Readers Write: How CMS Can Build a National Directory of Healthcare Providers

How CMS Can Build a National Directory of Healthcare Providers
By Justin Sims

Justin Sims is president and chief operating officer of CareMesh of Reston, VA.

Four weeks ago, CMS issued a Request For Information (RFI) to collect feedback on whether it should build a national directory of healthcare providers and services. They highlighted the problems the lack of quality provider information causes for consumers and the industry and asked for feedback on solving those problems.

But doesn’t CMS already have a provider directory?

NPPES is the closest thing that CMS has to a directory. It is used to issue ID numbers to healthcare professionals (NPIs) and covers almost all physicians (about a million) and many other healthcare workers (about five million). However, it suffers from infrequent update (the average age of an entry is 6.7 years old) and has gaps in the information it collects (it lists only 200k validated secure email addresses when there are well over a million).

There is also the Medicare Provider Enrollment, Chain, and Ownership System (PECOS). This is routinely updated by physicians every five years, so it is a little more current, but because it focuses on Medicare enrollment, it doesn’t cover all physicians and doesn’t collect the same information as NPPES.

Why hasn’t the problem already been fixed? In the words of Tom Hanks in “A League of Their Own,” if it wasn’t hard, everyone would do it.

There are several reasons that a single national provider directory has eluded us.

First, there’s scale. Maintaining information on a million of anything is hard.

Second, there’s the structure. Physicians often work for multiple organizations and keep a fluid list of physical locations with different contact information at each one.

Third, there’s content. Do you, for example, know your EIN? And how many physicians do you think know their Direct Address? (Not a lot!) Or their EHR end-points? (Even fewer.) Or can readily list the insurance carriers they accept at each organization and location? Not to mention that providers and their staff are busy.

If CMS is going to take this challenge on, and we hope that they do, we see four broad options:

Provider-Supplied Data

There are already regulations to encourage providers to submit information updates to NPPES and PECOS within 30 days. These rules have some teeth. For example, providers can be suspended from the Medicaid program if they don’t comply. As part of their strategy, CMS could certainly make it easier for providers and their staff to make updates and could increase penalties for those who don’t. But asking a million physicians and a further five million healthcare professionals to update their information manually will be a tough strategy to deliver success.

System-Supplied Data

In most cases, basic profile information about providers is maintained in the EHR. Another strategy that CMS might consider is to modify its Certification of Electronic Health Record Technology (CEHRT) standards and establish a process for EHRs to send directory information electronically using HL7 FHIR standards. While this would only cover EHR users, it would account for almost every prescriber in the country, and done right, it could reduce physician burden and result in continuously updated information, at least for some.

Combine Multiple Data Sources

While the EHR concept sounds promising, it would take some years to implement and a few more to iron out the wrinkles. Another approach that CMS might follow is to combine data from many sources. In addition to CMS data sources, there are many others, including state Medicaid agencies, medical licensing boards, Medicare Advantage plans, Medicaid MCOs, Qualified Health Plans, DirectTrust, and health system and provider group websites (many of which follow the schema.org standard), to name but a few.

By combining all of these sources and using statistical techniques to validate the data, CMS could create a more accurate picture of the provider than any single source alone. Minimally, it could use these techniques to identify where data quality issues may exist and then follow up with the provider.

Help Industry Solve the Problem

Finally, CMS could do more to help the industry solve the problem. Several companies, ours included, are already doing a combination of the above. But it would be much easier if CMS standardized its data (in NPPES and PECOS) and modified regulations to ensure that health plans, in particular, shared their information in a standardized electronic format.

For a problem as old as the US healthcare industry — states gained the right to regulate health and license doctors in the Bill of Rights in 1791 — we doubt that CMS will solve this overnight. But it is a challenge that most segments of the healthcare industry are cheering for, and one for which the ultimate solution will lie in a combination of the options described above.

Readers Write: Reversing RCM Brain Drain and Creating Revenue Cycle’s Digital Twin

Reversing RCM Brain Drain and Creating Revenue Cycle’s Digital Twin
By Jim Dumond

Jim Dumond, MS is senior product manager at VisiQuate of Santa Rosa, CA,

Across all industries, the need to retain knowledge of key processes and details has gained new emphasis as the labor supply has tightened and grown more expensive. In the revenue cycle space, health systems are competing against not only each other, but other industries to retain talent and ensure that their organizations run smoothly. The loss of seasoned RCM professionals is creating a knowledge gap or “brain drain,” which makes it harder for systems to keep their businesses moving, let alone do so efficiently.

As result, the question these organizations must answer is: how do we guard against this loss of RCM knowledge by having robust, prescriptive workflow systems in place that direct employees what to do, when to do it, and how to do it based on predictive analytics that mine data to suggest actions that successfully have solved the same issues in the past?

Health systems today are primarily reliant on their human “tribes” of users to pass key knowledge about specific payer processes, required details, and thousands of other minutiae. This has created a system where users inefficiently share that knowledge via occasional Zoom calls, PowerPoints or job aids, and often emails or hallway conversations (if they are back in the office) that don’t get recorded except in a single brain at a time. That verbal tradition of the health system is what is creating the impact that sites are seeing today as users leave for other systems or careers.

Why not create a centralized database of knowledge for all the activities that move an account through the revenue cycle from scheduling to a zero balance? We live in a proactive world. Amazon and Netflix use a recommendation engine to identify what we should buy or watch next. Why not utilize that same approach for the revenue cycle? Use all the available data and user history to provide specific next best steps help the user efficiently work the account.

Just like Waze takes real-time data from drivers, the recommendation engine could be further enhanced by crowdsourcing, gathering data from revenue cycle shops across the country and getting smarter every day.

A digital twin is a virtual representation of a machine, system, or other complex organism that exists in real life. Think of it like a simulated wind turbine in a computer program. You can run it through different kinds of environmental or mechanical break downs and make real-time design changes without costly real-world experiments.

In other words, digital twins are complete, virtual representations of all the actions and sequences of actions taken by a human agent performing a job. In the revenue cycle world, this means curating and combing through all the data signals that are created by a human worker, as well as signals that are coming from third-party systems like payer remits, to create a perfect representation of what the human is doing to a given encounter record.

Some might say that creating such system is unnecessary. After all, most systems have some form of bot automation. That should solve the problem just as well, right?

Automation and bots can be great for productivity, as once online they work endlessly and never skip a step. But bots have to be methodically crafted to perform specific sets of tasks in a specific order, and they require continual maintenance. Turnover contributes to the problem, when the employees who depart are the ones who developed the business rules for the bot.

The next step then is to start to combine intelligent process automation with the centralized, ever-learning, ever-adapting recommendation engine. That recommendation engine should continuously breadcrumb what a worker is doing and even allow workers to add new recommendations to a knowledge repository. That knowledge repository should be connected to incoming data signals so the engine can show the right knowledge to the right person at the right time for a given piece of work the staff member is doing.

Using the recommendation engine enables the system to visualize the end-to-end revenue cycle process, allowing organizations to see where those recommendations and changes lead to better performance or not. The digital twin provides the data and analytics to help revenue cycle leaders prioritize the right work for their users, determine process inefficiencies, help define where best to apply bots, and help those bots change over time. More efficient revenue cycle operations benefit the organization overall because its focus can be placed on the core mission of delivering exceptional patient care.

Readers Write: Lessons Learned from the COVID-19 Pandemic: How Data Sharing is Improving Chronic Disease Outcomes

Lessons Learned from the COVID-19 Pandemic: How Data Sharing is Improving Chronic Disease Outcomes
By Brett Furst

Brett Furst is president of HHS Technology Group of Fort Lauderdale, FL.

Although the worst of the COVID-19 pandemic is likely behind us, many Americans living with chronic disease will feel its effects for years to come. That’s because chronic diseases such as heart disease, diabetes, chronic kidney disease, and obesity increase the risk for severe and lasting illness from COVID-19.

According to the Centers for Disease Control and Prevention, this improved risk matters, because chronic diseases represent seven of the top 10 causes of death in the United States and six in 10 Americans live with at least one chronic condition, such as heart disease, stroke, cancer, kidney disease, or diabetes. Chronic diseases also are the leading drivers of the nation’s $3.8 trillion annual healthcare costs.  

