Defining Digital Health and Getting Clarity for HEOR


By Christiane Truelove


The phrase “digital health” encompasses a wide variety of things—telehealth, consumer wearables that track health data, the use of monitoring devices in clinical trials to generate hard data about clinical endpoints, and apps that are combined with a drug or medical device for treatment or can actually act as the therapeutic themselves. From data and health information technology to healthcare delivery and interventions, it’s easy to get lost in the maze of complexity posed by digital health.


HEALTH ECONOMICS AND OUTCOMES RESEARCH (HEOR) experts need the tools and terminology to be able to evaluate these new technologies. The problem is that the complexity surrounding digital health tools—including overly broad, vague terminology—makes it difficult to apply the same frameworks used to evaluate drugs and devices.

“Digitalization is basically cheaper information,” explains Zsombor Zrubka, MD, PhD, associate professor of Óbuda University in Hungary and a member of ISPOR’s Digital Health Special Interest Group. “Therefore, it’s simply just more information. And what does it mean? It means that it can make healthcare cheaper; it can optimize existing treatments; it can bring in new treatment opportunities that were unavailable before; and it can make everything more accessible.”

There are 2 ways that digital technologies are relevant for doing health economic research, according to Ariel Dora Stern, PhD, professor of digital health, economics, and policy at the Hasso Plattner Institute. Stern is also on the advisory board of the Peterson Health Technology Institute, which creates assessment frameworks for evaluating digital health technologies. “The first is that the digital technologies are themselves an intervention.” For example, there are apps for chronic disease management and apps that can deliver cognitive behavioral therapy for someone with substance use disorder. “The other way digital technologies are relevant in health economic assessments is actually using those technologies to collect data or any patient-relevant measures that would be difficult to quantify or otherwise be extraordinarily cumbersome to collect when it may have a really meaningful impact on patient quality of life,” Stern says.

Stern cites an example of how useful digital technology can be in gathering difficult-to-track data. In a recent study, she and colleagues from University Hospital Dusseldorf and Brigham and Women’s Hospital looked at the use of digital health technologies in neurology to measure such things as cognition, sleep tracking, and motion tracking. Stern says this is important because tracking sleep is key in evaluating the effect of many neurological diseases, and sensors can give hard data on sleep amounts or quality rather than relying on patients’ own recollections. These sensors can also collect data in a more patient-centric way.


“It [digitalization] means that it can make healthcare cheaper; it can optimize existing treatments; it can bring in new treatment opportunities that were unavailable before; and it can make everything more accessible.” — Zsombor Zrubka, MD, PhD


What to measure and how?

Stern notes there is already a framework for evaluating the tools used for data collection: the V3 framework—verification, analytical validation, and clinical validation to determine fit-for-purpose of biometric monitoring technologies. “Where it becomes interesting is when the digital technology is the intervention itself—because then we very quickly, for all sorts of reasons, slide into the mode of saying, “Well, we know how to do randomized controlled trials for healthcare products, and in many ways, this just looks like a new kind of therapeutic medical product.”

“We do randomized controlled trials for new drugs, medical devices, or surgical procedures—we know how to do this. And the instinct is 100% correct, which is that in the spirit of evidence-based medicine, we want to have evidence that technologies work before you have clinicians recommending them for patients.” And payers won’t want to cover a product if there is no evidence that it does anything at all, she adds.

Developing and understanding the endpoints HEOR experts will need to evaluate digital health technologies—whether a standalone app to treat a condition, a digital diagnostic, or something to evaluate a drug or medical device—is important because “we say what we would like is an intervention to be used when it’s effective, when it improves health, when it leads to better outcomes, when it increases efficiency, and when it gives patients personalization or the involvement in their own healthcare,” says Anita Burrell, founder of Anita Burrell Consulting LLC and chair of ISPOR’s Digital Health Special Interest Group. “That’s the promise of digital health—this promise to have efficiency improved outcomes, personalization, patient involvement, the possibility to be able to monitor patients more effectively so that we get a better understanding of how medicines may or may not be working or how their conditions develop.”

Standardizing digital health terms is important when it comes to the payers looking at whether they will fund digital health interventions, “their requirements for digital health have been far more diverse between different authorities granting reimbursement,” Burrell says. The problem when it comes to HEOR evaluations of digital interventions, however, is “they have far more components than the technologies that we’re used to evaluating,” Burrell says.

In a randomized controlled trial with a patient either taking a drug or a placebo, it is a fairly simplistic intervention. In a trial evaluating a drug, the class of the drug is known, even the subclass, and often the specific biological system pathway it is supposed to affect is understood. Within what Zrubka calls “the classical” HEOR fields, when it comes to pharmaceuticals, researchers know how to state their research questions: Who is the patient? What is the treatment? What is the comparator? What are the outcomes, etc. “By collecting this information, you can say, ‘This is a better treatment than that,’” he says.

