What is Virtual Health?: The Promise of Technology and the Chaos of Terminology

Thursday, May 20, 2021
Robert Graboyes
Darcy N. Bryan, MD

Virtual health is arguably the most important contemporary development in American healthcare. Remote communication and intelligent machines are poised to revolutionize the capabilities and outreach of healthcare providers. However, the terminology surrounding these technologies is chaotic, with broad disagreement over what is meant by “virtual health,” “telehealth,” “telemedicine,” and “autonomous health.” These terms are often used interchangeably and inconsistently, complicating research and confusing policy discussions at a critical juncture. Our goal is to provide some historical context to virtual health and more clearly define the terminology.

Long before the pandemic, physicians and other healthcare providers were interacting with patients via electronic devices. The earliest type of “remote care” was conducted by telegraph. Then, the telephone’s appearance in 1876 vastly increased the frequency of remote patient/provider interactions. Thirty years later, a doctor transmitted an electrocardiogram report from a lab to a hospital one mile away by telephone; in 1948, clinicians sent radiology reports 24 miles by phone; in 1959, University of Nebraska clinicians used two-way television to transmit neurological exams to medical students on campus; and five years later, the Nebraska team conducted video consultations and speech-therapy sessions with patients in a hospital 112 miles away.

In 1967, Massachusetts General Hospital used television cameras to establish a remote clinic inside Boston’s Logan Airport to serve distressed passengers. In recent decades, telehealth has even been used to conduct surgery and diagnose cancer in Antarctica and to manage an astronaut’s blood clot on the International Space Station. In the past two decades, Skype, FaceTime and other apps enabled laypersons to engage in interactive video communication.

Yet, despite the promise of virtual health, it remained a mere niche in the healthcare industry until COVID-19 emerged. In a year when social distancing became a lifesaving imperative, we saw a vast increase in healthcare at a distance. With patients, providers, payers, and regulators now fully acclimated to the service, virtual health will almost certainly remain a widely used component of healthcare long after the pandemic subsides.

Given that virtual health is likely here to stay, it would be helpful for the terminology to be consistent. For a glimpse of the problem, consider these three views of how virtual health has been defined in recent years:

  1. An Aetna International webpage suggests that virtual health is a synonym for video-based telehealth. “Virtual care is the capability to access a doctor through a video call on your phone or computer, anytime, anywhere in the world.”
  2. The Care Innovations consulting group describes virtual health as a narrower concept than telehealth, writing, “Virtual healthcare is actually a component of telehealth, which is a broader term encompassing the entirety of remote and/or technology-driven healthcare.”
  3. PriceWaterhouse Canada views virtual health as closer to what we imagine—a concept broader than mere telehealth: “Virtual health … includes health-care professionals who collect patient data and deliver care remotely, giving patients and caregivers more transparency (i.e., full visibility of their care plans, clinical histories, authorizations and more) and influence in how, when and where they’re treated. And virtual health goes beyond interactions with physicians, such as visits with chiropractors and therapists as well as interactions with pharmacists and insurance providers. It also includes an abundance of health and wellness apps and mobile monitoring devices.”

The Taxonomy of Virtual Health

Berkowitz, Ommen, and Halamka summarized all healthcare in one simple graphic. All care is either on-site (the patient and the provider are face-to-face) or virtual (patient and provider are in separate locations). Telehealth involves direct communication between patient and provider. Autonomous health involves interactions between a patient and a computer system, or between a provider and a computer system. Autonomous health can include the application of expert systems, artificial intelligence, and machine learning to support diagnosis, treatment, and compliance monitoring. These intelligent machines can be used to educate, monitor, advise, or treat patients.

Building on that diagram, in Figure 1, we add details. In place of “office visit,” we have “non-hospital visit,” which may occur in a provider’s office, the patient’s home or workplace, or elsewhere.

Figure 1: Taxonomy of Healthcare

The federal government’s Health Resources and Services Administration defines telehealth as “[t]he use of electronic information and telecommunications technologies to support and promote long-distance clinical health care, patient and professional health-related education, public health and health administration.”

The Center for Connected Health Policy identifies four distinct ways of delivering telehealth: video conferencing (two-way interaction); store and forward (“transmission of digital images, pictures, video, or text”); remote patient monitoring (“transmission of health or medical data”); and mobile health, or mHealth (“the use of mobile devices such as tablets and smartphones to transmit health information”).