Among many lessons learned since its start, COVID-19 highlighted the need for health equity, as some patient populations were affected more severely than others. For example, African Americans, Hispanics, and Native Americans have a disproportionate burden of chronic disease, COVID-19 infection, hospitalization, and mortality, primarily due to challenges associated with social determinants of health. Even among the general population, healthcare utilization dropped during the pandemic, with a decline in screenings and subsequent diagnoses for diseases such as cancer. Delayed screening and treatment for breast and colorectal cancers alone could result in almost 10,000 preventable deaths in the US, according to the CDC.

The lasting impact that COVID-19 has on individuals living with chronic disease, and the entire healthcare system, underscores the many lessons we can learn from the pandemic and the need for improved data sharing across all stakeholders. For example, researchers have yet to know the extent to which COVID-19 exacerbates chronic disease, causes chronic illness, or will be determined to be a chronic disease. Although long-term studies and longitudinal surveillance will help clarify these questions, much research remains.

The COVID-19 Research Database (RDB) is a leading industry example of how collaboration and improved access to patient ecosystems can accelerate innovation and understanding, improving immediate and future cost and quality outcomes. Several organizations were led by RDB to accelerate real-world pandemic research, knowledge of condition identification and treatment, and evidence-based healthcare policy.

With 85 billion HIPAA-compliant, patient-level records, RDB enables public health and policy researchers to access real-world data to understand better and combat the COVID-19 pandemic and future health-related events. The RDB provides a standard data schema that allows researchers to access linkable data sets — including claims, electronic health records, and consumer data — and has powered over 70 publications and presentations addressing the direct and indirect effects of the COVID-19 pandemic on population health.

Among the publications and studies resulting from the RDB is the publication of research in Nature Medicine examining the impact of COVID-19 infection on risk for neurological disorders and a separate study published in the Journal of Alzheimer’s Disease that showed a substantially higher risk for older adults in developing Alzheimer’s disease within a year of contracting COVID-19.

Accurate, comprehensive, real-world data represents the healthcare industry’s straightest path toward developing a deeper understanding of the connection between COVID-19, chronic disease, and population health. Data sharing and collaboration provide researchers, providers, and healthcare organizations with the keys to actionable insights, data-driven decision-making, and accelerated innovation related to critical issues like improving health equity and driving healthcare cost and quality outcomes across populations.

Readers Write: Applying AI to Improve Patient Care

Applying AI to Improve Patient Care
By Tomas Gogar

Tomas Gogar, MS is co-founder and CEO of Rossum of London, England.

Despite the technological advancements in healthcare over the past decade, the administration and quality of patient care has not kept pace. The industry is faced with the realization that if technological changes aren’t implemented at a foundational level, providers, payers, and patients won’t be able to realize the full value of the technology available to them.

The majority of medical institutions rely on electronic health records (EHR) to input, read, and upload critical documents related to patient care into online portals. The EHR concept, introduced in the 1960s, while valuable to the healthcare community, has yet to eliminate the need for manual paperwork. Paperwork is a huge drain and cost, taking time, energy, and precise attention to detail to ensure that all documents are properly scanned into the correct patient files.

Missing information can lead to delays in care, misdiagnosis, miscommunication around treatment plans, and the duplication of costly tests and procedures. Relying strictly on manual processes to manage such large amounts of information can be administratively crippling to a healthcare organization. The World Health Organization estimates that up to 50% of all medical documentation mistakes result from administrative errors.

By integrating intelligent document processing (IDP) into the systems, hospitals and healthcare institutions save time, reduce operational costs, and improve workflows. Introducing an IDP system into the EHR workflow means medical professionals across departments can easily scan and upload documentation into a secure SOC 2 and HIPAA compliant operating system. IDP efficiently captures, categorizes, extracts, and classifies data from documents, streamlining the workflow process and reducing the paperwork necessary for a patient file.

IDP also helps sustain HIPAA compliance, which can be challenging when dealing with thousands of physical documents stored in different formats and locations across a health system. Accounting for small margins for human error causes long input times and exhaustive efforts to safeguard physical documents containing patient information. With the implementation of IDP, this process eliminates any chance of human error in handling sensitive information and allows for patient data to be processed quickly, safely, and securely.

From a patient perspective, automating and streamlining document processing enables providers to get complete, accurate data straight into a patient’s hands via online portals. From the healthcare organization side, IDP can reduce document burnout that healthcare professionals are prone to experiencing.

For hospitals struggling with overhead operational costs, implementing IDP is a lucrative resource. By using IDP to process documents like prescription referrals, lab records, billing, and claims forms, manual data entry is drastically reduced, thereby reducing the need for resources associated with data entry into EHR and patient portals and enabling the healthcare organization to re-allocate them to more strategic tasks. In addition to labor costs, implementing IDP reduces costs associated with paper storage, security measures in place to store these documents, and any costs associated with administrative errors.

During a time when all our hospitals are critically understaffed and underfunded, ensuring that every worker is given the necessary tools and resources to adequately and efficiently perform their jobs is more crucial than ever.

Readers Write: Thinking Differently About OR Block Time

Thinking Differently About OR Block Time
By Michael Burke

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

The operating room is the hospital’s largest source of earnings, as well as the largest hospital cost category. Most OR time is allocated in advance to surgeons in chunks of time called blocks. Surgeons schedule cases into their allocated block time, such as Tuesdays from 7 a.m. to 3 p.m.

Block time often goes unfilled due to poor allocation decisions, case volume that can vary meaningfully from week to week, and surgeons neglecting to release block time when traveling or otherwise unavailable to use it. Often, OR time that sits empty can be filled with elective cases that have an average contribution margin of $2,000 per OR hour. Instead, the fixed costs from unused OR hours add up with no revenue to offset them.

Identifying block time that would otherwise go unfilled, getting it released, then refilling the time is something hospitals have attempted to do for quite a while. The process has been largely manual and has missed a meaningful portion of the opportunity, as evidenced by block utilization statistics.

New tools use machine learning to predict block time that is likely to go unfilled, along with mechanisms for seeking the release of the identified time and requesting the time. Finding more time, getting it released earlier, and getting it into the hands of those who can use it are all excellent reasons for adopting such a solution. Hospitals can make real gains with this approach. The core of the strategy is that any block time that would otherwise go unfilled should be filled with positive contribution margin cases whenever possible.

Surgeons are hesitant to release block time allocated to them, even if they don’t have cases to fill it. In most compensation scenarios, a surgeon has a financial incentive to hold on to any OR time allocated to them in the event that a case might come along later. Even if they are an equity holder in an ASC and benefit from facility earnings shared as dividends, they are still subject to a form of the prisoner’s dilemma. This affects their decision-making and can bias them against releasing allocated block time for which they don’t have cases to fill. Although some portion of unused time is collected from surgeons by proactive nudge reminders and the ad hoc efforts of the scheduling team, diverging incentives unnecessarily limit the amount of time that can be recaptured and repurposed.

In many ways, the math behind the predictions is the easy part. The difficulty lies in aligning incentives and driving changes in behavior. The structure of your incentives and your willingness to push will have as much or more impact on the success of an OR optimization effort as the predictive software you select. Maybe we should also consider taking lessons from other industries dealing with similar scarce resource challenges.

What if we thought of a hospital as an airline and an OR block day as a flight? Travelers or travel agents (schedulers) book seats on the plane (cases in the OR). However, from its predictive analytics, the airline knows that some seats will go unfilled, even if booked to capacity. The OR block appears to be booked to capacity in much the same way  since 100% of the block’s time is allocated to the block holder.

But we know the block holder won’t fill all the allocated time, just like the airline knows that without intervention, many more seats on the plane would go empty due to no-shows or missed connections. The airline uses predictive analytics to intentionally and confidently overbook the flight to account for this.

The hospital should consider a similar process because the block holder often won’t fill an entire block with cases. To be clear, you wouldn’t be overbooking, since the chunks of time into which you would book cases are empty and predicted to remain so. The math behind the predictions for an OR is different from that of an airline flight, but the analogy still applies. By adopting this strategy, hospitals could fill much more time in their OR blocks with a high degree of certainty that the block holder won’t need it. This approach bypasses the behavioral challenge of seeking permission from the block holder early enough for the unneeded time to be usable, resulting in more recaptured OR time and more contribution margin.

