Readers Write: Cutting Through the Hype: Navigating AI in Healthcare

Cutting Through the Hype: Navigating AI in Healthcare
By Michael Burke

Michael Burke, MBA is founder and CEO of Copient Health of Atlanta, GA.

First, a confession. Our company leverages machine learning in our operating room utilization software solution. As such, we stand to benefit from the AI hype machine that is running at full speed. But I promise you, the intent of this paper is not self-promotion; it’s to help you distinguish true AI value from mere marketing hype.

Understanding the categories of AI, and a little about how each tool set works, is essential. However, wading into the weeds a bit is an unfortunate requirement of doing this.

The Basics of AI: What Healthcare Executives Should Know

Before delving into the nuances of true versus misleading AI claims, let’s first understand some fundamental AI categories relevant to healthcare. The items below aren’t a comprehensive list, but they do capture some of the most common and important categories you’ll see:

Machine Learning (ML)

Machine learning is a branch of AI that uses specific algorithms to analyze and learn from large amounts of data. This “training” process, and subsequent testing, results in a model that can make predictions or decisions without being explicitly programmed to perform the task.

Training methods of different ML algorithms include:

• Supervised learning. Supervised learning is like teaching a child with the help of a teacher. The teacher knows the correct answers and provides guidance, and the child learns from this guidance. An example of supervised learning in healthcare could be predicting whether a tumor is malignant or benign based on a set of labeled tumor data. In this case, the “teacher” is a labeled dataset where each tumor is classified as either malignant or benign. The ML model is trained on this data and can then predict whether a new, unlabeled tumor is malignant or benign based on what it has learned.
• Unsupervised learning. Unsupervised learning is like a child learning through exploration without guidance from a teacher. The child learns about the world by observing and interacting with their environment. An example of unsupervised learning in healthcare could be patient segmentation, where healthcare providers group patients into different categories based on their health data. There are no pre-labeled categories here; the ML model must learn to identify patterns and structure in the patient data to determine how best to group the patients. For instance, an unsupervised learning algorithm could analyze patient data to identify clusters of patients with similar health characteristics, which might correspond to different risk groups or disease subtypes.
• Semi-supervised learning. Semi-supervised learning is a combination of supervised and unsupervised learning. The model learns from a combination of labeled and unlabeled data. A semi-supervised learning example could be if a hospital had a large amount of patient data, but only a small portion of the data is labeled. Perhaps only a small set of the patients’ records include a diagnosis for a certain disease. The ML model can use the small amount of labeled data to learn about the characteristics of patients with that disease and then apply this learning to the large amount of unlabeled data to predict which of those patients might have the disease.
• Reinforcement learning. Reinforcement learning is more like teaching a dog to perform a new trick. You don’t tell the dog explicitly what to do. Instead, the dog tries different actions, and you give it a treat (a reward) when it performs the action you want, like sitting or rolling over. Over time, the dog learns which actions will earn it a treat and starts performing those actions more frequently. In a healthcare context, consider a machine learning model that is trying to optimize treatment plans for patients with chronic conditions. To compare to our dog training explanation, the ML model is the dog, the different treatment plans are the actions the dog tries, and the patient health outcomes are the rewards. The model tries different treatment plans (actions) and observes improvements in patient health (the reward). Over time, the ML model learns which treatment plans lead to better patient health outcomes, much like the dog learns which actions earn it treats.

ML excels at tasks where patterns and structures can be discerned from data, such as prediction (predicting hospital readmission rates based on patient data), classification (classifying skin lesions as benign or malignant based on image data), and clustering (segmenting patients into different risk groups based on their health data).

Deep Learning (DL)

Think of deep learning as a team of detectives working on a case. Each detective looks at a part of the evidence and makes their own observations. They pass their findings to a senior detective, who then makes more complex observations based on the initial detectives’ findings. This goes on until the chief detective (the final layer of the network) makes a decision based on all these observations.

In the world of AI, each ‘detective’ is a layer in an artificial neural network. Each layer looks at some aspect of the data and passes on its findings to the next layer. This allows the network to learn from simple features at lower layers to more complex features at higher layers.

Let’s translate this into a healthcare example. Consider a deep learning model analyzing an MRI scan to detect a tumor. The initial layers might look for basic features like edges or colors. The next layers might recognize more complex patterns like shapes or textures. And the higher layers might identify the specific features of a tumor. Just like our detective team, each layer contributes to the final decision, allowing the model to accurately identify whether a tumor is present.

Deep learning excels at tasks involving unstructured data such as images, audio, and text. For instance, deep learning algorithms can analyze MRI images to detect tumors, listen to a patient’s speech to diagnose mental health conditions or analyze electronic health records to predict patient outcomes.

Natural Language Processing (NLP)

Natural language processing (NLP) involves the interaction between computers and human (natural) languages. This technology allows computers to understand, interpret, and generate human language in a valuable way. At its core, NLP involves machine learning to automatically learn rules by analyzing a set of examples and making a decision based on them. This decision could be understanding sentiment, translating languages, or converting speech to text.

In the healthcare sector, NLP can be used to interpret clinical documentation, analyze patient feedback, or enable natural language user interfaces (e.g., chatbots for patient engagement).

Generative AI

Generative AI involves creating new, previously unseen content. Think of it like an AI artist that creates new works based on styles it has learned from. Generative AI is not limited to any particular type of content and can generate images, text, music, and other types of data.

In healthcare, generative AI could be used to create synthetic patient data that can be used for research or training purposes without compromising patient privacy. For instance, a generative model could be trained on real patient data and then generate new data that maintains the statistical properties of the original data (like the distribution of different diseases or the average patient age) but does not correspond to any real individual patient. This synthetic data can then be freely used without worrying about privacy violations.

Computer Vision

Computer vision is like teaching a computer to ‘see’ and interpret visual data in the way humans do. This technology is extremely versatile, being used in everything from self-driving cars to facial recognition software.

In healthcare, computer vision is often used in medical imaging to detect diseases and conditions. For example, computer vision algorithms can be used to analyze X-rays, MRIs, or CT scans to detect tumors, fractures, or other abnormalities. It’s also used in telemedicine solutions, where computer vision algorithms can help monitor patients and detect abnormalities or changes in their condition. Computer vision is also used in robotics.

Knowledge Representation and Reasoning

Imagine AI as a detective solving a complex case. To do this, it needs  a vast amount of knowledge about the world, along with the ability to reason with this knowledge to draw conclusions. That’s what knowledge representation and reasoning AI do.

In the healthcare domain, such AI can be used in clinical decision support systems to aid physicians in diagnosing diseases. The AI system has access to a vast amount of medical knowledge and can reason with this knowledge to provide suggestions to physicians.

It’s hard to decide where to draw the lines when categorizing types of AI. For instance, that the problem of tumor detection often involves computer vision as part of a series of machine learning models that feed together into a deep learning network. Additionally, a field like robotics is sometimes considered its own AI category and other times considered an application that uses specific categories of AI. I’m confident that people smarter than me will sometimes disagree with my categorization choices.

Identifying Genuine AI: A Guide to Avoiding the Hype

While understanding AI categories is a good starting point, the key to discerning genuine AI applications in healthcare software lies in recognizing when these technologies add real value to a process or outcome. And remember: AI, at its core, should aid decision-making, not replace it.

It’s easy for marketing campaigns to dress up their solutions with the AI label, but there are several ways in which the reality may fall short of the hype.

The AI Imposter: Recognizing Automation Dressed Up as AI

AI, including ML, DL, and NLP, learns and improves from data over time, enabling complex decision-making that is generalizable and extends beyond predefined rules. In contrast, rules-based automation, though beneficial in certain contexts, lacks this level of complexity and adaptability.

Consider, for example, a software solution that sends alerts when patient vitals reach certain thresholds. This represents a rules-based automation system, not AI. A genuine AI solution might continuously analyze patient data, learn from it, predict potential health risks before they become critical, and even suggest personalized treatment plans. This shouldn’t imply that a solution that leverages AI is necessarily better than a rules-based automation solution. However, beware of vendors dressing up automation as AI to take advantage of the hype as a rationale to increase price.

The Overkill: Unnecessary AI Implementations

Some solutions may incorporate AI where it’s unnecessary, serving more as a marketing tool than a feature that adds value to the end user. An example could be an element of a software solution that matches tasks to individuals based on skills. It might use an unnecessarily complex ML categorization algorithm where a simple lookup table would have sufficed.

The Overstatement: Claiming Unrealistic AI Capabilities

Some vendors may oversell what their AI can achieve. While AI can indeed help predict patient outcomes, claiming perfect accuracy is unrealistic and potentially misleading. Also, prediction accuracy in machine learning is a moving target as training data sets change over time, sometimes in response to the ML-based intervention itself.

Downplaying the need for quality data can lead to overstating anticipated results. AI systems are only as good as the data they’re trained on. If a solution downplays the importance of data quality, quantity, or diversity, be skeptical.

