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Theory vs Practice

TheoryVPractice

A lot of ink has been spilt over the last 10+ years on the potential impact AI will have in healthcare.  In fact, it has been over a decade since it was reported that a prominent venture capitalist claimed that “Machines will replace 80% of doctors” because of AI. It turned out what he meant was “80% of what a doctor does can eventually be done by machines.” In either case, the point was AI was going to have an enormous impact on how physicians practice medicine. On the other hand, since 1995 when the Food and Drug Administration (FDA) approved the first AI algorithm to detect abnormal cells in pap-smear tests, the FDA has approved over 500 AI algorithms for clinical use. However, despite all theoretical predictions of the profession of radiology's impending demise because most approved algorithms are in radiology, this has not occurred. I should know because an interventional radiologist saved my life, and there is no way any AI algorithm could have done what he did. Period. I believe we can all agree on one point: AI advances in medicine over the last decade have not had the impact on medical practice that some predicted. There are two primary reasons for this:

1. The change management that healthcare provider organizations must engage in to successfully implement and benefit from AI is poorly understood. It is not as simple as saying, “Let me use this AI algorithm so I can fire physicians and become more efficient.”

2. There is much more to a physician's job than simply examining an image and diagnosing a disease; it requires a great deal of additional skills and the human touch.

That is not to say that AI cannot impact the practice of healthcare. However, to have that impact, we need to take a fundamentally multi-disciplinary approach, that accounts for the perspectives of clinical and non-clinical people in healthcare, businesspeople building companies to solve these problems economically, technologists building modern technologies and solutions, and possibly others. That is what we hope to explore in ThetaRho’s blog as we navigate this multi-dimensional universe. Let’s begin.

Macro Problem to Micro Problem

At the macroeconomic level, the problem with healthcare starts with two simple statistics.

You own your healthcare data.

You should be able to use it as you like, with AI you trust. Let's start the discussion.

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