“Telehealth” and “telemedicine” are not synonyms. Rather, telemedicine comprises a portion of telehealth. The American Academy of Family Physicians makes the distinction: “While telemedicine refers specifically to remote clinical services, telehealth can refer to remote non-clinical services such as provider training, continuing medical education or public health education, administrative meetings and electronic information sharing to facilitate and support assessment, diagnosis, consultation, treatment, education, and care management.”

In a telemedicine visit, the federal government’s Medicaid website refers to the provider’s location as the “distant site” or “hub site” and the patient’s location as the “originating site” or “spoke site.”

Next, Figure 2 further details the types of virtual health, separating both telehealth and autonomous health into synchronous and asynchronous components. Our definitions are aimed at describing the technology—not the law. For example, a telephone call between patient and provider is clearly an example of telehealth, but some jurisdictions or payors exclude audio-only telephone calls from their definition of telehealth, and do not reimburse them.

Figure 2: Components of Virtual Health


It is commonly stated that America faces a coming physician shortage. But as one co-author (Berkowitz) said, “the answer may not be in increasing the number of professionals available, but in using them more efficiently in a team-based fashion.” He also described how better use of automation, virtualization, and delegation would let a primary care physician and their team care for a much larger population more efficiently. Another co-author (Graboyes) argued that a more efficient, cost-effective healthcare system would come from “shifting some tasks from the physician’s calendar to non-physicians (like nurses or technicians), intelligent machines (like computer diagnostics), or patients themselves (by using home diagnostic equipment, for example). Digital technologies make this possible in previously unimaginable ways.”

These changes are not implying something is unnecessary or in need of replacement. Rather, as Modern Healthcare argued, “Virtual health is not about technology replacing humans in health care, but about augmenting and supplementing providers to improve the delivery of care—moving from a focus on bedside to ‘webside’ manner. Virtual care may help relieve clinicians of mundane, administrative, or routine tasks, affording them more opportunities to practice at the top of their license.”

As evident, the current terminology of virtual health is jumbled and inconsistent. Having a shared language so that a dialogue about the way to move forward can truly progress across clinical, research, regulatory, reimbursement, and other public health arenas would be helpful. By suggesting this set of pragmatic definitions, it is our hope that individuals making critical decisions about virtual health will benefit from a standard framework.

Finally, the terms “synchronous telehealth,” “asynchronous telehealth,” “synchronous autonomous health,” and “asynchronous autonomous health” might sound a tad abstract. So, Figure 3 shows real-world examples of each of these four categories.

Figure 3: Components and Examples of Virtual Health

Streamlining the Flow of Health Care Data

Wednesday, January 27, 2021
Robert Graboyes
Darcy N. Bryan, MD

A health economist and two physicians have some ideas for improving a new rule proposed by the Centers for Medicare and Medicaid Services. Read more at InsideSources

Simplicity, Interoperability, Symmetry, and Privacy Are All Important Goals for New Healthcare Information Rules

January 4, 2021

We are pleased to comment on the Centers for Medicare and Medicaid Services (CMS) request for information on the proposed new rule to address prior authorization and reduce any burden on patients and providers. The Mercatus Center at George Mason University is dedicated to advancing knowledge relevant to current policy debates. Toward this end, its scholars conduct independent, nonpartisan analyses of agencies’ rules and proposals. With that in mind, this comment does not represent the views of any particular affected party or special interest group. The authors of this comment include one health economist and two practicing physicians.

In general, we view the proposed rule as a positive development. We commend CMS for this action. Our comment has four main points:

  1. The proposed rule will reduce reporting requirements, but more can be done to simplify billing reporting, such as, specifically, anticipating developments in data input such as natural language processing.
  2. Electronic health record (EHR) interoperability is paramount, and the universal adoption of a standard, such as the Fast Healthcare Interoperability Resources (FHIR), is central to this task. The proposed rule is consistent with standardization, and we merely stress the importance of encouraging open and flexible EHR designs to accommodate future developments of data input and output.
  3. In adopting the FHIR standard, mandates for the use of application programming interfaces (APIs) should the same for payers and providers or at least allow payers to adopt the standard at the same pace as the providers do.
  4. The addition of “social risk data” seems important but also raises concerns over patient privacy that must be addressed adequately.