Readers Write: Five Lessons from the Five Years Since the EClinicalWorks Settlement

Five Lessons from the Five Years Since the EClinicalWorks Settlement
By Colette Matzzie, JD

Colette Matzzie, JD is an attorney and partner with Phillips & Cohen, LLP of Washington, DC.

The June 2017 announcement by the Department of Justice of a $155 million settlement with EClinicalWorks for alleged misrepresentation of the capabilities of its electronic medical record software heralded the start of a new area for health fraud enforcement. Both DOJ and the HHS – Office of Inspector General announced that investigations of alleged fraud involving electronic health records systems would be a top enforcement priority. Enforcement has continued at a steady clip, with DOJ bringing actions against six additional electronic health records vendors. There is every reason to think more will be forthcoming.

Most actions have been initiated by whistleblowers using the False Claims Act, but, at least two actions, including one resulting in a $145 million settlement, were initiated by the government.

Five lessons can be drawn from this period of robust enforcement.

DOJ and HHS-OIG have made good on their promise to investigate allegations of fraud in the development and implementation of electronic health records.

Since June 2017, five settlements and one additional intervention have been announced:

• February 2019 settlement with Greenway for $57.25 million.
• January 2020 settlement with Practice Fusion for $145 million.
• August 2020 settlement with Konica Minolta for $500,000.
• January 2021 settlement with Athenahealth for $18.25 million.
• April 2021 settlement with CareCloud for $3.8 million.
• March 2022 intervention in a pending qui tam against Modernizing Medicine.

The US Attorney in Vermont has led the way, but with US attorneys in Northern Georgia, Northern California, New Jersey, Southern Florida, and Massachusetts joining in. Five of the cases were initiated by whistleblowers. Three settlements (EClinicalWorks, Greenway, and Practice Fusion) required Corporate Integrity Agreements (CIAs) with OIG with ongoing federal oversight of software development, relationships with customers, and financial arrangements.

Financial relationships between electronic medical record companies and providers have been a major enforcement focus.

All but one settlement allege violations of the federal Anti-Kickback statute, which prohibits the payment of remuneration to induce referrals for items or services paid for by federal health programs. For example, DOJ alleged that CareCloud provided customers with credits, cash bonuses, and other payments to recommend the software and not to say anything negative. We can expect vigorous enforcement of the Anti-Kickback statute for health IT vendors where federal payments, whether under the Meaningful Use or Promoting Interoperability programs or otherwise, provide the necessary federal funding hook for allegations.

Kickbacks paid to EMR vendors by pharmaceutical companies and other third-party medical providers to influence clinical decisions are also ripe for enforcement.

Of major significance is the January 2020 resolution of criminal and civil charges with Practice Fusion for soliciting and receiving kickbacks from a major opioid company for utilizing its EMR to influence physician prescribing of opioid pain medication. Clinical decision support is an essential requirement for EMRs to deliver their promise of evidence-based clinical care. The Practice Fusion settlement brought scrutiny on EHRs leveraging their power to influence clinical decisions and extracting payments from pharmaceutical companies to implement CDS tools to increase prescribing of the sponsor’s drugs. This practice threatens to undermine the promise of EMRs to improve patient health in favor of profits for the EHR vendor.

Individual accountability has been an important feature of EMR enforcement actions.

DOJ’s interest in holding individuals accountable for corporate wrongdoing has peaked in the last five years and can be seen in a wide variety of industries. No less with EMR enforcement, DOJ has held accountable individuals for their participation in alleged misconduct involving EMR software. In EClinicalWorks, three of the company founders were held jointly and severally liable for payment of nearly $155 million, with three others responsible for smaller payments for their role. Health IT companies can expect continued scrutiny of the knowing decisions of individuals.

Future enforcement actions will include recovery of funds spent as part of the Merit-Based Incentive Payment System or MIPS.

Damages in the EClinicalWorks settlement recovered payments made under the Meaningful Use program. But recent settlements have also referenced recovery of payments under MIPS. There is every reason to think that DOJ will continue to seek recoupment from vendors of the portion of payments allocated for compliance with Promoting Interoperability requirements. Likewise, one should anticipate that DOJ and OIG will turn to enforcement of the Cures Act, including compliance with interoperability and information blocking mandates.

Readers Write: The Clinical Dilemma at the Tipping Point – How We All Can Drive Transformation in Healthcare

The Clinical Dilemma at the Tipping Point – How We All Can Drive Transformation in Healthcare
By Ted Ottenheimer

Ted Ottenheimer is VP of clinical data transformation for Ascom Americas of Morrisville, NC.

I have read countless articles regarding the shortage of staff in healthcare. I have experienced it myself in the pre-hospital EMS (emergency medical services) setting. So much of what I read provides a great depiction of the situation in which we find ourselves, yet few of them offer solutions. If you’re a nurse, administrator, leader, institution, or anyone interested in the healthcare field, I’m sharing my perspective here on how to be part of the change.

When I first left the military, I was looking for a nursing school to expand my career. The one I had intended to apply to, hosted right in the hospital, was closing its final year of the program. I was left to search for a college that I could attend in a traditional manner. As a single father, this posed a challenge, as I had to continue to maintain gainful employment.Hence, I entered the workforce. 

From time to time, I would look for that nursing opportunity. I finally found it two years ago in a program that runs on evenings and weekends with manageable clinical time during the standard work week. I wish I was 20 years younger, but I believe you are never too old to pursue your dreams.

Now is the time to invest in the programs necessary to continue turning out the critical staffing that the ever-changing healthcare industry is demanding – nursing aides, LPNs, RNs, RTs, and so on. What if these programs have more to offer for non-traditional students? What if programs are brought back into the health system? Partnerships between hospitals and higher learning can be successful, although I understand the prestige associated with being able to attend these opportunities for higher learning is a fierce battle of minds in which only the highest aptitude may attend. However, capacity continues to be an issue to provide the necessary staffing, and we need to think creatively to solve today’s challenges. 

To make a change requires a significant amount of effort and the ability to think outside the box. Let’s look at an example of a way that we made a change. The minimum provider level to staff an ambulance is an emergency medical technician – basic. There is an aide position that requires less training called an emergency medical responder. After years of work by some determined individuals, they were able to incorporate this into the local high school curriculum. The intention is to engage the students in assisting our local community. This is similar to having a CNA (certified nurse’s aide) program in high school or vocational / technical school. Both examples are great options to engage at an early age with hopes of pursuing a career in the healthcare field. It amazes me how many doctors and nurses I have spoken with whom have been trained in EMS, which drove them to continue in healthcare.

We see that there are policies in place for continuing education in nearly all of the health systems. Are you seeking out the employees with potential? I suspect with the current burnout rate it is difficult to think of continuing education. However, helping build one’s career is always rewarding in both directions. I will always remember those leaders who have taken the time for me and encouraged me to work towards improvement.

My last point is adopting technology. Clinical staff are caring for more patients than ever before. As the workload increases, the cognitive load grows as well. This situation will not diminish any time soon.  Technology can assist in capturing routine clerical entries, alerting clinicians to actionable patient events, provide collaboration tools, and clinical decision support that can reduce the burden on staff. It will reduce the negative outcomes we all worry about and want to avoid.  Engage the clinicians early in the process and watch them become the champions for you.  Take some time to see what is most important to them by reading our recent report, “Nursing Satisfaction: What Matters Most At Work.”

These are some simple concepts that can be the change that is so desperately needed in healthcare today.

Readers Write: Taking Clinical Natural Language Processing Mainstream for Effective Care Management

Taking Clinical Natural Language Processing Mainstream for Effective Care Management
By Kevin Agatstein

Kevin Agatstein is CEO of Kaid Health of Boston.

Across healthcare, clinical natural language processing continues to play an ever more influential role. Kreimeyer et al.’s “Natural language processing systems for capturing and standardizing unstructured clinical information” identified over 70 different CNLP systems in the literature, spanning multiple clinical domains. Unfortunately, few of these directly address the applicability of CNLP to care management. This lack of CNLP supporting care managers will and should change. Making this reality will require adapting the technology to the real-world needs of care management programs and the front-line clinicians who staff them. 

