Enriching Data Can Impoverish Reality


Medical historian Stanley Reiser wasn’t kidding when he entitled his best-known book, “Medicine and the Reign of Technology.”  To a large degree, technology has taken over medicine.  I am not talking primarily about our increased reliance on such technologies as advanced imaging equipment or assistive procedural devices.  In such cases, the technology remains largely a tool, and the wielder remains basically in charge.  I am talking about a far more pervasive and insidious form of technology whose very name tells a good part of the tale – health information technology.

Many physicians and other health professionals find health information systems clunky, perverse, and intrusive, but their problems go far deeper.  Underneath unwieldiness lies the temptation that we begin relying on such indicators to such an extent that we stop attending to our internal resources.  Consider the case of the patient said in his admission note to be “status post BKA” – below the knee amputation – but who turns out on rounds to have ten toes.  What happened?  DKA – diabetic ketoacidosis was mis-transcribed into the medical record as BKA, and the error simply propagated like a virus.

At stake is what we mean by knowledge.  Is what we know defined by our own experience – what we have seen, heard, felt, and perhaps even intuited in the presence of the patient?  Or do we instead rely on what is represented on a computer screen?  Which is a more likely occasion for us to exclaim, “That can’t be right!” – when what the computer screen indicates does not comport with what we have observed of the patient, or when what we have observed in the presence of the patient does not jibe with what the computer is telling us? 

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Digital Transformation and the Health Care Biz: My (Somewhat Skeptical) Takeaways For HBS


I’m deeply skeptical that I have much knowledge to impart to Harvard Business School (HBS) students.  After all, they’re the ones clever enough to pursue a two year advanced degree (“six months of education crammed into two years,” they joke), while across town, my classmates and I ran gels, plated cells, memorized structures, and took call for a decade or more (in some cases) — and all for the privilege of eventually working for our fleece-vested colleagues (see also this 2011 Scott Gottlieb piece, and my 2012 Forbes post).

Even so, I was recently invited to appear as a guest on a new podcast out of HBS called “Under The Datascope,” where I answered questions about my experiences and perspective as a physician, scientist, technologist, drug developer, and investor. The episode (here),released today, is part of a series hosted by Gabriel Eichler and sponsored by the Kraft Precision Medicine Accelerator (Go Pats!) at HBS, featuring interviews with people working on and thinking about data, analytics, and precision medicine.

There’s a lot of content packed into the nineteen minute episode, and I thought it might make sense to capture some of the highlights – though I suspect the entire episode, and the series more generally, is likely to be of interest to readers.

Biomedical entrepreneurs drive science into durable application. After struggling during my clinical and research training with the persistent gap between promising science and clinical application, I came to appreciate that biomedical entrepreneurship represents the distilled essence of the translational impulse. (See this 2005 Nature Biotechnologycommentary, for example, this related version that was published in the San Francisco Chronicle, and this and thisfrom Forbes.)

Biomedical entrepreneurship requires humility and humanity, not tech fetishization and solutionism. Driving science into application requires not only the best (more precisely, the most suitable) technologies that are available, but also a deep sense of, and respect for, the complexities of biology and what I described as the “humanistic center of medicine and patient care.”  (Regular readers will recognize this as a recurrent theme of this column — e.g. this 2011 post, “What Silicon Valley Doesn’t Understand About Medicine”).

Good doctors have always customized care. The mantra of precision medicine – “right drug for the right patient at the right time” – is not a radical new idea, peculiar to the molecular age. Admirable doctors have long tried to individualize treatments based not only on the biology of disease, as best it could be understood, but also based on the physician’s knowledge of the patient’s circumstances and preferences. It’s also critically important not to be excessively reductionist, and to recognize a person isn’t just the sum of their molecular mutations; everyone exists in a much broader context. See hereand here as well.

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Government Psychiatric Bed Policy and Incarceration Rates


I read a paper recently (1) on psychiatric bed policy with a focus on OECD (Organisation for Economic Cooperation and Development) nations.  The OECD has extensive data collection on their member nations and one of the metrics they collect is the number of psychiatric beds per 100,000 inhabitants.  I have demonstrated some of this data before.  For the purpose of this post I downloaded it to create the two graphs above that were used in the paper. One of the authors main points was transinstitutionalization – in this case sending people with serious mental illnesses to jails rather than psychiatric hospitals.  They demonstrate the rough inverse correlation between psychiatric beds and the rate of incarceration.  Throughout my career available psychiatric beds has always been a problem.  It has been a favorite topic on this blog.  I was interested in whether or not this group of authors had anything new to say.

In their introductory section, they provide the back drop with the numbers.  The American state hospital psychiatric beds fell 97% from 558,922 in 1955 to 37,679 in 2016.  In Minnesota, the drop was about 98.5% from 11,449 in 1955 to 175 currently.  Using the OECD data, the average was about 99 beds per 100,000 population in 1998 to 71 per 100,000 in 2015.  Only Germany trended in the other direction by increasing the number of beds.

They do a fairly good job of analyzing the risks of the bed shortage.  They cite rehospitalizations, prolonged stay in emergency departments, pressure to discharge patients from inpatient setting, more frequent involuntary treatment, and associated staff burnout.  They make the argument that higher rates of suicide are noted in community treatment compared to hospitals where suicide is less likely.  They believe acute inpatient care is less available to the acutely suicidal patient and that may account for some increase in the suicide rate. Scandinavian registry studies are cited as providing some confirmatory data with one group of authors stating that the reduction in beds was the “most probable explanation for the rising mortality.”  A similar study in Finland where more community resources were available and the beds were at OECD averages described fewer suicides.

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