Love them or hate them, computers are becoming more ingrained in 21st century medical care. And in an increasingly data-driven industry, medical education hasn’t kept pace.
Physicians might curse their computers for sucking time away from patients or turning them into “data entry clerks,” but computers aren’t to blame, according to two health policy researchers from Harvard Medical School. As algorithms gradually outperform the human mind, clinicians need to place more emphasis on data science to get the most out of advanced analytics and machine learning that could have a significant impact on medical care care.
“Today’s medical education system is ill-prepared to meet these needs,” Ziad Obermeyer, M.D., and Thomas H. Lee, M.D., who also serves as chief medical officer at Press Ganey, wrote in the New England Journal of Medicine. “Undergraduate premedical requirements are absurdly outdated. Medical education does little to train doctors in the data science, statistics, or behavioral science required to develop, evaluate, and apply algorithms in clinical practice.”
The authors say current medical complexities outmatch even the brightest human minds, but effective use of technology will allow clinicians to comb through troves of data to predict deadly illness like stroke and cardiac disease.
But that’s only possible if medical teams adopt new members: Clinicians well-versed in computer science that can interpret analytics designed to “systematically analyze every heartbeat” that can treat “tens of thousands of Americans who might otherwise drop dead unexpectedly in any given year.”
An inaugural Health Trends Report released by Stanford Medicine earlier this year touched on similar concerns, advocating for physicians to have more training on interpreting data, which will become a core function of their job.
That’s not to say Obermeyer and Lee see technology as a cure-all for healthcare. In fact, in an interview with NEJM, Obermeyer, who is also an emergency physician at Brigham and Women's Hospital, acknowledged that although algorithms are promising, there are numerous complexities, including the valid concern that developers could build in existing bias buried in EHRs.
He added that even if the industry could overcome significant hurdles associated with integrating patient data, there is currently no “killer app that would change the way doctors diagnose treat or prognosticate.” And while developers and computer scientists are often frustrated with the industry’s slow adoption of technology, validated clinical trials are a critical part of preventing the use of potentially harmful tools.
Which is why updating medical education is a necessary first step.
“The sooner that can happen the sooner we’ll be able to produce the kinds of people who can really be at the forefront of developing algorithms and doing that in a nuanced clinical way,” he told NEJM.