To fuel effective care management, actionable data is required across the entire workflow. Examples of actionable data include information that: 

• Identifies which patients require help.
• Stratifies patients for intervention.
• Summarizes the patient for the care manager.
• Determines the specific actions the patient needs.
• Uncovers the barriers to effective care.
• Measures intervention outcomes.

Claims data, lab data, health risk assessments, and motivational interviewing all meaningfully contribute to the above. While all of these are necessary, alas, they are not sufficient. For the care manager to meaningfully coordinate patient care, to accomplish the six steps listed above, they must have access to actual clinical data. They need the medical record. More precisely, they needs the nuggets of actionable insights buried in the massive EHR data set. Finally, they need it to be quickly digestible. Thus, CNLP can not only help, it is all but required.

This actionable data is almost all in the EHR; however, it can be hard to find. A patient’s medical record is often hundreds of pages of text, alongside hundreds of discrete data points (labs, medications, allergies, etc.) Within this morass of usually loosely organized data is the patient’s health history. While claims and labs can give some sense of the patient’s clinical experience, the chart has the diagnosed but not coded conditions, the written but not filled prescriptions, and more. It also has a plethora of exam findings, laboratory reports, radiologic data, and pathology findings that never get put into “structured” EHR fields.

Kharrazi et al., in “The Value of Unstructured Electronic Health Record Data in Geriatric Syndrome Case Identification,” found that the EHR text resulted in finding 1.5 times more patients with dementia than just reviewing the structured EHR data. That same ratio was 1.7 with decubitus ulcers, 2.9 for weight loss, and 3.2 for a history of falling.

Beyond traditional clinical data, the chart often contains insights into the patient’s family health history. It also has data on psychosocial barriers to care, limitations on activities of daily living, and other elements impacting the patient’s care journey. Just as crucial for care managers, the chart typically has data on the patient’s social determinants of health. While SDOH are almost never coded in claims, (and yes, there are ICD-10 SDOH codes), they are noted in charts. AI-powered healthcare data analysis and provider engagement platforms have found hundreds of SDOH in primary care, specialists, ED, and behavioral health charts. Kharrazi found similar results. For example, they found that it is 456 times more likely to find a patient with a “lack of social support” in the free text of the medical note than in the structured data.

For a care manager to do their job well, this data cannot be ignored.

More than just summarizing the patient’s health, the medical record can help translate the EHR text into a structured, actionable, trackable ambulatory care plan by summarizing the physician’s treatment plan noted for each encounter. Specifically, NLP can create a patient to-do list such as follow-up visits, getting testing or labs, addressing unhealthy behaviors, and more. These identified tasks can become the basis of a care management care plan or added to existing plans. As new data enters the chart, either as structured information or new medical notes, the to-do list can be updated. Tasks can be marked as completed, new tasks added, existing tasks amended, and more.

It’s important to remember that NLP algorithms do not digest a medical note the way a human does. Rather, they predict how a trained human would interpret the presented text. This is much more than finding key words. CNLP solutions also need to account for:

• Negation (“does not have cancer”).
• Family history (“the patient’s mother had an MI before age 55”).
• Uncertainty, (e.g., “initial lab findings mean early-stage chronic kidney disease possible, but additional testing is needed”).
• And more.

Making such determinations isn’t perfect, but making useful interpretations of clinical text has been proven possible. Moreover, CNLP does not fatigue as humans do. For example, Suh, et. al. found in “Identification of Preanesthetic History Elements by a Natural Language Processing Engine” that CNLP frequently identified salient clinical facts that a physician reviewer missed. 

Now, new data standards, notably FHIR, and regulatory mandates to share data combine to markedly simplify a CNLP deployment process. This, plus cloud and other emerging data exchange standards, mean CNLP go-lives can be measured in days, not months. By working with partners with rigid technological and workflow controls, extensive security training, and a culture of data security, the data can be processed safely as well.

For a real-world deployment, a care management CNLP solution should be intuitive to clinicians. It should be focused on the needs of care managers to anticipate the workflow. Care managers today deal with several different medical record and care management documentation systems. Effectively managing these variations, and the vagaries of existing workflows, comes only with experience. Most importantly, CNLP needs to add value for the user practically out of the box. They can, and they will.

Readers Write: Diagnostically Connected Data – The Key to EHR Clinical Usability

Diagnostically Connected Data: The Key to EHR Clinical Usability
By Dave Lareau

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

Clinicians are among the most highly trained knowledge workers in any industry, yet the systems they use to care for patients often actually hinder their ability to deliver care. We hear anecdotes from patients about clinicians spending inordinate amounts of time trying to find information in their EHRs – only to often give up rather than search through other sections of the chart to find a lab result, view past encounter notes, or try to correlate medications with problems or the course of a condition.

EHRs require users to spend too much time searching for clinically relevant information for the patient they are treating and, once that information is located, to go through a series of disconnected processes to complete their work.

This situation will only get worse once the floodgates of healthcare data interoperability are opened. Then, it will be even more challenging for clinical users to find what they need.

Consider the bright side of this data-driven conundrum: The effects of the 21^st Century Cures Act and TEFCA will make it easier for HIT systems to send and receive information. Plus, emerging terminology standards and the use of common codes such as ICD-10, SNOMED, LOINC, RxNorm, CPT, DSM5, CTCAE, UNII, CVX, and others will provide a basis for what is often called “semantic interoperability.” And today, the performance of natural language processing is getting more consistent and reliable, providing a means to convert free-text notes that use those same terminologies and codes.

So, does that mean that more coded data is a good thing?

Not necessarily – that is, unless clinicians can readily locate the information they need to assess, evaluate, treat, and manage a given patient and their clinical problems. With the widespread adoption of risk-based reimbursement through Medicare Advantage and similar programs sharpening the focus on chronic condition management, it will be increasingly crucial for clinicians to see a diagnostically focused view for each patient along with their medical problems. They need instant access to this view, without searching through disparate sections of the EHR.

Semantic interoperability facilitated by standard terminologies and code sets is a great start – and is necessary for sharing clinical information between systems. It will also drive better analytics and population health insights. But it will not make it easier for clinicians to find the data they need for the patient at the point of care (whether that patient is in-office or on a screen.)

Most existing EHRs, and the terminologies and codes for semantic interoperability, are structured in distinct “domains.” In an EHR, this typically shows up as separate sections or tabs – problem list, medication list, laboratory orders and results, procedures, encounter notes, discharge summaries, etc. Problems, meanwhile, have ICD-10 and SNOMED codes, labs have CPT and LOINC codes, medications have RxNorm or NDC codes, and other domains use other code sets. These codes were designed for their specific domain. They were not designed to work together for the clinical user.

The key to usability is to link these to the problem list, so that the user can click on a problem and immediately view the related medications, labs, procedures, therapies, co-morbidities, and findings from encounter notes that all are related to the problem. This diagnostically filtered presentation could be viewed longitudinally and supported by millions of mappings from standard terminologies and code sets.

Such a unique diagnostic relevancy engine would provide both the semantic – and diagnostic – interoperability that enables clinicians to not only see what they need at the point of care, but also to harness the flood of interoperability-driven data that will soon complicate their work.

Readers Write: Does Cryptocurrency Have a Future in Healthcare?

Does Cryptocurrency Have a Future in Healthcare?
By Curtis Bauer

Curtis Bauer is chief product officer of Sphere of Nashville, TN.

Cryptocurrency seems to have found itself used in some capacity in just about every industry. Well, every industry except healthcare.

Healthcare has been hesitant to adopt cryptocurrency, such as the well-known bitcoin, primarily because of cryptocurrency’s various nuances that are not inherent with more traditional forms of electronic payments. Those nuances include volatility, lack of consumer demand, increasing government regulation, and most importantly, challenges with reconciliation.

To understand cryptocurrency and its future in healthcare, you must first understand the underlying technology. Cryptocurrency is built on what is known as blockchain technology. Blockchain can be difficult to understand, in part because blockchain is not a plug-and-play technology, a silver bullet standalone technology solution, or an app that can be easily installed.

Blockchain is a decentralized, distributed, peer-to-peer network. As opposed to a system in which applications are controlled by one entity, a blockchain network enables control to be shared across a group of decentralized computers and networks.

Blockchain is built on distributed ledger technology, which creates a record of transactions over time while allowing for tracking and analysis, documenting the transfer of ownership, and ultimately serving as a means for proving ownership. The advantages of blockchain include the elimination of a need for third-party intermediaries to verify transactions and a high level of security.

