write checks and don’t even look to see what’s out in the marketplace. People say they are buying systems to please physicians who are not the ones writing the check. We’ve found that when they are operating hospitals or surgery centers, they are quite different, making sure they do due diligence to understand systems and capacity and spending money with some caution. I hope more of that comes back to the industry, because I think it’s been lacking in the last 10 years. I feel like we are well positioned to have a conversation with any organization and talk about some of the more important issues that get a little whitewashed at times. We haven’t seen enough due diligence in the last 10 years.

HIStalk Interviews David Bates, CEO, Linus Health

David Bates, MS, PhD is co-founder and CEO of Linus Health of Boston, MA.

Tell me about yourself and the company.

I have a PhD in chemical and materials engineering. I was in life sciences — applied biology, genetics, microbiology and biochemistry – and got into venture capital and new venture startups in 2009. I’ve done both the investment side and the operation side, especially bringing impactful technologies to market and trying to do paradigm shifts. Venture capital is about taking big risks to make a better future. 

Linus Health uses artificial intelligence to turn the sensor array of IPads and smartphones into high-quality medical devices that are capable of detecting and understanding brain function and giving primary care physicians and other providers the information they need to act on it in a timely fashion. We are looking to expedite neuroscience in the standard of care.

How would a primary care practice implement cognitive screening?

Right now, 76% do not do anything, or they ask the simple question, “Are you worried about your memory?” Those who do something use paper-based assessments. Those take a lot of time, require some training, are not very sensitive, and are subjective. Our tools allow them  do something remotely before they even come into the office, or in the office, where a medical assistant or even a secretary with a very little bit of training can administer our assessment. It takes about three minutes or less and the patient usually enjoys it. 

We get an incredible amount of insights, thousands of data points about their brain function that are processed through algorithms. It gives the provider an understanding of what’s going on with the individual, what their risks are, and what actions can be taken. We also provide the patient with a personalized brain health action plan so they can take agency over their own brain health and begin to make those lifestyle modifications that optimize their brain health trajectory. It needs to be very quick and it needs to be in the workflows of primary care. That’s what we focus on.

Can the screening be performed via telehealth or in settings outside the PCP’s office?

Our mission is to help bring brain health into its rightful place in the standard of care, making a brain assessment a standard like blood pressure and heart rate. All biology rolls up into the brain, but it is often neglected. Healthcare should start with brain health and brain function, and out from there, everything else. Any touchpoint in healthcare delivery, including remotely through telehealth, is an ideal setting to make sure that the brain is functioning as it should. If there’s any kind of wobble starting to occur, it gets addressed immediately.

Accumulated results for the same person allow you to detect both immediate problems as well as unexpected degradation over time?

That’s correct. The ones we use in the clinic are cross-sectional, because we have amassed enough data and understandings and there’s over 100 years of science behind it. But you’re precise in that with sensitive enough tools and using the individual as their own baseline, you can more easily pick up any kind of dysfunction or dysregulation that’s starting to occur and address it for that individual. That is certainly where we want to take things, to be more preventative so that someone has themselves as their own baseline. It’s very personal into what they are dealing with and their own personal situation.

Has interest in early detection increased as treatment options become available?

Absolutely. There’s a huge gap in information and understanding of brain health disorders, but that gap is closing. People thought, well, there’s nothing I can do about diseases like Alzheimer’s. It’s the number one feared disease for people over 55,  but most people think there’s nothing they can do about it.

But in fact, there’s a lot that can be done about it. At least 40% can be put off by just changing lifestyle, getting hearing aids, making sure your eyes are functioning correctly, nutrition, socialization, exercise, and all these kinds of things. They really work and contribute to optimize brain health. Giving every individual agency over their brain health, which is both information — that there’s something they can do about it, what matters, and how to optimize brain health — and giving them the tools to monitor it so that they know that, OK, I’m starting to experience something and I need to go take care of this right away. Maybe I need hearing aids, because 8% of dementia could be avoided just by using hearing aids. That’s just one example.

We want to partner and ally ourselves with these busy providers and enable them with tools to better serve their patients without increasing their burden. In fact, we want to remove some of their burden, lower the cost of the system, and empower patients under their care to have agency over their brain health. Patients want this, they want the information, they want to know. They are afraid of this disease. They are afraid of brain health ailments because they are so debilitating.

Now is the time. The science is advanced enough. There’s a lot of movement in the industry, and it’s time to take action. These digital tools are real. They are here to stay. We need to let go of the horse and pick up the car. I hope that we can work together on that in a new era of healthcare that incorporates the brain and puts it into its rightful place in the standard of care.

The recently published study that showed a strong correlation between untreated hearing loss and dementia was fascinating. How could the audiology component be tied into PCP dementia screening?

I typically lump it into two buckets. It’s all the people that have situations today. One in two over 45 will experience some kind of debilitating brain health situation in their lifetime. One in three over 65 will have at least mild cognitive impairment. So there is the population that is of age where there is a chronic illness, either detected or under the surface that has not yet manifested. What do we do with those folks? In the younger population, how do we work with those folks so they don’t get a severe neurodegenerative disease? Those two buckets are important. 

Primary care is the quarterback of healthcare. They manage care. Diabetes used to be treated only by endocrinologists, and hypertension was treated only by cardiologists. Both now are managed in primary care. We believe some of these very standard neurogenerative diseases and their causes will be managed in primary care.

But primary care is already overwhelmed. We need artificial intelligence. We need better tools and information to equip them, and even take care outside of the clinic into the daily lives of folks. 

That’s why I’m super excited about chronic care management opportunities, where health coaches are engaged to carry out the orders of the doctors and help people live their best life, their best self. That includes hearing, vision, and all those other things, making sure that it’s all coordinated and that information is tracked so that they can have that agency and optimize their brain health trajectory and overall health trajectory.

Will the company expand its offerings into other forms of testing or brain exercises?

We start with where we’re going and first enabling the patient to define what matters most to them in life. That is what we tie everything back to. Not just indefinitely — it’s what matters to you now. Is it playing with your granddaughter? Is it following a conversation? Is it driving your car? Is it playing golf? What matters to you? 

Then assessing their health, their lifestyle, what’s going on in their life, giving it some context and then bringing some objective assessment. We have those things in our platform. If there’s a reason to screen in primary care or to assess, they can use our IPad based tool to gain a clear understanding across multiple domains of what’s going on with this individual very early. That is then tied to recommendations and clinical decision support so that a primary care doc has specialist-level insights at their fingertips that were collected in three minutes. They know how to direct that patient next.That ultimately helps to generate a personalized care plan that can be implemented by the patient with help of their family.

But also I’m excited, as I mentioned, about chronic care management, where health coaches are engaged on a platform with asynchronous communication, rooting these patients on to implement the care plan and optimize their brain health trajectory. Then ongoing with the monitoring with our remote tools, being able to say, “The stuff that you are doing has stabilized or improved your brain health.” This is not just cognition, which is the highest-order function, but also mood. Do they need CBT for depression or anxiety? What are those other things that are happening? How can we equip them with the information, skills, even medications to optimize their health trajectory?

What is the company’s business model?

CMS has taken a good position. They reimburse our assessments for anyone over 65. I hope that goes downstream for the risk-based capitated plans, being able to identify people and what’s going on with them. That saves a lot of cost in running test after test and scans when all that was needed was a medication change. Or they had depression, or they were the worried well. Being able to have objective, concrete, and clear understanding of what’s going on with the individual and what to do next in a very short period of time optimizes the total cost of care and gets rid of a lot of wasteful spending in those risk-based plans. 

We have pricing models for both. We are much focused on return on investment of the practice, the healthcare system, or the payer network. We have dialed that all in because it’s important that we build not only fantastic products for providers and patients, but that we also have a sustainable business model that drives value to the entire system and every stakeholder in the healthcare.

What will drive the company’s strategy? 

Important to the company’s strategy is the acceptance of real, validated, artificial intelligence-enabled tooling that boosts the provider’s understanding, adoption, and necessary behavioral change to incorporate brain health in its rightful place in the standard of care. Younger doctors are more digitally savvy, but all doctors are savvy enough that if it is explained to them clearly, they will get it, adopt it, and their patients will love it. Getting more adoption, more openness, and that necessary behavioral change to bring it into the standard of care is the most critical thing for our success.

HIStalk Interviews Steve Cagle, CEO, Clearwater

Steve Cagle, MBA is CEO of Clearwater of Nashville, TN.

Tell me about yourself and the company.

I’m the CEO of Clearwater Security and Compliance. We are a national, healthcare-focused cybersecurity compliance and privacy services and software company. We work with healthcare organizations – hospitals, health systems, physician practice management groups, and digital health companies. Really any type of organization that serves healthcare. I’ve been CEO for five years. I have a 20-year background in healthcare.

How do you distinguish between security and compliance?

Compliance and security are very much intertwined, especially in healthcare. Certain regulations, including HIPAA, require organizations to meet certain specifications and standards in order to adhere to those regulations. Some of those of course involve security and privacy. We have other standards and frameworks that we use in those domains to build and execute programs that protect the organization — its data, its patient data, or third-party data — to ensure that it is kept private and secure.

There is some overlap. In healthcare today, compliance is extremely important. But from a security and privacy perspective, we need to go beyond what we see in some of those regulations, and most importantly, build programs that are ensuring that we are taking the appropriate actions that are relevant for the specific organization based on its size and complexity and its contractual agreements with third parties based on other requirements it may have from insurance providers and so on. Most importantly, based on its level of risk and its risk tolerance.

We see organizations getting better at at understanding risk, although not always going as far as they should — understanding risk, evaluating that risk, and then making decisions that are risk-based to secure and protect private information and to protect their organization’s operations from a cyber incident or other type of security incident.

Is ransomware still the predominant risk for providers?

It certainly is a top concern. We see in the headlines repeated ransomware attacks against healthcare. According to the FBI, healthcare is the most targeted industry out of all critical infrastructure industries for ransomware attacks. This year alone, there have been at least 19 attacks on hospitals versus 25 ransomware attacks in all of last year. 

Ransomware is extremely disruptive and dangerous when it comes to healthcare. Organizations aren’t able to deliver services at the same level of quality. It may be that backup systems are ensuring that patient care is of high quality, but we know there’s an impact when you can’t get test results, you have to reschedule procedures, or you have to wait longer to get care..

A good amount of data has come out recently that unfortunately shows that outcomes were impacted and the mortality rate increased following a ransomware attack. Even hospitals that are adjacent to a hospital that was affected by an attack have had overflow, increased wait times, and increased morbidity. There’s real data out there that shows that it is not only an extreme business risk, but also a patient safety risk. It’s a business risk because revenue is impacted. For smaller organizations, a ransomware attack can cause the loss of up to 30% of their total revenue. So from both a patient safety perspective and a business perspective, ransomware is a top concern.

Is email the primary vector of ransomware attacks?

I would clarify that a bit and say that people are the top vector. That could be email business compromise or other types of social engineering attacks. A lot of those attacks are coming through text messaging. Also phone calls, where the person on the other end purports to be somebody that they are not to try to get someone to give them information to further infiltrate the organization. Phishing and other types of social engineering are top concerns.

We have to continue to make sure that people are aware of all those tactics and techniques. We also want to have other types of security controls that limit the impact of a breach. If somebody were to be able to get those credentials, what can they do with them? Do we have controls in place, such as multi-factor authentication? Do we have controls in place that limit the amount of access that individual can get to? We want to have environments that provide for a zero trust approach, that they have to have repeated authentication to access certain applications even if they are able to get into through to a certain point. There has been a lot of focus on that area.