The Overlook: Ignoring the Need for Human Oversight

True AI applications in healthcare are designed to support and enhance human decision-making, not replace it. If a solution suggests that its AI can replace human judgment entirely, it’s likely overhyped. It could also be dangerous.

Conclusion

While Copient Health indeed benefits from the AI boom, we urge discernment when it comes to AI in healthcare. Understanding the basics and recognizing when AI genuinely adds value is critical. The future of healthcare is undeniably intertwined with AI. With a robust grasp of the subject, you’ll be primed to guide your organization into a more efficient, patient-focused era.

Readers Write: It’s Time for EHRs to Alleviate, Not Exacerbate, Clinician Burnout

It’s Time for EHRs to Alleviate, Not Exacerbate, Clinician Burnout
By Nancy Pratt, RN

Nancy Pratt, RN, MSN is senior vice president of clinical product development of CliniComp of San Diego, CA.

We all see the headlines – clinician burnout is taking an enormous toll. It is estimated that 47% of US healthcare workers may leave the profession by 2025. One of the most-cited sources contributing to burnout is electronic health records (EHRs), with nearly 60% of physicians in one poll suggesting that EHRs need a complete overhaul.

In my work with clinicians, their biggest frustration with EHRs is time. So much of their time is spent doing manual, EHR-related tasks, causing them to spend nearly twice as much time in the EHR as they do with patients. A poll by Stanford Medicine found that hospital-based physicians spent 25 of the 37 minutes on behalf of each patient in the EHR.

It doesn’t need to be this way. By collaborating with physicians, nurses, pharmacists, and other clinicians, EHRs can become a trusted part of care delivery processes, freeing clinicians to focus on their patients and recapture the most satisfying qualities of their professions. With a focus on human-centered design, EHRs can help reduce clinician burnout in three ways:

Capture documentation naturally as part of the workflow

Given the frustration with documentation, EHRs need to move beyond focusing on transactions to creating efficient, supportive workflows for all clinicians. One process that is often frustrating and burdensome is medication reconciliation. A well-designed EHR workflow can present this critical step at a natural point in the care process. By presenting a side-by-side comparison of what’s current and what’s needed for the patient, the process becomes a fast and accurate way for physicians to manage medications.

Bring device data into the workflow automatically

Using standard integration protocols, such as APIs, EHRs can automatically integrate data from bedside monitoring and other devices into user-focused workflows. Instead of requiring nurses to enter data manually, the EHR should be fully integrated, perform calculations automatically, and present in a user-friendly way. In addition to reducing errors and manual tasks, nurses working with monitored patients report saving as much as 15 to 20 minutes per patient per shift with this level of integration.

Remove downtime as a barrier

It’s surprising that system maintenance and upgrades still require planned downtime for many EHRs, slowing down care delivery with manual, paper-based workarounds. Unplanned downtime, experienced by 96% of organizations according to one study, can be even more onerous. EHRs should be built upon modern foundational architectures that don’t require scheduled downtime and have built-in redundancy to prevent unplanned downtime. Preventing downtime alleviates a common source of stress for clinicians when care delivery is hampered by lack of access to patient information.

It’s time for the industry to listen when so many clinicians say EHRs need to be revamped. Using flexible, well architected technologies and collaborating with clinicians, EHRs can enhance rather than hinder care delivery. At last, EHRs can support clinician wellness by enabling them to provide the highest quality patient care, bringing the joy of helping patients back into their day-to-day work.

Readers Write: Return Data to Hospitals and Researchers for Patients

Return Data to Hospitals and Researchers for Patients
By Amanda Borens

Amanda Borens, MS is chief data officer of Aridhia Informatics of Glasgow, Scotland.

As a data scientist formerly working in global health, I have firsthand knowledge of how challenging it is to recruit patients for clinical trials and observational studies, and how transformational a culture of open collaboration and data sharing can be. I have cancer diagnoses in my history, accompanied by so much fatigue from being extensively examined and poked and prodded, but I’m also a scientist in the health technology industry with appreciation for the way that humans advance medical knowledge.

I have eagerly signed up for clinical studies and taken on extra burdens, such as blood draws and filling out forms, to help researchers as well as those in training. Even when that means being examined by a nervous medical student and then repeating myself to a physician.

Then why did I balk when my health system asked me to participate by openly sharing my EHR data for research?

I collaborated with renowned medical ethicists who taught me to embrace the idea that a patient owns their data. That idea was cemented in me when I became a participant in various studies that led to peer-reviewed publication. I had been working in my non-profit world to aggregate data from multiple sources to learn more about rare diseases. I became frustrated that companies would keep shareable data confidential to avoid letting competition find insights that they had missed. I was shocked to learn that many scientists in academic settings hoard data in the same way, fearing that someone might find something they missed in the data and publish it.

In all cases, the stewards of the data seemed to forget that patients sacrificed time, blood, energy, and more to help all humanity, not the personal careers of their physicians or the bottom lines of sponsoring drug companies. That intention matters, and is worth honoring.

This brings me to my personal hesitation when asked to share my EHR data for research. I’ve been to data science conferences where abstracts were presented by employees of this health system. I know the kinds of questions my data would answer. Those questions tend to be focused on how to keep hospitals profitable. While I know that this is a valid concern for administrators in hospitals, I want my personal health information to be used to help other patients like me have a better experience, and I don’t much care what that would cost.

I want to be a piece of data that led to a cure for this, a better treatment for that, an earlier detection of my cancer for others, a less-invasive surveillance journey, or a better experience with caregivers. I want to share my EHR data with my incredible team of oncologists and researchers so they can learn. I want my data to be compiled with that of others so that they can learn more and faster.

However, I know that my doctor won’t be able to access aggregated data that way in my clinic. I know he had to use an Excel spreadsheet to keep track of data in the landmark oncology study he recruited me to join.

Conversely, aggregated claims databases can be used to answer questions about health economics. Some cost money to access, and sometimes researchers can access those external data sources with cost waived. Beyond payer-aggregated data sets, Epic has a respectable database and Cerner does too, both of which are valuable for review of de-identified patient data. But what about empowering researchers and clinicians in their unique patient communities? Shouldn’t we be honoring the patients’ commitment to advancing medical science by empowering clinicians and researchers to more easily use more data in their own hospitals? What does that look like?

The US government mandates that healthcare IT developers like Cerner and Epic provide their customer base with a certified FHIR API to support patient access to health information by December 31, 2022 as part of the 21st Century Cures Act Final Rule. This mandate requires that certified health IT developers publish “service base URLs” or “endpoints” for all customers in a machine-readable format at no charge. I hope this will be an inflection point.

Additionally, patient-focused drug development mandates are demanding greater listening to the voice of the patient, and that means tighter connection between pharmaceutical companies and real-world data from clinical settings. Hospitals with an investment in a next-generation research environment will be able to procure industry funding to collaborate in a secure, audited cloud environment that is dynamic and connected to anonymized EHR data alongside observational or interventional study data. A nice side benefit? That same (already funded) environment may provide a subset of hospital researchers with identifiable patient data that can be used to implement research findings into clinical practice in a timelier fashion.

As a patient, I’d donate my data to that hospital in a heartbeat.

Imagine a world where patients and clinicians collaborate to improve healthcare, then take a look at what Great Ormond Street Hospital for Children is doing with their research environment for a wonderful example. Pediatricians there have been studying precision dosing regimens and collaborating across continents to share dosing models where pediatric populations were excluded from clinical trials. This isn’t happening in one hospital’s data warehouse, and it isn’t happening with access to a single EHR or aggregated repository. It’s possible because of connecting different types of data of across borders and across time, but all in a next-generation research environment that connects people.

The transformative possibilities do not end there. What if a research hospital was able to collect clinical data from EHR and combine it with multiomics data from an academic research university where bioinformatics pipelines are optimized to provide a list of variants for each patient? How might they learn about patient subpopulations and disease progression or predict responses to interventions? That sort of collaboration should be the norm, but it requires us to think bigger than one data warehouse, one data type, or one organization at a time. Let’s make it happen.

Readers Write: Bringing the Call Center Into the 21st Century

Bringing the Call Center Into the 21st Century
By Ben Moore

Ben Moore is chief product officer of PerfectServe of Knoxville, TN.

Like some of you, I was compelled to dip my toe in the healthcare waters because of a personal experience.  It was over a decade ago, but I still remember it like it was yesterday.

My wife was pregnant with our daughter, who came weeks before her due date. Both my wife and daughter faced serious complications, and my stress level was exacerbated significantly by care delays that seemed almost entirely caused by inefficient communication. Simple questions and follow-ups that should have taken minutes ended up taking hours. These delays put both my wife and daughter’s health and safety at risk. 

