Source: Data Science Central | January 14, 2019 Author: William Vorhies Summary: Despite hundreds of projects and thousands of data scientists devoted to bringing AI/ML to healthcare, adoption remains low and slow.
A good portion of this problem is our own fault for failing to see the processes being disrupted through the eyes of the physician users.
Here we lay out the healthcare opportunity landscape but for data scientists following classical disruption strategies, it may be more of a minefield.
Part 2 of 3.
There are plenty of opportunities for good in applying AI/ML to healthcare.
But the track record of data scientists bringing these breakthroughs into the doctor’s office is poor.
Adoption is low and slow.
We reported in our first article in this three part series what we’d learned directly from clinicians and hospital CIO/administrators attending December’s AIMed conference.
First that only about 1% of all US hospitals have active data science programs.
That’s only about 50 out of the roughly 5,500 hospitals in the US (2018).
Second that while financial consideration play into this, slow adoption is laid squarely at the feet of we data scientists using our standard disruption strategies.
The lessons which will be spelled out in the third article in this series are that this is a unique culture with special needs, and one not particularly open to the idea of disruption.
The Big Picture In the first article we laid out the major segments of healthcare where AI/ML can have an impact and where it is succeeding now and can succeed in the near term.
Here we’ll offer more detail on these segments.
We organize these first around who pays, and then on the patient’s experience of the medical process.
Drug Discovery and Innovation Of all the AI/ML opportunities in healthcare this one is actually furthest along.
The primary reason is that it is big pharma who pays and is funded by capital markets, not by the payer/hospital/clinician/patient financial chain.
However, the payoff from these innovations may also be furthest in the future because of the risk of innovative research and the extreme costs and long approval times for new drugs.
While the press often covers advances in genetics and genomics as precursors to AI/ML assisted medical breakthroughs, closer in are the areas of personalized medicine and precision medicine.
The difference is evident in the names and includes using the patient’s own modified cells to combat disease (personalized) as well as creating drug combinations designed for the unique physiology and condition of the patient (precision).
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