That’s not the only vector. We’ve seen a lot of attacks, especially over the past couple of months, involving zero-day vulnerabilities or other vulnerabilities that have been exploited by bad actors. We have also seen that with third-party breaches, such as the recent MOVEit vulnerability. That has been a huge source of breaches for the healthcare industry over the past couple of months.

Will AI be better for hackers to launch cyberattacks, or will be be of greater benefit for defending organizations from them?

The AI wars have really begun. Artificial intelligence is not necessarily a new thing when it comes to security tools and techniques. There have been advances in applications being able to use those in a security operations center to assist an analyst in diagnosing or responding to a attack, certainly in identifying some sort of incident or potential incident that should be investigated.

But it’s being now used by bad actors to do all sorts of things, such as crafting more convincing email messages to learn about an organization’s defenses and to adjust the way that it is executing those attacks. From a social engineering perspective, it also allows creating deep fakes using video, photographs, and voice to trick people into giving credentials. The ability to detect an attack is getting better, but being able to execute those attacks is also getting more sophisticated. There will be continued advances and an ongoing battle in the world of AI and security.

How well do health systems evaluate the risks that are introduced by their business associates and vendors?

A lot of organizations are aware of the risk. There is more risk in third parties since we are using more third-party applications in healthcare, especially with digital transformation. We’re moving more to the cloud in healthcare. We are sharing information with more third parties, and it’s not just third parties — it’s fourth parties, fifth parties. It’s the whole supply chain. Understanding risk begins with understanding where your data is and where it’s going. Who are your business associates contracting with and how good are their security programs? How good are they assessing risk?

Healthcare is getting better, but the risk and the sophistication are growing  also. We are probably not catching up as fast as we want to consistently across the industry. Many organizations are assessing by sending out a spreadsheet or a questionnaire. Are they asking the right questions? Are they asking those questions at the right level of depth or depth when they are assessing the impact that particular business associate could have? How frequently are they doing it? What are they doing with those responses and how are they tracking?

That’s hard for a lot of organizations. They don’t have the time, resources, or money to do all those things. Some of the clients we’ve worked with get better at by helping to build better programs that optimize the resources. That’s a lot of what risk management is about, especially in healthcare, where there aren’t endless budgets. How do you become more effective at deploying those resources in a way that give you the most bang for your buck? There’s definitely opportunity there and those challenges can be improved or solved by being a bit more optimal in how you assess risk.

Do you see the Federal Trade Commission becoming more aggressive in the non-HIPAA security and privacy aspects of healthcare given its recent activities in consumer privacy and application practices?

Absolutely. The FTC has recently come down with settlements or resolution agreements with healthcare companies that have shared sensitive personal information in violation of FTC regulations. They have also been focused on the health breach notification requirement. They have been very clear that they are looking closely at health apps that might not fall under HIPAA regulations, but certainly could fall potentially under multiple FTC and other privacy regulations. Several fines have been executed this year. They have also asked for comments on updating some of the rules that are in place already.

Recently there was the joint notice that was sent out to about 130 hospitals between the HHS Office for Civil Rights and FTC, warning those hospitals and also telehealth providers about privacy and security risks from online tracking technologies. Office for Civil Rights had also issued guidance in December. There’s a lot of attention on on how information is being shared through the pixel and other tracking technologies with organizations like Google, Facebook, and other advertisers and marketers, how that information is being used internally and to ensure that it isn’t being used in an inappropriate way. I think we are likely to see additional action taking place from FTC and potentially from OCR as well. 

What are the challenges for health systems in recruiting or retaining cybersecurity expertise?

It’s definitely a challenge, and has been for a long time. There are only so many people qualified for these roles, and healthcare has been challenged with having the resources and the dollars available to be competitive in many cases. Some organizations are in areas where there just isn’t that talent available at all to begin with.

Healthcare is also unusual in terms of the environment that we are working with from a security perspective. It requires a good understanding of a clinical environment and the technologies, compliance, regulations, and the business of healthcare. It is different when you’re working with patients. A lot of unusual attributes go into making somebody successful in that role. That’s probably why we are seeing a lot of healthcare organizations outsource services that don’t make sense to do directly. 

We hope to see more support from the federal government in providing some of the resources that are needed to train professionals in cybersecurity. There have certainly been some talk about that in the national cybersecurity strategy and some of the legislation that was recently proposed, specifically for rural hospitals. But it’s a huge challenge, and the need for security professionals is only growing. We will continue to see some gaps over the next decade, even as we hopefully begin to bring more talent into cybersecurity.

What will be important in the company’s strategy over the next few years?

Our vision has been to be a market leader in healthcare, cybersecurity, and compliance. For us to continue to do that, going back to the talent question, we have to have the best possible people. We also have to have a good understanding of what the needs are for our clients going into the future. Being a partner and continuing to innovate. 

We always want to be thinking ahead about what our clients are going to need going forward. We spend a lot of time there, developing people, retaining people, and giving back to the industry. We hope that through our work, we can continue to provide insight, information, and sense of community that can help healthcare to work together to solve its cybersecurity challenges.

HIStalk Interviews Ron Remy, CEO, Mobile Heartbeat

Ron Remy, MBA is CEO of Mobile Heartbeat of Waltham, MA.

Tell me about yourself and the company.

Mobile Heartbeat is in the clinical communication and collaboration space, which is being renamed as care team collaboration as the market is changing. We’ve been in the business since 2011 with our existing product line. I’ve been the CEO since 2013 and been involved since 2011. We were acquired by a public company back in 2016 and have been a part of them for almost seven years now. We are fully deployed in the market with over 260,000 monthly users, predominantly clinicians in acute care facilities. We have been on premise and are in the process of coming out with our first cloud-based platform.

I have a background in electrical engineering, which is surprisingly pretty useful in the software space, and a minor in computer science, which back in the day was an interesting field that was relatively new.

How did the business change with the pandemic?

It was fascinating, particularly in the first six months. Clients that were fully deployed were really grateful that they had deployed communication capabilities in their facilities. Clients that had partially deployed were calling us and asking us to speed up the process of getting them fully deployed, which was challenging because getting on site in some of these facilities for our staff was not an easy task pre-vaccination. Those that were in the process of evaluating new technology acquisitions — not just us, but anything that was new technology — those opportunities just ground to a halt because there was so much they had to do. 

Our existing clients were extremely happy, and we made sure that we were there to support them to keep things running. It was a core to them being able to treat patients effectively. For those that were partially deployed, we sped up the deployments. They saw the value from the places that they had rolled out smartphones and our software, and they wanted it everywhere. Those that hadn’t made a decision to deploy this new technology just stopped. They couldn’t take on any new projects. That lasted until a year or 18 months ago and was a pretty consistent trend.

It works like this in our experience. A health system decides to deploy smartphones to its staff. They do analysis to determine how many smartphones to buy, how many units they have, and how many folks they want to give access to this technology. They put the phones in place and then look at one of the communication companies, Mobile Heartbeat being one of those, as a vendor of choice. They deploy, because the first tool that they need is a communication tool. That’s obvious. Immediately after the tool is deployed, they start seeing some pretty good return on their investment. Then they look at other capabilities that they can put on their smartphones to enhance the clinician experience, improve patient care, or decrease errors.

It’s fascinating how our clients have integrated different pieces of technology onto these smartphones, using Mobile Heartbeat software to glue them all together. That trend is accelerating, and we have clients that are making these integrations on their own. We don’t even know that they’ve integrated other products into our own because they have become so good at it. I’m excited about the trend of the in industry going forward because clients see this as a future-proof path to providing better care and providing a better experience for their clinicians. That’s what we set out to do back in 2011, and we are seeing that come to fruition. The pandemic accelerated how clients pushed their smartphones to their highest capability.

We saw during the pandemic that clinicians and then patients were untethered from traditional locations. How has that changed your strategy?

It changes it in one big aspect for us, which is that we’ve supported the telecare side of telehealth predominantly. For remote nursing or central monitoring, we’ve become the endpoint for those folks that are doing the remote monitoring to message and communicate with the actual point-of-care caregivers. 

Take a central monitoring scenario. You have a technician, nurse, or other clinician who is monitoring a number of patients. Suddenly, they notice something about a patient. They have to immediately get that information to the caregiver who is most likely to provide care for that specific patient. They need to communicate quickly and efficiently, and they can’t be searching around for the right person. 

In that scenario, the technology that Mobile Heartbeat produces has become a critical component of those systems. That has been the biggest change. The pandemic has sped up those telemonitoring scenarios. I believe that virtual nursing will be the next big trend, providing nursing care without being in the room all the time, but still with a presence in the room. 

What have you learned from analyzing how caregivers use messaging and mobile devices?

The first thing that you learn early on in deploying smartphones and communication capability is that you need to think of it as an enterprise product. Not just from a product standpoint, but from a value standpoint for an acute care hospital or system. Metcalfe’s Law was proposed in the early 1980s by Bob Metcalfe, one of the co-founders of 3Com. His law states that the value of your communication network is equal to the square of the number of endpoints on that network. It dates back to the days of fax machines being replaced by a 3Com network. The value of your network grows exponentially as more people can use it to communicate with one another.

A healthcare system’s investment in communication technology becomes exponentially more valuable, and your ROI increases, as you put more people on the network to communicate with one another. You‘ll see challenges if you only do one unit versus the entire hospital so that the whole hospital can communicate with one another. That’s the biggest lesson that we got from the earliest days of Mobile Heartbeat. We are seeing this come back 10 years later as we talk about new technologies going out into the hands of clinicians, making sure that that network grows and includes the entire continuum of care.

It’s not just those inside the hospital. Now it is the at-home capability of a physician who may be a referring physician and isn’t part of the hospital system. How do we bring them into the communication network? You’re going to make a big investment, so you want to make sure that the ROI is as high as possible for your system. You have to pick carefully which projects to fund, the ones that have real value to your patients and your staff.

When do collaborating caregivers prefer a synchronous voice call versus asynchronous texting?

That differentiation started immediately with our first customers. Our analytics tell us who is who is texting who, who is calling who. We can see it over time. Put an asynchronous communication system in place in a hospital, with smartphones and Mobile Heartbeat software, and the communication paths won’t be what you expect at all if you’ve enabled other parts of your facility. Those paths will be much broader than you expected. It’s not just physician to physician or nurse to nurse. There’s a lot more people involved, such as pharmacy, respiratory, and PT, that are key parts of the care of a patient.

The second thing you start seeing is that the trend to move to asynchronous happens immediately. People realize the value of sending you a message to read when you have time versus a phone call that interrupts whatever you’re doing. The value of asynchronous communication is immediately recognized, but it has a fascinating secondary effect, which is that once people are comfortable asynchronous communication — a phone call, a synchronous outreach via phone call — the recipient knows that that is valuable. They know that that’s important, because otherwise it would have been a text. The likelihood of the recipient picking up the call and actually starting a conversation is much higher because there’s a confidence level that you’re only calling me if you need me right now. 

That has an improvement on your overall communications capacity and the way people use the different tools and the best path. Asynchronous if you don’t meet need me immediately, synchronous if you need me immediately. Your chances of the communication being correct and actually occurring is much higher. I found that fascinating early on, watching the phone calls drop and the text messages grow. Then going back six months later and interviewing the clinicians, who say, those phone calls are still critical to us. When something is really needed, someone hops on the phone and I always answer, where before I would let things go to voicemail. That’s a fascinating change in human behavior based on new technology.

Are messages escalated or alerted if they aren’t delivered or answered?

