Culture of Health Blog

Nefarious cases of data sharing and data breaches are in the headlines on an uncomfortably regular basis. One recent exposé found period tracking apps were sending extremely personal information about millions of women directly to Facebook without their knowledge. This comes in conjunction with all-too-frequent corporate hacks—from credit cards to electronic health records and more—that leave consumers vulnerable and scrambling to reset passwords and freeze accounts. 

It’s a constant drumbeat that is feeding a climate of concern around our data: who has it, how safe it is, what it is being used for. 

Against this tumultuous backdrop, researchers around the world are launching studies that rely on smart phone apps and other digital devices to collect data. The hope is that these digital tools—and the data we provide through them—will enable more people to participate in studies and help accelerate medical discovery. But if researchers don’t act quickly, this turmoil around data privacy could upend their work.

When her regular job hours were cut, Lulu, who is in her 30s and lives in New York, couldn’t find a new full-time job. Instead she now has to contend with unsteady income and an erratic schedule juggling five jobs from different online apps to make ends meet. Cole, in his first week as an Uber driver in Atlanta, had to learn how to contend with intoxicated and belligerent passengers threatening his safety. Diana signed up to help with what had been described as a “moving job” on TaskRabbit. When she arrived, she had to decide whether it was safe for her to clean up what looked to her like medical waste.

Work is a powerful determinant of health. As these stories about taxi, care, and cleaning work from a new report show, it is a central organizing feature of our lives, our families, our neighborhoods, and our cities. And work—its schedules, demands, benefits, and pay—all formally and informally shape our opportunities to be healthy.

But the world of work is rapidly changing. Job instability and unpredictable earnings are a fact of life for millions. Regular schedules are disappearing. With “predictive scheduling,” a retail worker today is essentially on call, making everything from booking child care to getting a haircut impossible until the work schedule arrives. Health and other fringe benefits are less often tied to the job. Nearly six in ten low-wage workers today has no paid sick leave. Two-thirds lack access to employer-based health care benefits.

This $500K competition seeks proposals for studies that will further our understanding of mood and how it relates to daily life.

Are We The Source of Knowledge?

We started hearing about it a couple years ago: an ALS member of Patients Like Me had seen (and translated—it was in Italian) a medical conference poster with results showing lithium carbonate could slow the disease’s progression.  That study was a single-blind trial of 16 treated patients and 28 controls.  The results spread through the ALS community and soon, patients began talking their physicians into prescribing lithium carbonate off-label. PLM soon had 348 members reporting on the effects of their use of the drug.

 PLM realized they had an opportunity to study the experience of their members who were—effectively—experimenting with the drug.  PLM couldn’t randomize, so they developed an algorithm and matched 149 treated patients to 447 controls based on the progression of their disease course.

 On Sunday, the journal Nature Biotechnology published PLM’s findings showing after 12 months of treatment, lithium carbonate had no effect on disease progression.  PLM reports that subsequent clinical trials reached similar conclusions.

 What’s important here is to recognize the potential to conduct research using patient self-reported data from an online social community.  PLM’s sweet spot is social communities for ambiguous diseases (that is, diseases we’re still learning about, diseases that don’t have clear, effective treatment protocols) where the patient does a lot of care at home.  To be sure, PLM is a pretty sophisticated community, but it’s intriguing to think about where we might be in 10-15 years.

A couple of us met last week with PLM’s Jamie Heywood and Dave Clifford.  We had a ranging discussion—hard to avoid with Heywood—that included linking patient self-reported data with clinicians, conducting research with this data, and business models.  A fundamental question Heywood is exploring is “whether it’s faster to get to learning health system through the current confines of the health system or through something like PLM.”

Given the growing ability and inclination of patients to capture and share details on their own experiences, how powerful a role is there for the analysis of this sort of data in our efforts to accelerate the discovery of new treatments for disease?

This commentary originally appeared on the RWJF Pioneering Ideas blog.

The Innosight Institute, the non-profit think tank founded by Harvard B-School Professor Clayton Christensen, put on a conference last week called, A Forum on Disruptive Innovation in Healthcare.

Prof. Christensen developed the theory of disruptive innovation and is currently working on a book on the subject. One of his co-authors is Jason Hwang, MD, MBA, who served as a judge for the Disruptive Innovations competition Pioneer sponsored through Changemakers. Those of us at the meeting were treated to a glimpse of the still-being-drafted book, which was pretty interesting. I’m keen to read the final version.

Elliott Fisher, MD, of Dartmouth Atlas fame, set the stage for the forum by taking us on a flyover of "everything that’s wrong with health care in America." Fisher then presented seven causes, which I thought was a pretty succinct list:

1. There’s a lack of clarity in the US on the aim of health care;
2. There’s inadequate evidence to evaluate the effectiveness of both biologically-targeted interventions and delivery systems. Fisher asserted that the current discussions around comparative effectiveness were not paying nearly enough attention to the effectiveness of different types of delivery systems;
3. There’s a public assumption that more care is better care (Fisher has published results demonstrating that more care can actually lead to poorer outcomes);
4. Medicine is practiced (and taught) in a model of professional autonomy and authority that is outdated;
5. There’s a lack of accountability for capacity, quality and costs;
6. Current quality measures reinforce fragmentation, in that they’re too focused on performance within individual care settings and don’t track quality across the continuum of care; and
7. Payment incentives are flawed.

Wow.

Another big chunk of discussion focused on the development of more precise diagnostic tests, how they will drive the move to personalized medicine and disrupt the current paradigm of “trial and error medicine,” according to speaker Mara Aspinall, former president of Genzyme Genetics, which provides diagnostic services. As example of new precision, Aspinall noted that we can now diagnose 38 different types of leukemia and 50 different types of lymphoma. That increase in diagnostic precision tracks with the increase in five-year survival rates.

Looked at through the lens of Disruptive Innovation, what you see is a technological innovation—increased diagnostic precision—commoditizes expertise. The growing development and use of more precise diagnostics moves us closer to rules-based—and evidence-based—practice.

And the ability to use rules to guide activity—care—is an important pre-condition that permits a Disruptive Innovation. Once you have evidence-based rules that determine a course of action, you don’t need someone with the highest level of training to take that action, because you don’t need as much judgment and intuition. In the case of health care, this means less expensive caregivers can do more complicated things.

Then came Christensen’s discussion of how Disruptive Innovation can transform the health care system. Christensen’s take is that the network effects of the existing health care ecosystem (the relationships among hospitals, providers, plans) make it impossible for our current system to change sufficiently to solve the problems we have with health care today. I think he would assert that the current system simply can’t improve its way out of its current limitations, so the only way to fix the problems we face is through disruptive innovations.

The cool part came when Christensen reframed of the current business models in health care in such a way so as to identify opportunities for disruption. I don’t want to steal the thunder of the forth-coming book, so just when this blog is getting interesting, I need to stop. But suffice it to say, I plan to read this book carefully when it’s published.

This commentary originally appeared on the RWJF Pioneering Ideas blog.