Readers Write: Finding the Elusive Insights to Improve Surgical Outcomes

Finding the Elusive Insights to Improve Surgical Outcomes
By Dennis Kogan

Dennis Kogan, MBA is co-founder and CEO of Caresyntax of Boston, MA.

America’s operating rooms have an international reputation for driving surgical innovation. But they are also the setting for high variation in performance, as evidenced by the fact that 10 percent to 15 percent of patients experience serious post-surgery complications. This means millions of patients are at risk, yet insight into the root causes of performance variation remain an elusive “black box.” In the absence of this understanding, some hospitals cite the uniqueness of its patient cohorts as the primary driver of variation.

That has the unsettling ring of blaming the patient for his or her subsequent complications. Further, it raises the question of whether or not the hospital has a reliable risk stratification methodology for its patient cohorts, and if not, why not? We can predict the reason and it’s a valid one. Risk stratification at scale depends on data insights, and most perioperative data—a full 80 percent of it—is either uncaptured or unstructured.

To establish perioperative best practices, hospitals first need to harness the massive volume of data where actionable insights currently hide. With the convergence of IoT medical technology and healthcare analytics, they finally can.

Significant workflow enhancements can be made, for example, via performance analytics that consume structured preoperative and postoperative data from the EMR, surveys and patient outcome assessments. But real actionability is made possible with the addition of point-of-care data acquired within the operating room itself, largely from various connected medical devices. Combined with structured preoperative and postoperative data, this provides clinicians with both aggregated and granular data visibility. Now enabled with the clinical full picture, clinicians can focus on putting the data into action.

Circling back to risk stratification, let’s take a closer look at how this works. First, providers must document an individual patient’s risk factors. Then, using a validated risk calculator, a personalized risk assessment can be created (and communicated to the patient). Then, it should be included in an aggregation of patient risk assessments. From this collection of data, along with other data sources that include data pulled during the patient’s surgery, automated risk stratification reports can be immediately available for ICU managers to help prioritize and tailor recovery pathways. These reports could also indicate complication risk and compliance percentages versus targeted benchmarks.

All patients are inherently unique, but that doesn’t mean most of the variation in surgical outcomes or costs is unavoidable. In fact, a significant amount of variation can be reduced by meeting targeted benchmarks—say, for reducing infection, readmissions, length of stay, or even amount of pain experienced post-surgery. These benchmarks and best practices can be crystalized after aggregating and analyzing procedure and surgical documentation, such as reports, vital charts, videos, images, and checklists.

One strategy used in operating rooms around the world is to automate the collection and aggregation of operating room video recordings with key procedure data, including some of the above mentioned checklists and vitals data. Advanced technology can also retrieve surgical videos and images from any operating room integration system. Once surgery and vitals are recorded in a synchronized way, the ability now exists to identify and create a standard protocol that can go into a pre- or post-operative brief.

An additional use for this data includes streamlining post-operative report building, especially for payer reporting and internal quality initiatives. While there is a little time left to report 2017 data for the first official year of MACRA MIPS, this will be a continuing need.

Pre-operative risk scoring is sporadic at best, again, due to the lack of an ability to harness the necessary data. But the same data aggregated to create benchmarks and best practices can be used to create robust and highly accurate risk scoring to see what the possible harm could be to a surgical patient. In parallel, protocols also identified from the data can help to mitigate this risk.

In a hypothetical example, perhaps in one hospital more than 11 percent of patients undergoing non-cardiac surgery experience post-op infection. Predictive analytics reveal that the number of times certain thresholds were reached during surgery correlated with outcome measures. Evidence from this research can be incorporated into a decision support system that monitors the patient’s score and sends alerts when care plans are veering off course. Reductions in infections—and corresponding length of stay and readmission—soon follow.

Persistent opacity into root causes of variation is untenable. Quality-based reimbursement programs such as MACRA MIPS rely heavily on analytics of surgical performance, with a full 60 percent weight given to quality. Meanwhile, patients are aging and becoming frailer. This could increase post-surgery complications to an even higher rate than it is now.

Clearly it is time to innovate not just how we perform surgery, but also how we improve performance.

Readers Write: Almost Real, But Not Quite: Synthetic Data and Healthcare

Almost Real, But Not Quite: Synthetic Data and Healthcare
By David Watkins

David Watkins, MS is a data scientist at PCCI in Dallas, TX.

We all want to make clinical prediction faster and better so we can rapidly translate the best models into the best outcomes for patients. At the same time, we know from experience that no organization can single-handedly transform healthcare. Momentous information hidden in data silos across sectors of the healthcare landscape can help demystify the complexities around cost and outcomes in the United States, but lack of transparency and collaboration due to privacy and compliance concerns along data silos have made data access difficult, expensive, and resource-intensive to many innovation designers.

Until recently, the only way to share clinical research data has been de-identification, selectively removing the most sensitive elements so that records can never be traced back to the actual patient. This is a fair compromise, with some important caveats.

With any de-identified data, we are making a tradeoff between confidentiality and richness, and there are several practical approaches spanning that spectrum. The most automated and private method, so-called “Safe Harbor” de-identification, is also the strictest about what elements to remove. Records de-identified in this way can be useful for many research cases, but not time-sensitive predictions, since all date/time fields are reduced to the year only.

At the other extreme, it is possible to share more sensitive and rich data as a “Limited Data Set” to be used for research. Data generated under this standard still contains protected health information and can only be shared between institutions that have signed an agreement governing its use. This model works for long-term research projects, but can require lengthy contracting up front and the data is still locked within partner institutions, too sensitive to share widely.

What’s a novel yet pragmatic solution to ensure that analytics advancement is catalyzed in healthcare industry? We are exploring “synthetic data,” data created from a real data set to reflect its clinical and statistical properties without showing any of the identifying information.

Pioneering work is being done to create synthetic data that is clinically and statistically equivalent to a real data source without recreating any of the original observations. This notion has been around for a while, but its popularity has grown as we’ve seen impressive demonstrations that implement deep learning techniques to generate images and more. If it’s possible to generate endless realistic cat faces, could we also generate patient records to enable transparent, reproducible data science?

The deep learning approach works by setting up two competing networks: a generator that learns to create realistic records and a discriminator that learns to distinguish between real and fake records. As these two networks are trained together, they learn from their mistakes and the quality of the synthesized data improves. Newer approaches even allow us to further constrain the training of these networks to match specific properties of the input data, and to guarantee a designated level of privacy for patients in the training data.

We are investigating state-of-the-art methodologies to evaluate how effective the available techniques are at creating data sets. We are devising strategies for overcoming technology and scientific barriers to open up an easy access realistic data platform to enable an exponential expansion of data-driven solutions in healthcare.

Can synthetic data be used to accelerate clinical research and innovation under strong privacy constraints?

In other data-intensive areas of research, new technologies and practices have enabled a culture of transparency and collaboration that is lacking in clinical prediction. The most impactful models are built on confidential patient records, so sharing data is vanishingly rare. Protecting patient privacy is an essential obligation for researchers, but privacy also creates a bottleneck for fast, open, and broad-based clinical data science. Synthetic data may be a potential solution healthcare has been waiting for.

Readers Write: Report from AWS Re:Invent

Report from AWS Re:Invent
By Travis Good, MD

Travis Good, MD, MS, MBA is co-founder and CEO of Datica of Madison, WI.

AWS Re:Invent has become one of the most important technology conferences of the year due to the sheer size of the Amazon Web Services cloud and the rate of technology innovation announced. The influence of the conference on health IT has grown over the years as well.

Cerner

There was not an industry-shattering Cerner announcement as was rumored in the CNBC article the week prior. Cerner held a session focused on a few interoperability topics that was well received by the deeply technical audience. But nowhere during its session, nor the daily keynotes, was the announcement made. We bumped into a few Cerner individuals at the event who all commented that they are excited about the future capabilities of AWS’s international regions. International expansion is a priority lately across many health IT vendors and it appeared both Cerner and AWS have similar ambitions based on the Cerner conversations we had at the event.

The amount of money Cerner makes on managed services (which can be largely interpreted as hosting) and support and maintenance (which one can presume has a large amount of hosting-related support) dwarfs the company’s revenue from licenses and subscriptions. The international market is the greatest area of growth for its core revenue model, but international data centers are exponentially harder to build and maintain yourself vs. co-location in the US. Cerner has built and maintained its  own data centers nationally.

There are legs to the rumored CNBC story as well as credibility to the other rumors around population health-related partnerships, but the best insight from Re:Invent we can lend is that any rumored partnership is much more about hosting management than it is about APIs or cloud-based data interoperability.

Compliance

Without question, compliance and security were the two most important topics at the conference. Simply charting the messaging from vendors demonstrates the point: at least twice as many vendors were touting compliance and security management tools, while at least half as many vendors were there to market developer empowerment tools. It’s like the cloud grew up to an enterprise option in the last 12 months.

This is also backed by our observation of the number of C-suite attendees at the event. Supposedly the attendee count jumped from 30,000 last year to 45,000 this year—a number and rumor that was floated often throughout the conference. If true, from our vantage point, the 15,000-person increase was a major jump in “suits” who were there to evaluate how to make this cloud thing work rather than developers who are already leveraging the cloud for projects.

As such, compliance and security was the buzz amongst serious enterprise and healthcare buyers, while the general zeitgeist amongst developers was machine learning and artificial intelligence. But, as we all know, health IT is always woefully behind!