Simplifying Billing Reporting Requirements

Implicit in the proposed rule is a recognition that EHRs will be (and ought to be) critical for patient care, along with a recognition that present-day EHRs are not doing the job they ought to. (The three of us have coauthored one other related public interest comment on that topic.) Also, the proposed rule recognizes the need to make the process of prior authorization of reimbursable expenses faster and more predictable, and it recognizes the tightly integrated relationship between EHRs and prior authorization. We accept these assumptions.

Although we praise the bulk of the proposed rule, we urge CMS to err on the side of flexibility and adaptability. We urge CMS not to amplify the rigidity of present-day EHRs and not to hamstring the prior authorization process with excessive specificity. For a positive example in this direction, CMS is in the process of simplifying billing through changes to 2021 evaluation and management coding requirements. Previously, CMS had imposed specific requirements for billing, such that doctors must report the exact number of organ systems examined to justify charges according to a pay scale. This requirement has led to what is often called “note bloat” to satisfy CMS reporting requirements. (Note bloat is the tendency of providers to copy and paste old records into a new EHR, resulting in duplicate information and a larger volume of text to sort through to find the desired clinical information.) Under the proposed rule, doctors need only document the total time spent in their encounters or the level of difficulty in their assessments and plans.

It is also important that EHRs and prior authorization processes be sufficiently flexible and adaptable to dovetail with future developments in remote monitoring, artificial intelligence, and machine learning. To fulfill their maximum potential, future EHRs will have to incorporate data in currently unknown formats.

Enhance Clinical Value of Electronic Health Records

We are particularly pleased that the proposed rule recognizes that EHRs will be essential to healthcare in coming years. A reliable, easily accessible, and easily readable record of an individual patient’s health history will streamline providers’ search for information on a given patient and the ability to analyze that patient’s needs in the context of large-scale population data.

Present-day EHRs are far from ideal, in large part owing to their well-documented issues with decreasing provider efficiency. The quest to record detailed billable activity has led to note bloat and has added little value. There is considerable opportunity to decrease these inefficiencies, thereby improving the clinical value of EHRs. Thus, we strongly agree with CMS’s aspirations stated in its press release and in the proposed rule. In our earlier public interest comment, we write, “today’s EHRs are primarily designed to support billing and record keeping and only secondarily designed for patient care.” There is widespread perception among providers that today’s EHRs hinder their work more than they help. They divert the time of physicians and other providers toward bookkeeping rather than patient care. EHRs contain highly valuable information that is hard to aggregate and transfer across different platforms, thus diminishing their value.

The Goal of Interoperability

Interoperability—a seamless flow of data from one EHR to another—has represented the Holy Grail for healthcare professionals. And thus far, the quest for interoperability has largely failed, as different systems cannot talk to one another as easily as they should. Perhaps the most glaring indicator of this failure is the ubiquitous use of fax machines—devices that had become obsolete by the end of the 20th century—to transfer information between institutions. Under the proposed rule, payers in Medicaid, the Children’s Health Insurance Program (CHIP), and Qualified Health Plans (QHPs) would have to construct APIs to facilitate the exchange of data and prior authorization. APIs would have to meet the FHIR standard. According to CMS, “The FHIR standard is an innovative technology solution that helps bridge the gaps between systems so both systems can understand and use the data they exchange.”

Systems should be flexible enough to incorporate future, not-yet-imagined types of healthcare data. In particular, it’s likely that future providers will increasingly enter clinical data into EHRs via natural language processing (NLP), as opposed to standardized box checking, and may extract clinical data similarly. Excessively specific data requirements and formats could impede the development of alternative inputs and outputs. We see no obvious dangers in the proposed rule and merely suggest that this potential conflict be kept in mind. In a paper published by the National Institutes of Health, Na Hong and coauthors explore the challenge of integrating NLP-generated data and standardized, structured-query data into EHRs under the FHIR configuration. They investigate “whether the standardization process causes a loss in performance,” and their results show that NLP and structured data entry could coexist in the FHIR framework.

