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Reviewing “The Myth of The Paperless Office” for the New Yorker in 2002, Malcolm Gladwell argued that if the computer had come first, and paper didn’t exist, someone would have had to invent it. Paper, it turns out, is a lot more useful than we typically appreciate.
It occurred to me that perhaps the same might be said of another product we seem to take for granted in the digital age – medicines. (Disclosure: I work at a company that makes them.)
Medicines – you know, those little white pills that everyone loves to critique – are in many cases remarkably effective solutions to very difficult problems; it’s actually kind of amazing how useful some of these products can be. What an incredibly powerful idea – addressing a difficult and complex health problem with a simple pill you can pop before breakfast.
I read a tweet recently asserting that physicians may soon prescribe health apps as an alternative to medications; my initial reaction: good luck with that one. It’s certainly easy enough to envision how magical thinking about the power of health apps will soon be replaced by disappointment as app developers realize something drug makers have known for years: it’s hard to improve health, and it can be very difficult to get patients to stick with a treatment long enough to make a difference.
At the same time, it’s clear there are profound opportunities in digital health; I imagine the most effective applications will find a way to complement and enhance traditional therapeutics, rather than position themselves as “alt apps” – the alternative medicines of the digital age (you can just see the eBook now: “Health Apps ‘They’ Don’t Want You To Know About”).
There are at least two major areas where digital medicine might be expected to play a significant role. The first opportunity is in helping to motivate behavior change by spurring patient engagement, whether in something as basic as completing a full course of antibiotics (I could easily imagine a motivational app being useful here) to a task as monumental as achieving sustained weight loss (a goal of many apps, of course, though it’s not clear any have proved to be broadly game-changing).
The second key area is in measurement, a topic I’ve discussed extensively (see here, here, and here), and around which I’ve co-founded a new academic initiative, the Center for Assessment Technology and Continuous Health (CATCH), together with MGH Chief of Medicine Dennis Ausiello and several Boston-area colleagues. The basic idea is that improved phenotypic measurement – measurement of relevant parameters in a fashion more comprehensive and more continuous than typical patient data – could immediately improve care while also advancing future science.
An interesting underlying challenge associated with both of these areas that we must confront is the need to figure out how to do more than preach to the choir – see this characteristically elegant discussion of this phenomenon by Duke University‘s Mark DeLong.
The specific issue for digital health is that the costs and burdens of healthcare are not evenly distributed, and a relatively small number of people drive most of the costs and also bear most of the suffering. I’m not sure these patients are always the ones who are eagerly sampling the new health apps or at the leading edge of the quantified self movement (although the participants in PatientsLikeMe and similar communities may represent important exceptions). Finding a way to bridge this gap will be important to demonstrate a meaningful impact on health – and also to provide a sustainable business model in this cost-focused era.
The flip side is that the need for improved measurement of real people is so pronounced that if you embarked on a serious effort here – as CATCH plans to initiate – and could achieve more comprehensive measurements in a broader selection of people and patients, there’s a good chance it could generate results that might improve health delivery almost immediately. The key hurdles will be the logistical obstacles associated with actually collecting these data, as highlighted in Chapter Two of this recent Kauffman Foundation report, and discussed extensively at the recent Sage Commons Congress. But if acquired, these data are likely to render healthcare more efficient and effective, and can help us ensure we do a better job of understanding current practices and getting a better sense of what works best – acknowledging, importantly, that there’s usually not going to be a single best approach that should be applied reflexively to every patient, as discussed here and here.
Less certain, however, is how these digital approaches can help us improve care in a revolutionary, not just evolutionary, way (as I’ve previously discussed in context of Steve Jobs). It’s terrific to understand what sorts of approaches to antibiotics and physical therapy work best for cystic fibrosis patients (see here), but how much better would it be for patients to have a new medicine that fixes the underlying problem completely and permanently?
To put it crudely, the development of an effective vaccine did a lot more for the treatment of polio than applying the best design thinking to the construction of an iron lung ever could. I worry a bit that in our fascination with technology and design – which matter a lot for patients in the here and now – we’re neglecting the need figure out some way to get at the difficult biological questions that remain at the root of disease. I really don’t believe a clever app is going to cure cancer – though one might improve and help optimize the experience of patients now suffering from the disease.
What I can imagine, however, is that the focus on patient measurement will highlight the importance of understanding disease in the context of a person, rather than in a petri dish or a model organism, and beyond that, will lead to the development of technologies that make the study of human physiology, and pathophysiogy, increasingly robust. Perhaps the ability to characterize cancer cells more precisely in a living patient could help identify more effective treatments, for example.
The good news is that there seems to be a lot of interest now in balancing classic reductionism with a more physiologic perspective; this includes a renewed emphasis on phenotypic screening (see here and here), and an interest more generally in understanding diseases though patients rather than model organisms. For example, a scientist responding to a previous piece noted that the development of the recently-approved Vertex drug for a variant of CF reflected a more patient-based approach than was typical for industry.
A final point to contemplate is how big pharma should view the nascent efforts in digital health; I think Avado’s Dave Chase (who certainly gets my vote as contributing the most consistently insightful writing about the evolving digital health landscape) nails it in this recent piece, in which he describes the pharmaceutical industry as essentially watching with bemused interest and applauding politely from the sidelines. On the one hand, they’ve more than a sneaking suspicion they need to change their business model, but on the other hand, they’re sitting on a ton of cash, and seemed inured by this point to the endless invocations of a burning platform – it’s almost as if they’ve decided this is more of a PR problem than a core business issue.
The thing is, biopharma companies may be positioned better than almost anyone to take advantage of the opportunities in digital health; as I’ve suggested, they have a unique understanding of the complexities of the healthcare system, and have a deeper familiarity with the many stakeholders. They also would be operating in a space where many potential competitors fear to tread.
David Shaywitz is an Adjunct Scholar at the American Enterprise Institute.
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