HIPAA, HITRUST, GDPR, GxP, FedRamp, and others were the topics we continuously heard discussed. Interestingly, there are so few options to help truly manage these complex compliance frameworks on AWS. Ultimately, the sentiment we gathered across the healthcare landscape is no one is really helping, especially with HITRUST, GxP, or GDPR. No one had a true GDPR message or product. (Datica will be GDPR ready in Q1 2018.)

AI, ML, and AWS Services

John Moore from Chilmark Research once told us that he goes to Health 2.0 to see what’s going to happen and HIMSS to see what’s already happened. Re:Invent has similar characteristics as Health 2.0.

The pace of innovation and accessibility to digital health developers is so fast that the products and changes to health IT are going to become ever more rapid despite the industry’s best efforts to slow it down. The sense that the AI revolution is just around the corner was one of the strongest observations from Re:Invent. That more AI tooling is being made available to health IT developers on AWS’s cloud means that better products more adeptly addressing patient care and reducing costs are going to come at an ever faster pace. It’s going to be an interesting next few years.

Readers Write: The Challenges (and Benefits) of Anesthesia Data Capture

The Challenges (and Benefits) of Anesthesia Data Capture
By Douglas Keene, MD

Douglas Keene, MD is chairman and founder of Recordation of Wayland, MA and an anesthesiologist and co-founder with Boston Pain Care Center.

As part of the American Recovery and Reinvestment Act of 2009, hospitals and clinics were required to demonstrate conversion to electronic medical records (EMRs) by the end of 2014. However, despite government incentive programs totaling in the billions, the program initially faced a myriad of hurdles and proved harder to implement than initially anticipated. Fast-forward to nearly a decade later and the initiative is back on track, with over 90 percent of healthcare facilities using EMRs as their universal standard.

With that said, one segment of the healthcare market has lagged in EMR adoption: anesthesia care providers and the adoption of anesthesia information management systems (AIMS). Despite the critically important role the operating room plays in a hospital’s ecosystem –typically the source of about 60 to 70 percent of a hospital’s revenue – the majority of healthcare facilities have been hesitant to make substantial monetary investments in AIMS.

To bring the EMR revolution out of the doctor’s office and into the OR setting, physicians must reflect on the factors that have led to slow AIMS adoption,and consider the key features and components needed in order for physicians and administrators to overcome these implementation hurdles.

Anesthesiology departments have grappled with many of the same challenges initially faced by healthcare facilities looking to adopt EMRs. These include reluctance to share information with competitors, software from different vendors that can’t interoperate or communicate, lengthy and complex implementation phases, and the overall high price tag of such systems.

In addition to these obstacles, AIMS adoption faces an even more challenging hurdle: adoption inertia by anesthesia providers. While all EMR software faced some initial skepticism by healthcare providers in general, this aversion has been far more vehement among anesthesia care teams for several important reasons, and stemming from the complexity of real-time anesthesia-related documentation.

Early AIMS were difficult to learn to use and implement. They relied upon larger, expensive computers with relatively lower processing power and faced challenges with interfacing reliably with anesthesia equipment and hospital information systems. Anesthesia workflow and efficiency often worsened with the introduction of early AIMS technology.

Advances in computer technology and interface design have improved some aspects of the overall user experience. However, the drawbacks from early AIMS still linger in the minds of many anesthesia providers.

While many academic and larger surgical facilities have adopted AIMS made by the vendors of the existing hospital information systems, there are numerous community hospitals and ambulatory surgical centers that have not yet transitioned to electronic anesthesia records, based upon their smaller sizes and budgetary constraints.

As a result, many of today’s anesthesiologists and CRNAs who underwent their initial training using AIMS in academic facilities ultimately enter practices that still rely on handwritten documentation.

As economic and regulatory forces increase pressure to consider the adoption of electronic anesthesia records, teams that include administrators, information management specialists, clinical managers, and anesthesia providers are sharing the decision-making process.

As a board-certified anesthesiologist, pain management, and clinical informatics specialist, I am certainly familiar with the complaints physicians have had with AIMS. In my opinion, with the modern technologies now available on the market – and many now available at more reasonable price-points – there is no good reason that surgical facilities and anesthesia departments should hesitate to consider the adoption of anesthesia information technology. The benefits of AIMS and the potential perils of not adopting such a system are far too great to ignore.

In choosing an AIMS, the type of facility in which it will be implemented should be considered and the characteristics of the facility should be embodied in the AIMS. As an example, ambulatory surgery centers (ASCs), while among the slowest to adopt AIMS, are beginning to realize that their survival will depend upon information management.

ASCs must provide patient care with a focus on safety, quality, and operational efficiency, but often have smaller budgets to implement information technology. Therefore, a sensible approach would be choosing a cost-effective AIMS solution designed to facilitate perioperative documentation in a fast-paced anesthesia workflow environment that is focused on providing easily available data for process analysis and improvement.

ASCs also need to streamline the sharing of information from and with numerous sources, including primary care providers, surgeons, patients, and hospitals, and therefore should choose an AIMS solution that focuses on interoperability and that is easy to implement. These factors will benefit all of the ASC’s stakeholders and will lead to better patient care and assure the long-term financial viability of the facility.

From the point of view of the AIMS end users, the anesthesia care team must view the AIMS solution as benefit rather than an obstacle. Instead of placing a barrier between physician and patient as some feared AIMS would do, early adopters have found that well-designed AIMS empower physicians and CRNAs to be more vigilant with respect to direct patient care during surgery.

Instead of using handwriting to create what is sometimes partially illegible documentation during a surgical procedure, many AIMS are able to capture vital signs such as pulse oximetry, end-tidal CO2, volatile agent concentrations, and other numerics automatically, enabling providers to spend more time monitoring the patient and focusing on quality of care. The result: better data, accurate documentation of measurements, and improved patient outcomes.

Other improvements to modern day AIMS includes intuitive user experiences and interfaces, the ability to easily customize workflows, as well as increased interoperability with existing EMR systems. For AIMS users, and especially for ASCs, ease of use and system integration is of utmost importance as the success of an ASC depends on the ability to seamlessly share information back to the host system of a hospital or provider during transfer of care.

In addition to interoperability, today’s AIMS solutions are designed to mimic traditional interfaces and workflows with which anesthesia providers are already familiar. In fact, adopters of well-designed AIMS can become comfortable with their use after just a few surgical procedures.

There will always be new documentation requirements, new monitoring data that must be recorded, and new information that will need to be shared with providers. Practices that adopt modern AIMS solutions will be able to weather these changes far more easily than those who continue to create handwritten anesthesia documentation, as well-designed clinical solutions respond to these changes and guidelines in anesthesia technology, monitoring, and standards of care.

In summary, a well-designed AIMS provides a cost-effective alternative to handwritten documentation in that anesthetic records can now be based upon high resolution electronic data capture, with computer-validated information that can be aggregated into databases that form the basis for continuing quality analysis and improvement studies.

In the end, with a relatively small investment in anesthesia information technology, even the smallest community hospitals and ambulatory surgical centers can implement technology that will empower the facilities to say with confidence, “We’re doing a great job and here’s the proof.”

Readers Write: Tell Me More: Documentation Support in Telemedicine

Tell Me More: Documentation Support in Telemedicine
By Patty Maynard

Patty Maynard is senior vice president of business development with Health Navigator of La Grange, IL.

A successful telemedicine platform provides value beyond the latest technology or reduced healthcare costs. The most effective platforms focus on workflow, from resource allocation to staff education. In fact, a recent REACH Health survey showed telemedicine can improve outcomes, access to care, and efficiency.

Clinical documentation support (CDS) facilitates reaching these goals. From the chief complaint to the pre-visit, “tell us more” step, CDS can improve workflow. It captures shareable data for medical call centers, telemedicine providers, hospitals, and primary care providers. This data can simplify the pre-visit process, saving time and money. In addition, it provides patients with a familiar and comforting medical interaction, but in a digital format. CDS is part of the back-end content and workflow that make the digital health experience run smoothly.

The more information a healthcare professional has, the easier it is to make decisions. In telemedicine encounters, an easy-to-navigate questionnaire about the chief complaint or symptom can help move the process along.

Imagine knowing a patient’s chief complaint, symptoms, and demographic information before they reach the clinic. This may sound too good to be true, but modern platforms can provide a patient-facing checklist or Rapid Medical History that prompts patients to provide information. Clinicians can review a patient’s Rapid Medical History or use the CDS tool to record patient responses.

For example, a patient using a telehealth application may respond to two of five questions in a pre-visit checklist or Rapid Medical History. In a follow-up call, the clinician reviews the responses and asks any unanswered questions. The clinician then collects relevant information from a standardized CDS checklist and gives care advice.

CDS checklists also help providers ensure staff follow safe, consistent processes with patients. Checklists are especially important in crisis or high-stress situations when staff may forget details. In the long run, checklists help:

• Ensure consistent workflows
• Improve communication
• Reduce provider risk, and
• Save time.

For every chief complaint, there is related information telemedicine providers need to know. The ideal telemedicine platform should have access to content that automatically links a chief complaint to a Rapid Medical History template. A platform that connects chief complaints to a standardized list of questions can save time and improve efficiency. These custom templates can also improve accuracy of care advice.