Short of a mandate that Medicare Advantage plans meet the FHIR standard, we recommend minimizing any obstacles for meeting the standard voluntarily. The same holds true for other plans not included under the proposed rule. A patient history that incorporates only periods when the patient participated in Medicaid, Medicare, CHIP, or QHPs is not a complete patient history, after all. The proposed rule acknowledges this: “Neither the provisions in the CMS Interoperability and Patient Access final rule nor the proposed provisions here would preclude any payer from implementing these proposed policies regardless of whether the payer is directly impacted by the rule. We believe aligning these policies across all payers would benefit all payers alike.”

Prior Authorization

Currently, prior authorization is a highly labor-intensive, time-consuming, often-arbitrary endeavor—in part owing to the irrelevant data offered by today’s EHRs.

As CMS Administrator Seema Verma said in her blogpost on the proposed rule, “Prior authorization processes can drain significant time from the very purpose of medicine – caring for patients – in favor of often mindless nitpicking and wrangling with distant bureaucracies. The interminable delays and back-and-forth make prior authorization the top cause of physician burnout. These processes can delay needed care for patients who will sometimes unnecessarily pay out of pocket or even forgo important care just to avoid the inevitable slog.”

Admirably, the proposed rule seeks to expedite the process. The rule proposes “a maximum of 72 hours for payers, with the exception of QHP issuers on the FFEs, to issue decisions on urgent requests and seven calendar days for non-urgent requests.”

For the most part, such time frames seem reasonable. Two caveats come to mind, though. First, some modest percentage of prior authorizations may legitimately take longer than 72 hours, so it might be wise to build in the capacity for a limited number of penalty-free exceptions. Second, the expedited prior authorization is likely dependent upon improved EHRs, and arbitrary time limits without adequate infrastructure can lead to perverse incentives. For example, the United Kingdom established a maximum four-hour wait time for emergency rooms, but resources were inadequate to meet emergency room demand. What ensued was gamesmanship—for example, patients wait for extended periods in ambulances because the four-hour clock does not actually begin until the patient passes through the emergency department doors. In addition to causing providers to fail the four-hour test in a de facto sense, this lack of infrastructure has led to excessive demand for ambulance time. CMS would do well to contemplate how insurers might respond to infeasible time demands and how perverse incentives might be minimized.


The CMS announcement says of the potential advantages of improved EHR–prior authorization interaction, “If just a quarter of providers took advantage of the new electronic solutions that this proposal would make available, the proposed rule would save between 1 and 5 billion dollars over the next ten years.” In a healthcare system currently spending $3.8 trillion per year, such savings are a mere drop in the bucket. (Using these figures, savings would amount to no more than around 0.01 percent of total healthcare spending.) But that seemingly small number doesn’t particularly bother us. First, we suspect that those numbers do not fully capture the full benefits that success would entail. We suspect, for example, that the $1 billion to $5 billion figure does not capture improved health of patients, psychic well-being of patients and providers, and better population data for improving the standards of care. Second, we also suspect that savings to the US healthcare system will likely come in the form of many, many drops in the bucket, rather than one-shot deals.

Another small but worthy goal in the proposed rule is that of reducing the use of fax technology. The healthcare industry is one of the last holdouts of this now-antiquated technology. Continued use of fax machines should be considered prima facie evidence of failure.

The proposed rule would make the API standards mandatory for payers, but voluntary for providers. This condition raises the possibility of private insurers investing heavily in information systems that are scarcely used by providers. Our colleague Elise Amez-Droz has speculated that the new standards will place small providers at a competitive disadvantage against large, well-capitalized providers. These questions are worthy of consideration. Perhaps API mandates on private payers could be made contingent upon sufficient buy-in by providers.

Finally, the proposed rule seeks to incorporate “social risk data” in patient histories: “We recognize that social risk factors (e.g., housing instability, food insecurity) influence patient health and health care utilization. And, we understand that providers in value-based arrangements rely on comprehensive, high-quality social risk data.” This aspiration cuts two ways. First, healthcare usage explains a little over one-tenth of the variation in health status across the population. Therefore, it is not the only determining factor of health. And second, the inclusion of social risk data raises some concerns over patient privacy that must be addressed adequately.

In conclusion, we strongly commend CMS for its proposed rule and merely suggest a few areas for further study.

