A recent Fortune article on big data in healthcare quoted Dr. Brennan Spiegel, a physician-scientist at Cedars-Sinai Medical Center as saying, “Digital health is not a computer science or an engineering science; it’s a social science and a behavioral science,” in the context of a discussion on the hype-evidence gap in digital health. He tweeted the same line back in August, eliciting hundreds of retweets and shares.
Spiegel has cited his own failure experience from attempting to build health technology whilst not addressing behavioral factors like patient motivation, engagement, and long-term adherence. He has elaborated, “Creating the tech isn’t the hard part. The hard part is using the tech to change patient behavior.“ As behavioral scientists, we are encouraged to see increasing value placed on behavioral science in a space that has generally been a slow adopter of it—after all, chief behavioral officers in digital health companies are still unicorns.
In opposition to this emphasis on social and behavioral sciences, John Nosta penned a Forbes piece titled “Digital health isn’t a social science.” He suggests digital health is a technological endeavor that is supported by the social sciences, citing the lack of social and behavioral scientists in his laboratory and the plethora of “engineering, medicine, chemistry, material science, computer science and many of those with techy-type degrees.” This would seem to imply that the technology fields lead while behavioral and social sciences play a secondary role. He states, “I don’t think that social science is the defining aspect of digital health, but (and here’s the key point) an essential element to its success!”
As behavioral scientists, we wholeheartedly agree that behavioral science is not the defining aspect of digital health. We do not view digital health as belonging wholly in our disciplinary silo; in fact, our efforts have been toward the opposite—behavioral scientists are voraciously exploring opportunities to blow the lid off of the silo to embed behavioral science into other disciplines and sectors including STEM, medicine, digital health, and policy.
As a thought experiment, consider inserting any discipline (e.g., engineering, computer science, materials science, biology, robotics, human-computer interaction) into Nosta’s statement, “I don’t think that [insert discipline] is the defining aspect of digital health, but an essential element to its success!” You will quickly see that no discipline is the defining aspect of digital health and all of them are essential to its success. There is no lead actor in this movie—only an ensemble cast.
We fear that Nosta’s piece misses the point of what digital health needs to be: a transdisciplinary pursuit. The first step towards this endeavor is recognizing all the fields that need to be at the table. Transdisciplinary teams bring all disciplines relevant to a problem together and devise solutions via shared goals, respect for differences, communication, and an openness to conflicting opinions and approaches. Transdisciplinary work is hard. The hardest part is letting go of tribalism. In the digital health space, teams have been just as Nosta described and Spiegel lamented—technology lopsided. If “transdisciplinarity” can be accomplished, more effective innovations will emerge that no single discipline could have conceived of on its own. In science, it is well documented that transdisciplinary approaches produce the biggest breakthroughs.
A Deep-dive Into Specialty Pharma
A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.
If the digital health space does not start embracing transdisciplinarity, it risks further widening the hype-evidence gap. When one discipline tries to tackle a problem alone they often ignore crucial aspects of the problem they cannot see. Far too often we see digital health tools that produce one desired effect only to fail at being useful because the tool does not attend to equally important indicators of success. For example, one might design and optimize a technology to measure a biomarker with little consideration to patient engagement. The result is a tool rarely used long enough to produce any actionable insights or meaningful impact.
The true defining aspect of digital health is our common mission to improve health by advancing our ability to effectively and efficiently diagnose, treat, and prevent disease via digital technologies. The success of that shared mission will hinge entirely on our ability to create the right teams to solve the right problems–together.
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