The traditional, pre-visit process can take a significant amount of time, time that could be spent elsewhere. Incorporating CDS reduces time spent gathering patient background information and allows staff to get to the root of the problem quickly. This leads to faster, more accurate diagnoses and care recommendations. It also creates an alternative to ER or urgent care visits for low-urgency conditions, which make up a large part of telemedicine encounters. CDS can also be used to augment EHRs with data that improve patient tracking.

A standardized clinical documentation support process can transform the telemedicine experience, creating a faster diagnostic process and reducing unnecessary visits. CDS can improve patient outcomes, safety, and satisfaction by delivering a consistent experience for patients and staff. This can help patients feel empowered and gives them tools to make appropriate healthcare decisions. In short, CDS is a building block of a better telemedicine experience with more valuable data.

Moving forward, the healthcare industry will see more of this data processed through artificial intelligence (AI) like natural language processing (NLP). NLP directly relates to CDS because this “narrow AI” produces the standardized, follow-up templates for each chief complaint. These two technologies can improve all areas of telemedicine.

Some of the major areas of opportunity for telemedicine lie in services like tele-ICU, tele-psychology, and triage. CDS allows these services to deliver a richer, data-driven experience. These areas are only expected to grow, and CDS helps telemedicine providers meet patient and provider needs.

As telemedicine falls under new legislation and continues to evolve as a covered benefit, expect to see new guidance on standardization and use. CDS provides data that makes telemedicine visits valuable, fitting into value-based payment models. Telemedicine providers can expect to see increasing demand for these convenient services as employers and health systems work to provide cost-effective, accessible care.

Readers Write: HIT Talent Trends to Watch in 2018

HIT Talent Trends to Watch in 2018
By Frank Myeroff

Frank Myeroff is president of Direct Consulting Associates of Cleveland, OH.

What’s in store for 2018 when it comes to HIT talent? Here are eight talent trends that will help to shape the HIT workforce in the New Year.

Widespread Adoption of People Analytics

As Millennials move into HIT management roles, they’re turning to analytics much more than their predecessors as a way to better understand the effectiveness of people practices, programs, and processes. Millennial HIT managers are creating employee dashboards like Microsoft’s MyAnalytics to help people better understand how their time is spent and as a means to measure progress of organizational HIT goals and initiatives.

Cybersecurity Needs to Improve

Cybersecurity in 2018 needs to become a top priority. In 2017, the WannaCry outbreak brought serious attention to security in the healthcare industries. The security of digital health data has not kept up with its growth due to a lack of investment in people and technology, but that is starting to change. Healthcare IT hiring managers and HR executives could be in a good position to lure cybersecurity talent in 2018 because healthcare is the hottest hiring hotspot when it comes to cybersecurity.

Explosive Growth in Telemedicine Services

According to an IHS Technology Report, the telemedicine services field is expected to grow to include 7 million patient users, nearly twice what is was in 2016. Telemedicine is a huge change to healthcare because it can help extend care and reach of patient monitoring, consultation, and counseling to those individuals who cannot make it to a doctor’s office. Plus, once it reaches its potential, telemedicine will allow doctors to help more patients in less time. According to a survey done by Becker’s Healthcare, only one percent of respondents had no plans to implement telemedicine in the future. This fast growth means that HIT professionals will play an event bigger role when it comes to developing telemedicine services. By helping to create the telehealth infrastructure, HIT professionals can help make telemedicine a fixture in healthcare delivery.

Robotics and AI Represent Greatest Transformation in Healthcare Services

While this has been a high-growth area in recent years, we see it skyrocketing in 2018 and beyond. The main areas of healthcare that will benefit the most from robotics and AI are direct patient care such as surgery and prosthetics, indirect patient care in the areas of pharmacy, medical goods delivery, home health, and disinfection that will interact with people having known infectious diseases. This high demand in robotics and AI will add a plethora of new jobs in the areas of highly skilled data specialists, algorithm specialists, robotics engineers, software developers, and technicians.

Expected IoT Job Boom On Hold

The healthcare industry only saw an 11 percent boost in Internet of Things (IoT) network connections between 2016 and 2017. That ranks the healthcare industry behind four other key industries: manufacturing, energy / utilities, transportation / distribution, and smart cities / communities according to “The Verizon State of the Market” report. While IoT devices clearly offer new benefits for healthcare provider organizations, adoption remains limited due to the IoT standards, security, interoperability, and cost. Therefore, the hiring of developers, coders, and hardware professionals will not be needed to the extent previously thought.

Continued Rise of Freelance Economy

There’s high growth when it comes to freelancers, temporary workers, contractors, and independent consultants within the HIT space. New technologies, cost factors, and a whole new generation of HIT professionals wanting to work in a gig economy are fueling the growth. Organizations should, now more than ever, look at building new strategies or evaluating what is already in place to keep these workers motivated and engaged. If they don’t, they risk losing this highly skilled talent to their competition. By 2020, it is anticipated that 50 percent of all US workers within various industries will be contingent workers.

Candidate-Driven Job Market Continues

In most industries across the US, we’re experiencing a candidate-driven job market and the HIT industry is no exception. Those who do have the right skills are in a good position to find the best job offer. They have far more power and latitude to be very selective regarding opportunities and employers. In fact, HIT professionals tell us that they have a pipeline of opportunities to choose from and are getting up to 20 recruiting calls per day. There’s no doubt that healthcare organizations are feeling the impact of the heightened competition for their attention.

Diversity in Technology Still Needed

With the retirement of the baby boomer generation in full swing, worker shortages are of great concern. The fact that the information technology field can’t seem to attract a more diverse population doesn’t help the situation. The IT workforce is predominantly white males. Even though many organizations announce diversity initiatives on a regular basis, hiring managers complain that they can only hire from the worker pool that is available. By introducing science, math, engineering, and technology (STEM) to minority students (including females) at an early age plus having a diverse group of educators throughout their schooling, the amount of diversity in the field as a whole can increase.

Readers Write: Preparing Nurses for Opioid-Addicted Patients

Preparing Nurses for Opioid-Addicted Patients
By Jennifer David, RN, BSN, MHA

Jennifer David, RN, BSN, MHA is vice-president of clinical operations for Avant Healthcare Professionals of Casselberry, FL.

President Donald Trump declared the opioid crisis a national emergency. As a reaction to this announcement, Intermountain Healthcare, a Utah-based hospital chain, pledged to decrease opioid prescriptions by 40 percent in 2018.

Making a commitment to addressing the crisis is a crucial first step. However, only addressing the patient-use side is not a holistic approach to the problem.

The mental health of the nurses and doctors who care for overdosed patients are not considered in the opioid equation, yet every day they feel the magnitude of the epidemic and they are left alone to manage their pain. Ultimately, they may leave their job or the profession altogether without support in facing this problem.

Nurses are the frontline warriors in this epidemic. Several times each day, they’re responding to the screams of withdrawal, managing the inherent chaos of addiction, and dealing with family members who demand an immediate solution. For many patients, it’s the second, third or tenth time to the emergency department for the same problem. Families are desperate, angry, and looking for someone to blame, often defaulting to the nurse.

I’ve had nurses from hospitals around the country explain that they feel that they’re enabling drug-addicted patients by administering pain medications. However, “managing pain” is an important aspect of HCAHPS. Nurses are conflicted between caring for a patient and adding to the problem. This conflict can lead to anger, stress, and frustration among nurse staff, and in some cases, could drive nurses to quit.

Some hospitals have made steps in protecting nurses against these patients with a patient code of conduct, which states that violence and verbal abuse against staff will not be tolerated. A large hospital in New York has their patient code of conduct displayed throughout their hallways and another facility in Missouri strictly enforces that their staff will not be disrespected by patients. When these rules are up against patient discretion on HCAHPS scores, they become harder to enforce.

The best thing hospital leadership can do is to mentally prepare nurses to care for these difficult patients. This will also reduce staff turnover and improve employee communication.

The first place to start is to recognize the potential of a problem. I make personal visits to our nurses on assignment and always ask them how they are dealing with opioid-addicted patients. It is not always easy or possible to give individual attention to every nurse on staff. However, it is important to identify who is having issues. Surveying the nurse team to ask if they feel respected at all levels and supported in their job challenges is a great strategy to begin with.

Once honest communication begins, explore what support nurses want and need, then put a plan together. It should include a healthy dose of continuous learning intended to help build understanding and empathy for patients’ needs. Seeing how our nurses were affected, we now incorporate training on how to care for drug-addicted patients in our curriculum as well as provide consistent follow up while nurses are on assignment. We want to pre-expose them for what they might face and be there for them when they face it.

There will likely be multiple tiers of support needed,  varying from the occasional discussions about a particularly challenging patient to more intense, personalized support from the human resources department. Everyone has different experiences and belief systems about addiction, so allow for that. One of the hardest things to address is that opioid-addicted patients should not be discriminated against.

Not all days are the same when dealing with these patients, and some days might be especially challenging. Consistent follow up is necessary to maintain a healthy staff and also allows for positive patient experiences. If nurses feel that their employer constantly empathizes with them, they will feel the support they need when caring for such patients.

Readers Write: Tips for Selecting EMR Training and Activation Support Vendors

Tips for Selecting EMR Training and Activation Support Vendors
By Kevin Smith

Kevin Smith is CEO of TrainingWheel of Fort Myers, FL.

The contracted EMR vendor often does not deliver experienced staff for the activation. The go-live is the first time some of the vendor’s elbow support resources enter a hospital without being a patient or family member.