Technical Expert Panel on Electronic Health Record Data Quality Best Practices for Increased Scientific Acceptability

October 30, 2020

We are pleased to be able to comment on the National Quality Forum’s draft report titled “Technical Expert Panel on Electronic Health Record Data Quality Best Practices for Increased Scientific Acceptability.”

The Mercatus Center at George Mason University is dedicated to advancing knowledge relevant to current policy debates. Toward this end, its scholars conduct independent, nonpartisan analyses of agencies’ rules and proposals. With that in mind, this comment does not represent the views of any particular affected party or special interest group.

Electronic health records (EHRs) will be an essential component of healthcare in the future—a means of extending the capacity of the healthcare system and increasing the positive effect of that system on the health of the American population. To fulfill that promise, however, EHRs will have to evolve far beyond their current state. Today’s EHRs focus mainly on facilitating billing, not on enabling clinical applications of use to patients and providers. One can argue that contemporary EHRs are more of a hindrance than a help to care—an often-frustrating bureaucratic artifact that encumbers a healthcare provider’s time and distracts from the task of care.

If they are to fulfill their promise, future EHRs will need to incorporate broader data sources, including patient input. Entries should include more natural language, in contrast to today’s highly structured drop-down menus and checkboxes. The data will have to be more portable across platforms and applications—the much-vaunted, but elusive, interoperability. In addition, EHRs will have to meet two market tests absent in today’s environment: do patients and providers find the EHRs useful and use them voluntarily?

The National Quality Forum’s draft report makes a positive contribution in this direction and deserves a great deal of praise concerning its insights and thoroughness. Many of the draft report’s recommendations deserve a place in any future manifestation of EHRs. However, the draft report does not fully escape the environment that is the downfall of today’s EHRs. In our view, a more useful regime of EHRs would rely less on mandates and top-down specification of data and more on bottom-up, organically designed data configurations that constantly adapt and evolve in accordance with input by patients and healthcare providers. This approach will require greater reliance on natural language inputs, broad interoperability, and adaptive structure of data and user interfaces.

High Points of the Draft Report

We share the task force’s overall goal: to improve the quality, availability, and utility of healthcare data contained in the nation’s EHRs, and there is much to like in the draft report. The first paragraph makes excellent points, which are echoed throughout the report. In this comment, we address the first four points made in that initial paragraph. The first of those reads, “One of the promises of electronic health records (EHRs) is that they enable automated clinical quality measure reporting.” We view vastly improved, restructured EHRs as essential to the task of improving the efficacy and efficiency of healthcare, improving health and doing so in more cost-effective ways than the status quo.

The second point states, “EHR systems are primarily designed to support patient care and billing, not necessarily capture additional data . . . to support quality measurement.” We would amend this statement by stating that today’s EHRs are primarily designed to support billing and record keeping and only secondarily designed for patient care. In general, patients have little interaction with their EHRs, and to a large extent healthcare providers detest them. EHRs have been cited as a significant motivation for healthcare provider retirements. We stress that this is a function of how EHRs are structured today. Designed differently, EHRs could serve patients and healthcare providers alike and still perform their role with respect to billing and record keeping.

The third point states, “However, since EHR data routinely collected for patient care can be used for clinical quality measures, they can be reused to reduce provider burden associated with public reporting and value-based purchasing programs.” We agree that this reuse is a desirable goal, but one not well served by current EHRs. At this point, rather than reducing healthcare provider burdens, EHRs often are a provider burden—and a significant one at that.

The fourth point states: “Despite high adoption rates in multiple care settings, the promises of EHRs haven’t yet been fully realized because of considerable variation in data quality, due to a number of factors . . . .” Data quality is certainly one problem with present-day EHRs, but they have other serious problems. EHRs tend to create fragmented data lakes; that is, a given patient is associated with numerous disconnected EHR programs in the possession of different healthcare organizations who all may document information differently. The user interfaces for healthcare providers are often cumbersome, with rigidly defined data fields or simple free text. The ability of current EHRs to interface with nontraditional data sources, such as fitness watches and other remote telemetry, is limited or nonexistent. Patients have virtually no way to input data into their EHRs, resulting in EHRs that include data points from only the brief periods when the patient is actually in the presence of a healthcare provider. Nor can patients easily dispute the data entered by the healthcare provider, leaving them further disenfranchised from their own record.