The alerting capability is pretty much in place today. What will be fascinating in the future is the ability of AI tools to make sure that these orders workflows are done, they’re done in the right order, and that people are reminded if they’ve not completed.

The digital playbook for a stroke stroke patient is different from hospital to hospital. If everyone is in a channel that has access to the digital playbook, it will be followed. You’re making sure that everyone takes care of their steps in the playbook, and you’re using some assistive AI technology to predict what the next step should be. That’s a big plus and that is a big win for the patient, the hospital, and the healthcare system. It’s a really good use of new artificial intelligence technology. I think we will see that coming relatively soon. 

Alerting when things don’t happen properly, if messages don’t get sent, is already pretty much there. It’s the keeping track of what should be done in a playbook manner for each patient and for each condition that will be the future.

Are health systems doing anything to integrate messaging with the EHR?

Almost everyone stores the actual messages, archiving them or keeping them in a offsite facility. They choose how to long to hold this. But very few want this to be a part of the medical record, for good reason. In many cases, these are conversations happening between clinicians. It’s like a hallway conversation. Would you really want every hallway conversation written into the medical record?

There are places where you need to be written in, and other places where you need it to be accessed down the road, but you’re going to clutter up a medical record with an awful lot of chatter around a patient if you wrote every conversation into the record. That being said, you may want to access it at a future date, so you need it to be archived, but you don’t want to bring back hundreds of pages of conversation in the medical record around the patient. You’re asking people to search through a lot of data for limited value.

Phone use went from calls to texts and then to two-way video like FaceTime. Are you seeing health system demand for that video capability?

There is demand, and we are moving down that path. The big use case is, “I need to show you something, but you’re not with me at the moment.” That’s the use case of video. You and I are working together. We have a patient or something of interest in common. We are in different facilities across town, you need to show me something, and I can’t be there standing next to you.

The video will give that opportunity. You have a smartphone in your hands that has camera capability. I can receive it. I can look at something with you on using the voice side of it. I can illustrate what I am seeing, ask for an opinion, or let someone know of something that will interest them that they should be aware of.

The challenge is that video is bandwidth heavy, and wireless requirements in healthcare systems are growing exponentially. If you’re going to add a lot of video onto your network,you’ll have to do some physical infrastructure planning to support it.

You have an unusual perspective in being a vendor that is owned by hospital operator HCA. How is the business environment changing for digital health companies?

I’m fortunate, and our team here is fortunate, to see how our parent company operates and how they make decisions around not only acquiring technology, but also business decisions around staffing and growing the business into different areas. 

One trend that doesn’t seem to be going away, both in our world but also in that of our customers, is the pressure on staffing and cost. Staffing costs are going up, and you have a couple of choices to try to address that. One is to hire more staff, which is difficult because the people just aren’t there. If you look at the number of nurses entering and leaving the profession, you have a potential 10 to 20% staffing gap in five years in just that individual role alone. Roll that across your whole system and that’s a pretty big gap. 

You can’t hire your way out of the problem, so what can you do? You can decrease the quality of patient care by assigning more patients to each caregiver, but that’s not a very good thing when the quality of care begins to slip. Now you look at other ways of mitigating this issue, and technology plays a role. It’s not a panacea. It won’t solve every problem. But it certainly serves a role, along with making operational changes. 

If you can reduce operational challenges using technology — make the clinician available to the patient more frequently or cut down their non-productive time so that they are practicing medicine instead of standing in front of a workstation on wheels – you have a chance of solving this problem. We are  looking at the operational side of the clinician’s world and how our communication capabilities can improve it. How can we make them more efficient? How can we increase their job satisfaction? How can we increase their time spent with patients and decrease the time they spend on administrative tasks?

Everything we are working on is aimed at that. It’s a problem we see both in our parent company and across the industry. That problem of staff cost and shortages is just not going away.

HIStalk Interviews Bart Howe, CEO, HealthMark Group

Bart Howe, MBA is CEO of HealthMark Group of Dallas, TX. He is also president of the Association of Health Information Outsourcing Services (AHIOS).

Tell me about yourself and the company.

I’m a problem solver. I like to work on big projects. I started in finance and quickly determined that my entrepreneurial bug was a little too strong to stay put there, so I started a solar energy technology company and worked in molecular diagnostics before getting into my career in health information management.

I’m the CEO of HealthMark Group, which is a digital health information management solution provider that is most known for our work in the secure and digital exchange of medical records. Oftentimes that’s referred to as release-of-information. That function is perceived at times by the rest of the healthcare industry as a bit archaic, but it can, and has, benefited greatly from the evolution of technology and the way that we maintain records and transmit records. HealthMark is committed to changing that dynamic by providing technology that drives digital self-service and immediate access to patient health information.

I’m an unapologetic patient advocate, so I’m always trying to think from the patient’s perspective. I am a consumer of healthcare, as are all of the end users of the clients that we serve. I always try to have my patient perspective hat on when we’re looking at how we can do things different and better at HealthMark.

I am also, as of a few months ago, president of the national industry association that represents release-of-information vendors. It’s called AHIOS, the Association of Health Information Outsourcing Services. In that, I take a pretty active role in speaking with regulators and legislators around the evolution of the health information management industry from a regulatory perspective. There are tons of opportunities to work with ONC, OCR, and even FTC as we see them starting to regulate more in this space, to make sure that we are creating the right pathways and incentives for organizations, providers, and digital health app developers to engage more in the interoperability solutions of the future. I’m a believer, and I think that opportunities are on the horizon that people don’t even realize exist yet in terms of what can happen when information flows more freely.

How has technology changed the release-of-information process over the past five years?

It continues to change regularly, so it has changed quite a bit in the past five years. I’m sure the next five will as well. What drove me to the space was the rapid change in the way that health information is managed and being transmitted.

I had a personal challenge in one of my prior roles. I was at a molecular diagnostics company, and we were pushing the bounds of scientific discovery in some of the tools that we were using to do oncology diagnostics and provide therapeutic guidance. One of the challenges was getting access to the longitudinal patient information to demonstrate that our diagnostic tool was actually generating better outcomes for patients, and therefore should justify better reimbursement. I saw a need for a better solution for accessing and transmitting health records. When I came across HealthMark, it struck a nerve as an opportunity to jump into an industry that is changing quite a bit and that has a lot of opportunities for improvement.

But to answer your question more acutely, the way that it has changed over the past five years is that everything is going faster. Medical records requests take all sorts of shapes and sizes and they come from all sorts of different parties, such as patients, other physician practices, attorneys, and insurance companies. But expectations for turnaround time for delivery of those records have increased dramatically. They will continue to increase until we can truly hit that target that I’m shooting for with our organization, which is immediate. We want to be able to provide immediate access to that medical information for a variety of different purposes while maintaining the security and privacy of that information.

As EMRs have proliferated throughout the healthcare ecosystem, a lot of that information is now stored digitally instead of on paper, where it was copied or scanned and delivered via snail mail. Today, we try to digitize as much of that delivery as possible. You would be surprised how much of that information is still being requested via a snail mail pathway, but in every chance that we get, we’re pushing requesters towards receiving and ingesting that information in a digital form.

The molecular diagnostics example is a near real-time, business-to-business transaction. How you see the line drawn between release-of-information versus interoperability?

That line is blurring entirely, and that is a good thing. From my perspective, the release of information function, again, has historically been perceived as relatively archaic and lagging behind much of the rest of the industry in terms of moving towards interoperability. I would challenge you to look at HealthMark a little bit differently. We are definitely embracing interoperability as a tool to be able to help deliver digital self-service and immediate access to those records.

To your point, we deliver records for both B2B purposes as well as B2C purposes or B-2-patient purposes. It covers all aspects of what we do. I’m incredibly excited about the trajectory of the industry from an interoperability perspective, and I really want HealthMark to be a leader on the forefront of that push.

On the patient side, how has the Cures Act change how patients request and receive access to their medical records?

I don’t think we’ve seen yet the inflection point of adoption that I hope that we will see at some point, in terms of the adoption of FHIR endpoints and the delivery of information through API methods that will enable a digital healthcare app ecosystem that doesn’t yet exist. We certainly have elements of it and we’re starting to see more of it, but we haven’t hit the inflection point yet.

Do hospitals see release-of-information as a necessarily evil or as an opportunity and a touch point for patient engagement?

If they are not looking at it as an opportunity or as a necessary touch point for patient engagement, then they are looking at it the wrong way. It is absolutely one of the areas that can cause the most abrasion between patient and provider if they aren’t given timely access to their information. They certainly need to think about that as a core competency of either their organization or of a partner that they’re working with to help facilitate that information flow as easily and seamlessly as possible. Maybe it used to be viewed as a necessary evil, but certainly it is an opportunity today.

Much of the information requests that the release-of-information association or partners fulfill are still continuity-of-care requests, so a lot of that information used to treat patients is still flowing through those means. It is critical to the patient as well that they get access to that information for those purposes.

You offer services related to the Family and Medical Leave Act. What kind of information requests are involved?

It’s not just FMLA. There are disability requests and requests for other information that require the physician or physician practice to complete information related to that patient’s care or related to that patient’s treatment regimen. It’s not something that you can pull directly out of a discrete data field. It often takes physician know-how of the situation, or specifically what the request is about, to complete that information. We work on behalf of our healthcare provider partners to alleviate some of that administrative burden.

Ultimately, HealthMark is trying to alleviate, across the ecosystem of our clients, the administrative burdens that we see in our US healthcare ecosystem, which is two to three times the administrative burden that we see in other developed nations. We think there are opportunities to streamline a lot of that information flow, and FMLA paperwork is one of them.

There are requests for that paperwork on a regular basis, ranging from simple requests related to a pregnancy or a surgery all the way up to things that are much more complex. Provider practices are required to fulfill that information request on behalf of their patients because it’s necessary, often for the patient to get a paycheck, so it’s critical to that patient experience.

Everything that we work on ultimately drives back to that patient experience. We are completing that paperwork on in conjunction with the provider partners that we work with to make sure that information doesn’t get stuck with the front desk staff or stuck with an MA and ultimately fall to the bottom of the priority list because it doesn’t involve treating a patient right there in front of you. These things are still critically important to the patient. We are helping make sure that we can streamline the flow of that information.

It’s vexing as a patient to go to your regular medical practice that uses an EHR and having a clipboard full of empty forms immediately shoved at you every time, especially when you know that everything you are being asked to write is already on the computer screen five feet from the clipboard. Why does that happen?

Honestly, I ask myself why that is still so often the case. Filling out paper on a clipboard should be a thing of the past. There is virtually no other situation where we complete information on a clipboard. We provide a digital patient intake solution to help streamline the flow of that information. In this case, not out of the EMR or the practice management system, but into it. We are providing a digital experience for patients to be able to bring healthcare into the modern world, into the 21st century of technology adoption.

I understand why there is a laggard nature to the healthcare industry in terms of a adopting technology. It’s a heavily regulated environment where it is difficult to make changes overnight. That has created a situation where healthcare providers are slower to adopt technology than in other industries, but I think we see that changing as well. Certainly with the pandemic, we saw a rapid overnight need to adopt technology for solutions for things that didn’t exist previously. Telemedicine skyrocketed during that period, as did things like digital patient intake, pre-registration forms, and remote check-in opportunities. We are coming along and we are making progress, but it still baffles me when I walk into a healthcare facility and I’m handed the clipboard and a pen.

Where do you see the company in three or four years?

We are going to continue to lean into the interoperability landscape. I know that is a buzzword that has been around for decades, but I hope that we are reaching the inflection point for both technical and regulatory pathways to make true interoperability a reality. There is a ton of potential in things like the Cures Act and TEFCA. As we lean into that, it will open up downstream use cases for organizations like us, where we are a trusted partner of the healthcare providers that we work with and a steward of that most precious protected healthcare information that they hold on behalf of their patients.