For example, to reach the specialist, a nurse would have to call the hospital operator, who then had to manually track down and page the correct specialist on call, which would often start a process of telephone tag among nurses, operators, and physicians. The physicians were, and in some cases still are, carrying one-way pager devices, which would cause lots of disjointed one-way communications.

At that time, we had purchased the first-generation 3G-connected iPad for my wife while she was in the hospital. She was able to instantly text friends and family from her hospital bed and engage in social media. I was perplexed. Why weren’t the care teams in the hospital equipped with similar state-of-the-art communication tools? My wife was able to engage in real-time and media-rich communication, but hospital staff were limited to 140-character alphanumeric pager messages, telephone callbacks, and voicemails. The contrast was stark, and to be frank, seemed a bit ridiculous.

I can tell you that archaic technology and processes like the ones I described above are still prevalent across healthcare today. As just one example, even though over 70% of communications into and out of a hospital run through the call center, these antiquated tools are still, in many cases, a major part of directing that very important traffic. That’s alarming.

By upgrading technology that has its roots in the 1990s, the call center can become a true hub for patient engagement and clinical collaboration. Here are four steps you can take to bring the call center into the 21st century. 

1. Head for the cloud. Eighty percent of call center systems still use on-premise technology. This is problematic, because you’re only going to reliably connect stakeholders across all sites of care with a cloud-based solution. I once witnessed a fan blow up on a hospital PC server, and it completely knocked out communication with incoming ambulances. With a cloud-based system, this kind of risk is gone. If your EHR is already in the cloud, your call center should be there, too.
2. Unify clinical and patient communication. Rather than forcing operators to play middleman, go with a system that allows providers to communicate directly with patients. This kind of setup can be achieved within a broader ecosystem that facilitates both clinician-to-clinician and patient-to-clinician communication, meaning all communication is initiated and captured in one platform. This means less complexity for providers, easier access for patients, and greater transparency — especially with EHR integration — for anyone who needs to reference communication history.
3. Integrate, integrate, integrate. Speaking of integration, I actually think it should be a bigger focus for the call center than things like analytics or standard performance metrics, which you find in most call centers today. A properly executed integration plan can reduce the manual labor of operators by over 80%. Key integrations should include the EHR, patient flow systems, CRM, and scheduling platforms to provide operators with a single pane of glass.
4. Embrace smartphones. You need a call center platform that embraces smartphones. The vast majority of physicians use them, and integrating them with the call center ecosystem allows for things like time-sensitive communication — say, a team alert initiated by an agent about an incoming hip fracture patient — that can actually be monitored to verify that recipients have received and read all necessary information. No more guesswork! And please, whatever you do, make sure to ditch the pagers. No agent wants the page-and-pray technique to be central to their everyday duties.

Clinical call centers — like hospital switchboards, patient transfer centers, and answering services — can have a tremendous impact on everything from care coordination to patient experience to health outcomes. After decades of neglect, it’s time to give them the modern infrastructure they deserve to unleash their true potential.

Readers Write: Should Health Systems Become Banks?

Should Health Systems Become Banks?
By David Stievater

David Stievater, MBA is a partner with CWH Advisors of Boston, MA.

Are healthcare organizations expected to now run banks in addition to delivering patient care services? To help patients pay for their care, providers have become lenders, offering patients the equivalent of unsecured, zero-interest loans backed by hospital balance sheets. It should come as no surprise that with additional financial stress lingering from the COVID pandemic, providers are looking for additional funding sources and financing alternatives for their patients.

Overall US healthcare spending hit 18% of GDP in 2021, up from 5% in 1960. Total patient out-of-pocket spending, not including insurance premiums, totaled $433 billion, according to the latest numbers from CMS. The out-of-pocket dollars owed by patients has risen at twice the rate of US GDP growth since 1960. More and more, patients find they do not have the cash on hand to cover medical expenses.

The financial challenges facing patients are well documented. In 2021, the Federal Reserve Board reported that 17% of adults had major, unexpected medical expenses in the prior 12 months, with the median amount between $1,000 and $1,999. The same research also said that 23% of adults went without medical care due to an inability to pay. These types of findings are corroborated by other reports, including Synchrony Financial’s Lifetime of Healthcare Costs study in 2022 in which one in four individuals said they delayed a recommended procedure due to cost. The Synchrony study also indicated that when care was delayed due to cost, respondents ended up with additional medical issues 50% of the time.

Patients will benefit from more funding sources and options to reduce and eventually pay off balances owed to healthcare organizations.

Unlike other industries, providers generally bill patients after care is delivered. They carry the patient balance owed to them as a receivable, to be collected once insurance has calculated its portion. By billing after the fact, providers essentially make unsecured loans to patients.

Many decades ago, when the patient portion of provider revenues was relatively small, it was common for unpaid patient balances to simply be written off. It wasn’t worth the effort to pursue them. Providers did not tend to accumulate large unpaid patient balances on their books for long.

Over time, as the size and frequency of uncollected patient fees grew, provider organizations looked to new propensity-to-pay algorithms, early pay discounts, early out vendors, recourse financing, pre-service collection of co-pays, in-house payment plans, and other tactics to increase the yield on the amount owed by patients. Debt collectors were asked to pursue patients deemed able to pay. In some unfortunate cases, the most aggressive health systems have sued patients (including their own employees) to recover unpaid balances.

Provider executives say they work hard to balance compassion for patient circumstances with a desire to collect from patients who they believe can afford to pay at least some of what they owe. Additionally, they would prefer not to be in the lending game and tie up so much of their balance sheet with unpaid patient balances.

We estimate that 45-55% of a typical health system’s patient balance, after insurance (including full self-pay), is never collected and is converted to charity care, some other form of financial assistance, or written off as bad debt. Providers too will benefit from offering patients more options to fund their care and reduce and eventually pay off balances.

Our 2022 PatientPay study — which included 38 in-depth interviews with executives at health systems, hospitals, and large single/multi-specialty medical groups — indicated that providers will increasingly look outside their organization for solutions. One hospital CFO said that “a lot more people are going to finance their portion of what they owe,” Another revenue cycle executive said, “We are definitely going to provide more third-party financing options and less in-house over the next two years.” Overall, 61% of the executives said they expect to make greater use of third-party patient financing over the next 24 months.

Part of the impetus to look externally for payments solutions is that the COVID pandemic has made it harder for healthcare organizations to staff their revenue cycle operations. Still, much of the motivation to seek third-party assistance is simply the need to find more flexible payment and financing options for patients that free up the health system’s balance sheet.

The study noted that the biggest investments by large provider organizations are centered on creating a more retail-like payment experience for patients. COVID pushed more dollars here immediately in the form of contactless terminals and accelerated efforts around card-on-file and other portal-based technologies. Patients can expect improved communication via portal-driven emails, text to pay, and mobile applications.

Entrepreneurs are also putting their creative minds to work and generating new solutions to help providers match their patients with available financial assistance and philanthropic programs. Companies such as TailorMed/Vivor, Annexus Health (AssitPoint), RIP Medical Debt, and Atlas Health claim their approaches are generating meaningful ROI for providers.

Longer term, providers must focus on providing patients with an accurate estimate of their costs. This will enable them to collect the patient responsibility before care is delivered, without waiting for insurance adjudication and without turning their healthcare organization into a lender.

Readers Write: Rural Hospitals Need Federal Assistance to Strengthen IT Security Posture

Rural Hospitals Need Federal Assistance to Strengthen IT Security Posture
By Kate Pierce

Kate Pierce, MSMIITA is senior virtual information security officer and executive director of the Subsidy program of Fortified Health Security of Franklin, TN.

The majority of my career in healthcare IT has been dedicated to working for a small and rural hospital, leveraging technology advancements to improve patient care and to keep those systems safe from cyberattacks. I spent 21 years with North Country Hospital in Vermont, starting as a systems analyst and working my way up to chief information officer and chief information security officer.

Growing up in a rural community in northeast Vermont, I have a deep understanding of the challenges faced by smaller hospitals, especially those in rural settings. I also understand how vital these organizations are to the communities they serve.

When I was asked to testify before the US Senate’s Homeland Security and Governmental Affairs Committee on the challenges that small and rural hospitals face in managing an effective cybersecurity program, as well as barriers to adequate funding and human capital constraints, it was an honor to do so, as this is a topic near and dear to my heart.

Even without the cybersecurity challenges, rural hospitals are experiencing unprecedented staffing and budget constraints. More than 40% operate in the red, and nearly one in three is at risk of closure. When it’s a daily challenge to deal with basic healthcare delivery while managing higher labor costs and shrinking margins, cybersecurity isn’t a top priority for most hospital executives.

Anyone who has ever worked in a hospital knows that change is constant. However, a cyberattack is among the most disruptive and devastating events that can occur within a healthcare environment. A 2021 study by the federal Cybersecurity and Infrastructure Security Agency (CISA) found that hospitals hit by ransomware often experience additional stressors that can be correlated with higher patient mortality rates.