Here are a few helpful tips based on lessons learned to save organizations time and money:

• Know what the organization wants and what it is paying for. Consider more than the proposed training and support cost. For example, the cost may be different because the vendor provides licensed clinicians, while others may provide non-clinical rounders with no hospital for a lower cost.
• Insufficient planning can lead to less-experienced resources. What recourse does the contract include? If a bank teller or oil rigger joined the firm last week, are they prepared to help the clinicians? A body is not what matters to the clinicians. They want someone helpful to them as they learn how to do their work using new tools.
• Ask the vendors to provide resumes, CVs, immunization records, background checks, and proof of experience in advance. Vendors often slide inexperienced people into a project and shuffle them around. They want to maintain high resource numbers, but the clinicians are not getting the support.
• Does the vendor rely on one or more third-party companies to provide trainers and support staff? If so, it will be hard to know what type of resources are being provided. This is important because many vendors subcontract to the same companies. There may be two different bids, but the organization ends up with the same subcontracted company. If the primary vendor can’t answer basic support questions, the organization may already be in trouble. An experienced vendor will match clinical support personnel to support areas based on their clinical role and/or experience.
• Can the vendor present a full project cost proposal with a support schedule, detailed expense projection, and a list of their proposed resources after one walk-through of the facility? Staffing ratios help, but are not always accurate. If a vendor doesn’t understand the makeup of the organization’s staff and the layout of the facility, how can they give an accurate estimate of clinical support resources?
• Does the vendor develop curriculum and clinical scenario-based training or does training simply cover system functionality? If training only covers functionality, then users will require more elbow support because they won’t be prepared to use the system for their real-time clinical workflow. The #1 complaint from clinicians in EMR training is that it only teaches them navigation and what each click does. This leaves clinicians anxious and also forces every clinician to come up with their own approaches and workflows.
• Can the vendor recognize issues in the build and offer recommendations based on past client experiences? The training partner should be an asset to the team, identifying issues in the build that may come up during the training. Better to know this ahead of time and make corrections than during or after the go-live. Make sure the organization and the vendor have a joint commitment to be open and sharing in this regard.
• Does the vendor pursue continued improvement and feedback? Are they as committed to quality as the organization?

Vendor involvement is an integral part of implementation success. As an organization, ask the necessary questions to guarantee the right vendors are selected.

Readers Write: How Hard Is It?

How Hard Is It?
By Frank Poggio

Frank Poggio is president and CEO of The Kelzon Group.

In the October 28, 2017 issue of HIStalk, Mr. H made this critical observation and raised an important question. He wrote (finishing with tongue in cheek):

For those with short memories or short healthcare IT careers, it’s time to relearn the oft-repeated lesson that big companies dip their toes into and out of the healthcare IT waters all the time with little loyalty to anyone except shareholders. McKesson bailed out this year and now GE is apparently mulling its exit after wrecking a slew of acquisitions over many years. Siemens is long gone. Nothing good ever comes from conglomerates licking their chops at what they naively think is easy money and higher growth than their other verticals (see also: Misys and Sage). How hard could this healthcare thing be?

GE of course isn’t alone, but they may hold the prize for most kicks at the can. This will be their third time since 1970 — three tries and billons later and nothing to show for it. Ironically, GE has had great success in medical devices, so one could assume they know more about the healthcare business than a Revlon, Apple, IBM, NCR, Martin Marietta, Lockheed, Oracle, SAP, Microsoft, et al.

After some 45 years working in the healthcare IT arena, I believe I have the answer to Mr. H’s query. My qualifications in support of my response are:

• Over four decades, I was a hospital CFO and CIO at a major teaching hospital.
• I spent two intermittent decades as an industry consultant working with healthcare providers and system vendors of all sizes.
• In the middle of my career, after my CIO stint, I founded a HIT startup that built both clinical and administrative systems, went public, and was later acquired by one of today’s major vendors.
• Most importantly, I have designed clinical and administrative software systems, led installations, and written more than my share of program code.

To summarize, I have seen it from all four sides; buyer, builder, advisor, and patient.

There are four reasons that make healthcare IT hard, really hard.

Organizational Structure

Many people new to the healthcare readily compare it to commercial industry. Why can’t hospitals do as banks, or airlines, or Google, or…?

One reason is they are not organized like these entities. What other industry has as its primary customer the same person that sells and then performs the core services? That same person also defines the product and further determines how it is delivered and implemented. That person is the doctor. The PhDs at GE do not make the final decision on how to make a jet engine or how to deliver it. GE is run by a CEO and the buck stops there. Hospitals are run by a troika (or committee) of the board, the administrative CEO, and the chief medical officer.

In 1974, Professor William Dowling, University of Washington, published the book “Prospective Reimbursement for Hospitals,” which did research on hospital operations. His studies showed that the CEO of a typical community hospital directly controls only 25 percent of the resources and operations. The other 75 percent is controlled by the doctors. They decide what tests to run, when to run them, and what happens next. The fastest way for a CEO to lose his job is to directly challenge the medical staff.

What other industry is organized like this? If you are in the business trying to build and sell million-dollar systems, you had better understand this organizational dynamic and accept the fact it will take years to generate an acceptable return on investment.

Regulatory Quagmire

All businesses are struggling with regulation. I submit that healthcare far exceeds all others.

Case in point: in what other industry does the payer define the structure and content of the bill down to the very last data element? One that comes closest is the defense industry, and many of its idiosyncrasies are incorporated in healthcare regulations. In 1999, Price Waterhouse CPAs completed an analysis of how many pages in the federal register addressed income tax laws. They compared income tax against the number of regulatory pages need to create a payable UB bill for all payers in a given state. The results were 11,000 pages of regulations for taxes and over 50,000 for a hospital bill.

A further complication is the person receiving the care is not the one paying the bill. Sometimes the patient never sees the full bill, and when they do, they are inevitably confused.

Training, Structure, and Definition

Computer systems thrive on definition and structure. The easiest applications to develop are those where the target domain has a history and library of definition and structure. Lack of definition and structure are a programmer’s nightmare. Today there are many tools to help address gray areas, such a fuzzy logic and neural networks, yet learning and applying these tools significantly raises the complexity of the system, thereby increasing development time and costs.

A doctor’s adherence to medical terminology and structure is highly dependent on which medical school they attended. As an example, a study at the Milken Institute SPH at George Washington University found that physicians whose residencies were in higher-spending regions spent 29 percent more on average than their peers who had trained in lower-spending areas of the country. Different protocols for different regions based on training. The federal government spent $30 billion on EMRs and yet we still have wide gaps in medical lexicons, protocols, and the structure and content of EMRs.

Moving Targets

In IT, this is classically called a rolling design, again a developer’s nightmare. But the delivery of healthcare and the practice of medicine are rife with this burden. Medicine is in constant change, with new protocols, test procedures, quality measures, etc. presented every week. Old protocols are challenged on a routine basis, e.g., mammography screening, PSA testing, knee replacements, tonsillectomies, and more.

What if you were assigned to develop a production management system for an auto manufacturer and every month the manufacturing engineers told you that process A — which we coded last month — has now changed to process B? The solution in commercial industry is to freeze the design by freezing the process. Can’t do that in medicine — freeze your protocol and tomorrow it could be the basis of a malpractice suit.

Medicine has always been in constant change, and with personalized medicine around the corner, variation and complexity will grow by leaps and bounds. Scientists have been trying to reverse engineer the human body since the first autopsy a thousand years ago. If only when you were born your mother gave you a 5,000-page human spec sheet with schematics and diagrams, a user’s manual, a troubleshooting guide, and a 1-800 number to call when all else fails. They exist for every car, dishwasher, plane, and other device and sure make software development a lot easier.

When I was a CIO at the end of a difficult IT implementation, the dean of our medical school said to me, “There is a reason we called it the practice of medicine. If we practice long and hard enough, someday we’ll get it right.”

Many of these issues exist in other industries and disciplines. I submit that the depth and interaction to which they exist in medicine and healthcare is what makes IT development hard, very, very hard. All those big companies (and many small) that came into the healthcare industry failed because they did not allow for the depth and interaction of these challenges, and hence they did not prepare for them, lost patience and millions, then chose to cut their losses and run.

From the outside looking in, healthcare is twenty percent of the gross national product, which could support a very attractive business opportunity. It’s a beguiling number which has proved to be siren song for many a big and small firm.

Readers Write: Six Myths Debunked: The True Significance of Social Determinants of Health

Six Myths Debunked: The True Significance of Social Determinants of Health
By Erin Benson

Erin Benson is director of market planning with LexisNexis Health Care of Alpharetta, GA.

Predicting future health risks has always been important, but the ongoing move toward value-based care and the emphasis placed on health outcomes is driving the need for greater prognostic accuracy.

At least 25 cents of every healthcare dollar goes toward the treatment of diseases or disabilities that result from potentially changeable behavior. If you can identify risk factors in patients, you can potentially intervene and initiate change. That’s where social determinants of health come in, but first it’s important to separate the myths from the truth.

Myth #1: Adding socioeconomic data to the patient file causes information overload and makes it difficult for providers to zero in on what’s important and relevant.

Medical care determines only 20 percent of overall health, while social, economic, and environmental factors determine 50 percent, making them too significant to ignore. The National Quality Forum, Centers for Disease Control, and World Health Organization have all acknowledged the importance of socioeconomic data.