As it relates to healthcare, although there are significant challenges that need to be overcome before cryptocurrency will ever become mainstream, there is a strong likelihood that the underlying blockchain technology will become a staple in the world of payments and other business segments that have a need for authenticating transactions and minimizing the risk for fraud.

Blockchain is changing the world in several ways, as there are 300 million estimated global cryptocurrency users, according to a report from the US Department of Health and Human Services’ Office of Information Security. Consider bitcoin, which is itself just one of thousands of cryptocurrencies in existence. Approximately 17% of the US adult population owns bitcoin. It has a global market cap of $775 billion. It is accepted by more than 15,000 businesses for payment globally.  Crypto-economics, which is a field of economics based on blockchain technologies, is now a recognized academic field. Non-fungible tokens, which are also built on blockchain, are selling for millions of dollars.

Yet if blockchain and cryptocurrency are so innovative, exciting, and cutting-edge, then why have they been so slow to penetrate the healthcare market? There are several reasons, and they begin with the extreme volatility of cryptocurrency.

The value of cryptocurrency fluctuates wildly in short periods of time, sometimes by the minute. In general, cryptocurrency lacks the stability of traditional government-issued currencies, and that is unlikely to change any time soon.

Similarly, given cryptocurrency’s extreme volatility, exchange rates with other types of currency are constantly in flux. For example, between the time a transaction is agreed upon and the buyer obtains the product, the cryptocurrency-to-dollar exchange rate may have substantially changed.

The next problem is regulation. The federal government views cryptocurrency not as legal tender, but more like a piece of property or even gold from a tax perspective. Every time cryptocurrency is bought or sold, it must be reported to the Internal Revenue Service, so attempting to use cryptocurrency similar to cash or credit cards becomes a tax and accounting nightmare. Healthcare providers who begin to accept cryptocurrency would be subject to the same accounting and income tax implications when they move to convert or exchange the cryptocurrency back into fiat money or government-issued money that is not backed by a physical commodity, such as gold or silver.

Finally, there is the Catch-22 situation of consumer and merchant demand when it comes to cryptocurrency. Consumers are reluctant to embrace it because merchants don’t accept it, and merchants are hesitant to devote time to learning how to accept it because so few consumers use it.

The credit card is likely to remain king well into the future for healthcare transactions, making cryptocurrency unlikely to make much of an impact on healthcare any time soon, and possibly ever. But the blockchain technology that undergirds cryptocurrency is another story. Due to the premium that the technology places on security and transparency, there are two notable use cases for blockchain in healthcare.

• Patient record-sharing. The healthcare industry has traditionally faced substantial barriers in ensuring patient access to all their health records across all service providers in order to have a complete view of medical histories, while ensuring their records are secure. However, blockchain-based medical record systems can be linked into existing medical record software and act as an overarching, single view of a patient’s record without placing patient data on the blockchain. This approach provides patients with a comprehensive, single source for accurate medical records.
• Supply chain transparency. Blockchain is ideal for enhancing the transparency of the healthcare supply chain, particularly for pharmaceuticals. To combat prescription counterfeiting, the industry must be able to track each package’s end-to-end movement from the point of origin, including manufacturers, wholesale, and transportation. Blockchain enables stakeholders throughout the prescription drug supply chain to verify the authenticity of medicines, expiry dates, and other important information.

For healthcare transactions, the credit card is likely to remain king for some time. In that regard, one concrete step providers can take now is to steal a page from retail and focus on minimizing shopping cart abandonment. In other words, providers must ensure a convenient and easy buying experience that enables patients to quickly submit payment. To this end, providers should store payment information electronically for patients, so payment is fast and seamless when they log into patient portals and practice websites.

Though cryptocurrency will likely continue to grow in popularity across many sectors, it’s unlikely that healthcare will embrace it as a mainstream payment option any time soon.

Readers Write: The Retail Revolution is Changing Modern Medical Care and Healthcare Organizations Need to Act Now

The Retail Revolution is Changing Modern Medical Care and Healthcare Organizations Need to Act Now
By Shelley Davis, RN

Shelley Davis, RN, MSN is VP of clinical strategy at Lightbeam Health Solutions of Irving, TX.

As patients and healthcare providers continue to navigate a post-pandemic world, we have begun to see an overlying trend — especially among the younger generation — that favors convenience and transparency in the way healthcare services are obtained. Retail health is defining a generation of patients who are taking healthcare into their own hands and steering away from the relationship-based patient-PCP (primary care physician) system that older generations have followed.

This can come with some benefits, as more convenient healthcare makes treatment accessible for a wider patient population. However, this new healthcare trend also has potential downsides.

To grow and change with the world around us, health systems must be able to answer two questions. Why are these changes are taking place? How can this new mindset be leveraged to make healthcare more accessible and forge a positive, meaningful impact on many lives?

Consumers Can Shop for Anything They Need, Including Healthcare Providers

The retail health phenomenon comes at a time when digital fluency is high. Most Millennial- and Generation Z-aged patients prefer to choose provider offices that offer a better patient-centered experience or with the highest reviews, much like shopping for a new product or home appliance.

In the past, one might have found it odd to receive an eye exam or mammogram at Walmart or to check into a walk-in clinic instead of contacting a PCP when you become sick. Nowadays, a person’s first thought when it comes to their healthcare options is, typically, convenience. This can be due to any number of reasons. Patients may prefer:

• To be seen at a moment’s notice.
• To come in late or on weekends.
• To have financial transparency or listed prices.
• To see a provider without having insurance.
• To go to the clinic or office closest or within walking distance to them.
• To multi-task, such as grocery shopping immediately after receiving a check-up or vaccine.

Many of these scenarios can be tied back to the health inequities that impact a patient’s ability to acquire the medical care they need. It makes sense that patients have to make decisions based on whether they will have finances, transportation, or even shelter. However, while the convenience of retail health does offer benefits, its drawbacks cannot be ignored.

Benefits and Downsides to the Retail Health Model

When taking it at face value, the trend toward retail health might seem appealing. After all, having this level of convenience allows providers to see patients at intervals flexible with many schedules. More benefits include:

• Retail health pushes organizations to be more transparent with costs to compete with these convenience-based clinics.
• Retail health overall is more patient-centric. Moving toward a patient-centric approach rather than provider-centric overall prioritizes the needs of patients.
• Through retail health, many patients can receive basic care who otherwise would not receive medical attention at all, even though that care may not be the highest quality.

But with these positives come some clear drawbacks. When patients are given this degree of autonomy over their own health journey, it puts an enormous responsibility on their shoulders. Patients who adhere to the behaviors of retail health must act as their own medical historian, care manager, and health expert.

From the patient side, these concerns are rooted in an extreme lack of consistency and continuation of care, stemming from little to no engagement or follow-up after an appointment, as well as disjointed health record tracking. When patients go to multiple places for care that do not communicate with each other, an information silo is created, resulting in reduced efficiency, lower quality services, and potential treatment duplication.

Additionally, the use of medications is significantly higher in patients who use retail health. If a patient does not see a physician or care team consistently, many things can be missed or misdiagnosed. Preventive screening recommendations take a back seat to addressing acute needs and new symptomatology. It also puts a provider at a disadvantage to not have all the information they need, such as family history, past illnesses, symptoms, allergies, and drug interferences. This, in turn, increases the consumers risk of medication compatibility issues, treatment gaps, and single symptom management.

How Can Healthcare Organizations Bridge the Gap Between Convenience and Quality?

Retail health is setting some great precedents that can be harnessed to elevate the more traditional healthcare model to one that is more inclusive, accessible, convenient, and transparent. Opening healthcare information while respecting HIPAA guidelines and privacy could solve many of the issues that are associated with data silos while giving providers more access to important patient information and taking the onus off the patient to act as their sole care manager.

Telehealth has the building blocks to be a great alternative for easier access to care while maintaining consistency and quality. Its capabilities include:

• Remote patient-provider visits that reach wider audiences and encourage patient engagement.
• Online or virtual classes to encourage medical literacy for chronic conditions that patients may not know how to best manage on their own, such as diabetes and hypertension.
• Improved coordination of care between multiple providers.
• Encouraged patient communities that benefit from cohort-learning or developing interpersonal relationships with others in their group

Going beyond the digital environment, larger healthcare organizations can also take actions to forge partnerships with after-hour facilities or clinics within their communities to bring the high-quality care they provide to those who rely on convenience.

