Unbiased Analysis of Today's Healthcare Issues

Machine learning and physician employement

Written By: Jason Shafrin - Dec• 06•16

An article by Ajay Agrawal and Avi Goldfarb (“The Simple Economics of Machine Intelligence“) provide an interesting perspective on how machine learning will affect employment with a nice example from the health care sector.

All human activities can be described by five high-level components: data, prediction, judgment, action, and outcomes. For example, a visit to the doctor in response to pain leads to: 1) x-rays, blood tests, monitoring (data), 2) diagnosis of the problem, such as “if we administer treatment A, then we predict outcome X, but if we administer treatment B, then we predict outcome Y” (prediction), 3) weighing options: “given your age, lifestyle, and family status, I think you might be best with treatment A; let’s discuss how you feel about the risks and side effects” (judgment); 4) administering treatment A (action), and 5) full recovery with minor side effects (outcome).

As machine intelligence improves, the value of human prediction skills will decrease because machine prediction will provide a cheaper and better substitute for human prediction, just as machines did for arithmetic. However, this does not spell doom for human jobs, as many experts suggest. That’s because the value of human judgment skills will increase. Using the language of economics, judgment is a complement to prediction and therefore when the cost of prediction falls demand for judgment rises. We’ll want more human judgment.

How will machine learning affect physician employment?  The answer is unclear.  The value of physicians for prediction purposes will drop.  Why ask a doctor what drug works best when you can ask Watson.  However, the value of physicians for judgement will expand.  How do physicians take into account different patient preferences?  Can physicians identify patient-specific idiosyncratic factors not readily apparent in prediction models that make one treatment better suited for a given patient than another.

One option is that physicians will become more of a luxury good and low-cost, Watson prescribing could be used for less complex patient cases with more straightforward recommendations.  Physicians could be used more for more complex cases and also for higher income individuals or less tech savvy individuals who do not want to or are not able to interact with a machine learning technology.

Nurse employment may become more valuable because all the treatment still need to be administered, likely by a person and not a machine.

In short, machine learning is likely to provide huge benefits for patients, but the effect on who will deliver care is unknown.

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