Incorporating the data into existing workflows and integrating it with electronic health record (EHR) systems makes risk assessment more efficient, not time-consuming. A Socioeconomic Health Score, for example, can provide an immediate picture of unforeseen and avoidable risks. It can then drive informed decisions regarding that patient’s care, as well as offer opportunities for patient-provider discussions about lifestyle.

Myth #2: Social determinants of health include everything related to a person’s lifestyle, environment, situation, and behaviors.

Only certain types of data have been clinically validated to predict health outcomes. Even when attributes are clinically validated, they may correlate to different outcomes with different accuracy strengths.

Improving predictive ability is not just a matter of adding more data. It is a science to determine which datasets enhance predictive power and how they should be weighted in drawing insights about a patient.

Myth #3: A patient’s socioeconomic attributes considered individually allow you to make accurate predictions about a patient’s overall health risk.

Any attribute examined on its own is not adequate to develop an accurate risk score understanding. A combination of relevant attributes—ranging from social and community circumstances to economic stability and education, to neighborhood and built environment—provide context and are critical to developing a complete, holistic picture of the patient.

Myth #4: Socioeconomic data comes from demographic data or must be gathered through the use of surveys.

Demographic data may be too limiting and census data tends to get outdated quickly. Survey data, too, can become outdated. Furthermore, the value of survey data depends on the accuracy of the patient supplying the information and on the staff member who manually enters the results into the system.

Research has shown that public records are a better source of socioeconomic data. Those records are vast, comprehensive, and reliable. Clinically validated information on social determinants of health can be extracted from these records to paint a picture of a patient’s social, environmental, and economic situation and predict future health outcomes.

For healthcare providers who have traditionally relied only on medical and pharmacy data, socioeconomic data can now help fill gaps in understanding the patient and provide actionable insights that can be used to improve patient care.

Myth #5: To personalize care for a patient, you can rely on aggregated data at the ZIP code or census level.

Aggregated levels of data can be useful for expanding a health system’s market share or determining resource allocation. They are not, however, suitable for predicting a patient’s individual health risk.

Within a single ZIP code can be a wide variety of income levels, crime rates, and other factors that are critical components of social determinants of health. An individual’s actual address allows for the collection of social determinants that are more accurate indicators. However, even address data alone are not effective predictive tools. They ignore the influences of education, economic stability, social context, and other important variables that impact health.

Myth #6: Socioeconomic data must be used in combination with clinical data and is not an effective risk predictor on its own.

Even in the absence of clinical data, using socioeconomic data has proven to more accurately predict risk based on total cost than traditional age/gender predictions alone. Small increases in accuracy of as little as a percent or two can have a substantial impact and should not be ignored.

Because higher-risk patients account for the majority of healthcare costs, using socioeconomic scores to more accurately identify them gives providers an opportunity to proactively address their care. The result can be a 10-20 percent savings over traditional age/gender model risk stratification alone.

Healthcare is on the brink of a significant transformation largely driven by the availability of vast amounts of socioeconomic data and advanced analytics. Now that we’ve separated fact from fiction, it should be apparent social determinants of health have great value as a reliable predictor of healthcare risk.

The truth is we’ve only scratched the surface of what can be learned and how the insights gained can be applied. What is clear now is that organizations that embrace using social determinants of health will be better able to understand and manage health risk in their patients, resulting in improved outcomes and reduced healthcare costs.

Readers Write: Why Healthcare Organizations Take So Long to Make Buying Decisions and How We Can Fix It (Part 4 of 4)

Why Healthcare Organizations Take So Long to Make Buying Decisions and How We Can Fix It (Part 4 of 4)
By Bruce Brandes

Bruce Brandes is founder and CEO of Lucro of Nashville, TN.

We have previously discussed the impact of organizational misalignment and lack of trust on slowing the buying cycle in healthcare. Once you decide which projects are worth tackling and you streamline getting the scoop from your trusted network, now you must challenge and simplify the deeply-rooted, legacy workflow to make a decision.

Let’s illustrate two examples of antiquated steps in most every vendor selection process in healthcare and discuss potential solutions.

Meetings about Meetings

For projects that require alignment and collaboration across many key stakeholders, unnecessary, unproductive meetings complicate and delay making a decision.

Politics and mistrust may artificially swell the number of required participants (healthcare is more than a catered buffet away from adopting Amazon’s two-pizza rule). Something as simple as setting a date for a meeting when everyone is available can push a process out by weeks, if not months. Inevitably, some important contributors will have a last-minute conflict. The original meeting agenda often devolves into a pontification session with distracted participants checking emails on their phones and little advancement toward a decision.

How can we ensure fewer meetings, using our precious time together to be true decision-making events to advance a project? The most common answer to date has been an untenable volume of fragmented emails, spreadsheets, SharePoint files (people really still use this!?), and other databases that do not spur action.

A better solution is to enable a more efficient platform for asynchronous collaboration among key contributors. Ideas, comments, and assessments can be solicited and shared at the availability of each stakeholder and captured in context of the problem statement, current solutions, or potential solutions being considered. Project owners control the appropriate balance of privacy and transparency to minimize duplication of effort across the organization.

Better asynchronous collaboration can ensure fewer people physically attend fewer meetings and calls,  and when they do, key information and opinions will be understood in advance, reserving meeting time to make decisions that more rapidly advance a project. Let’s stop having meetings about meetings that don’t offer any action items or decisions and ensure everyone’s time is spent more efficiently.

RFIs / RFPs

In an effort to de-risk a decision, get a fair price, and discern truth amid vendor marketing claims, we in healthcare add months to a procurement process with a request for proposal.

The greatest advance in the healthcare RFP process over the last 15 years is replacing FedEx’ing boxes of binders with emailing word documents and spreadsheets. Not exactly disruptive innovation on the most universally dreaded, antiquated step in the buying process (drawing disdain from both healthcare organizations and vendors alike).

Every RFI or RFP is created and sent as if it were being done for the first time and yet rarely is an original question asked. Just ask the vendors who jump through clerical hoops to nuance their library of prepared answers to meet the requirements and format of each set of questions, always wondering if they are wasting resources on a CYA exercise for a decision that has already been made.

Further, manual effort for project owners to cut and paste siloed answers among Word documents, spreadsheets, and PowerPoint to score, assess, and present results amplifies frustration.

How can health systems ensure the diligence and risk mitigation benefits of an RFP without the exorbitant time, resources, and costs associated with their current methodology?

A network of engaged healthcare organizations that share common challenges and opportunities can collectively engage with vendor partners in a new way that is more efficient and effective for all involved. Common questions and answers can be crowdsourced to minimize unnecessary duplication.

The future state of an RFI can be little more than an appropriate search and application of filters to instantly identify a relevant shortlist. For a deeper dive, posing general RFP-like questions in a common platform (with appropriate privacy controls) can help buyers ensure a more comprehensive question set while helping vendors avoid answering the same topic more than once. Enhanced by automatic comparison, simplified assessments and flexible analysis of results, the expectation is we can minimize decision-making risk for more vendor selections being made without the formal RFP we know today.

By reimagining meetings and RFPs, the healthcare industry can simplify and antiquated process and enable decisions to be made with significantly fewer resources, less elapsed time, and lower costs.

Readers Write: Detecting Healthcare’s Data Dilemma

Detecting Healthcare’s Data Dilemma
By David Lareau

David Lareau is CEO of Medicomp Systems of Chantilly, VA.

“It is a capital mistake to theorize before one has data.” – Sherlock Holmes, “A Study in Scarlett” (Arthur Conan Doyle).

The great detective Sherlock Holmes understood the important role that data plays in decision-making. Whether you’re sleuthing or delivering patient care, you need data in order to make sense of things.

Not long ago, before EHRs were pervasive across health systems, providers struggled to obtain the data they needed for good clinical decision-making. Today healthcare has an abundance of clinical data, along with a new data dilemma: finding the right data at the right time.

In a recent webinar, our team asked 76 healthcare IT professionals and physicians about their biggest data-related challenges. According to 43 percent of the respondents, the top struggle was not a lack of data, but finding the right data at the right time. An additional 25 percent claimed they did not have access to the data they needed; 9 percent said they did not have enough data; and 6 percent complained of having too much data.

In other words, providers are challenged by the inability to access the data they need when and where they need it.

Consider what happens when a physician sees a patient and lacks ready access to their medical history, problem lists, medications, and test results. If the physician does not have access to the results of a critical test, the provider may re-order the identical test, possibly wasting healthcare resources and creating confusion about the accuracy of the patient’s records.

Interoperability advances are making it easier to share data, but true interoperability continues to be a struggle, thanks to a lack of standards, inconsistent data, and inadequate monetary incentives. We asked our webinar participants about their biggest barriers to achieving interoperability and 42 percent pointed to the challenge of data exchange. An additional 35 percent expressed difficulty applying data and making it actionable, while 20 percent reported difficulties organizing the data they received.

New exchange standards, such as FHIR, are making it easier to send data, but typically the incoming data is highly disorganized and not stored in an easily searchable format that adds value for clinical decision-making. In fact, the flood of incoming, unorganized data is creating new concerns about potential medical liability risks for providers. For example, a physician that inadvertently overlooks a critical abnormal finding that’s hidden within an incoming record could be held accountable for any ensuing patient complications.