As we sit in that position and start to facilitate better, cheaper, faster information flow, that opens up a ton of opportunities downstream for things like analytics and focusing on the potential for using things like AI to provide relevant insights from that data back to the provider and back to driving better treatment outcomes for the patient.

This is stuff that I care deeply about, and as I mentioned at the beginning, I am an unapologetic patient advocate who tries to think about things from the patient perspective and how to make their experience better. A better experience for them is a better outcome for our clients.

HIStalk Interviews Chakri Toleti, CEO, Care.ai

Chakri Toleti is founder and CEO of Care.ai of Orlando, FL.

Tell me about yourself and the company.

This is my fourth gig in the healthcare space. My brother Raj and I have done business together, and this is my own project. I started the business three and a half years ago to bring ambient intelligence to healthcare.

I don’t have a pure technology background or healthcare background. I worked for Disney Ideas, then went to film school. That has nothing to do with any of this stuff, but I always was intrigued with other industries and how they adopt technology to bring process automation and efficiencies to deliver consistent, better solutions. That is my background and my passion.

Looking at healthcare, many processes can be efficiently automated to impact the care delivery process itself. I looked at ambient intelligence and felt that there is a significant gap in healthcare. I saw the transformation that was happening even in your home, like a smart home, the ability to get control of what’s going on in real time. That was the genesis of Care ai.

How will your business change as new types of health-related sensors are developed?

The technology has evolved dramatically. We can deploy high compute engines like GPUs in a smaller form factor with less power consumption. We have several provisional patents in terms of how to scale and do edge computing in a much more efficient way in the healthcare setting. We can roll out across tens of thousands of rooms without bringing the network down. We are really good at being able to get the appropriate data, clean data, to run these AI models on the edge.

If you want to draw parallels, look at Nuance’s 10- or 15-year-old technology with Dragon. When you have enterprises like Google, Microsoft, and Amazon spending billions of dollars on NLP-based workflows, that has become commoditized dramatically. Amazing large language models are being deployed in enterprise settings to be able to deliver the same kind of results, and much better results, for a fraction of the cost. That’s the transformation that is happening.

What we’ve built is bringing these operational clinical workflows together, building a scalable command center, and shifting the paradigm of what clinical data capture or operational data capture will look like in healthcare.

A lot of the old-school monitoring in the ICU went beyond sensor-based instrument alarms and instead involved an experienced clinician asking questions or observing the patient. Can value be added by analyzing audio and visual information?

That’s exactly what we do. Imagine a Tesla car sitting in a room. That’s what we’ve built — inferencing, audio-visual, three-dimensional volumetric data to give you a lot more information of what’s going on, how many people are in the room, how long did they stay there, did the patient eat food, how long have they been sleeping in the same position. All the environmental data, coupled with the data capture of every action that’s happening, is the fundamental difference that we are enabling to truly build a smart patient room.

I wake up every day from the dream that I’m going to kill the EMR. EMRs are the most antiquated way of data capture. They are required, but were built for a specific purpose 10, 15, or 20 years ago with an archaic way of data capture. It would be unthinkable if workers in an Amazon warehouse had to stop and input information about everything that they are doing. Yet we take the most talented and expensive resources in healthcare and make them do data entry in a crappy interface with all these clicks, forms, and flows in a complex form of data capture. All it is doing is generating a bill.

Obviously the clinical data is important, but we all know that every unit in every health system has skewed dark data. If you look at the respiratory rate, it is magically the same, 14 or something, in every unit. It’s like muscle memory. It gets worsen as you go through the ecosystem. Post-acute reimbursement is completely based on data capture. They have something called ADLs, activities of daily living. They have to capture all of that, and it’s a manual process.

Some hospitals have created command centers and are interested in remote patient monitoring. What will the hospital of the future look like given the opportunity to separate the services from the hardware capabilities of the room or having people enter the room regularly?

An accelerator for us is that the staffing shortage and the staffing crisis is elevating the need for solutions like these that can give the bedside care teams the scale that they need. Also, they have to think outside the box. The EMR cannot be the universe of every way of capturing information. Every health system recognizes that, and that’s why we are getting traction.

Also, the technology has become democratized, in that the cost to deploy these solutions is fractional. If you go to most of these organizations, they are still moving computers-on-wheels from older companies from one room to another, paying $10,000 or $30,000 per cart. For a fraction of that cost, we can wire up a true smart patient room that gives you real-time visibility into operational and clinical workflows with the ability to analyze audio, video, three-dimensional volumetric data visualization and capture of that information with super high accuracy.

How will AI change the way we think about healthcare software and how technology is developed or deployed?

It will be a once in a generation change in terms of how you look at delivering care. There are two sides to it. One is innovation, drug discovery and all the other aspects of AI. But when it comes to the four walls of operations of a hospital or post-acute facility itself, real-time AI will fundamentally change how we monitor and how we deliver care in an efficient way and at higher standards of quality. If you look at generative AI and all the innovation that is happening at an accelerated rate, healthcare will have a huge impact on that.

When we talk about AI in a healthcare setting, people talk about taking a few algorithms and applying them to the dataset that we have. That is good, and you need it. But a lot of the data is dark data. It’s skewed. How did we capture that information? Is it accurate? You have to go back and look at how you bring true, clean data into the system. 

Imagine a self-driving car. They send out these cars, capture real-time information about the roads, then teach the neural nets to look for the most efficient way of driving. More and more you will see those kind of implementations and adoption of AI into healthcare in a different way. It could be a radiology or a CT scan that’s happening in real time. The ability for it to recalibrate itself using AI to get more accurate scans will also be a part of the entire ecosystem. Rather than just, hey, I’ve scanned, so let’s apply AI to identify abnormalities. There are different aspects of AI that have not fully been leveraged in healthcare settings.

How should a mid-sized healthcare technology company look at incorporating large language models that are changing so quickly?

We should be looking at a problem and then seeing if applying AI to that problem will solve it. Does it even require AI? Once you have identified a problem like nursing shortages — we have a virtual nursing infrastructure — but then how do you look at AI being more integrated into the platform? Understanding the workflows within healthcare and using the frameworks with the right set of data to impact that workflow. That work will be a key way for these organizations to succeed. 

Cerner or Epic were designed before a lot of these innovations happened. For example, for controlled substances, two people have to sign off in the room, logged into the EHR on the same computer. That was designed like 10 years ago. There’s no way for one person to be virtually beaming in and one person in the room. EMRs don’t have the ability to do it. They would have to re-architect everything in the new way of doing things. That would be a big lift for them. 

Newer companies have an advantage to look at a clean slate and say, what’s the most effective way in today’s technology landscape to implement the most effective solution for that problem? If they truly understand what real-time AI can do, then the sky’s the limit to transform healthcare.

You started the company right before the pandemic began. What is different now about starting, running, and selling a digital health company?

I would strike out the last one. If someone is building something with the objective of selling it, then that’s the wrong way of going about it. You have to solve a problem, and whatever the outcome is, it will be good, whether you sell the company or stay with it. 

The landscape has dramatically changed. For us, we had an advantage in that we started the business when the pandemic hit, which propelled and accelerated our growth. I don’t think I could repeat the same kind of growth again in my career. We were at that inflection point.

Also, health systems have changed their thought process. The pandemic exposed the weaknesses that are inherent in the care delivery system and processes. That is in the forefront of the leaders in these health systems for them to solve. They are much more open to new, innovative companies, so it’s a great time to bring innovative technologies to these institutions that are more open to newer ideas and newer companies to innovate for them. They know that the status quo has a lot of weaknesses that are built into their systems today. It’s a great time if you have the right solution to help them be more efficient and deliver the same or higher standards of care.

What will be key to the company’s strategy in the next three or four years?

It will be extremely important to understand the impact of AI and how it will change the client’s businesses. If companies don’t look at new ways to solve problems, be nimble about it, and adapt aggressively, it will be tough in a dynamic environment. The technology landscape is changing at a much faster pace than we’ve ever seen in our careers. They have to be at the same speed as what the technology is changing. ChatGPT 3.5 versus ChatGPT 4 or Bard are coming up at lightning speed, and startups and new companies that are trying to go to market need to have the same agility.

HIStalk Interviews Patrice Wolfe, CEO, AGS Health

Patrice Wolfe, MBA is CEO of AGS Health of Washington, DC.

Tell me about yourself and the company.

I am a industry veteran. I’ve been in the healthcare space, particularly the healthcare technology space, for over 35 years, and have worked both on the provider and the payer side. I think I’ve seen every possible permutation of the challenges that we face in this industry in one form or another. I’ve been with AGS for almost four years. I’m so grateful to be in the revenue cycle space because we are at the core of the challenges that health systems are facing. 

AGS is a global provider of revenue cycle products and services, mostly to large health systems, but also we work with a lot of big physician practices and other players in the market.

What is the financial situation of health systems?

It is dire for a lot of health systems. A few dynamics have converged simultaneously. The rise in inflation impacts the cost of goods that hospitals and providers use and the wages they pay. Inflation is impacting margins across the board. Then you add to that the dynamics around clinical staffing and administrative staffing, where we’ve seen huge turnover across the health system space. They are left with fewer people to do the jobs that need to be done. Then when they try to fill these positions, particularly the clinical ones, they can’t find people, so they are using temp staff at triple the cost. You have huge cost pressures from that. 

On top of that, there’s the drop in investment income that some health systems have seen based on the markets. 

The news came out last week that the federal government will be rescinding a lot of the additional Medicaid coverage and other types of protections that were put in place during COVID, so a lot of health systems will be left with even more indigent care. 

The financial pressures are coming from every single angle for providers. We hear it from our customers. We see the anxiety that they have around, how am I going to cut costs? How am I going to increase revenue given all of these headwinds that I’m facing?

How will technology such as robotic process automation, natural language processing, and generative AI contribute to revenue cycle management?

Some of them are a little more near term than others. RPA has probably been around the longest. There are a lot of good use cases for using automation. I see across our customer base plenty of use cases where they’ve brought in automation to do some rote manual activities. We do quite a bit of RPA to drive out some of the  low-value tasks that you don’t need a human to do, so that the humans can focus on the more complex work. The low-hanging fruit has already been plucked in many cases, but there is an endless supply of additional use cases. It’s dependent on the health system’s ability to harness the data from their EHR and other types of systems and have the ability to attach the RPA to whatever the process is that they are focusing on. It’s a lot harder in practice than it sounds when you are planning it out.

There are some fantastic use cases in the HIM or coding area with the greater sophistication that is available in machine learning and natural language processing. You can see 10, 20, even 30% improvement in coding efficiency. With that comes increased revenue, because if you are getting your coding correct, that can then drive more accurate representation of things like case mix index, which then drive higher revenue. 

These types of tools are still in earlier stages of maturity. But with what we are doing in computer-assisted coding, we have some clear examples where customers are generating additional revenue from implementing these types of tools.

A lot of it comes back to data. You have to be able to extract from your EHR all the right information to take advantage of these tools. That is a critical success factor.

We have been playing around with not just ChatGPT, but some of the other OpenAI tools. We’ve implemented a couple of use cases for our internal use. Voice to text is important in the work that we do because we are often calling payers on behalf of our customers. Sometimes we’re on hold for 40 minutes or an hour, and the conversations that are taking place to follow up on claims can be lengthy and complicated. We’ve been able to use some of the OpenAI tools to turn those lengthy voice discussions into text so that we can do better quality assurance on our own folks as they are in these calls. We’ve implemented a few other use cases. There’s a lot of promise here, but I roll my eyes a little bit at some of the statements that are being made about how it’s going to revolutionize healthcare in the near term. I think it’s more of a long-term play.