This can happen at any facility, but criminals shifted their focus to attacking small and rural hospitals in 2022. Even though a successful attack against a smaller facility may yield less patient data or a lower ransom to release data, the reality is that they are often easier to breach and invariably connected to larger facilities.

When an urban or suburban hospital is hit with a cyberattack, it may inconvenience patients, but they often have other care options nearby. That’s not the case for rural hospitals. The nearest facility may be 40+ miles away, which doesn’t make it feasible to simply divert patients. Even if patients are diverted, nearby facilities can become overwhelmed, creating a cascading crisis throughout the community.

The stakes couldn’t be higher, as evidenced by a 2019 attack on an Alabama hospital that knocked out the hospital’s IT systems for three weeks and is believed to have resulted in the nation’s first fatality attributed to ransomware. According to the lawsuit, patient monitors were offline while the plaintiff was in labor, leading to insufficient monitoring of a fetus that was born unresponsive with the umbilical cord wrapped around the baby’s neck. Although the child was resuscitated, brain damage occurred, and the infant died nine months later.  In a recent 2022 attack, a rural Washington State hospital was so overwhelmed that an ER nurse called 911 for help.

The urgency of improving the security posture of these small and rural facilities continues to escalate every year.

As the sophistication of cyberattacks continues to grow, the federal government should be stepping in to help secure these hospitals and keep patient data safe. As I testified to the Senate committee, implementing these four measures could improve the state of cybersecurity for our small and rural hospitals.

First, we must move beyond guidance and recommendations and create minimum standards for cybersecurity that all healthcare organizations must follow. These standards must be reasonable, effective, achievable, and continually evolving as cybersecurity requirements change over time.

Based on the items outlined in the Health Industry Cybersecurity Practices (HICP) document, recommendations can be grouped into five basic categories:

• Email security and protection
• Access management
• Asset management
• Network management
• Incident response

Simply put, regulators must spend less time suggesting and more time providing concrete solutions.

Second, we cannot leave our small and rural hospitals behind. We must create funding opportunities to allow all hospitals to meet the standards. Options include:

• Subsidies, which have found success among rural hospitals in other initiatives
• Grants, which may prove more difficult as smaller hospitals often don’t have grant-writing resources
• Incentives for small and rural hospitals to enhance security, a “Meaningful Security” type program modeled on Meaningful Use
• Enhancements in Medicare and Medicaid payments for eligible facilities, with hospitals showing how additional funds were used to boost cybersecurity

Third, we need better coordination of government cyber efforts for healthcare. While the guidance and services from government are appreciated, there is often a knowledge gap regarding the unique healthcare challenges that must be considered when applying cyber best practices in this sector. Due to time and budget constraints, many rural hospitals find it challenging to access or use available resources, so coordination must be streamlined to be effective.

Fourth, the federal government should establish a cyber disaster relief program, much like the assistance provided by the Federal Emergency Management Agency (FEMA). Such a program would provide this vulnerable sector with valuable resources in the event of attack, assist organizations in their recovery process, and increase the likelihood that hospitals could keep their doors open following a cyber-attack.

Overall hospital operating margins have been in negative territory for the past 12 months, according to a February hospital report from Kaufman Hall, and margins have decreased year over year for the past eight months. Operating margins are often higher for larger facilities that have outpatient clinics and more ancillary services than a smaller hospital can offer.

Adding to the challenging complexities, nearly 700 healthcare data breaches of 500 or more records occurred in 2022, according to the Office for Civil Rights. While the number of breaches is basically flat, the number of breached records topped 51 million for the first time, apart from the anomalous 2015, when just two breaches exposed 90 million records. Cyber insurance rates also continue to increase, with insurers demanding more monitoring and detection technologies that smaller facilities may not have if facilities can obtain insurance at all.

Because healthcare records are so valuable, hackers aren’t going to stop. Small and rural hospitals need help to protect their systems and patients, and these simple measures are a sensible path forward.

Readers Write: Turning Data into Action to Address Social Determinants of Health

Turning Data into Action to Address Social Determinants of Health
By George Dealy

George Dealy, MS is VP of healthcare applications of Dimensional Insight of Burlington, MA.

Social determinants of health (SDOH) are widely recognized as critical factors that influence the health outcomes of individuals and communities. SDOH refers to the various environmental, economic, and social factors that impact a person’s health, including food and housing insecurity, social isolation, and lack of reliable transportation options.

While most people are aware of SDOH, they often struggle with how to derive meaningful insights from the available data. Therefore, it is essential to explore practical ways to turn data into action to address SDOH and reduce health disparities.

Failing to address social determinants of health can have dire consequences, particularly for underserved populations. CDC data shows that certain populations, such as minority groups, are disproportionately impacted by SDOH. The health outcomes of these groups are often comparable to those in third-world countries.

One of the most striking examples of this is the maternal and infant mortality rates among certain minority populations. For instance, maternal and infant mortality rates are significantly higher among African American women than among other racial and ethnic groups in the United States. Addressing social determinants is a critical step in reducing these disparities and improving overall population health. This highlights the need to use data related to SDOH for actionable change.

One practical way to address SDOH is to use data and analytics. It’s first important to know where to find data related to SDOH. The data can be obtained from various sources, including national surveys, government agencies, and community organizations. One such resource is the US Census Bureau American Community Survey, which provides valuable data on community demographics, including economic and social characteristics at the neighborhood level. As SDOH information is very geographically specific, data at this level can help identify specific needs and target interventions more precisely. Another important resource is the US Agriculture Department’s Food Desert Map, which helps to identify communities where residents lack access to healthy food options.

Additionally, many non-profit organizations aggregate data from various sources into information resources. These resources include the County Health Rankings Project, run by the University of Wisconsin and the Robert Wood Johnson Foundation, and Community Commons, which is a collection of tools and resources for democratizing data related to advancing equitable community health and well-being. These data-centric resources can empower healthcare providers and policymakers with the necessary insights into the needs of the community and identify potential solutions.

The next step is to analyze the data to identify trends to better understand the specific challenges that a community is facing in relation to SDOH. One practical approach is to use technology to analyze and visualize the data. This can help identify trends and patterns more efficiently and communicate findings in a clear and compelling way.

Leveraging data on SDOH can help in the development of targeted interventions tailored to address the specific needs of each community, such as expanding access to healthy food options or improving transportation services. For instance, data mapping tools can identify areas with high rates of poverty and food insecurity, with this information then used to target interventions in those areas. Predictive analytics can forecast potential health risks based on social and economic factors. The use of data and analytics can be a powerful way to identify trends related to SDOH, develop appropriate interventions, and measure their effectiveness.

Improving population health requires a comprehensive understanding and focus on social determinants of health. While healthcare plays a crucial role, it is only one piece of the puzzle. Addressing SDOH requires a practical and collaborative approach that involves analyzing data, leveraging available resources, and recognizing the dire consequences of inaction. By turning insights into action, we can make meaningful progress in improving the health outcomes of communities, particularly among minority populations, and ensure that every citizen has access to the care they need and deserve.

Readers Write: HIMSS23 Recap

HIMSS23 Recap
By Mike Silverstein

Mike Silverstein is managing partner of the healthcare IT and life sciences practice of Direct Recruiters, Inc. of Solon, OH.

The HIMSS Global Health Conference & Exhibition was held April 17-21 in Chicago, where over 40,000 professionals gathered for educational sessions, visited vendor booths, and networked. Our company was excited to get back to HIMSS as a larger group and share these observations.

Trends

AI and tools such as ChatGPT are getting adopted into healthcare quickly. There were a few innovative companies already showing off their new ChatGPT features, which was extremely cool. We anticipate this trend continuing, with ChatGPT having strong use cases in many areas of healthcare tech. We’re just at the tip of the iceberg.

We noticed a pivot towards partnerships. With hospitals struggling, tech companies are putting a big focus on partnerships. Fundraising remains in a slowdown. However, we continue to hear about investments being made for companies going from seed to Series A or to A to B, not in later stages. Series B/C companies are conducting more inside rounds to sustain cash needs.

Direct-to-provider meetings seemed to be relatively slow. We saw a continued trend of companies who sell direct to providers that were looking at ways to break into the health plan space. As hospitals are taking a long time to buy and are very ROI-driven, RCM services are staying strong.

Interoperability has been a theme for over a decade now in healthcare and remains today. Conversation has shifted from inside the four walls of the hospital to how technology receives data and information from what is available globally. A “Designated Record Set” is pushing for provider compliance to ensure their technology integrates with all systems (HIE extender).

Hiring seems to be ramping back up, especially looking into Q3 and Q4. The emphasis remains mostly in revenue-generating roles at the moment. There is also a need for senior finance and accounting. Product and operations roles are hardest to come by.