Along with telehealth, other solutions that can be leveraged to match the convenience and transparency of retail health are:

• Deviceless or device-based remote patient monitoring.
• After-hours hospital clinics to capture patients who need care outside of the traditional 8 a.m. to 6 p.m. window.

The new mindset surrounding healthcare and how medical services are obtained is not going anywhere anytime soon. Larger health organizations should listen to the needs of their communities and extend their capabilities to match those needs as best they can. Ultimately, the key is to meet patients where they are.

Readers Write: Digital Care – The Opportunity and Threat for Metropolitan, Community, and Rural Hospitals

Digital Care – The Opportunity and Threat for Metropolitan, Community, and Rural Hospitals
By Cody Strate

Cody Strate is managing partner of Upward Spiral Group of Boulder, CO.

In 2002, I began my career on the vendor side, helping hospitals move away from NCR forms and embosser cards towards centralized e-forms that could be printed on demand, which was some serious eyebrow-raising stuff back in the day. For the next 18 years, I had the pleasure of working with some wonderful people to institute digital solutions to vexing paper-based processes at over 1,000 hospitals spanning more than a dozen countries.

I stepped outside the acute care space in 2019, gaining exposure into how leaders in other industries fundamentally think about the market they serve, the importance of value, proactive versus reactive mindsets, and intent towards consumer experience.

After dancing with technology solutions and problem solving in other industries, I can clearly see that there are some threats not too far afield for hospitals that the all-too-pervasive status quo thinking approach to operation, mindset, and leadership is ill suited to handle. I wanted to write this article to call attention to a few things in hope that it opens a few eyes and facilitates some fresh thinking.

Specifically, I’m going to focus this piece around one single theme, which is the opportunity and threat brought about by the ability to extend care across great distances thanks to advancements in technology, a large and reliable communication network infrastructure, and the prevalent adoption of smartphones.

Caveat alert: given the massive institution that is the hospital and health system ecosystem, there are exceptions to every situation and rule. In other words, this is a generalist view derived from the aggregate of my experience of thousands of interactions with healthcare leaders.

The Stakes: Revenue

Just so we’re clear upfront, the cold hard calculus of the following is that pretty much everything comes down to revenue opportunity versus revenue threat. Revenue is the lifeblood of any company in any industry, and hospitals are not immune to this fact. I could elaborate on this, but I don’t think readers need any Finance 101 lessons from me, so let’s just leave it at: (a) lots of revenue = good, versus (b) little revenue = bad.

The Digital Attack on Proximity-Convenient Care

This situation cuts a couple of different ways that we’ll get into, but before that, let’s get straight on what’s happening. It’s a basic principle and rather self-evident that as technology progresses, it consistently renders “impossible” into “possible.” Case in point — the vast geographic distances that kept people isolated from each other, education, services, and so on, are now being bridged through technology.

Proximity Based Care: The Way It’s Always Been

Community hospitals generally exist in an orbit around a metropolitan center, where total beds decrease as distance from the metro area increases. This geographic distance has set the stage for the conventional model we see today, where care is largely accessed and delivered based upon these geographic constraints. In other words, if you live in a rural area, your choice of care is largely dictated by geographic proximity to care. This was my situation as I grew up in rural northeast Texas, where driving into Dallas for big-city healthcare was out of the question. Simply put, geographic proximity to care correlates to convenience of care, which up to this point has served as the primary basis for choice of care.

Potential Winners: Metropolitan Hospitals

Thanks to the combination of (a) 85% of the US population has a smartphone in hand, 84% in suburban areas versus 80% in rural areas; and (b) the emergence of digital capabilities to offer care through these devices, metropolitan hospitals can extend their reach out into suburban and rural areas. If I’m a metropolitan hospital, I would be creating targeted ads regarding specific services to even more specific personas and deploying them through the Facebook ad network, Google display ads, YouTube, TikTok, and the like.

The siren’s song of getting big-city care in palm of your hand can be tempting to people who traditionally are separated from the big-name healthcare due to physical distance. Marketing access to big-brand name healthcare that’s convenient and digitally accessible to these populations can be a lucrative practice for metropolitan hospital systems looking to add more revenue and/or recuperate revenue lost to specialty care service organizations.

Potential Losers: Community and Rural Hospitals

Whereas metropolitan hospitals have the potential to go on the offensive to bring in more revenue, suburban and rural hospitals are a greater risk of having the patients within their community, along with all accompanying revenue, effectively poached. Convenient access to quality care through one’s smartphone is here, and it will only continue to become more mainstream. This places leadership at community and rural hospitals in a precarious situation. The question is how these smaller facilities will strategically position themselves going forward.

The Mindset Problem: BWADITW Thinking versus Proactive Thinking

After stepping away from the acute care industry for a few years and seeing how other industries operate, there’s one thing that’s clear. Generally speaking, the mindset of hospital leadership is largely one set on BWADITW (“because we’ve always done it this way”) versus opportunity and/or threat-based agility found in many other industries.

This should not be a surprise given that most hospitals have the two things required for BWADITW thinking to flourish: (a) size, since these are very large organizations; and (b) time, since many hospitals are long established serving many generations of their community. However, BWADITW thinking stands as a tremendous threat to community and rural hospitals as it offers an alluring false appeal of safety. Building a fixed strategy based upon what’s worked in the past is folly given technology’s acute ability to alter the landscape of the future. If you want to apply lessons from history, consider a more Darwinian lesson of “adapt or die.”

A more vile and derivative threat born from BWADITW is thinking your patient in your area owes your community or rural hospital unwavering fealty. This is complacent leadership at its worst, thinking that your patients owe you something rather than you owing your patients something. Your organization may have been the only game in town for decades, but that is no longer the case. This begs the question — are you working to earn your patients’ business, or are you resting on laurels expecting your patients’ business?

Move Quick and Focus on Earning Your Patients’ Business Rather Expecting It

In industries that are rife with aggressive competition, there is an understanding born from survival. If you want to earn the business of your consumers, you have to offer more value than the other guys. Value is the key here, and it comes in many forms ranging from quality, convenience, cost, convenience, customer experience, and so on. My fear is that certain hospitals may have grown complacent due to a lack of competition, which does not bode well for protecting future strategic interests.

Metropolitan hospitals, it’s a smart move for you to pursue and seize revenue opportunity by leveraging technology to extend the service boundaries of your organization. If you can offer a service that tangibly has more value for the end consumer, then fair game.

Community and rural hospitals, you should act fast to get in front of this threat and seize the opportunity to leverage technology to offer your longtime customer base the best possible consumer experience before they explore any lures from the big-city hospitals. Your goal should be to proactively provide better service, offer more value to your patients, and lock in new consumer behavior patterns. Be proactive in exploring ways to expose your customer base to a new and more convenient way to access the care offered by your facility as a first line of defense, while still having them come to your facility to access face-to-face level care. Do this and you will develop new behavioral patterns in your consumers / patients that any outside competition will find difficult to break.

Simply put, by focusing on providing a quality consumer experience, you will concurrently better serve your patients, continue to fulfill your hospital’s mission statement (often built around how you exist to serve your community), and protect your financial interests from outside invaders looking poach your you patients and revenue.

Readers Write: AI in Imaging: Improving Outcomes Across the Care Continuum

AI in Imaging: Improving Outcomes Across the Care Continuum
By Calum Cunningham

Calum Cunningham, MBA is SVP/GM of healthcare diagnostic imaging for Nuance Communications of Burlington, MA.

The role of AI in medical imaging has been top-of-mind for a number of years, particularly as deep learning algorithms continue to improve to the point they can even identify findings that are not detectable by the human eye. As these solutions gain traction and adoption in clinical practice, more radiologists acknowledge the need for familiarity with the role AI plays in imaging and how to successfully integrate it into radiology workflows. In fact, RSNA recently launched its first-ever certificate program for imaging AI.

While the benefits to radiologists are significant and broad  — enhanced efficiency, greater satisfaction by automation for repetitive and mundane tasks, and freedom to focus on what matters most — AI in imaging, more specifically an integrated imaging network, adds value across the care continuum.