Assuming that all the important chart information is in digital form, wouldn’t generative AI be ideal for coding and abstracting, perhaps replacing humans in the same way that speech recognition has done? 

Given how long I’ve been in this industry, I have a hard time saying that I think things will be completely eliminated. I can’t believe that we are still using humans to post payments, which should have gone away 15 years ago. 

The promise is there that these tools will be able to take over a good chunk of that work. But we have seen too often that a human is required to do some of the more nuanced review. It may be that AI can eventually code a chart, but the human is most likely going to need to continue to do some of that auditing. There’s a lot of hype around things like autonomous coding in certain discrete specialties like radiology, where the clinical nomenclature is  limited. But at this point, only a subset of those charts are able to make it through the autonomous process. You have to have humans on the back end to take what falls out and to do that auditing. We have the promise to be able to automate far more of this work than we have to date, but as a grizzled industry veteran, I don’t think we will get to that 100% automation level.

Everybody is unhappy with the prior authorization process. Can it be automated or eliminated?

That is probably the single most expensive activity that takes place in the revenue cycle. I’ve seen estimates of $80 for each prior authorization if you add the cost of the payer and provider to adjudicate. That’s a huge pain point, and there will be a lot going on in the industry to address this. My understanding is that Epic has been working on this for a couple of years now and has some payer partners that they are doing some development work with. The EHR is part of the solution, but I don’t think it is going to be able to completely solve that problem. 

Without a doubt, some of these front-end activities are  getting more complex. We hear from our customers that the barrier to prior authorization has only gotten worse since the pandemic. Some of that may be a reflection of staffing and other problems on the payer side. They are having the same kinds of issues providers are with the great resignation and other types of administrative challenges. We are seeing the prior auth space get worse, so any kind of automation that can be done in this area is of enormous importance to providers and to payers. 

It’s a huge pain point to physicians. We are doing patient access work, including prior auth, with one of our customers. Their physicians are unhappy when they get involved in these peer-to-peer conversations with payers, or when they have to re-review the documentation that was submitted. We are doing a lot to try to minimize how much the physician actually has to get involved in these types of interactions, because it takes them away from their core mission, which is to care for patients.

It’s probably not the best example of healthcare consumerism, but how is patient payment processing developing?

This is an area where every single person has a personal opinion. Earlier in my career, when I was running a patient payments business, any time I talked to anyone, they had a story about trying to pay for something or trying to understand what they were supposed to pay for. This is a visceral topic for a lot of people. 

There has been a lot of progress made, particularly around the patient front door type of growth. I see progress in patient registration and all the things that we try to push to the front of the revenue cycle that can and should get done before the patient shows up. Some of the mobile-based and text-based tools that are out there are pretty good. As always, getting them correctly integrated with the EHR is often a problem for these providers.

So many people are comfortable with mobile-based payment activity that it makes me happy when I go to a doctor’s office personally and have to do a mobile-based registration process. There has been good progress made in this area, and while I don’t think we are done yet, we want to meet people where they want to be, and most people want to do mobile-based banking and other kinds of financial activity. I feel fairly hopeful about that.

How have operational and market conditions influenced the appetite for innovation in health systems?

That’s such an important question. In the course of my conversations with our customers, I see two camps. One is that I see the organizations that are using this as an opportunity to double down on innovation, but I would say innovation with clear ROI expectations. What we are trying to do a better job of at AGS is quantifying the revenue impact we are having for our customers. Our space historically has patted itself on the back in terms of its ability to reduce customer expenses, which is a great thing, but that’s not enough right now. In this environment, it’s about how you will positively impact a health system’s revenue. Organizations that are  a little more risk-taking and forward thinking are willing to double down on innovation, but they want to see those metrics, which is completely reasonable.

On the other end of the spectrum, we are seeing health systems that are sitting around the boardroom, with the CEO and CFO saying, “I need $5 million in expense reduction out of you, I need $10 million out of you, and $2 million out of you. I don’t care how you do it, just go out and do it.” In those organizations, we are seeing more things that are retrenchment, such as cutting IT spend or vendor use. That’s a challenging situation, because they will regret some of those cuts two years from now. I completely understand the pressure they are under, but there are wise ways to make those cuts and maybe not so wise ways to make those cuts. It’s a challenging position for a lot of these health system executives.

What is the company’s strategy over the next few years?

A few things really matter to us. Our mission is to drive great financial health of our customer organizations. A few things are top of mind. There is a continued need to bring technology to bear in what has often been an inefficient and human-intensive process. As technology matures, whether that is AI or other types of technology, we want to be there thoughtfully using it on behalf of our customers.

The other thing is that we we are fairly acute care focused. We work with a lot of large specialty physician practices, but mostly large health systems. Many of these health systems don’t just do hospital-based care for their patients. They offer ancillary service lines such as home care, skilled nursing, ambulatory surgery centers, et cetera. We have to continue to think about and define the revenue cycle more holistically on behalf of these organizations, because there are opportunities for them to gain efficiencies and drive more revenue out of some of these other parts of their organizations. Those other parts might use different EHRs. They might be managed completely outside of the standard revenue cycle. There could be some good efficiency gains in some of these other areas over the next few years, particularly as we continue to see consolidation in the market.

HIStalk Interviews Christine Swisher, PhD, Chief Scientific Officer, Project Ronin

Christine Swisher, PhD is chief scientific officer of Project Ronin of San Mateo, CA.

Tell me about yourself and the company.

My background is in healthcare, mostly in oncology, but also in building predictive models and AI as software and as a medical device. I’ve worked at Philips Healthcare, which is in the Fortune 500, as well as several startups. I’ve led from idea to FDA clearance and expansion in the US and in Europe.

I am passionate about responsible AI and what that means, to deliver AI that is impactful in healthcare and that improves the lives of patients at scale.

At Ronin, we are fortunate to have a wonderful network and partners such that we are set up to achieve our mission of improving the life of cancer patients at scale and impacting all four of the Quadruple Aim verticals. We build technology such that an oncologist and the clinical care team that cares for cancer patients can see at a glance, and understand, their patient’s journey. We look through all of the structured data, clinical notes, and documents and bring that forward, so there isn’t that 30 minutes of clicking to prepare for a visit, but help them understand their patient at a glance. We also bring in the patient’s voice to understand what’s happening to the patient outside of the hospital and render that in their clinical workflow.

We have a mobile application that engages patients, not just for having a better understanding of the patient, but to empower clinicians with predictive information so they can take actions earlier and prevent adverse events and avoidable hospitalizations or emergency department visits and also better manage symptoms so that patients can stay on treatment longer.

What is the extent of genetics and genomics data that can be used to make clinical decisions?

A lot of that is about contextualizing that information. There’s a big jump from what scientists have discovered and where we are in this, especially in the genetics field. How do we deliver that to have meaningful outcomes in clinical care? How can we contextualize that information alongside their patient record of what’s happening, their entire patient record such as comorbidities, social determinants of health, and patient-reported outcomes? What’s happening to them at home? How can we bring all that together to have a total patient understanding, including their patient preferences?

With that total patient understanding, we can make the best choice for that particular patient. It’s a critical piece of information, especially things like EGFR mutations that are so impactful for treatment decisions that they can be lifesaving. We need to bring them into care decision making.

ChatGPT feels like an overnight success, but probably isn’t to experts in the field like yourself. How will your work be changed by its capabilities and popularity?

It definitely impacts the work that we do it. In fact, I think it enables the next level of technology if we are thoughtful in how we deliver that.

It didn’t happen overnight from my perspective. In 2012, we witnessed a similar event in AI, where there was a technological breakthrough with convolutional neural networks, rectified linear units, and dropout that allowed us to have computer vision perform as well as humans for general domain tasks in classification. That particular event sparked the deep learning revolution.

From 2012 to 2020, there were about 100 FDA-cleared applications, 88 of which were computer vision or in the radiology space. That happened quickly and the characteristics of these winners that were able to deliver on deep learning at that time. Radiologists, pathologists, and recipients of this technology were skeptical, just as skeptical as they are now.

It’s slightly higher publicity now because so many people are using things like ChatGPT in their work. But it’s a lot of mirroring to what happened in the 2010s, when the AI winners in healthcare did three things. One, they prioritized interpretability and risk mitigation. Two, they focused on super-powering the clinicians versus trying to compete with them, and companies that said they were going to replace a clinician were not successful. Third is that they delivered a complete solution, and those solutions fit seamlessly into the clinical workflow. They delivered on the CDS five rights, which means that it was the right information, the right person, the right format, the right channel, and at the right time. That’s the key to success.

None of those things have really changed about healthcare in the past 10 years. There was a technological breakthrough with the transformer architecture in 2017, and then a new generalizable method, which was GPT- based models. We had a new generation of applications like ChatGPT, Stable Diffusion, Dall-E, and all of these generative AI technologies. It’s very much like what we saw in 2012.

If we can take those learnings about what success looks like, and bring those into how we think about this new innovation or new class of AI-powered applications, we’re going to be a lot more successful. I am really excited about generative AI, but I think that it has to be delivered the right way.

We heard way too much back then about big data, which is rarely mentioned using that name today. Will AI and ML help deliver that promise?

We’ve been doing things that are interesting. AI has helped identify sepsis patients earlier and to identify ischemic strokes so that patients can be treated within the golden hour. It’s been able to better detect breast cancer, lung cancer, and prostate cancer earlier. It’s already impacting people’s lives. That was with big data. It’s already living up to, maybe not at the scale that was predicted, but it is actually improving people’s lives at scale.

Now what we are seeing with this new class is new ways that we can better improve people’s lives. Generative AI can help scientists and researchers better discover new drugs, new treatments, and new therapies for cancer and other diseases.

It’s going to enable a better understanding of the patient’s journey, just like what we are doing at Ronin, being able to dig through the 80% of the EMR that is unstructured data documents, clinical narratives, and notes and have a better understanding of patients at an individual level and at a population level. That means that we are going to be able to better predict things like mortality, progression, adverse events, toxicities from treatment, and acute care utilization like emergency department visits. Then by being able to predict them and see what caused them, we can better inform on actions. I’m really excited about the technology, as long as it’s delivered safely and ethically.

The new book “Redefining the Boundaries of Medicine” notes that medicine is based around huge population studies that may lead to the wrong conclusions when a specific intervention doesn’t appear to be effective collectively, but works on subgroups of patients who share particular circumstances or comorbidities. How would a data scientist look at that issue?

This is very core to our Ronin mission, to deliver care decisions that are personalized to that particular patient versus based on population averages. So many decisions in oncology are based on population averages. By bringing data of what happened to patients like them — what happened in terms of their progression, their quality of life, the toxicities that they experienced — we can look at the patient in a comprehensive way, thinking about their demographics, social determinants of health, their cancer and treatment specific risk factors, their comorbidities, symptoms, active problems, and biomarkers as well.

If we bring that together to then say, what happened to patients like my patient, we can provide more personalized decisions. We can also empower the care team, oncologist, patient, and caregiver with data to make that decision.

Previous technologies were implemented as advisory rather than a closed loop system that would require FDA approval. How prepared is FDA to evaluate AI technologies and are the usual retrospective studies adequate to do so?