General Observations

From an overall size and attendee standpoint, the conference seemed to be back to pre-COVID levels. It was key to have pre-booked meetings, but even then, some companies were no-shows. Microsoft’s booth (and Nuance), and surrounding areas were always packed with people. It was great to see a renewed energy at HIMSS despite the ups and downs of the industry over the past few years.

Constructive Feedback

The main hall was split into two sides. The north side felt a bit forgotten, and some did not venture out much to the booths on the fringes. In addition, there seemed to be a lack of places to sit, grab a good coffee, and most importantly, charge your phone.

The last piece of feedback is to bring back the carpet. We noticed one person trip and fall (luckily, they were OK) because of the lack of carpet-to-carpet transition at one of the booths.

Overall, our team is excited about the connections made and the new technologies we saw at HIMSS. We are energized by the passion and innovation of the industry as a whole and look forward to what’s to come.

Readers Write: Labor Augmentation Technology Solutions Automating Manual Processes

Labor Augmentation Technology Solutions Automating Manual Processes
By Kelly Feist

Kelly Feist, MBA is managing director of Ascom Americas of Morrisville, NC.

General care floors represent one area of the hospital that has experienced the most change post-COVID, including increased clinical staff pressure resulting from caring for patients with higher acuity levels than in the past. This continues to be the area of care where continuous monitoring is the exception rather than the rule, and the ability for care givers to have patient contextual information at their fingertips is challenging.

As a result, the possibility of patient deterioration over time goes unrecognized until the patient becomes symptomatic, often resulting in unplanned ICU admissions, activation of rapid response teams, and sometimes other adverse events, such as codes.

By using vendor-agnostic medical device integration capabilities solutions, nursing staff can collect inputs from devices such as patient monitors, spot check monitors, laboratory information systems, EHRs, ventilators, CPAP devices, IV pumps, and more. This information is interpreted via pre-defined algorithms to determine a patient score that is regularly updated and trended. Automated alerts are generated and communicated to appropriate recipients — such as rapid response teams, charge nurses, and physicians — when a change in patient score indicates. The outcome is delivering the needed change in care prior to an adverse event occurring.

Automated, non-latent Early Warning Scoring is a vital tool in preventing unrecognized deterioration on the general patient care floors, ensuring improved clinical outcomes for the patient and financial outcomes for the hospital. There is a real and measurable ROI attached to well executed and automated early warning workflows.

Early Warning Scoring is one of several high-value workflow automations these kinds of MDI solutions can bring to bear to improve nursing efficiencies, create nursing practice safety nets, improve patient outcomes, and ultimately contribute to institutional financial goals.

COVID has had an impact on nursing capacity and on staff-to-patient ratios, resulting in the need for technology to become the force multiplier in the clinical space. There’s incredible opportunity through this technology to elevate the quality of care hospitals can provide today.

Readers Write: Healthcare Needs to Slow-Roll Fast-Moving ChatGPT

Healthcare Needs to Slow-Roll Fast-Moving ChatGPT
By Jay Anders, MD

Jay Anders, MD, MS is chief medical officer of Medicomp Systems of Chantilly, VA.

Now that the initial hype surrounding the chatbot ChatGPT has peaked or perhaps plateaued, its strengths, weaknesses, and applications are being scrutinized.

Perhaps one of the most visible applications revealed recently was the AI tool correctly answering 60% of the United States Medical License Exam (USMLE) medical board exam questions, a task that many top-tier students fail to achieve. This raised a number of concerns about how the technology could, and should, be used in healthcare.

Granted, as an AI language model, ChatGPT has a number of applications in healthcare today, including administrative tasks, triaging patient inquiries, and performing preliminary analysis of medical data. However, ChatGPT is not a trained, certified medical professional and should never be relied upon for clinical guidance or diagnosis. Just like a Google or Bing search, it can provide limited general health information, but it is certainly not a substitute for professional medical advice or treatment.

As a physician, my primary concern with ChatGPT and other large language AI models is that patients accessing the technology will begin to distrust the advice of medical professionals when a disagreement occurs.

Here’s an example of how such a disagreement can go awry. Years ago, a patient came to our practice and told me she wanted to feel like ‘that guy surfing in a wheat field’ in a popular ad for an allergy medication.

When I inquired about her allergy symptoms, she said she had none. She argued that the drug would help her anyway. So, when I would not write her prescription, she switched doctors to one of my practice colleagues. My colleague asked why she was making the change, and I told her. My colleague then revealed that this same patient argued with her as well and then switched to the clinic down the street.

I am a staunch advocate of transparent patient information that is accurate and science based. In this case, a little knowledge could be a dangerous thing. At the time of the dispute, the patient was taking a medication that would interact with this antihistamine and cause a severe reaction.

Although ChatGPT and AI weren’t available at the time of this encounter, the danger is clear. There is a genuine risk that some patients, particularly those without access to primary care or those trying to avoid the inconvenience or expense of an office visit, might rely on AI technology like ChatGPT for medical guidance. This could lead to incorrect self-diagnoses, misinterpretation of symptoms, and any number of potentially harmful consequences. It is essential for consumers and patients to understand the limitations of AI in healthcare and always seek professional medical advice for their health concerns.

AI and the role of the clinician

What is the clinician’s role in this learning curve? Healthcare providers (and naturally, developers of AI solutions) should emphasize the importance of using AI as a supplementary tool rather than as a knowledgeable substitute for professional medical care.

The real issue is the lack of reliable, trustworthy information for patients. Patients, especially those with a rare disease community or with complex conditions, can’t advocate for their own health and care if they don’t know anything about the condition they are battling. Reliable academic medical information isn’t as freely or easily available to them, so they often rely on what they find on the internet to supplement what their doctors tell them for peace of mind and, in some cases, survival. The patient advocacy community calls the patient administrative burden associated with this lack of reliable information “information toxicity.”

That said, patients are already using AI to self-triage, so it’s really up to the medical and technology communities to establish parameters to prevent people from using the technology in lieu of trained medical professionals, or educate them on how to do it safely. Ultimately, it would seem that both communities would work to make the AI better able to do it better.

In my experience as a physician, I’ve encountered many patients who consider themselves quasi-medical experts and excellent researchers. Still, some patients don’t particularly care if the information they unearth is accurate. They just don’t want to feel left in the dark about their symptoms. After all, a wrong answer is still an answer.

Overall, patients want and need to be collaborators in their own care, and with the availability of information being what it is, they are moving forward in the best way available (to them). Unfortunately, the burden is on the physician to correct the misinformation, and that will need to be included in the job description of physicians and nurses going forward. With technologies like this on the rise, with questionable, though increasing, accuracy, there is no choice.

The responsibility is on health systems to educate patients on how to use these technologies and other more reliable websites to research and also regularly share population health information with communities to combat disinformation. Additionally, efforts should be made to ensure equitable access to quality healthcare for all, reducing the reliance on AI technologies for primary medical guidance.

Harnessing AI to supplement clinical decision support

Looking back at those USMLE licensing exams, consider this. The exams are written very discreetly. “A patient presents with X, Y, and Z. What is the diagnosis?” It’s based on a set of facts, and is possibly multiple choice. Humans do not operate that way. Consider a 65-year-old with high blood pressure, elevated cholesterol, diabetes, osteoarthritis, and spinal stenosis. That is not a single question, it’s multiple conditions. Physicians are trained to mesh those conditions together because a treatment for any one condition may exacerbate another. An exam would not approach it this way.

Physicians need to learn how to use AI to augment their practice, knowledge, and skill, not the other way around. Harnessing AI as a supplement to clinical decision support is a promising option.

For now, ChatGPT is out there, and it will be used, sometimes for medical advice. That’s all well and good until it makes a mistake or doesn’t surface something of importance. Meanwhile, there are technologies in use that work with clinicians, in their workflow, and present clinically relevant information regarding conditions in a way that mirrors the way they think and work.

The human element is, by necessity, still very much at the center of healthcare. So, for now, let’s slow the roll on ChatGPT. Let it mature. Crosscheck it. See how it evolves as its models are further trained and deepened. The technology holds tremendous promise, but is still in its infancy.

Readers Write: HLTH, CHIME, ViVE, HIMSS — Choose and Invest Wisely

HLTH, CHIME, ViVE, HIMSS — Choose and Invest Wisely
By Steve Shihadeh

Steve Shihadeh is founder of Get-to-Market Health of Malvern, PA.

Many vendors in the health technology space have just attended ViVE and/or are preparing for HIMSS to showcase their offerings, meet with clients and prospects, and engage with their investors. For most of our clients and friends in the industry, these shows represent a significant investment of time, money, and education for their teams.

Execute these shows right and reap the rewards in new contracts, bought-in clients, and investors who get your plan. Do it not so well and you miss out on the year’s biggest potential market exposure for your company. What can you do to maximize this opportunity?