The radiology workflow is at the heart of a patient’s journey, informing healthcare decisions at the point of care, but also traveling both upstream and downstream. Once a patient’s images have been captured, radiologists depend on a range of technological platforms to integrate those images, relevant patient data, and AI services (such as automated 3D visualization solutions) from across the healthcare ecosystem.

When a radiologist reads a mammogram, for example, they need more than just the images from the patient’s most recent study. The patient’s previous images, information from the medical record (e.g., history, demographics, and symptoms), and increasingly, AI-powered diagnostic models are all essential to helping the radiologist accurately, comprehensively, and efficiently prepare the report.

The radiologist’s report likewise becomes part of the integrated imaging network, which breaks down the siloes that traditionally exist between care teams, administrators, payers, and other third parties. Sharing key imaging data with administrative, coding, and billing teams, for example, means supporting the organization’s financial resilience. These teams depend on accurate, comprehensive reports to protect the organization’s revenue cycle. When radiologists harness the power of AI to deliver more content-rich reports, insurance claims may be cleaner, which can result in appropriate reimbursements from payers. Similarly, sharing imaging data in this way can also streamline prior authorizations for treatments, surgeries, and prescriptions.

The downstream impacts don’t end there, however. Health plans and self-insured employers can take advantage of the integrated network to improve coordination and collaboration in ways that can address care quality and cost. Life science companies can make use of these insights to identify candidates for clinical trials.

An integrated imaging network also benefits healthcare provider organizations, which can apply AI-generated insights from diagnostic imaging to support earlier disease diagnosis as well as inform treatment options and planning. Perhaps one of the more exciting aspects of an integrated imaging network is the potential impact on patient follow-up adherence. When a radiologist includes an imaging follow-up recommendation in their report, only about half of those recommendations are adhered to. Not only does this represent a significant risk to the patient in terms of treatment and outcomes, but it can also create liability risks for the providers, not to mention the adverse impact on financial performance for the healthcare organization. The integrated imaging network can help close the loop on these follow-up recommendations, automating certain aspects of provider and patient communication to reduce the risk of delayed diagnosis.

In short, by seamlessly sharing clinical and imaging data and applying AI-generated insights and automation, organizations can maximize the value of existing healthcare IT infrastructure across the care continuum while improving patient outcomes.

Readers Write: What’s Missing from Healthcare’s Consumerization Conversation is Physicians

What’s Missing from Healthcare’s Consumerization Conversation is Physicians
By Casey Jenkins

Casey Jenkins, MBA is VP of product management for Epocrates of Watertown, MA.

The consumerization of healthcare is a trend that’s spreading like wildfire in the HIT industry. Healthcare organizations are increasingly turning to technology systems that create more consumer-friendly interactions for patients with the intention of ultimately improving access to care and boosting patient satisfaction.

Recent research supports this, finding that 74% of healthcare leaders are planning to invest in technologies that drive consumerization. This includes investing in technologies like telehealth, self-scheduling, text correspondence, online payment applications, and accessible patient portals. However, there’s one vital group that’s missing in healthcare’s consumerization conversation — physicians.

Due to the growing pressure to give patients the consumer-grade experience they are accustomed to in other areas of their lives, the physician experience has been deprioritized as a result. That needs to change, as rates of physician burnout are skyrocketing due to factors like information overload and administrative burden. It’s vital that the HIT industry also prioritizes the physician experience as the trend of healthcare consumerization continues to grow. Physicians are consumers, too – they’ve learned and adapted to everyday technology, and physician-facing technology needs to reflect this shift as well.

There are numerous ways that more consumer-friendly technology for physicians can alleviate their pain points in practice. One way is incorporating more personalization capabilities into the clinical information-seeking process. Today’s clinicians simply don’t have enough time in the day to stay abreast of the latest clinical information and can often feel overwhelmed. In fact, a recent study found that 64% of physicians reported not having time to stay up to date in their field.

Stronger personalization capabilities in clinical information tools can help by providing more curated information that’s updated and relevant to a specific physician’s practice and area of interest. This concept emulates the level of personalization that companies like Amazon, Facebook, and Netflix have created and that we’ve all come to expect across all our technology interactions. Personalization in clinical information tools can take the form of a data feed, much like a social media-style feed, that understands what information a specific physician is researching or seeing in the clinic and then presents relevant information in an organized and logical manner. This empowers physicians to sharpen their focus during the information-seeking process and ultimately make a more positive impact around patient care.

The push for more consumerization in physician-facing technology also includes a need for a stronger translation of science into technology applications to help curb information overload. In a recent survey, 89% of physicians reported that more clinical data isn’t always the answer. The right data at the right time is what is most important. When we can connect the right person and information at the right place and time within the clinician / physician workflow, that is when physicians will be able to provide the best-quality care.

Additionally, the HIT industry can help streamline workflows by putting the tools clinicians need in front of them for a more intuitive, easy-to navigate experience. This includes incorporating more thoughtfully designed interactions and experiences with the tools physician most frequently use.

Beyond that, the HIT industry can turn to familiar user experiences like a navigation bar. For example, navigation bars on social media apps include things like notifications, direct messages, or saved posts. In a HIT platform or technology system, a navigation bar could provide quick access to the key features a clinician needs at the point of care. like the patient’s medical chart or the medical news reel with the latest clinical developments. The HIT industry should lean into what people find familiar when determining how to put technology at a clinician’s fingertips.

As there continues to be a societal shift in what we expect from technology, the HIT industry needs to bring the physician experience into the fold of healthcare consumerization to truly improve healthcare outcomes. Stronger consumerization in physician-facing technology has the potential to reduce feelings of information overload, streamline workflows, and empower physicians to provide the best care possible to their patients.

Readers Write: Project or Program: Why It’s Time to Rethink Your Approach to Cyber Risk Management

Project or Program: Why It’s Time to Rethink Your Approach to Cyber Risk Management
By Jon Moore

Jon Moore, MS, JD is chief risk officer and SVP of consulting services for Clearwater of Nashville, TN.

The modern healthcare enterprise constantly expands with new technologies, services, and devices. Still, few have a reliable process to ensure that these new additions meet their cybersecurity standards or are added to their risk analysis. Most do a point-in-time risk analysis or conduct their risk analysis using only a sample of their information assets, or worse, both.

Point-in-time risk analysis in a complex healthcare organization will be outdated nearly as soon as it is completed. Sampling information assets is risky. Any number of assets outside the sample could threaten an organization.

A firewall is not enough to protect an asset, system, or network. Effective, compliant cyber risk management is not just about implementing and protecting the electronic health records (EHR) system.

Today’s cyber risk management should be comprehensive, including all aspects of daily operations and supporting systems, evaluating applications and systems both on-site and in the cloud. That can be challenging for even the best teams to manage and even more difficult for smaller organizations where access to skilled professionals, risk landscape intelligence, and financial re sources can be hard to come by. It’s further complicated in mid-size to larger healthcare organizations, where technologies, software, applications, and configurations can vary from location to location and sometimes from department to department.

Without accurate, up-to-date asset, software, and system inventories, a team can quickly fall into siloed risk management practices that focus on the known, leaving security gaps with the unknown.

Adding more challenges to the mix is the growing third-party risk that healthcare organizations face as their vendor and partner lists grow, especially in new applications or devices that streamline patient care. Owensboro Health CISO Jackie Mattingly recently spoke about the challenges in keeping up with vendors, systems, and programs that are brought into the organization by various departments. “Most of these major EHR systems have a pretty good grip on security for their systems. We use Epic, and they have things pretty well buckled up,” Mattingly said. “They’ll notify us if they detect an incident, but the many other ancillary systems we use pose a greater threat. You have to assess risk across the enterprise.”

A recently released Cyber Readiness Report found that some 74% of healthcare organizations haven’t yet implemented comprehensive software supply chain risk management policies. The report noted that more than 90% of respondents struggled to measure and implement software supply chain risk management policies in healthcare. That should be alarming considering the number of successful healthcare breaches recently resulting from vulnerabilities in third-party software solutions.