I have two answers for that. The first is that regulatory and best practice groups are moving quickly in response to the innovation and excitement around generative AI and AI in general. Three seminal documents were released just in the past few months. The White House delivered a blueprint for an AI bill of rights, NIST delivered their risk management framework, and the Coalition for Health AI delivered their “Blueprint for Trustworthy AI Implementation Guidance and Assurance for Healthcare.”

When you look at these three documents, five themes emerge across them. You need validated, safe, and effective systems. You need protections against bias. You need privacy and security. You need interpretability and explainability. Finally, you need transparency and human factors.

Whether or not it’s FDA-cleared 510 (k) software as a medical device, a CDSS, a CLIA-validated laboratory developed test, or AI for another application that doesn’t fit it under those regulatory guidance, it’s still important that it delivers on those five principles. In fact, those actually expand past healthcare.

Those are the things where we will see guidance from groups like CHAI on how we concretely deliver on those principles. The principles have been defined, and now these groups are working very quickly to define the next steps. I also think that infrastructure cloud vendors and AI tooling vendors will, at some point, start to provide certified tools to companies like Ronin and others to accelerate our ability to deliver AI safely. That’s a huge market opportunity.

AI in healthcare, particularly with our last AI revolution in the 2010s, was most successful when it was partnered with clinicians to make them super-powered clinicians. If you look at other domains, the same thing is true. AI did not replace as many jobs as people thought it would.

You could also look at things like when we went from animators hand drawing to CGI. CGI just expanded the scope of what they could deliver, how productive they could be, and allowed them to work at a higher level with the tedious tasks taken away. It’s the same thing of going from FORTRAN to C++ to Python and how we develop AI.

If we look at how those industries are impacted, there’s as guiding principle that AI empowers people and takes the tedious things off their plate so that they can operate at a higher level and deliver higher quality. That’s true in healthcare as well.

How will the availability of complete, representative, and unbiased training data affect the market for AI technologies?

Protections against bias is a key theme in those three seminal documents that I just talked about, and something that we need to do proactively and continuously. It’s not a one-time event where you look at your patient population, see how it performs in subgroups, and then write it up in a medical journal.

It has to be part of your system, where you are continuously monitoring for bias. Then when you detect a bias incident, you need to have the systems in place to rapidly mitigate that issue. One of solutions is representative data, but we need a three-pronged approach, where the first prong is like the brakes in your car, the second prong is the seatbelt, and the last one is the airbag.

The first prong, our brake, is about preventing any foreseeable bias. So that when you are developing the model, you have representation of the populations that you intend to serve. You have subject matter experts that understand that there isn’t bias built into the actual ground truth data or the data feeding into the model. That the way it is delivered from a user experience will not exacerbate currently existing biases in the system, so that there’s a lot of voice of the customer or human-centric design that has representation of the populations that we intend to serve. That’s the brake.

The seatbelt and the airbag are two pieces. The first is that you need to have proactive and continuous monitoring for bias across important subgroups. Things like social determinants of health. Do they have access to transportation? What about their insurance and demographic groups? We need a comprehensive understanding of the different ways that we could introduce bias that causes harm to different types of groups, then detecting that and being able to diagnose any problem quickly before it causes patient harm.

Then knowing that you have a problem, the next step is to fix the problem, so having the systems in place so you can rapidly retrain a model and you have the technology or ability to mitigate bias quickly. The machine learning operations, MLOps needs both infrastructure and practice to mitigate that and then deliver that fix quickly before there’s patient harm. In addition, there are human factors in how it’s delivered so that you can mitigate risk as well.

IBM Watson Health failed at trying to do years ago what people think is possible now. What has changed?

For those that will be successful, what’s different now is the user experience and real-world validation of the technology. What is the AUC, area under the curve, of a model? All these abstract metrics that AI practitioners tend to focus on … instead of focusing on those, focus on the meaningful measures. Does the AI plus the human better prevent acute unplanned care? Does it keep patients on treatment longer with their symptoms better managed? Does it increase progression-free survival? Going back to what a meaningful measure is and evaluating the performance of your models against that, versus abstract measures, is one of those key pieces.

The other one is thoughtful, human-centric design. With those pieces together, that’s where you have meaningful impact. Companies compete too much on model AUC, accuracy, or F1 score. A 5% difference sounds good on paper, but it’s the execution of that. When you delivered in clinical workload, did you live up the CDS five rights? If that’s true, you’re going to have a bigger impact. Focusing on the meaningful measures versus the abstract measures is key.

Is there a tension between the absolutes of data science versus the frontline practice of medicine that incorporates variables that are personal, local, or perceptual?

Especially for CDSs that rely on predictive models, machine learning, or statistical methods, it’s crucially important. It is written in the FDA’s guidance that you need to share the basis of the prediction and the relevancy of the training of the development data. Both of those things need to be shared.

At Ronin, we show that in a way that is accessible to the clinician. You don’t have to have statistical knowledge or machine learning knowledge to understand that. It’s right there at the point of making the decision, the relevance of the patients that are similar that are giving this insight for this particular patient. The basis of that prediction is right there during clinical decision versus buried in a user manual or peer-reviewed publication that might be behind a paywall.

For things like generative AI and language models, we still need to innovate and develop the methods for transparency in sharing the basis of our prediction. When we look back to things like convolutional neural networks, there was innovation on how we do that. Things like saliency maps were invented and the methodology to do that. Semantic segmentation was another innovation that allowed us to provide that type of insight.

We probably will have to invent some new methods, and I’m excited and hope that we continue excited about what that will be. We would like to be a part of that, and I am hopeful that our research community will gather around this challenge.

Will we see a trough of disillusionment with generative AI?

There will probably be a realization of the challenges, limitations, and areas of success. We’re going to learn that. We’re still learning about what this technology can do. How do we really understand what’s going on underneath the hood? How do we get it to explain the basis of its predictions?

People who are skeptical now — especially if they start to use it to help with writing, as a second reader, or to write code – may start to see a lot of value in it. On the other hand, we’re going to learn about its limitations. I think we might see the more skeptical folks being more embracing, and the ones that are less skeptical becoming more skeptical, as we learn more about the limitations.

What will the next few years bring to Ronin?

We are realizing that personalized, data-driven, total patient understanding in care decisions for cancer patients empowers clinicians. We can use AI, machine learning, and data science informatics for that and to bring the patient patient’s voice into it as well, where they can say what’s happening to them outside the home and their preferences can be brought in to care decision-making, even in the data that is driving those care decisions. There’s a huge opportunity to deliver on that vision, and we are already doing it.

HIStalk Interviews Julia Regan, CEO, RxLightning

Julia Regan, MBA is co-founder and CEO of RxLightning of New Albany, IN.

Tell me about yourself and the company.

I’m a long-time health tech innovator. I carried a bag in pharma and started my career in sales. Early on in my career, in the infancy of health technology, I worked for a manufacturer organization. I fell in love with the opportunity to connect different people, roles, and responsibilities across the healthcare continuum to try to create a better experience and world for patients.

The specialty medication market is one of the fastest growing spaces in the industry for drug spend, representing 52% of dollar volume with high-cost medications such as biologics, infusion meds, cell gene therapies, and even those involving personalized medicine. RXLightning brings that specialty medication process and journey for patients and providers into the digital arena.

Our end-to-end platform automates multiple steps of this process while connecting doctor, patient, specialty pharmacy, and drug manufacturers and  their support teams. Our digital platform, for the first time, creates visibility into the experience. The goal is to reduce administrative burden and waste in the healthcare system for the providers and those organizations that are working to help patients, but ultimately to get patients on therapy quicker in a more affordable way.

What is the overlap between specialty medication prescribing and prior authorization?

Prior authorization is definitely a component of gaining payer access and approval for these medications. But it’s not just the prior authorization, it’s also the cost component, which for these medications could range from tens of thousands of dollars up to a million dollars. Because the cost is so high, there’s an affordability component. Drug manufacturers create programs to help patients get access to therapy, helping go through that benefit investigation and that prior auth process, and also more affordability programs. That could be a bridge program, where patients get samples of the drug while they are navigating the access barriers; free drug for people who can’t afford it; and research around foundations and grants. It’s everything from access through affordability as well. We are a little different than the PA, but the PA is still a component of the journey.

What is the manual process that you replace?

If a specialty pharmacy is used, the doctor will send the prescription to the pharmacy and then wait. The pharmacy will reach out to them and say that a prior authorization required, so they will either complete a paper form or use a digital solution. The next step involves affordability. The pharmacy traditionally works through that process, but because the prescription doesn’t have any of the clinical information or patient financial information, there’s just a lot of back and forth among the pharmacy, the provider’s group, the payer, and even sometimes the manufacturer and their programs. This paper-based system is slow and creates inefficiencies due to missing information or ineligible information.  

RXLightning has created a technology solution for just under 1,300 medications that turns those processes into a single solution that walks a provider through that process digitally and also allows them to track their patients throughout. Instead of using Post-its, Excel, or manual processes that live outside the EHR, our technology system tracks that journey with a CRM type of tool.  

Why do manufacturers choose the specialty drug distribution model and what information do they require?

Because of restrictions and cost, a lot of parties along the way want validation that the clinical steps that are required for approval for a given patient have been documented. The traditional prescription information is one component, but it’s also contact information and caregiver information. Sometimes it includes the clinical history, not only from medications, but also height, weight, allergies, and medications that have been tried and failed. Many components that are part of that traditional prior authorization process are part of these referral forms and enrollment forms.

Then there are REMS medications, which are in the FDA’s Risk Evaluation and Mitigation Strategy because of serious safety concerns. Those have different criteria around authorization codes and compliance that in some cases must be submitted monthly.

Another component is consent, opting into different programs for the patient to share information from a HIPAA compliance perspective, as well as the provider consent to allow another party to work on behalf of them to help navigate through that experience. Also for sharing household income information if they are looking at grants, foundations, or free drug programs.

How laborious is the provider’s process and how long does the patient have to wait for approval before starting the drug?

The work of going through access, affordability, and patient data collection isn’t done while the patient is in the office. A patient who is sick now may have financial constraints with affording a medication that can change their life or even save their life. The provider has to call the patient and ask them to fill out forms. They either have to come in to the office or have it mailed to them, which could get lost.

That paperwork process can take weeks or months. With RxLightning, we see it done sometimes in less than 10 minutes. We communicate and capture the patient consent and information via text and email. The majority of referrals are completed in less than an hour compared to the 2-3 weeks it was taking before. 

What is the implementation process? Do you work individually with providers in a health system, or do they need to reach consensus as a group?

Our platform is extremely flexible and nimble, so we can support all of the different scenarios that are out there. If a large health system wants to install it, we go through a corporate business associate agreement, because PHI and patient data is being entered into our system. We traditionally go through security assessments, and we are HITRUST certified.

We have crawl, walk, or run approaches to implementation. We have a standalone portal that providers and users can be up on within minutes once we get through the business associate agreement and security assessment, if it’s required. The crawl approach is that we use our standalone portal and power it with Secure File Transfer Protocol, or SFTP, data exchange. That’s really just around how we are going to exchange information, pulling exports out of the EHR, having that load patients into our system, and then pushing the data from our system back into the EHR.

Our run is being able to do fully single sign-on capabilities or API integrations with the EHR and embedding our platform into those systems. That requires an implementation group and technical support from the health system. Our standalone platform is completely free to provider groups. 

How are insurers managing biosimilars? Are they asking patients to change their specialty drug prescriptions or do they require a different process?

That’s a really interesting question, and I don’t think there’s a exact answer. Each payer is going to create their clinical policies into their rules based upon what their clinical team assesses coverage should look like. There are multiple steps in this process, and our platform does pharmacy referrals. If a health system doesn’t have access to limited distribution and it’s at a single-source distribution pharmacy, they can send the clinical information and package it up over to that pharmacy. Then we close the loop back to the health system pharmacy with the details so they can create a better experience for the patient.