ViVE

If you just went to ViVE looking to find a vast field of potential customers on the exhibit floor, you probably came up wanting. One well respected colleague called it as “six vendors for every buyer.” The main live customer engagement seemed to come from the well-run Hosted Buyer Program in the ViVE Connect Lounge. This is a “pay for x meetings” gig, where the matched potential customer is trading their time to hear your pitch for reimbursement for ViVE fees and possibly T&L.

Based on our experience, about half of these meetings have potential value for you, but it is in your hands. It is really speed dating. You must be on message, be quick to read the buyer, and put your best foot forward.

ViVE is smaller (although just about every company in the space made sure to have someone there), more posh, and easier to get your head around than HIMSS. If I was mostly intent on getting time with investors and partners, I would have gone to ViVE. If I was mostly focused on potential buyer organizations …  let’s go to HIMSS, which comes up in a few weeks.

HIMSS

HIMSS is the grand old show of the health technology business. In its heyday, 40k+ attendees and healthcare IT companies sent everyone from the CEO to their junior sales reps. COVID and the way HIMSS handled the associated cancellations knocked the show for a bit of a loop. Their breakup with CHIME was the next issue. Now ViVE, in association with CHIME, is giving them competition that they did not have before, along with HLTH.

HIMSS has historically been gigantic and hard to navigate. However, it had most everyone in the business in one enormous convention center, along with a sea of potential buyers evaluating systems. HIMSS attendees slant heavily towards IT staff and leadership, some clinical and financial executives who are in the market for new systems, and an occasional CEO. Given its size, HIMSS takes extra work, extra people, and extra prep to get the most of your investment, but it is too tempting to pass up for most every company in the business.

Given the hip and more accessible vibe of ViVE (sorry, could not resist), I suspect HIMSS will make some adjustments. Good competition will do that for you. It will be great to be at HIMSS and get a real pulse of the industry post-COVID and in the new AI-everything world.

A Few Words on CHIME and HLTH

CHIME (College of Healthcare Information Management Executives) is the single most important event for CIOs and those who are on a CIO career track. They run a fabulous boot camp for people in the field and have an agenda aimed squarely at the CIO. Vendor participation is carefully managed and expensive, but several key luminary vendors in the field, such as Epic, have built their business around important relationships that it established with CIOs via CHIME. If CIOs are crucial to your business, CHIME is the place to be.

HLTH is a relatively new show hosted by the same folks who run ViVE. It is well run, quite upscale, targeted mainly at investors, and attracts enough potential buyers to make it interesting. For those who have been to the JP Morgan Healthcare conference, HLTH is a friendlier environment to tout your plans, show your wares, and get quality time with all the key investors in the space.

Where to Make Your Investments and Our Top 10 Tips to Maximize Your Return

Given how expensive any of these shows can be, we have some suggestions on how to get the most return.

1. Have a presence, even just one key person, at as many of the shows that you can. It is a great way to drive meetings and prospect engagements, e.g., “Are you going to HLTH?” A lot of startups that cannot justify the fees of the show still get mileage by being around the event and meeting potential partners at local hotels.
2. Based on your company’s priorities, pick your most important show for any exhibit booth investments. Securing, staffing, and running a booth at a show is a significant investment.
3. Send only your best, most committed people. Buyers will judge you by your staff.
4. Be fully committed to setting up meetings in advance. Know who in your target market is attending and schedule meetings and interactions. Do this whether you are exhibiting or not.
5. Train in advance on any new announcements and have your team arrive early for last minute retraining.
6. Trade shows are a fantastic selling opportunity and need to be treated as such. Booth personnel need to be focused on facing clients and prospects and not connecting with industry friends. The best-run booths are hardcore about this. Be best.
7. Booth hygiene matters. Have a dress code and stick to it. No food. No cell phones. No rep-to-rep chatting.
8. Get a modern lead tracking tool and train your booth staff on it.
9. Force (gently) all inquiries to your front desk so you can properly capture their info and direct them to the right staff in your booth.
10. Get your leads into your CRM system ASAP and send immediate follow-up notes to all visitors.

Trade shows are a big lift, so make sure that you and your team are aligned, prepared, and motivated to have a great show. Hone your messaging so that it works from a buyer’s perspective. Practice your demos until they are crisp. Worry about the details and have a fantastic event.

Readers Write: The Myth of the Golden Health Record

The Myth of the Golden Health Record
By Peter Bonis, MD

Peter Bonis, MD, is chief medical officer of Wolters Kluwer Health.

In 2009, President-elect Obama signaled his plan for the federal government to support the adoption of electronic medical records (EMRs). His expectation was that broad adoption would “…cut waste, eliminate red tape and reduce the need to repeat expensive medical tests,” adding that, “it just won’t save billions of dollars and thousands of jobs; it will save lives by reducing the deadly but preventable medical errors that pervade our healthcare system.”

The subsequent Health Information Technology for Economic and Clinical Health (HITECH) Act, part of the larger American Recovery and Reinvestment Act, achieved his directive, at least in part; most healthcare systems now use EMR systems. However, the strategic objectives of reducing costs and preventing medical errors have been elusive. Preventable medical errors remain common, growth in healthcare spending has not been reduced materially, and healthcare providers frequently cite EMR systems as being an important contributor to professional burnout. 

In this backdrop rests a common belief that the full promise of EMR systems has yet to be obtained. It will only be achieved once patient data can flow unimpeded from system to system, permitting healthcare providers (and other stakeholders involved in healthcare services) to have a comprehensive view into patient care wherever it is delivered, a concept referred to as interoperability. Over the years since the HITECH act was passed, many barriers posed challenges for achieving such a vision including concerns related to data privacy, deliberate blocking of information flow (especially when it interfered with business models), and approaches to gathering and making sense of intrinsically messy data.

Nevertheless, the journey has continued; key pieces of legislation and advances in technology have led to demonstrable improvements in interoperability.  Most recently the federal government gave the objective a boost by advancing standards and designating qualified health information networks intended to establish a universal floor for interoperability across the country. As a result, the healthcare system is marching toward a comprehensive, golden health record.

But once we have it, will the golden record enhance the quality, safety, and effectiveness of care? The answer is unsurprisingly no unless more is done to use the data effectively. Primary care providers would need almost 27 hours a day to deliver all the guideline-recommended care, according to one estimate. In this context, more information is not better.

Healthcare data must be delivered in ways that are useful for busy healthcare professionals working in varied settings. Information must be high value and organized into consumable payloads and workflows not only for time-pressed individual clinicians but for extended care teams. It should ideally support decision-making and subsequent actions while saving time, reducing cognitive burden, decreasing administrative overhead, measurably improving the quality and safety of care, and reducing costs. The golden health record is a welcome enabler, but will not in itself accomplish these objectives. 

So, what is needed? Foremost is recognition that the matter is critically important. The core of healthcare delivery is an interaction between providers and patients where decisions are made and care implemented. There is much to be gained by making it easier for healthcare professionals to take care of patients.

A greater sense of urgency is needed. Burnout and other challenges are leading to attrition of healthcare professionals. There will be a shortage of primary care physicians and hence a need for advanced practice providers (such as nurse practitioners and physician assistants) to take on greater responsibilities. They will need help. At the same time, healthcare services are becoming more distributed to new sites of care, such as retail pharmacies and to digital health technology companies, creating greater challenges for coordinating care and optimizing flow of useful information. The proliferation of devices that generate healthcare data adds further complexity.

EMR vendors need to expand capabilities, focusing on metrics that are directly relevant to the experience of various user types and ultimately to the quality of care delivered. Financial incentives and payment models must justify the investment for both EMR vendors and providers.

Healthcare professionals want to deliver exceptional care for their patients. Let’s make their needs a priority. The golden health record is a worthy goal, but the usability of the data should get equal attention.

Readers Write: Healthcare Delivery Must Evolve to Meet the Needs of a Generation in Crisis

Healthcare Delivery Must Evolve to Meet the Needs of a Generation in Crisis
By Bob Booth, MD

Bob Booth, MD, MS is chief care officer at TimelyCare of Fort Worth, TX.

A recent new report from the CDC shows startling trends about the never-before-seen levels of hopelessness and suicidal thoughts among teens.

The findings for teenage girls in the CDC’s 2021 Youth Risk Behavior Survey were particularly grim. Nearly three in five teen girls (57%) said they felt “persistently sad or hopeless,” the highest rate in a decade. And 30% said they have seriously considered suicide, a 60% increase over the past decade.

While boys generally fared better overall, more than 40% of boys and girls said that they had felt so sad or hopeless within the past year that they were unable to do regular activities, such as schoolwork or sports, for at least two weeks.

The members of Generation Z, born in 1997 or later, are experiencing unprecedented levels of stress and emotional turmoil. While some of this is likely to ease as they age out of adolescence, adulthood is certainly no cure for depression, anxiety, loneliness, and other stressors. Additionally, double the number of Generation Z members report feeling emotionally distressed compared to older Millennial and Generation X groups.