While forward-looking security teams are trying to keep pace with healthcare innovation and the adoption of new technologies, it’s important to remember that the data in legacy systems may also be at risk. Late last year, a healthcare organization in Canada discovered a breach that could have affected data dating back to 1996. Although its EHR appears unscathed, data was taken from legacy administrative systems like those used for reporting and patient satisfaction surveying. The breach affected 13 different but overlapping data categories, such as medical and other information, and impacted others, such as an affiliated non-profit that purchases IT services and file storage from the core agency.

If you’re still approaching cyber risk as an annual project or initiative, it’s time to rethink this approach. While nothing can guarantee that a cyberattack won’t become a breach, having a comprehensive ongoing program in place means that even in the worst-case scenario, you’ll be prepared to show that you did what was reasonable and appropriate to protect your systems and patient data. This goes a long way when the Office for Civil Rights investigates a breach or audits your organization. It can save you countless hours, resources, and money by resulting in a short investigation and more favorable determination.

Unsure of where to begin? Consider:

• Adopting reasonable and appropriate security controls across all of your information assets. Be sure to account for the legacy data you may have in storage somewhere. It needs protection, too.
• Employing identity and access management processes that limit access to patient data to only what is needed for an employee to perform their job.
• Segmenting your network as appropriate to reduce the ability of threat actors to move laterally through networks and systems.
• Using a risk management software solution to power an ongoing risk assessment and risk management program so you always know where your risks are and how to address them
• Working with an expert to develop a comprehensive risk management program for your organization, including seeking out program weaknesses and making plans to mature it over time.

Readers Write: The Next Frontier for Healthcare Consumerization

The Next Frontier for Healthcare Consumerization
By Aaron Fulner

Aaron Fulner, MS is senior director of product marketing at Edifecs of Bellevue, WA.

The pandemic accelerated consumers’ move to the driver’s seat of their healthcare. Consumers are using digital health devices now more than ever and are demanding a personalized, on-demand healthcare experience. To meet these heightened expectations, the healthcare industry needs to better stitch together all of the pieces – telehealth, artificial intelligence (AI) and machine learning, and interoperability standards – to create a comprehensive delivery model. A model, where data can be exchanged quickly and accurately. This is the next frontier for the consumer healthcare movement.

To reach this next stage, the healthcare industry needs to ensure data can be shared seamlessly across the healthcare system to best equip providers with real-time health information. Here’s how we get there:

Expand Telehealth from Acute Care to Chronic Condition Management

It’s no secret that telehealth use skyrocketed during the pandemic. Even following its peak in 2020 and 2021, telehealth usage is still 38 times higher than prior to the pandemic. Patient and provider muscle memory is changing, and telehealth is now the go-to for urgent care needs. The next chapter of the telehealth story includes a movement from tele-urgent care to telehealth for chronic care management and preventative care. This is especially important as 40% of adults in the U.S. reported avoiding medical care due to the pandemic. This means providers are playing catch up to ensure patients’ routine and preventative care needs are met.

Additionally, as providers work to offer comprehensive care to patients, they must have access to rich datasets about each patient. This includes using administrative information from encounter and enrollment files as well as clinical data stored in electronic medical records.  A comprehensive view of a patient enables the provider to be an informed partner in the care of a patient.

Integrate Artificial Intelligence and Machine Learning into the Internet of Things

Beyond telehealth, consumers are embracing the Internet of Things (IoT) to monitor their health at home. These digital health devices – smart watches, exercise bikes, glucose monitors, etc. – are used by more consumers today than ever before. For example, a recent Rock Health analysis found that digital health wearable ownership increased from 13% of consumers surveyed in 2015 to 45% in 2021.  The question remains: How can the industry enhance the impact of these tools with machine learning and artificial intelligence (AI)?

Remote monitoring devices combined with machine learning and AI create a powerful tool for providers by delivering real-time information and changes in patient health. For example, if a patient’s glucose is low, a glucose monitoring device with a layer of machine learning and AI proactively sends a notice to their provider to reach out and determine if a change in their care plan is appropriate. Ultimately, this gives providers the data to intervene earlier, work with more patients at a time, and improve margins.

Connect Consumer Digital Health Device Data with Provider Workflows

This increase in digital health tool utilization is ushering in a flood of new patient data. To move forward, the industry must integrate siloed data into provider workflows so providers can deliver holistic care. Connecting consumer health data with provider workflows signifies the convergence of healthcare and business data management standards.

To date, the Health Insurance Portability and Accountability Act (HIPAA) and recent interoperability rules declare the consumer as the agent of personal health data. Meanwhile, Health Level Seven International (HL7)’s Fast Healthcare Interoperability Resources (FHIR) and the Da Vinci Project have established the technical standardizations with consumer data and how it is exchanged across stakeholders.The Office of the National Coordinator for Health Information Technology (ONC) is leading the final push through its Trusted Exchange Framework and Common Agreement (TEFCA), which outlines business rules for data exchange and determines allowable uses for payment, reimbursement, and care data without consumers’ permission. The last mile for closing this gap between consumer data, payer, and provider workflows lies in empowering service providers to adopt these standards industry-wide.

The key to the future of healthcare consumerization is to determine how data is exchanged across the healthcare industry to better inform care, improve provider workflow, and integrate digital health data solutions into one place.

Readers Write: The ABCs of Using NLP for SDOH

The ABCs of Using NLP for SDOH
By Marty Elisco

Marty Elisco, MBA is CEO of Augintel of Northbrook, IL.

It has been established loud and clear: social determinants of health (SDOH) have a huge impact, even more than physical health, in determining the overall well-being of individuals. Yet obtaining an understanding of how specific SDOH factors affect individual patients is extremely difficult because SDOH data is not methodically collected by clinicians and social workers. This is a problem.

Unfortunately, it is just too difficult and time-consuming for clinicians to make sense of all the SDOH data because most SDOH data is buried in patient notes. This ultimately inhibits their ability to consume the data to inform decisions about individuals receiving care.

Natural language processing (NLP), a key discipline of AI that uses computers to understand the written word, tackles this problem head on. I encourage hospitals and health and human services organizations to explore NLP in their practices, particularly as technological innovation in this area is rising across healthcare.

Listed below are the simple ABCs of why health and human services organizations and hospitals should adopt NLP to make sense of SDOH. But before diving into the list, I want to emphasize: the technology is now here. NLP has matured significantly over the last five years, and it is now a proven method to extract key concepts in narrative healthcare data, such as SDOH, from text.

Cost Savings and More Efficient Care

Clinicians and case workers spend an overwhelming amount of time combing through narrative data — for example, reading typed or handwritten patient notes and case notes — to understand the status of their patient and think through potential courses of treatment. All of this time spent reviewing unstructured data is time that could be better spent in any number of ways, such as spending more time with patients.

The beauty of NLP is that it automatically highlights impactful indicators and trends across case or patient notes, thereby quickly revealing SDOH to the case workers and clinicians on the case. An NLP platform relieves health and social services workers of the time it takes to comb through the staggering amount of records by readily highlighting SDOH across a case.

Improved Outcomes

NLP empowers caseworkers and clinicians with the information they need to make impactful decisions and allow supervisors to maximize quality of care delivered. This is because NLP provides a deeper understanding of a patient or case.

The Gravity Project is a national public collaborative creating diagnostic codes for SDOH factors with the goal of having those codes incorporated into the existing list of medical diagnosis codes. The idea for the Gravity Project originated in 2017, and prior to then, hospitals and health and human services organizations had no way of incorporating SDOH into their care besides entering it in free-form into patient notes, even though it is now widely understood that a range of social, environmental, and economic factors impact health status often greater than the actual delivery of health services. NLP can extract the information in unstructured data and translate that to Gravity codes to support the diagnostic process. These diagnoses can lead to treatments and interventions that improve outcomes.

Risk Mitigation

NLP enables organizations to quickly identify patients at the highest level of risk so interventions can be provided. I firmly believe that you can only truly identify risk by understanding what is included in the narrative data. Most risk stratification systems today simply look at claims data to do this. But claims data is an incomplete picture. If care coordinators had a full picture through SDOH, then they would have a much better tool to identify those who are at most risk, and where early interventions can be referred to prevent serious health conditions from occurring.

The case for using NLP is as easy as ABC. Described above are three tangible benefits to hospitals and health and human services organizations for incorporating NLP into their practices. All players in the healthcare ecosystem share the ultimate goal of improving outcomes while saving costs, and NLP is a surefire way to do just this.