The much-vaunted goal of interoperability has fallen far short; moving data among a patient’s different healthcare providers can be difficult or impossible. In March 2020, Katie Keith posted on the Health Affairs blog, “Despite gains in many providers moving from paper records to electronic health records (EHRs) over the past decade, interoperability has been an ongoing source of frustration for providers and patients alike.” That post was in part a response to the release of two official documents addressing these problems: a 474-page final rule from the Centers for Medicare and Medicaid Services (CMS) and a 1,244-page final rule from the Office of the National Coordinator for Health Information Technology within the Department of Health and Human Services.

The most vivid and iconic commentary on the failure of EHRs and interoperability is the fact that the atavistic fax machine remains a critical tool in medicine.

The draft report is heavily concerned with the utility of EHRs in producing aggregate data across the patient population. All the shortcomings we have described affect the capacity to accumulate useful population data. We suggest as a general principle that if patients do not provide regular input to EHRs, if patients rarely see or use output from EHRs, and if healthcare providers find EHRs to be a stumbling block rather than a tool, then the usefulness and quality of the data will never remotely approach the potential of the technology.

The draft report makes some valuable contributions. It discusses the potential value of unstructured natural language inputs into EHRs. In addition, as it should, it discusses the challenges involved in converting such inputs into coherent, accessible information for a given patient or for a population. By contrast, quite a bit of the draft report is devoted to further refining structured data. Both the use of unstructured natural language and the refinement of structured data are worthy goals, but there is some danger of overemphasizing the second at the expense of the first. Unstructured natural language is innately more valuable in supporting positive provider–patient interactions.

The draft report includes ample discussion of incentives designed to encourage implementation and innovation. We have some concerns that many of these ideas require someone in central authority to prospectively judge which actors will best advance the development and implementation of EHRs. For an analogy, imagine a program in the mid-1970s, pouring development funding onto Data General and Honeywell rather than Microsoft or Apple. Current experts on and users of clinical data may not be the ideal agents for envisioning the future. As information technology has showed over the past 50 years, the greatest innovations tend to come from unexpected innovators in unexpected places, with advancements growing rapidly and organically from constant interaction among users and innovators. On January 9, 2007, Steve Jobs, introducing the first iPhone, said, “Every once in a while, a revolutionary product comes along that changes everything,” and then pronounced his new device such a product. He was correct, and no one could have imagined that panels of government officials, academics, and established corporate leaders would ever have envisioned anything remotely resembling the iPhone—or identified in advance that Jobs would be the one to produce this world-changing product.

Our Recommendations

In 2016, two of us (Graboyes and Bryan) proposed a tentative set of principles for reimagining EHRs. We went so far as to term our vision digital health biographies (DHBs) because of the significant revulsion by healthcare providers at the very notion of EHRs. We thought this rebranding could help avoid many of the preconceived notions about current and future EHRs. In a larger work in 2018, we tweak and expand upon those principles, based on our conversations and research in the interim. We plan to revisit these principles again soon in an upcoming research paper. No doubt, our thinking on some will have changed. Nevertheless, we think the list presented in the 2018 paper still holds up rather well, at least as a conversation starter. We present those principles here verbatim from the 2018 paper:

  1. As a default, patients, not doctors, should own the DHBs and the data contained within them.
  2. Each patient should have precisely one DHB.
  3. A patient’s DHB should incorporate data from multiple providers—primary care physicians, specialists, hospitals, nurse practitioners, emergency rooms, pharmacists, therapists, and so on.
  4. The DHB should also incorporate data from wearable telemetry such as Fitbits, insulin pumps, and heart monitors.
  5. The DHBs should incorporate subjective data entered by patients, including family history, childhood illness recollections, fears, and feelings.
  6. To the greatest extent possible, data entry should use natural language (ordinary spoken or written sentences) rather than structured queries (such as drop-down menus).
  7. Machine learning capabilities should extract and organize output for specific users, limiting the output as much as possible to the needs of each specific provider or of the patient.
  8. Input and output should be recognized as very different functions that require different software, allowing vendors at both ends of the system to compete on the basis of functionality and aesthetics.
  9. A common protocol or protocols should be set up to minimize the cost and difficulty of shifting from one input or output vendor to another.
  10. To maximize competition among vendors, the government should not mandate or subsidize any particular vendors or data requirements.
  11. DHB usage should be voluntary on the part of healthcare providers so that the systems must continually prove their worth.
  12. The prime motivation of DHBs should be improved patient health and provider efficiency.