In order to meet the healthcare needs of Gen Z, particularly mental and behavioral health, the industry needs to become more proficient in its use of digital and virtual care tools. However, not all of these tools are equally effective or designed to meet these young patients where they are.

The digital-native generation that has never known a world without the Internet or smartphones expects that their preferred technology will deliver relevant information and an engaging experience as part of treatment. It’s something we can expect to see more of as part of the future of care for younger generations.

Artificial intelligence (AI) can play an important role in care delivery and engagement if the algorithms enable a highly personalized and patient-centric experience. For example, not all young adults are ready for, or want, 50-minute, one-on-one counseling sessions with a mental health professional. AI can accurately analyze and interpret intake screenings, so a patient’s selected care pathway is the most relevant and takes into account their unique health and personal needs. AI can even help guide digital-only care pathways through content and activity selection based on the young adult’s interactions with the solution.

The promise of AI is that it delivers an even more personalized experience as its algorithms learn more about young adults, which accelerates their growth and motivation to improve their mental health and well-being. These engagement-building concepts have been understood and applied in other consumer-facing technology for years. Healthcare is finally catching up, and that’s good for young adults and healthcare overall. It’s exciting to see where this will take us in the future.

Gen Z needs a solution that leverages personal technology to enable access to mental health and well-being at their fingertips. By seeking tech-enabled help from a healthcare platform that is designed for them and understands their unique challenges, Gen Z can develop the skills and resilience to help them prepare for college and beyond.

It’s time for healthcare to look beyond traditional models of healthcare delivery and meet a generation who so badly needs care where they are.

Readers Write: The Impact Intelligent Automation Can Have on Healthcare Costs

The Impact Intelligent Automation Can Have on Healthcare Costs
By Krishna Kurapati

Krishna Kurapati is founder and CEO of QliqSOFT of Dallas, TX.

RPA stands for robotic process automation. RPA uses technology to automate repetitive human interactions with a computing system. In other words, instead of a human clicking a button over and over to generate a desired outcome, the system automatically connects and completes the stipulated work process, eliminating significant amounts of manual steps and time for the care team.

A similar approach applies to robotic workflow automation, where a chatbot can automate manual and repetitive tasks between a care team member and a patient. For instance, in healthcare administrative and clinical support tasks, the end-to-end steps include reminding a patient of an appointment, sending them digital forms to complete before the visit, automating patient check-in, and reinforcing instructions after the visit. Each task’s workflow comprises a number of work processes to gather, upload the patient’s information to an electronic health record (EHR) system, and to communicate with and guide the patient.

To illustrate, let’s examine the case of patient intake: Today, the office staff creates a paper clipboard and shares it with the patient on arrival, who takes five to 15 minutes to complete the paperwork. Staff then looks up the patient record, scans and uploads the requisite forms to the EHR, and checks the patient in. Humans can be removed entirely from this sequence of steps with RPA and chatbots, which automate the workflow to capture and process the requisite patient data to meet clinical and billing purposes.

The benefits of intelligent automation in healthcare

Faced with a never-ending need for reporting and data entry, healthcare organizations must manage high volumes of administrative duties. A recent study found that the average employee spends 60 hours per month on easily automatable tasks, making healthcare an ideal use of RPA to digitize and scale manual, routine processes. The upshot is dramatically reduced labor costs while optimizing workforce usage for lower costs.

In a January 2023 paper by the National Bureau of Economic Research titled “The Potential Impact of Artificial Intelligence on Healthcare Spending,” the authors calculated that hospitals employing AI-enabled use cases could achieve total annual run-rate net savings of $60 billion to $120 billion (roughly 4% to 10% of total costs for hospitals) within the next five years using today’s technologies, without sacrificing quality or access. The Academy projected that 60% of clinical workflows can be automated through AI, including bots, signifying untapped potential in new revenue and cost reduction.

The role intelligent automation will play in transforming healthcare

Digital health is about delivering care and managing data electronically. Unfortunately, many patient experiences at healthcare systems and practices are handled through traditional communications, including paper transfer, phone calls, snail mail, and fax. This can lead to disconnected patient communication, misdiagnosis, medical errors, waste, and poor quality care. Digital capabilities help providers, innovators, payers, and other stakeholders come together collaborating in an agile, more communicative way to solve problems, overcome scalability limitations, empower patients, improve efficiencies, and speed up throughput.

Once digital infrastructure and capabilities are built, the robotic process automation sits on top to automate workflows. The conjoining of digital and RPA accelerates and scales processes and elevates innovation to create a new standard for the patient experience.

Current use of intelligent automation

Automation started in revenue cycle management processes and is relatively new to the clinical side of healthcare, where the initial focus is processing and management of large quantities of paper into the EMR or content management systems.

Although automation is now happening on the clinical side, it’s not yet well adopted. The most common focus areas are patient communication regarding appointment reminders, appointment scheduling, patient intake, billing, procedure readiness, documentation management, and evidenced-based content for patient education.

The future state of intelligent automation in healthcare

Automation’s ability to simplify healthcare is limited only by our imagination. The cost of labor has skyrocketed to 64% of total operating costs, creating new pressure to reexamine workflow and adopt automation. Healthcare has two broad categories where automation can be of service:

• Eliminating work by automating existing manual, repetitive administrative tasks staff are doing today.
• Supporting automated communication and monitoring needs not possible today because of staffing limitations, such as readmission prevention.

I expect intelligent automation to play a larger role in healthcare for years to come. The time is now to blend clinical and business efficiencies to improve operations and provide relief to overworked and understaffed healthcare professionals.

Readers Write: A Glimpse of Telehealth’s Future: Five Takeaways from ATA 2023

A Glimpse of Telehealth’s Future: Five Takeaways from ATA 2023
By Lyle Berkowitz, MD

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

My recent visit to the American Telemedicine Association (ATA)’s annual meeting offered an opportunity to briefly reflect on how far the industry has come, as well as provided a few glimpses of what the future of telehealth might hold.

When ATA was founded three decades ago, broadband internet was a rare commodity and telehealth visits were primarily via phone calls. Fast forward to today and it’s obvious that telehealth is leading healthcare transformation in multiple areas, from urgent care to women’s health needs to lifestyle medications – while pushing how we can use virtual care tools to simultaneously improve the patient experience, quality, and cost.

Here are five of my key takeaways from the ATA 2023 Annual Conference and Expo:

1. The rise of femtech and women’s health. Numerous startups are developing solutions that leverage telehealth to address women’s health issues. For example, Nest provides virtual same-day lactation support and has partnered with several hospitals to improve infant health outcomes. Separately, SimpliFed partners with caregivers before an infant is born to develop feeding plans and delivers support to patients through a virtual breastfeeding provider network.
2. Increased focus on hybrid care. In this context, hybrid models refer to those that offer patients access to telehealth visits which can coordinate with in-person care, based on a patient’s individual care needs. Corporate giants like Amazon, CVS, and Walmart are lurking around in this space, but health systems have the greatest potential to own it. That’s because it is far more straightforward, simple, and cost-effective to add a virtual care partner to a robust office-based health system than to bolt on office-based care to a virtual care company.
3. A new market for hearing aids. Over-the-counter hearing aids are now available to the public, thanks to a ruling by the US Food and Drug Administration last year. As a result, companies like Audicus have jumped into this market to serve customers via telehealth. In this easy and convenient process, a hearing test is performed online, a hearing aid is shipped out, and any adjustments are done via a video visit.
4. The rise of remote patient monitoring. Like telehealth, remote patient monitoring (RPM) technology has been around for decades, and while various startups have different approaches for obtaining data, they all have the same vision in mind. For example, some companies use a wearable patch for continuous monitoring, others use Bluetooth to connect to devices a patient may have, and “device-less” companies use a chatbot that allows a patient to self-enter data. Some may even combine these tools or add others. Then all of this data is sent to a dashboard for analysis and display so that a virtual team can appropriately monitor and engage with patients, and then identify outliers which need to be escalated to office-based providers. However, the real trick is knowing where to apply RPM and align incentives. The post-acute care area has been popular for years; the chronic care space has experienced slow growth but offers strong potential; and the new hot area is clearly hospital at home.
5. Niche products. It has become easier for companies to focus on specific use cases for virtual care monitoring and management. For example, I came across the super niche startup Staling Medical, which has created an at-home urine diagnostics tool that uses a patient’s smartphone microphone to listen to their urine stream, with a goal of improving outcomes for recurrent urinary tract infections, urinary obstructions, and chronic kidney disease.

It’s a fun time to be in telehealth. I’m looking forward to seeing what’s up next at ATA 2024!

Readers Write: Value-Based Care Arrangements: Four Ways Specialty Care Providers Can Prepare for Claims Data

Value-Based Care Arrangements: Four Ways Specialty Care Providers Can Prepare for Claims Data
By Tyler Johnson

Tyler Johnson is VP of strategic partnerships at Ursa Health of Nashville, TN.