Despite new standards and APIs that facilitate the exchange of data, much of the data exists in unstructured formats that are difficult to organize, include too many duplicates, and are not easy to search. In fact, an estimated 80 percent of all health data is stored in unstructured formats, such as free-text or scans.

Many in healthcare are optimistic that new technologies such as natural language processing (NLP) and artificial intelligence (AI) can be leveraged to convert dictated chart notes to free-text, and free-text to data that is in a format that is actionable for clinicians. The reality is that these solutions are still not sufficiently mature for most healthcare applications: the error rates for converting speech to text to data are, at best, between 8 and 10 percent, which is not reliable enough to support clinical decision- making.

Not all data is created equal – and not all data is equally usable for advanced analytics or for clinician use at the point of care. In order to be actionable for providers and usable for AI and analytics applications, data must be structured and organized in a way that facilitates viewing across clinical domains. One way to do this is to leverage technology that intelligently identifies, interprets, and links medical concepts and maps them to standard nomenclature, such as ICD-10, SNOMED, RxNorm, and LOINC.

Once disorganized data is converted to structured, actionable formats, it becomes more accessible to clinicians, allowing them to easily find the information they need during patient encounters and within their normal workflows. The structured data is also properly formatted for input into AI systems that use advanced algorithms to deliver clinical insights.

To ensure the ongoing creation of high-quality, structured data, we need to give clinicians the ability to capture coded clinical data as a byproduct of the documentation process and within their normal workflows. With more usable data, physicians can more readily access actionable information at the point of care. Organizations can more easily exchange quality information, and not just chunks of data that must be manually interpreted and organized. And, health systems are better equipped to harness the power of AI and the advanced analytics that enhance the delivery of patient care.

Detective Holmes understood that he could not optimally perform his job without data. To optimally deliver healthcare, providers need more than just data, which is why the industry must embrace technologies that make it easy to access the right data at the right time.

Readers Write: How Technology Could Aid Amazon’s Prescription Drug Play

How Technology Could Aid Amazon’s Prescription Drug Play
By Thomas Borzilleri

Thomas Borzilleri is the founder and CEO of InteliSyS Health of San Diego, CA.

Amazon’s plans to enter the healthcare market may not be ready for prime time, but it is high time for the healthcare industry to take Amazon’s threat seriously. A recent report from Goldman Sachs notes that with 2016 prescription spending (net of rebates) topping $400 billion, high repeat purchases, lack of price transparency, and an increasing cost burden on the consumer, it’s no surprise that prescription drugs have attracted the attention of the 800-pound e-commerce gorilla. But the Goldman Sachs report goes on to warn Amazon of the complexity of the drug supply chain and the high barriers to entry for new players.

This is all certainly true. Most patients still buy their drugs the old-fashioned way, at retail pharmacies or through mail order services that reflect negotiated prices between public and private payers and pharmacy benefit managers (PBMs). These relationships will be challenging to break up. But as customers pay more for their prescription drugs, they will increasingly clamor for more price transparency and the ability to shop around, as they are starting to do for other healthcare services. As consumers increasingly drive decisions about their own care, Amazon may well find a path to success. The key is to partner with emerging technologies to bypass prescription drug middlemen and deliver real savings.

Amazon could wade in slowly to the healthcare sector. It could easily sell durable medical equipment online. There is no prescription needed and Amazon could most certainly lower prices if it can capture enough volume. At the very least, the e-tailer could offer free shipping as an incentive to choose Amazon over smaller DME vendors. Amazon could also, reasonably easily, get into the market to sell over-the-counter medicines, as discount clubs like Costco and BJ’s have already done with great success. Again, these purchases do not need to involve insurers or physicians, and Amazon can appeal directly to the consumer.

But this approach only scratches the surface of the opportunity for Amazon. If Amazon wants to go after the big Kahuna—prescription drugs—it will have to meet three essential goals

Inspire Trust

Amazon must inspire trust throughout the healthcare ecosystem, among patients, first and foremost, but also among doctors who agree to write e-prescriptions to a preferred pharmacy. Amazon may also have to inspire trust among insurers if it decides to partner with them to get access to drug coverage, formularies of preferred drugs, and guaranteed patient volume. They must convince all parties that the drugs they will deliver are as safe and effective as those found in retail pharmacies.

Promise Convenience

Amazon must offer added convenience over traditional mail-order pharmacies. The holy grail of two-hour or same-day delivery will require a large number of distribution centers, all of which need to be inspected by the US Food and Drug Administration and will require a high level of security. These demands, including added manpower to run these facilities such as highly-paid pharmacists, will be costly.

Deliver Value

Amazon can only succeed if it can offer a lower price point, and it can only offer better prices if it can capture significant patient volume. If Amazon simply seeks to replace existing pharmacy benefit managers by winning brand-new contracts with insurers, this will be a challenge. Amazon will not, starting out, have the volume-based discounts that PBMs enjoy. Meanwhile it will face a variety of new startup costs. Additionally, Amazon will face a fierce battle from entrenched PBMs.

Amazon must, as it has done in other markets, forge an entirely new path by connecting directly with consumers and offering reliable products conveniently and affordably. Amazon has chosen a fortuitous moment, as the consumerization of healthcare is finally gathering steam. Consumers are paying more out-of-pocket than ever for prescription drugs due to rising deductibles, co-pays, and co-insurance. Meanwhile, most patients have no idea that the price of drugs varies widely and that buying medications retail may be cheaper than their co-pays. This sets up a unique opportunity for Amazon to help lift the veil on drug prices and offer patients lower-priced alternatives.

By operating independently of insurers, Amazon can sidestep a head-on fight with established PBMs. Instead of spending resources trying to pick off patients one insurer at a time, they could choose to woo patients directly, competing on price at the point of care where prescribing decisions are made.

PBMs ostensibly exist to provide volume discounts to insurers on drugs. But in reality, these “discounts” are riddled with padding. Insurers are receiving volume discounts off the manufacturer’s retail price, but PBMs tack on something called “ingredient spread” while also charging a per-transaction fee. Meanwhile, prices vary widely from one pharmacy to the next, even in the same town, and consumers typically have no idea. Consumers who buy their drugs using insurance coverage usually accept the PBM-mediated price at their closest pharmacy because they don’t have the knowledge or the ability to shop around.

Instead of allying with manufacturers and insurers (the supply side of the prescription drug transaction), Amazon would do much better to join forces with the demand side, appealing to consumers and doctors. But how can Amazon get into a doctor’s office? Send sharply dressed salesmen and women like the makers of Lipitor and Viagra? Since the Sunshine Act and other rules regulate interactions between healthcare providers and the pharmaceutical industry, drug sales personnel have less access to providers than ever.

Amazon could instead leverage new technologies that help doctors and patients access real-time drug prices at nearby pharmacies. Amazon’s prices could be listed along those at traditional retail pharmacies to allow patients and doctors to choose the lowest-priced vendor, depending on whether the retail price or the co-pay is a cheaper option. These tools, if accessed seamlessly within the e-prescribing workflow, would integrate Amazon into the existing marketplace of drug retailers and give the e-tailing behemoth a seat right in the exam room alongside the doctor and patient, giving Amazon direct and immediate access to patients across different private health insurers, public payers, and the uninsured/underinsured.

Amazon would still face the challenge of establishing mail order centers and getting them certified by the FDA. But they wouldn’t be seeking to replace one middleman—pharmacy benefit managers—with another. By going straight to the consumer and the provider at the point of care, Amazon would have a unique opportunity to disrupt both the supply chain and the pricing models for prescription drugs. This could potentially have far-reaching benefits for consumers by causing overall downward price pressure and further exposing price-gouging that PBMs engage in, even while promising discounts.

The Goldman Sachs report posits that Amazon’s best chances for success rely on choosing a partner that can help it get into the market, such as an existing PBM. While this approach may be a boon for Amazon, it won’t do much to disrupt drug prices and transparency. If Amazon is serious about remaking the way Americans buy their prescription drugs, the e-commerce behemoth should look to cut out the middlemen—PBMs—and appeal directly to consumers to help tackle the prescription drug affordability crisis in the United States.

Readers Write: Why Healthcare Organizations Take So Long to Make Buying Decisions and How We Can Fix It (Part 3 of 4)

Why Healthcare Organizations Take So Long to Make Buying Decisions and How We Can Fix It (Part 3 of 4)
By Bruce Brandes

Bruce Brandes is founder and CEO of Lucro of Nashville, TN.

In Part 2 of this series, we discussed the importance of first clearly defining and aligning regarding the problem a buyer seeks to solve before evaluating products. The next key element is to invite trusted colleagues to contribute insights and experience in the context of that problem and possible solutions.

Below we will share more about the type of buying decisions most impacted by a lack of trust and explore how healthcare buyers can gain more confidence in the choices they must make.

There are three buckets of non-labor spend in health systems:

1. Supply chain — traditionally influenced by group purchasing organizations (GPOs.
2. Pharma — traditionally influenced by pharmacy benefit management companies (PBMs).
3. “Everything else,” which falls into a category known as purchased services (traditionally managed less centrally).

Purchased services represent 20-30 percent of a hospital’s expenses and include a wide array of vendors ranging from health IT and digital health to outsourced professional and ancillary support services. Many health systems are seeking to drive standardization, minimize duplication, and realize greater value related to purchased services.