However, when looking at a digital health intervention, “we’ve had this explosion of digital technologies and everything comes under this big umbrella,” Burrell says. Terms such as digital health, eHealth, mHealth, or telehealth are not very well defined from each other, making these interventions much more difficult to evaluate.

“Are we able to conduct research to extract that evidence—to synthesize that evidence—with the same effort, efficiency, or effectiveness as we do with for drugs?” Zrubka asks. In analyzing more than 500 systematic reviews, he and Burrell found the terms digital, mobile, telemedicine, and eHealth have more than 100 definitions. “And we found that each year, 10 new definitions were created.” Drilling down further yielded 67 more secondary terms including telehealth, telestroke, telesurgery, and teledermatology, he adds.

According to Zrubka, “If we do research this field, we need to communicate using clear terminology, and then we are able to help all the users because they can get the information that they need for the decisions.”


How would digital health RCTs function?

Stern says while it would be difficult to do blinding in a randomized controlled trial of a digital therapeutic, there are ways a digital therapeutic trial would have advantages over a traditional drug or device trial. “You have a much richer set of data because digital products, by nature, come with a lot of metadata,” she notes. Another way trials for digital therapeutics may have an advantage in evidence generation compared with those for conventional drugs or devices is in the tracking compliance. “Only the most diligent pharma trials will have adherence measures built in, and that’s typically because they’re for medicines where it’s really important that a patient take that drug at the same time every day,” Stern says. “It’s very difficult to measure compliance, so we measure ‘intent to treat,’ which is different than ‘Did the patient actually take the drug and then what is the effect?’"

While it’s not always possible to track compliance with a digital therapeutic, it is far more likely that there will be “digital exhaust” that can be tracked, Stern says. “Let’s just imagine this is some form of behavioral therapy that the patient is doing in an app-based way. If they’re supposed to spend 14 minutes per day doing their cognitive behavioral therapy exercises, you can actually see if they had the app open and were engaging with it when things are time stamped.” These kinds of data can create a number of new opportunities for studying these products, she adds.

“I’m a big supporter of practicing evidence-based medicine, but the strategies that we typically employ for evidence generation for new medical products are just not perfectly suited—and certainly not at all well-suited in the long-term—for studying digital products,” Stern states. “And that’s where we, as a research community, have to be honest with ourselves and then get creative about methods and not compromise our standards.”

It’s also important to set these standards for evaluating digital therapeutics to alleviate the frustrations of manufacturers, Burrell and Stern note.


“I’m a big supporter of practicing evidence-based medicine, but the strategies that we typically employ for evidence generation for new medical products are just not perfectly suited—and certainly not at all well-suited in the long-term—for studying digital products.” — Ariel Dora Stern, PhD


“The requirements for digital health have been far more diverse between different authorities granting reimbursement,” Burrell says. In the United States, a manufacturer may be able to obtain reimbursement from 1 state Medicaid system, but not another. “The funding is more piecemeal.”

According to Stern, “There’s this frustration that you hear from companies that are doing diligent work to study their products,” Stern says. “They’re running appropriately powered trials yet having a very difficult time differentiating themselves from the massive offerings out there, which include a number of products for which there simply isn’t any high-quality evidence available.”

Establishing standards for determining the value of digital health products “certainly raises the bar, but in a way that will incentivize manufacturers and other organizations to do higher quality research and will stimulate good value-creating products because it will actually create a market for them,” Stern says.


Digital health and ISPOR

Zrubka is the co-chair of ISPOR’s Delphi Study on Defining Digital Health Interventions. He and Burrell have aimed to define digital health terms for HEOR in “How Useful Are Digital Health Terms in Outcomes Research?” This paper advocates that umbrella terms should be accompanied by medical subject headings terms reflecting population, intervention, comparator, outcome, timing, and setting (PICOTS). A functional classification system that creates standardized terminology for digital health interventions will allow researchers to focus evidence summaries for outcomes research. The new PICOTS-ComTeC framework is a flexible and versatile tool, intended to assist authors in designing and reporting primary studies and evidence syntheses, yielding actionable results for clinicians and other decision makers.

“Hopefully, the PICOTS-ComTec checklists that we’ve produced and the push to rationalize some of the requests from reimbursement authorities for digital health technologies will actually start to improve the efficiency, the outcomes, the personalization, and the patient involvement through digital health,” Burrell says.



Christiane Truelove is a healthcare and medical freelance writer.

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