We handle the investigation, pricing, and coverage. There is a PA component of our platform that could be used. It’s very modular, though, so if they already have a solution in one of those, we could plug those into the platform. Then we handle all of the foundation, grant, free drug, and affordability components in our platform. What we’ve looked at is that across that end-to-end experience, we’ve created a tool where it’s up to the health system, providers, and users on how they want to navigate through it and use it. 

Regardless of what the payer criteria are or the decision-making, around the biosimilars, for example, offices can use our platform to navigate those decision points, and complete the processes for all of them in one location, to navigate the patient quickly and efficiently to a therapy that the payer is going to cover and approve.

How have market conditions affected your strategy?

They haven’t impacted our strategy. So many inefficiencies exist across this journey that health systems and provider groups need a solution. RxLightning has approached it from a brand- and drug-agnostic perspective. We haven’t isolated it to one therapy, one disease state, or a limited portion of drugs. We’ve opened it up and said that we are going to try to solve this process for all of these medications across all of these different steps, which today is being done by different vendors or organizations, most of the time on paper. Organizations see that our platform solves many inefficiencies on their team and the work that they are doing. RxLightning helps alleviate provider burnout  because it makes this process so efficient.

It’s not just about the efficiencies upstream, because when you use paper and faxes, inefficiencies happen while you are awaiting a response. The communication back to the provider’s offices creates call lags and call volumes and it’s sometimes uncontrollable for organizations. We work to plug into the different destinations across this journey — manufacturers, different specialty pharmacies, different parts of the process — to close the loop with information back.

If a provider has a patient who needs a cancer medication and can’t afford it, they can go in our system, see all the grant information, and make a decision whether to apply for a grant. If the grants aren’t of open and foundations aren’t open, they can do the manufacturer’s program. We will provide the response back around the approvals or the denials so they don’t have to constantly look, make phone calls, or answer phone calls. That gives transparency through that whole process while also allowing the patient to see updates across the journey.

What will be important to the company over the next few years?

We are looking to expand our provider base. We know that when our platform is used, it saves much time for offices and helps patients get on therapy much quicker in a more affordable way. We are used by some of the largest healthcare systems today, so growing that base and then providing all the digital connectivity points into the drug manufacturer programs, the hubs, and the specialty pharmacies to have a 100% digital, interoperable ecosystem that exchanges information is critical to the success for the industry, patients, providers.

HIStalk Interviews Frank Harvey, CEO, Surescripts

Frank Harvey, RPh, MBA is CEO of Surescripts of Arlington, VA.

Tell me about yourself and the company.

I have been interested in healthcare since I was six years old. My father used to take me on Saturday mornings to the soda fountain at the local pharmacy. I was interested in what our local pharmacists were able to do with patients and the members of the community. From that time on, I’ve wanted to be in healthcare, specifically as a pharmacist.

I’ve been in pharmacy throughout my career. I have been fortunate to be a part of life sciences, with Lilly and Hoffman-LaRoche, and companies such as Liberty Medical and Mirixa, which is a medication therapy management company. I ran my own venture fund for bit. I was excited to get the opportunity to come to Surescripts because it’s such a wonderful company. Surescripts is a mission-driven health information network that is focused on enhancing the prescribing process and forming care decisions. Our mission is to continue to lower the cost of healthcare, improve patient safety, and improve the overall quality of care.

How has the role of the pharmacist, along with the technologies and data that are part of their work, changed?

During COVID, pharmacists really raised their level and used the full scope of practice of their degree. It was critical during that time, because in many cases, physicians weren’t available because they were tied up with so many COVID patients. Pharmacists stepped in to do much more, such as administering vaccines and  counseling chronic care patients.

We expect pharmacists to continue operating through the full scope of their license, particularly because there’s such a shortage not only of primary care physicians, but also of endocrinologists and rheumatologists. We’re seeing a burnout of physicians and many of them are retiring. Pharmacists will have the opportunity to step up their level of their practice to be operating more at the full scope of their license.

How has the Surescripts network changed over time?

When Surescripts first came into being over 22 years ago, prescriptions were transferred back and forth, either by patients carrying the prescriber’s handwritten prescription to a pharmacy or having it called in. Surescripts was put in place to make that process electronic, as the first health interoperability network, if you will. Now the vast majority of prescriptions go from the physician to the pharmacy electronically through our health information network. 

We have continued to expand far beyond that to help with price transparency and to support pharmacists and physicians being able to message each other electronically, with no more faxes or having to jump on the phone. We’ve continued to focus on enhancing the prescribing process and informing the care decisions that physicians, nurse practitioners, and PAs make by providing medication histories of the patients to the physician.

Has the launch of a competing e-prescribing network changed your strategy?

No. We will continue to focus on being a mission-driven company and will continue to enhance the prescribing process and informing that care decision. Competition is always good. We welcome competition that helps move our mission forward. Whether it’s Surescripts doing it or other companies doing it, we’re happy about that.

How will you continue to enhance the Surescripts network?

Even in the last four years, we’ve improved the quality of prescribing, the prescriptions coming across, by about 85%. We continue to focus on enhancing that prescribing process. The other thing we continue to work on is ensuring that, from an administrative standpoint, we’re providing the right information at the right time to physicians, so they don’t have to cull through volumes of information to get to what’s important at care decision time.

How much emphasis is placed on inserting the connectivity result into the prescriber’s EHR workflow?

It is really critical that it’s in the workflow. We’re integrated in every EHR across the country. Last year, over 2 million practitioners prescribed over 7 billion transactions. All of those were integrated into the electronic health record that the physician was working with.

An example is that at the time of prescribing, when the physician is with the patient, transparency apps allow the physician to see not only the therapeutic alternatives, but also the pricing of each based on the insurance coverage that the patient has. It allows a physician to make the right therapeutic decision for the patient as well.

Are you seeing benefits for both the prescriber and the patient?

Absolutely. That’s one of the most important things about having a real-time prescription benefit tool in the physician’s EHR. They can see everything about the prescription and the therapeutic alternatives. Before, they would write a prescription without understanding the price consequences. The patient would take it to the pharmacy, find that they couldn’t afford that medication, and then ask the pharmacy to call back to have the prescription changed to a different medication that they could afford. Integrating that into the overall workflow cuts down a lot of demonstrated burden of the physician, the pharmacy, and the physician staff.

Have you seen statistics documenting outcomes improvement since cost issues might have led to the patient either not having the prescription filled or taking it in lower doses to stretch it out?

We absolutely have. Most recent studies shows that the prescription pickup rate increases by 3% to 5% with use of a price transparency tool with real-time prescription benefits. The patient knows what they are facing from a pricing standpoint, they’re more likely to pick it up, and the doctor is more likely to have written a medication that is affordable to the patient. The most expensive medications are the ones that the patient never picks up, because they never get their health condition taken care of. These tools help the patient.

How has the federal government influenced interoperability?

Micky Tripathi and his team have done a tremendous job. They have so much energy behind their efforts. Interoperability is so critical in being able to get that full patient’s record. A new proposed rule focuses on advancing that interoperability and improving transparency, supporting the access and exchange of electronic health information. 

The role that Micky and his team have played has been critical to moving us forward more rapidly than would have happened without their participation, their urging, and their hard work over a long time. We are a great example of what interoperability does, with 21.7 billion transactions a year across all of our products. We are looking forward to everything that’s happening with TEFCA.

What will the company’s strategy be over the next few years?

We are going to continue to focus on what has been our bread and butter, which is our mission of improving the quality of care, improving patient safety, and lowering cost. We will do that by broadening the areas that we work on across enhancing prescribing as well as informing care. We are looking to work to help broaden the care team, to enable the care team as it expands and pharmacists take a more active role, to make sure that they’ve got the right data to make the right decisions and can communicate that information back into the health record. We will continue to lobby for the right legislation to be in place to enable and empower pharmacists to do what they’re able to do, in partnership and collaboration with physicians, nurse practitioners, and physician assistants.

Healthcare in this country is at a critical phase. We are seeing the continued burnout of our healthcare practitioners and a lack of enough healthcare practitioners, particularly in rural and urban areas. We have areas where patients may have to travel 100 miles to see a physician. It will be important that pharmacists can play a larger role. I believe that we will see, over the next five years, that the healthcare team will continue to evolve, and that will be the best thing for the patient.

HIStalk Interviews Jamel Giuma, CEO, JTG Consulting Group

Jamel Giuma is president and CEO of JTG Consulting Group of Miami, FL.

Tell me about yourself and the company.

I studied finance in college, but I was always in IT. I started working for a retail company while I was in high school in their corporate IT department, and did that through my first couple of years of college. I got tired of working for corporate America pretty quickly and started applying to IT jobs in Jacksonville, Florida, where I was raised. The first place to call me back was the University of Florida health system. I started working in their desktop support group, and one of the areas I was responsible for was the laboratory. I was replacing the lab director’s computer and she said, have you ever thought about becoming a systems analyst? I said, what’s that? I fell in love with the lab, and here I am 16 or 17 years later.

I was recruited by the University of Miami to start their lab team and manage that to grow it to what it is today. I worked at Sunquest as director of interoperability in their product strategy group for a number of years. The travel got out of control, especially being in Australia for a over a year and missing family and friends. I left to work for a five-year-old startup, and after nine months, decided that I was smart and hardworking enough to do this on my own. I started JTG five years ago in September.

Lab was always a healthcare technology pioneer, being the first to recognize the benefit of scale, to implement barcoding and tracking systems, to integrate with systems inside and outside the hospital, and to create a market for health IT that included the formation of Meditech and Cerner with lab as their first offering. What are the lab’s biggest issues today?

Historically, lab leaders were not always the best businesspeople to sell their service, either internally to the health system or externally. I’ve definitely seen a change in lab administration, where we’re starting to see more MBAs and MPHs who understand the business side and can take the lab to the next level. Taking advantage of excess capacity, economies of scale automation, and overall delivery of service for providing providers the first point of diagnosis.

Lab has a huge impact in the ecosystem of a patient’s journey. Without the lab, very few decisions can be made. If you have no radiology or no labs, you have no diagnosis in most cases, or it’s harder to make a diagnosis. With the onset of enterprise EHRs becoming the standard, we’ve seen things change from integration projects to workflow and optimization projects in health systems that allow providers to get more rich data and get it more quickly. It has been interesting seeing the evolution from best-of-breed lab systems to enterprise systems that have that best-of-breed technology embedded in them.

What laboratory-related external technology connections add value?

Folks are looking at more genetic data and genomics. That’s a lot more data than they can even handle. It’s more of a concern at times for providers because of the liability of missing something and understanding and interpreting those more complex and lengthy reports. Hospitals want to provide those services to their providers and patients, but they are also taking a close look at the risk of offering those tests, not just the financial risk of being reimbursed, but also how to interpret these results.

How do we ingest these results? Some of these new reports are 50-plus pages long, where historically doctors are used to receiving a metabolic panel or a CBC that has more discrete results with 20 or 30 assays in it as opposed to interpretative results that are more qualitative and quantitative results that impact how they make decisions to place those orders. They want to be able to provide the patient care, but if they can’t interpret the results or don’t have enough time to review and understand what the results are telling them, then are they adding any benefit to the patient’s overall care?

Are health systems changing their policies or technologies to comply with the Cures Act requirement to release electronic results immediately to the patient?