Vendor selections for purchased services are generally complex, collaborative processes. With input required from so many key stakeholders for a purchase commitment of a significant amount, how do risk-averse health systems ever make a buying decision? The challenge is exacerbated by an array of third-party sources of market insight, including consultants, industry associations, purchasing groups, etc., all with their own opinion about the “best” vendor for that area of focus.

If you are responsible for this buying decision, who do you trust? How can you efficiently synthesize so many disparate data points of opinion about the best product in the market to put them all into context to make the right decision for your organization?

Healthcare buyers take many of these outside opinions with a grain of salt, skeptical of the motivations of some. Vendors may compensate the “non-partisan” organizations to endorse their products. Unsolicited information received from cold calls or spam emails can rarely be trusted.

When you are making a strategic hire to your team, do you ever expect the personal and professional references given you by the candidate to say anything too negative? More commonly, even before an interview, it is wise to do a quick check of LinkedIn to seek common connections that you know will give you more clear and honest insight (this works in both directions for the candidate and organization).

Similarly, buyers want an efficient, private way to tap into trusted colleagues across their professional network,  those who have experience in tackling this same problem in an organization like theirs.

The time and cost associated with deciphering vendor claims, vs. hype, vs. reality are untenable for the entire industry. With today’s time and financial pressures, physical site visits and reference calls are (finally) antiquated. Thus, it is critical to gain necessary insights from those you trust to reduce the risk associated with strategic vendor selections.

The value of a trusted network is compounded when this collaboration is contextualized with the problem and potential solutions under consideration. As trusted communities come together, the entire industry can benefit.

News 11/1/17

Top News

CMS clarifies the information blocking requirement of MIPS for providers. Eligible providers must attest that they:

• Haven’t disabled the interoperability capabilities of their EHR.
• Have implemented their EHR without taking any actions to limit interoperability.
• Quickly respond to requests from patients and other providers who ask for information retrieval or exchange.

CMS advises that no documentation is required to be submitted as part of the attestation. It also notes that while attesting providers aren’t expected to understand or enable the technical side of interoperability, it’s their job to let their implementers and EHR vendors know that they will be attesting and hold them accountable for enabling that capability.

Reader Comments

From Good-Time Charlie: “Re: Practice Fusion. I didn’t remember until your poll that they were even still around.” They are, but seemingly barely, well out of contention as an ambulatory EHR disruptor despite having raised $200 million from investors under that premise. ONC places Practice Fusion as the #9 most commonly used ambulatory EHR based on attestation data, which isn’t so good given that their product is free (and the products that follow them on ONC’s list aren’t really major-name EHR vendors). The company seemed unstoppable when doctors flocked to its product to collect $44,000 in Meaningful Use money in return for spending nothing, but the company’s business model was questionable, they were selling practice data in less-than-transparent ways (such as not naming who was buying it and for what purpose), its self-reported usage figures were head-scratching at times, and there was always the question of just how deeply practices engaged with the product since they had no skin in the game (no hardware cost, contract, training cost, etc.) The company fired founder and CEO Ryan Howard in mid-2015 right before a planned IPO, promoted a replacement with skimpy credentials for taking a company public, and largely fell off everyone’s radar.

From Dinky McQueen: “Re: GE’s rumored discussion about selling its healthcare IT business. Is it everything from Centricity on down, or just smaller, unaligned divisions such as the GE-Intel Care Innovations JV?” I haven’t heard specifics but would be interested. The health IT business includes the many products labeled as Centricity, workforce management (the old API Healthcare), Health Cloud, diagnostic-related software, and some analytics stuff. It’s hard sometimes to figure out where healthcare IT begins given the overlap with the company’s diagnostic business, which I assume won’t be dealt off. GE has built a poorly managed, unfocused portfolio of acquisitions that will be hard to sell off to a single buyer, which will probably elongate any process to make its unlamented healthcare IT exit.

From Just Saying: “Re: speech recognition. You mentioned years ago using speech recognition to write HIStalk. Do you still do that?” I don’t. Three or so times I bought the latest version of Dragon and got excited about how much easier it was to “write” HIStalk by talking instead of typing, but the sheen wore off due to Dragon’s heavy system usage, occasional mysterious errors that lost what I had dictated, and the time required for me to fix its mistakes (nearly always caused by my not articulating crisply enough). I’m thinking about trying again with LilySpeech, a cloud-based system that uses Google’s speech-to-text system. I don’t need voice-controlled system automation – I just want to dictate into a text box and paste the result into my editor or Gmail. I’ll give the 30-day free trial a shot.

HIStalk Announcements and Requests

I enjoyed reading a recent interview with freshly named economics Nobel winner Richard Thaler and was therefore happy to see his “Misbehaving: The Making of Behavioral Economics” pop up as a free Prime Reading Kindle book this week. I was engrossed and entertained by it, to the point that I ordered a softcover version (at full price) just so keep it handy and to be able to lend it out. This book and his earlier “Nudge” explain why humans don’t always follow rational economic thought, which of course has healthcare implications along with big-time business impact. I highly recommended the first book and will no doubt do the same for the second once I’ve read it. He’s a great explainer and pretty funny besides.

The only iOS device I have left is my iPad Mini, but even that is now a little closer to Android – I replaced the default iOS mail app with Gmail and like it much better, especially for deleting endless streams of spam without having to swipe emails individually and trying to recall the difference between “archive” and “move to trash.”

Webinars

November 8 (Wednesday) 1:00 ET. “How Clinically Integrated Networks Can Overcome the Technical Challenges to Data-Sharing.” Sponsored by: Liaison Technologies. Presenters: Dominick Mack, MD, executive medical director, Georgia Health Information Technology Extension Center and Georgia Health Connect, director, National Center for Primary Care, and associate professor, Morehouse School of Medicine;  Gary Palgon, VP of  healthcare and life sciences solutions, Liaison Technologies. This webinar will describe how Georgia Heath Connect connects clinically integrated networks to hospitals and small and rural practices, helping providers in medically underserved communities meet MACRA requirements by providing technology, technology support, and education that accelerates regulatory compliance and improves outcomes.

November 15 (Wednesday) 1:00 ET. “How Hospitals and Practices Can Respond to Consumerism by Better Engaging Patients Through Price Transparency and Payment Options.” Sponsored by: Change Healthcare. Presenters: Kathy Moore, president, Moore Martini Medical; Linda Glidewell, VP of business development, consumer payment solutions, Change Healthcare. Healthcare consumerism and high-deductible health plans require providers to offer upfront estimates and payment options throughout all points of service. In his webinar, we’ll discuss consumerism as a critical area of opportunity in revenue cycle management and review numerous areas across the revenue cycle where your staff interacts with patients and leaves lasting impressions. From your first interaction with patients on the phone to discuss financial responsibility; to collecting payments at all points of service; to offering payment plans and various payment options — these are all areas that can be game-changing. With the right approach to consumerism, you can improve patient collections and optimize revenue from the start while also improving the overall patient experience.

Previous webinars are on our YouTube channel. Contact Lorre for information.

Acquisitions, Funding, Business, and Stock

Meditech announces Q3 results: revenue up 9.8 percent, EPS $0.47 vs. $0.68. Product revenue jumped 50 percent, but a slight drop in services revenue and an 18 percent increase in operating expense drove earnings down 31 percent.

Roper Techologies (Sunquest, Strata Decision Technology, Atlas Medical, Data Innovations, CliniSys) announces Q3 results: revenue up 23 percent, adjusted EPS $2.36 vs. $1.96, meeting revenue expectations and beating on earnings.

Germany-based Ada Health, which offers consumers an AI-powered health chat app that also connects users to doctors, raises $47 million and announces plans to open a US office.

Sales

Hospital for Special Surgery (NY) will expand its use of Docent Health to provide customized, whole-person support that addresses each patient’s needs, fears, and concerns.

Orlando Health chooses Phynd to manage the information of 25,000 providers across nine hospitals.

People

Geoff Hogan (Imprivata) joins Diameter Health as chief commercial officer.

Impact Advisors promotes VPs Paula Elliott and Bill Faust to leadership roles over its quality services and strategic implementation services practices, respectively.

Announcements and Implementations

Casenet’s TruCare is named Best Population Health Management Software Provider in a UK healthcare and pharmaceutical awards program.

HIMSS announces that Alphabet Executive Chairman Eric Schmidt will deliver the opening keynote at HIMSS18 in the “still seems weird” time slot of Monday, March 5 at 5:00 p.m. PT. Schmidt provided the very long keynote at HIMSS08 in Orlando, where he pitched since-failed Google offerings like Health and Flu Trends. 

Government and Politics

Politico reports that HHS privacy leader Deven McGraw, JD has left government service to join an unnamed Silicon Valley health technology startup.

Publicly traded, Cincinnati-based Chemed, which owns the country’s largest for-profit hospice chain, will pay $75 million to settle False Claims Act charges that it billed Medicare for services involving patients who were not terminally ill and paid its employees bonuses for recruiting new patients. Three whistleblowers will share in the payout. Trivia: Chemed’s other big holding is Roto-Rooter. 

Other

Vanderbilt University Medical Center (TN) will go live on Epic this week.

The April 1 Cerner go-live at Medical Center Health System (TX) led to billing delays that left it behind for the fiscal year ending September 30. A consultant’s report says that hospital executives blamed staff for lack of commitment and not paying attention during training, but most employees said that training was insufficient for doing their jobs. 