Companies that are bringing new specialty care models to market face a big early hurdle when partnering with plans, full-risk provider groups, or self-insured employers: working with claims data. Although lacking some clinical context, clean, well-organized claims data is vital for creating longitudinal patient views and the main fuel for analytics (which of course become even more powerful when supplemented with clinical and other data sources). Trusted analytics, in turn, are the first step toward optimal operations and outcomes, as well as the financial reconciliation between partners that value-based contracts require.

With everything else involved in launching or expanding a new business, specialty care providers (SCPs) may be tempted to put data and analytics planning on the back burner. Those that delay too long, however, could find themselves scrambling to get new partnerships off the ground or to keep up with an ever-changing landscape. In the best-case scenario, late-night heroics save the day but inordinately stress the team. In the worst, lack of planning leads to lost sales, crumbling partnerships, and dwindling rather than growing healthcare impact.

SCPs must ready themselves to consume claims data from their partners in four key ways.

The first concerns the security review. Organizations are very particular about how and where their data gets shared. Convincing business or clinical leadership to try a novel intervention is tough, but convincing security and InfoSec folks that others can be trusted with their most prized possession is another obstacle altogether.

Before any data is shared, an organization will ask its potential SCP partner to submit to a comprehensive vetting process to ensure the SCP’s technical and administrative safeguards are strong enough to meet both internal and HIPAA requirements. To prepare for the review process, the SCP should:

• Create very tight and easy-to-understand documentation around its technical architecture, including where data is going to live and what people, tools, and processes are going to touch it.
• Create an overview document that summarizes its security posture.
• Organize employee business and security procedures for easy reference.
• Devise a system for retaining answers to assessment questions to expedite the next review.
• Consider being HITRUST and SOC2 certified, which can quickly ease the security team’s concerns. Because the level of effort isn’t trivial, working with a technology vendor that is already certified can help organizations that do not have the internal resources to pursue certification themselves.

Second, an SCP needs to prepare is its tech stack. The contracting and security assessment process can feel a lot like hurry up and wait, but the reality is that this is a task in a very long queue, and once the organization assigns resources to complete that task, it will expect a new partner to be ready to roll.

If the SCP can tap into and pull from the organization’s existing infrastructure for hosting data, great. If not, it needs its own secure cloud storage mechanism (e.g., Amazon S3, Azure blob storage) into which data can be dropped, as well as a pre-defined process for granting access to it. In addition to transmission mechanisms, a database/warehouse and any data modeling and transformation tools must be up and ready to use.

The potential partner is also going to expect the SCP to quickly provide feedback and ask questions about the data. If the environment is ready to go in advance, the SCP can spend more time on loading and investigation instead of provisioning cloud resources. It is also extremely helpful to get answers to data questions while the company’s technical resources are still engaged and informed.

As a final note, SCPs should think in advance about how they will assess the quality of the incoming data, both in a general sense (e.g., data completeness) and regarding specific data points or lineage that is important to their analyses.

The third way to prepare is to ensure a scalable approach exists for organizing and analyzing data. Without a proactive approach to a data model, an SCP can very easily stack up technical debt — in the form of silos of logic and code that are custom to analyzing data from a single source — that becomes a nightmare to untangle down the road and will prohibit efficient scaling of its business.

Once it has defined the data model, the SCP should apply transformation logic to all incoming raw data sources to map the data to that standardized structure. Rules and algorithms to interpret data for specific use case(s) should only be authored on top of that standardized data model, an approach called hierarchical data modeling. This approach facilitates scalability while making it easier to marry up claims data with other sources of information: for example, clinical data from the EHR, patient engagement data, and internal product data.

The final way to prepare is to identify how the claims data will be used to provide insight into their operations and business. This planning should occur before any value-based contract is signed, let alone claims data is shared, to help determine whether other sources of data will be needed — for example, other patient data or industry-available supplemental data such as value sets and provider rosters. Armed with a clear understanding of what insights need to be derived, SCPs can more intelligently articulate their needs and the anticipated value to partnering organizations.

Effective partnership in the co-management of patient populations demands a strong data foundation paired with trusted, useful analytics. Bad data in results in bad data out. SCPs embarking on new value-based partnerships can increase their chances of success and make life easier for both parties with some basic preparation. With a solid and scalable data foundation in place, technical resources can shift their attention away from non-strategic data wrangling work and focus on building the special sauce that differentiates it from competitors and adds the most value to its customers.

Readers Write: The Cost of Doing Nothing: Five Learnings from the Build versus Buy Debate

The Cost of Doing Nothing: Five Learnings from the Build versus Buy Debate
By Kimberly Hartsfield

Kimberly Hartsfield, MPA is EVP of growth enablement at VisiQuate of Santa Rosa, CA.

It’s a conundrum that health system executives regularly face. Build a much-needed software solution in-house or buy it from a vendor?

Once hospital leaders identify the need for a solution that requires new functionality, the debate is on. Revenue cycle management (RCM) solutions are no different.  While many hospital IT departments are no doubt capable of designing, constructing, and implementing new RCM solutions, leadership must decide whether taking this route is likely to yield the best business results.

Often, it starts with hospital leaders surveying vendors, seeing the price tag, and deciding to embark on the journey to complete the project internally, with the premise that it will be at a much lower cost. The decision frequently backfires. Rather than making the investment and having the technology that hospitals need to support their RCM operations efficiently, do-it-yourself health IT projects often end up taking years to fail and costing hospitals far more than if they would have signed with a vendor in the first place.

Health IT leaders see a pretty Tableau or Qlik dashboard and think, “We can do this ourselves.” When it comes to data visualization, they probably can. What they don’t consider is that the data aggregation, normalization, and transformation work that happens under the hood is actually the challenging part of RCM transformation.

The following are factors to consider when considering whether to build or buy a new RCM solution.

Complex health IT projects require more than health IT

IT departments sometimes believe that because they have their own developers and analysts, they can design, build, and implement complex health IT systems on their own. However, complex health IT projects require far more than technical skills. There must be business knowledge and experience married to that technical skill. Frequently that is where the projects break down because the people with the business knowledge already have full time jobs in the organization that are not related to building a platform.

Indeed, the reality of large IT projects is that they frequently exceed timelines, go over budget, or sacrifice important functionality. For example, one in six large IT projects have an average cost overrun of 200% and a schedule overrun of almost 70%, according to Harvard Business Review. Similarly, 56% of IT projects fall short of the original vision, according to a study by McKinsey.

It’s all about speed to value

Leading RCM vendors have been waking up every day for years thinking about how they can work to evolve revenue cycle analytics and deliver value and ROI to clients. Vendors have the benefit of having seen and evaluated RCM systems from healthcare organizations of many different shapes and sizes across the country. They understand best practices, having implemented RCM solutions alongside numerous electronic health records systems. This experience enables the ability to identify idiosyncrasies that hide within data and frequently uncover gaps that clients didn’t know existed.

While hospital do-it-yourself RCM projects may take years to complete, leading vendors can perform an installation in 90 days, delivering immediate insights and ROI.

RCM processes are broken and technology is the fix

It’s an unprecedented time for healthcare. There is no model for the circumstances the industry is undergoing, given labor shortages, supply chain constraints, and the financial after-effects of the COVID-19 pandemic. Across the nation, hospitals are pushing for more automation to augment staffing issues, letting their staff focus on tasks that require decision making, not repetition.

In many cases, RCM processes are broken, and technology is the only route hospitals can take to do more with less. Hospitals must lean into technology and automation, leveraging data to build predictive models and using artificial intelligence and machine learning to boost efficiency.

Unless hospitals are large, mature, and complex, they typically don’t have the resources to handle a large RCM project internally. Smaller hospitals often lack resources like a database administrator, a data warehouse, and data scientists who can build predictive analytics models, for example.

RCM processes continually evolve

It’s easy to forget that RCM projects typically are not “build it and you’re done” solutions. In addition to building RCM solutions, hospital IT departments must provide ongoing support and maintenance. These projects continually evolve, with new requests for additional reports or functionality upgrades. This often requires analysts, engineers, and other highly paid technical resources that are difficult to find and are only growing more expensive.

Further, it’s an open question as to whether build-it-yourself solutions deliver enough value and differentiation to be worth the time, expense, and effort. For example, if all an organization’s competitors can simply build their own systems to accomplish a certain objective, then that system is hardly a source of competitive advantage.

Move from descriptive to predictive

RCM employees cannot manage by spreadsheets. The industry is moving beyond rows and columns. RCM employees need to be able to visualize data to detect patterns to quickly identify outliers and manage by exception. Additionally, hospitals must move their RCM processes beyond descriptive analytics to predictive and prescriptive analytics.

It is no longer acceptable for hospital leadership to simply understand what happened yesterday. Hospital leaders must look to the future with the ability to anticipate and predict what will happen tomorrow, next month, or even in six months. Through automation and advanced data analytics, leading RCM solutions drive those insights.