Health systems historically were risk averse to releasing those results. They don’t want patient going to Dr. Google to figure out how to interpret these results, whether it’s right or wrong. But with the onset of things like Meaningful Use and other technologies that have been embedded in these systems, they are having to release these results. If it’s being sent to a reference lab, patients are getting savvy enough to know that they can register with Labcorp, Quest, or Sonic to create a patient account login and get those results before their provider. A lot of EHRs and lab systems now have automatic release of those within certain parameters. Certain tests, such as STIs and other infectious disease results that are more sensitive, might be released within five to seven days if the provider hasn’t reviewed it. But overall, health systems are becoming more open to the fact that they have to do it, and we are starting to see that paradigm shift at larger health systems.

The introduction of artificial intelligence will bring a lot of opportunities to health systems to provide even better economies of scale to their providers, who can interpret the results before they are released and decide whether they need to add comments. We’ve seen Epic talking about utilizing ChatGPT and Cerner is talking with the FDA on some AI tools as well. AI can be powerful and potentially dangerous, but with the right guardrails, it will help providers, patients, and health systems take advantage of the data that’s already there.

Generative AI seems ideally suited to turn medical language into patient-understandable reports or instructions. Will that effort be led by companies like yours, or vendors themselves?

We are going to all have to partner together to take advantage of those new opportunities and tools. With lab, I’ve seen things like CellaVision, who has been doing artificial intelligence before it was called AI in identifying different cell types in a hematology slide. We’re also seeing things like the Copan WASPLab, a microbiology total lab automation tool that can take pictures of Petri dishes, interpret what’s growing, and group them for the tech to review. Their machine learning and algorithms are getting better every day to help the tech skip things that aren’t value-add, like no growth on a micro plate, and also categorizing things for them to review and confirm.

We will see more of that in chemistry and other areas, doing anything we can to avoid having a tech review a result, using a confidence interval set by the lab’s medical director to allow auto-verification. That will reduce turnaround time and hopefully improve patient care by getting a diagnosis sooner.

Telehealth, remote patient monitoring, and other virtual medical services are limited by the last-mile problem of collecting lab specimens and delivering prescriptions. Several companies have attacked the second issue. How are they approaching the lab collection challenge?

Direct-to-consumer labs is a great example of that. Because of the EUA that the FDA approved for COVID testing, we’re starting to see restrictions and legislation change on patients being able to order their own lab tests. It’s only a matter of time before it crosses all of our states. Across our country, providers and health systems are looking at ways to make it more convenient for patients. Going to a hospital, parking in a garage, and finding the right location are going away. We are starting to see Walgreens, Walmart, and Safeway embedding labs in those shopping centers. You park in a parking lot easily, walk in, get your test done, pick up your Starbucks after you are finished fasting, and you’re out.

That’s one step. But direct-to-consumer, where patients can order the test and self-administer the swab or void into a cup, is another example where we will see this evolve. The concerns that people are raising are also valid. Was the test collected correctly? Is it the same patient who ordered it? Who is responsible for that authorizing provider and interpretation of that result for the patient? There’s still a lot of work to do, but health systems know that to compete with Amazon, Walmart, and CVS they are going to have to change. That will also require lobbying work with the government to make that direct-to-consumer testing possible.

Which of your services are in highest demand?

With the great resignation, it’s difficult to get people to go to work for some reason, so staff augmentation is a big part of our business. We embed full-time employees at organizations to augment the needs of positions they can’t fill. A lot of the work we do can be done remotely, and the pandemic was a great representation of what we can do without having to physically be on site. We’ve done big implementations of new lab systems and EHRs with other vendors and consulting firms that were completely remote, and it’s incredible the amount of work that can be done remotely. Those are some of the big things that are being requested. Also, folks are looking to upgrade their systems constantly and they just don’t have enough people or time with all the competing projects.

Integration work is top of mind for health systems, laboratories, and even private reference labs, being able to interop with their clients, vendors, and patients. Those are quick wins. We are starting to see demand for talking about digital pathology and what that could do for the pathologist, automating some of their workflow and providing remote capability for the pathologist who was historically eyes on a microscope. That still has way to go, but we’ve seen some good headway in the last couple of years.

What have you learned in starting a company and setting its strategy?

From the beginning, I knew that we had to remain focused and not try to be everything to everyone. We’re not afraid to turn down business that doesn’t align with our goals, competencies, and strengths. We are laser focused in the laboratory. and there’s enough business in the laboratory space for not just JTG, but for other firms and vendors. We are happy that we’ve been successful in remaining focused and providing that excellent service to our customers.

HIStalk Interviews Tyler Smith, CEO, Health Data Movers

Tyler Smith, MBA is CEO of Health Data Movers.

Tell me about yourself and the company.

I began my career on Capitol Hill during the recession, but most importantly, it was during the passage of the Affordable Care Act. The HITECH act from the stimulus propelled the digital health transformation forward. Being on the Hill during this time, it was an exciting time to get into healthcare. I left after one year and went to work in the consulting space. I got certified in Epic, spent a little bit of time at Cumberland before they were acquired by Tegria, and then started building what became Health Data Movers.

I focused on application work, while one of my two co-founders focused on data migration and the third brought in our first software development. From the beginning, Health Data Movers has had a technologist focus. Today we offer six core services that we break into two categories. Signature services is software development, integration, and data migration, while foundation services is applications, PMO, and advisory. We are a technologist-first firm. While a lot of great firms that came before us have moved into a desire to be a Big Four advisory-type firm, we enjoy getting our hands dirty and doing the work.

How have health system financials changed the demand for consulting services and affected their technology acquisition process?

I realized as a leader during the pandemic that that change is the only constant. When we saw how the scaling back of fee-for-service could affect budgets, it taught me that have to be flexible and shape-shift with the needs of customers. During that time, we saw pauses in projects, but then when the government acted and figured out ways keep health systems solvent, we saw all these paused projects back in high demand. We were scrambling to put together teams to get these projects kicked off.

At the same time, we also saw a lot of interest in digital health that was created by the pandemic. You saw a lot more digital health firms. We saw more interest from biotech firms that needed to integrate data in and out of the system of record, which is the EHR.  

In terms of our investment, it is staying focused on what we do best, but understanding that ultimately our customers — health systems, biotech firms, digital health firms –have their strategic imperatives and we need to be able to support them from a technology perspective. The directions that they choose to go determines a lot of the direction that HDM ultimately goes.

Are health systems and their technologies prepared to integrate real-world data, life sciences research, and FDA marketing surveillance?

Companies are formed based on ideas and also pre-existing alignment. Especially consulting firms, because folks who have worked together in the past and liked it will work together in the future. One of our co-founders came from a company called OTTR, which was a transplant EHR that was acquired by CareDx, which is a massive life sciences biotech company in the transplant space. We always say that if you can move transplant data, you can move any type of data. We have roots with life sciences because of our transplant background. We’ve been working with CareDx for over three years to ensure that the data from their tests makes its way to the EHR and vice versa. 

As this massive influx of information comes about as these biotech firms are maturing, it has been awesome to see the data from tests that are done by the specialty labs that we work make its way into the EHR. Is the real-world, evidence-rich data entering into the EHR? One hundred percent yes. A lot of the work that we have been doing recently with Epic Aura is getting specialty lab data in and out of the system of record. 

I think we are going to see some really cool improvements in patient care because of the integration of this data. It’s exciting to see how the EMR is moving from being a system for charting and ensuring that there’s billable outcomes to a place where we can have true insights from the interface with the patient and physician that will improve care.

What will the healthcare impact be of new tools such as cloud services, ChatGPT, and low-code systems that make it easier to develop applications?

That’s really exciting. We have an internal team of developers and the energy and excitement they have behind ChatGPT is evident in the Slack channels that I’m lucky enough to lurk in. There’s a specialty that is required in healthcare development, which we see as a competitive advantage because we have a line team of developers. But we also have developers who are well versed in the intricacies of healthcare – HL7 v2, FHIR, and HIPAA-compliant cloud. If there’s now a way for developer to speed up execution, then we can get a lot more done with smaller teams and we can scale the projects that we’re able to take on at a greater pace than just simply scaling headcount.

I get asked a lot about this metric – how big is your headcount? That is important in time and materials type work, but if we are able to empower our engineers to be more efficient with their work, then we can talk more about how many projects were we able to execute. The projects are the work that is pushing healthcare forward. If we can leverage the geniuses that we have in house to take on projects with biotech and digital health firms and essentially scale their skillset, we will be about to move our integration and software development portfolio of work faster, which then ultimately we believe will make healthcare more interoperable, user friendly, and efficient.

How are health systems and their new digital-type C-level officers approaching consumer-facing projects as they begin to compete with big technology and consumer companies?

We have been working on some digital front door related projects with strategy firms and more payvider-type organizations. Then you look at the One Medical-Amazon combination and see the rise of concierge medicine and advanced primary care. We talk about the retail health side a lot internally. Patient experience is going to be a critical next piece where technology is applied. Our core is with the healthcare organizations. We are seeing a lot more interest in the patient experience. I’m excited about this next wave of technology that supports that and I’m confident that Health Data Movers will be involved in that next transition.

How does your experience as a Stanford MBA student influence how you run the company?

It’s crazy how much Stanford influence there is at Health Data Movers. We have a board member who was my professor at Stanford, another board member who was the associate CIO at Stanford Health Care, and then both of my co-founders were either full-time or consultants at Stanford Health Care. But to the broader question around the Stanford influence, it’s just insane how much innovation has come out of a really tiny piece of US geography. When you are around it, you understand the energy behind creating something new and using technology to change the world. It’s not just something people say, it’s something they believe.

I could give you a laundry list of all the positives that I got from my Stanford experience or from being around Stanford, but I’ll focus on the idea that we are all capable of making the world change no matter what discipline we decide to do that in. Going to business school at the time when we were developing the company made me realize that yes, we are a services firm, but services firms can create massive change. Having lofty goals to actually improve healthcare through the implementation of EHRs, optimization of EHRs, and software around EHRs is something that can be achieved if we work extremely hard and have total dedication to the mission and vision.

Are you encouraged that healthcare has the ability and the incentives to implement technology that will truly make a difference?

I’m encouraged because there’s been massive adoption. Everyone had to get on the grid. I think Paul Kenyon from OTTR sums it up really well. It’s like the land grant colleges, or the land grants in general, in the Midwest. The government had to create a reason for folks to move to a certain part of the country, and then they were able to step away. We had to HITECH to get everybody onto the grid, and then we had the pandemic. The pandemic was awful,  but in a lot of ways, it also brought in more interest into healthcare. 

As much as people like to write off the EHR vendors as being resistant to change, it’s impossible to separate that there’s so much interest now in healthcare and digital health. Even though some tourists have left, a lot of folks have stuck around. We will see innovation that comes from a lot of brains and energy being in this space.

Just look at your MyChart interface over the years. It is always improving. While the rate of change is slower in healthcare, it will continue to evolve. I am fully confident that it is continuing to become better. I am excited about everything that will be possible now that we are on the grid and folks are interested in making healthcare better through technology,

My dad was seen at two health systems with what turned out to be Parkinson’s. It’s crazy that they operated on his rotator cuff even though they could have looked in his record to see that it wasn’t necessary. Here I was five years ago, working at a healthcare technology firm, and I’m literally sitting there helplessly with him knowing that the physician isn’t getting the full record. That’s when it really clicked to me. We all have these personal stories and that’s just one of millions. A lot of what we are working towards is to become non-existent as a company, because at that point, we will have created truly interoperable patient care that will fix so many avoidable errors. This is also the platform where innovation from life sciences and biotech companies will be made available to the providers who are delivering the care.