A study finds that 10 percent of Massachusetts residents who were revived from opioid overdoses with rescue drug Narcan died within a year, highlighting that only five percent of overdose survivors receive longer-term treatment drugs such as Suboxone.

Brilliant: a company in Japan gives non-smokers an extra six days of vacation, both as an incentive for smokers to quit and to give non-smokers the same amount of non-productive time enjoyed by smokers.

Weird News Andy helpfully notes that maybe the patient should have considered flossing. Doctors examining a woman with a decades-long history of nosebleeds believed to be caused by rhinitis finally determine the cause – a fully grown tooth embedded in her nasal cavity. Dentists say “supernumerary teeth” can grow in odd spots on the face in the 4 percent of people affected, but having them erupt in the nasal cavity is extremely rare. 

Sponsor Updates

• Meditech, which has 20 customer sites in Puerto Rico, donates money to United for Puerto Rico.
• AdvancedMD will exhibit at APTA’s Private Practice Section event November 1-4 in Chicago.
• HIS 2017 recognizes Agfa Healthcare in the IT Industry category – RIS and PACs during its annual awards ceremony.
• AssessURHealth publishes a new white paper, “The Missing Piece: Holistic Care through Preventive Screenings.”
• Bernoulli Health will exhibit at HealthAchieve November 6-7 in Toronto.
• Besler Consulting will present at the Minnesota HFMA Regulatory Conference November 2 in Bloomington.
• CSI Healthcare IT completes the first wave of Epic go-lives at Lovelace Health System (NM).
• Clinical Architecture will exhibit at the AMIA 2017 Annual Symposium November 4-8 in Washington, DC.
• CoverMyMeds, CTG, and Cumberland Consulting Group will exhibit at the CHIME Fall CIO Forum October 31-November 2.

Blog Posts

• Revenue Integrity – Not Just for the Billing Folks! (Impact Advisors)
• Peninsula Regional Medical Center Improves Patient Care Coordination With Spok (Spok)
• Powered by Passion: MEDITECH, Physicians, and CIOs Connect Over Personal Health Stories at Annual Physician and CIO Forum (Meditech)
• Using Technology to Build an Integrated Practice #3 of 6 (AdvancedMD)
• Do you have $360k to build compliance into your digital health product yourself? (Datica)
• Finally Following the Patient Journey (CarePort)
• How Population Health Can Move Beyond Analytics to Achieve Wider Benefits (Casenet)
• The Secret to Gaining Staff Buy-In for RTLS (CenTrak)
• Voice Dictation Enables Physicians to Recover Lost Hours from a Packed Workday (ChartLogic)
• Decentralized vs. Centralized Access at Health Systems: The Patient’s Perspective (Culbert Healthcare Solutions)
• 3 Ways Measure Factory will Enable Data-Driven Decision Making (Dimensional Insight)

Contacts

Mr. H, Lorre, Jenn, Dr. Jayne, Lt. Dan.
Get HIStalk updates. Send news or rumors.
Contact us.

Readers Write: Interoperability and Standards Will Be Areas of Focus Through Year End

Interoperability and Standards Will Be Areas of Focus Through Year End
By Michael Burger

Michael Burger is practice lead, EHRs and EDI, for Point-of-Care Partners of Coral Springs, FL.

While there are many uncertainties in healthcare, interoperability and standards will undoubtedly be areas of focus through the end of 2017. To that end, the government and industry will continue to refine existing standards and address interoperability challenges. This involves activities by the Office of the National Coordinator (ONC) and ongoing efforts by standards development organizations (SDOs) and electronic health record (EHR) vendors.

Despite potential severe budget cuts, ONC says it is committed to interoperability and standards as main areas of emphasis. For example, ONC is putting the finishing touches on its Proposed Interoperability Standards Measurement Framework, the final document for which will be issued this fall. It also is accepting comments through November 20 for the Interoperability Standards Advisory, which is a stakeholder-informed catalog of the standards and implementation specifications that can be used to meet interoperability needs in healthcare. The newly created Health Information Technology Advisory Committee will also be influential with regard to standards and interoperability. Its recommendations to ONC doubtless will be translated into rulemaking and policy.

The next few months also should see continued progress by SDOs in refining standards for interoperability with a focus on practical use cases by EHR vendors.

One example is FHIR (Fast Health Interoperability Resources), which is one of the newest standards from Health Level 7 (HL7). Vendors are beginning to embrace the most recent iteration of the standard for various clinical use cases and FHIR is being used to extract relevant clinical data from EHRs.

Also, the National Council for Prescription Drug Programs (NCPDP) is refining the SCRIPT standard to facilitate the transition to electronic prescribing of specialty medications. Today, specialty prescribing is largely a manual process that isn’t easily adapted to existing electronic prescribing workflows. An NCPDP task group is looking at ways in which new data elements could be added to the SCRIPT standard to handle enrollment for specialty medications, which accompanies the prior authorization that is required for nearly all such medications. The goal is to enable enrollment and electronic prior authorization (ePA) for specialty medications. Changes to the standard will enhance the ePA functionality, which EHR vendors have already built for non-specialty medications.

There are still obstacles that must be overcome to move health IT interoperability down the field. Three come to mind:

• Lack of a national patient identifier. One of the biggest interoperability challenges is the lack of a national patient identifier. While industry solutions are being developed, they are one-offs that are not totally standards based. True interoperability cannot be achieved unless this problem is solved.
• Changes in business models. There is much talk around data-blocking by EHRs, but this is not so much a technology challenge as a business one. The competitive nature of healthcare delivery is primarily what prohibits the exchange of clinical information, as competitors don’t want to make it easy for patients to seek care outside of their networks. When there is demand among customers to connect systems, software vendors respond by building and selling connectivity solutions. The most successful of these solutions rely on standards that have been created and vetted through SDOs.
• Variations in standards implementation. Other interoperability challenges are created by variations in how standards are used in application program interfaces (APIs) with EHRs. Sometimes these APIs rely on technology that is not standardized, thus adding to the complexity and inconsistency in how data are exchanged among EHR platforms. The goal of using standards to achieve interoperability can only be met when standards are interpreted, implemented, and used consistently.

These are but some of the opportunities and challenges we see in the waning months of 2017 when it comes to standards and interoperability. These issues are not going away anytime soon and will continue to occupy stakeholders’ attention in 2018.

Readers Write: The Untapped Data That Can Improve Lives and Lower National Healthcare Spending

The Untapped Data That Can Improve Lives and Lower National Healthcare Spending
By Kurt Waltenbaugh

Kurt Waltenbaugh is founder and CEO of Carrot Health of Minneapolis, MN.

Ask 10 mechanics which costs more — preventive or corrective maintenance — and each will likely give the same answer. It’s cheaper to change a car’s oil regularly than to repair a seized engine. The same principle holds true for healthcare.

In 2015, US healthcare spending reached $3.2 trillion. More than half of that went toward hospital care and physician / clinical services, which increased by 5.6 percent and 6.3 percent, respectively, according to the Centers for Medicare and Medicaid Services (CMS). The surge in payouts for these services was due to “non-price factors,” specifically an increase in “use and intensity of services.”

This makes sense given that the coverage expansion under the Affordable Care Act (ACA) gave more Americans access to healthcare than ever before. But at a time when the public and healthcare professionals have centered their focus on reducing insurance premiums and the cost of care, there is one question missing from the debate. Could the need for some of these services have been prevented?

The answer lies in a well of big data that has, until recently, been untapped by the healthcare industry.

In the health insurance market, there exists a disconnect between medical costs and an individual’s health quality. Behavioral and socioeconomic factors determine roughly 60 percent of their overall health, yet 88 percent of the country’s healthcare spending goes towards medical services, which impacts merely 10 percent of a person’s healthiness.

A study entitled “Health and social services expenditures: associations with health outcomes” compared spending by 11 nations on medical care against social care and the impacts on health outcomes. The findings showed that not only was the US the only country to spend more on healthcare than social services as a percentage of GDP, but that a higher ratio of spending on social services was also associated with better outcomes in infant mortality and life expectancy.

Access to this socioeconomic and behavioral data gives payer organizations a clearer picture of a member’s health risks. For example, detailed knowledge about where a person lives — such as neighborhood crime rate, average household income, and availability of healthy food — provides more predictive information than higher-level information on the coverage region, data that delivers far more accurate insights into quality of life. Environmental factors like “walkability” can help determine how easy it is to exercise, while air quality can indicate a person’s risk for lead exposure. For individuals living in a low-income, high-risk area, education and local job opportunities can determine their probability for upward mobility and, by extension, how likely they are to improve the socioeconomic factors impacting their health.

On the surface, proponents of data privacy might argue that these companies would push to use this information to raise premiums for those whose socioeconomic and/or behavioral patterns make them more susceptible to life-altering medical conditions. A deeper examination, however, reveals an opportunity for payers to cover more individuals with less-costly interventions without losing any competitive ground. By connecting these individuals with services that help address social and behavioral determinants of health, payer organizations help them improve their lives while also reducing the potential need for higher-cost care interventions, such as emergency room visits or hospitalization.

In fact, this approach has the potential to change the way insurance operates throughout the country. Rather than balancing enrollment with enough low-risk members into a health plan to cover the care costs for high-risk members, a strategy centered on preventive care through social and behavioral interventions means payers become more invested in their members’ total quality of life, thereby creating a healthier population.