In a key takeaway from that paper, we cite the federal government’s role in creating the internet as a potential model to emulate: “The model for this principle lies in the shift in the early 1990s from the federal government’s tightly controlled ARPANET [Advanced Research Projects Agency Network] to the wide-open internet. The government made a set of interoperability protocols available to the public but did not mandate or heavily tilt the market toward the adoption of those protocols. The code was merely available for web developers and users. Adoption was widespread because the code worked, it was relatively unobtrusive, and these protocols passed the test of the market. The question of whether clinical-quality EHRs can emerge from decentralized processes rather than from a centralized command may be the most contentious point in determining the success or failure of EHRs or DHBs.”

Our paper also includes a number of caveats to these principles. When writing our DHB paper, the two of us (Graboyes and Bryan) were unaware of the work of our coauthor on this comment (Berkowitz). In a 2010 presentation at the Mayo Clinic, Lyle Berkowitz shared ideas that anticipated some of Graboyes and Bryan’s ideas on DHBs. In the Mayo Clinic speech, Berkowitz stated that EHRs were “dead.” (He actually used the related term “electronic medical record,” or EMR, though we will continue to use EHR here.) Berkowitz attributed the failure, in part, to EHR designers doing what doctors had asked them to do, which was to make the EHRs “look like paper.” Speaking to information technology specialists behind the EHRs, he said, “You created something that looked like Word, that looked like Excel, tried to make it look like paper. Not much innovation there.” These stylistic features, the three of us argue, are as important as the structure of the data. On the basis of Berkowitz’s work, some of our additional thoughts include the following:

  • Use of the Health Information Technology for Economic and Clinical Health (HITECH) Act to focus on rewarding only features, functions, and outcomes was a mistake. The process should have started with insistence on a common data model so that (a) all the systems could communicate with each other and (b) the focus of EHR vendors could be on creating better top-level (user interface) systems versus creating and maintaining their own proprietary data models.
  • Because a common data model does not exist (yet), the initial focus should be on how to harmonize all data into a single, unified national database that can then be used for measurement, reporting, and so on. This approach is technically possible, and various groups are doing it to some extent, but a true national plan is lacking.
  • There is a tradeoff between trying to capture data in a structured way up front and using simple free text; it is more difficult and takes more time and thus is costlier. Meanwhile, natural language processing and artificial intelligence are enabling an easier conversion of free text into structured data elements.
  • For the topic at hand, one must clarify what problem is being solved (i.e., this more about sharing data, or reporting out data, and so on?). Then, one must develop the proper incentives around that approach, and let the technology experts help make it happen.
  • The structure of the data must be flexible and evolutionary. Data should not be pigeonholed into an immutable structure.

For financing innovation, we would urge that consideration be given to retrospective prizes, as opposed to prospective grants that often dictate to the innovator. Perhaps the best-known of such prizes today are the XPRIZEs, awarded by the XPRIZE Foundation. (Currently, the foundation is offering a number of prizes to be given to those who meet certain research and development goals with respect to the COVID-19 pandemic.) With such programs, a goal is stated up front, and money is awarded retroactively to whomever achieves the goal first. Thus, central authorities do not need to guess ahead of time the identity of the best innovator, as they must with prospective grants. Such prizes have been around since at least Great Britain’s Longitude Prize of 1714. In this vein, Congress created the Eureka Prizes as part of the 21st Century Cures Act.


We appreciate the goals laid out in the National Quality Forum draft report and the thought and detail that went into its production. However, we also advise CMS to focus on ways to enable decentralized innovators to autonomously develop the means for improving the efficacy, efficiency, and proliferation of EHRs. This approach requires flexibility, interoperability, and a highly competitive environment, with both traditional and nontraditional agents offering innovations from the bottom up. Such an environment is well served by ensuring that patients and providers have the capacity to accept or reject products. This organic, evolutionary method contrasts with previous top-down approaches involving encyclopedic specifications and heavy-handed mandates