FEATURE
GUIDING THE FUTURE OF ARTIFICIAL INTELLIGENCE Real Intelligence and Early Action Needed by Physicians
A
Colin G. DeLong, MD, Penn State Hershey Department of Surgery
s a general surgery resident, I had not put much thought into the prospect of artificial intelligence (AI) and its potential impact on my training and career until one particular patient phone call. “Hello, this is Dr. DeLong. How can I help you?” An exasperated voice answered back. “I’m calling to cancel my hernia surgery. The reminder I received mentioned robotic surgery. I will NOT allow a robot to do my surgery. I’ll find a real surgeon!” He hung up the phone, leaving me stunned. If this innovative—yet entirely human-driven—surgical technology could cause such distrust, I shuddered to imagine what additional challenges patients and physicians alike will face with the increasing role of AI in modern healthcare. The concept of AI, admittedly, can be difficult to grasp. How we define AI and what does—and does not—constitute AI technology can be conceptually nebulous questions to answer. Historical perspective can help to bring clarity by framing the current state of AI development within a broader timeline of computerized healthcare innovations. The early 1970s brought about the first attempts to develop and implement electronic health records (EHR) and it is interesting to note that while the term “artificial intelligence” was yet to be popularized, these actions set in motion substantial, lasting changes in healthcare delivery—the incorporation of computerized systems into physicians’ daily practice. An early paper describing EHRs by McDonald et al, published in the American Journal of Public Health in 1977, described an “automatic 4
BUCKS/MONTGOMERY PHYSICIAN
fall 2021
physician guidance” system1. Designed to warn against drug interactions and prompt physicians to schedule proper followup tests, the authors optimistically touted that “a computer system such as ours will significantly reduce the physician’s error rate and his time investment in clinical bookkeeping.” Fast-forward forty years and enthusiasm for EHRs has withered into frustration and cynicism. In the now-famous 2019 article, “Death by 1,000 Clicks: Where Electronic Health Records Went Wrong”, authors Fred Schulte and Erika Fry lament how “electronic health records were supposed to […] make medicine safer, bring higher-quality care, empower patients, and […] save money” yet have become a “tragic missed opportunity”2. With over $36 billion invested by the U.S. government alone in the last decade, the system has become an “unholy mess”—despised by providers and dangerous for patients. And worst of all, the system is—in all practicality—unfixable. In four decades, the most publicized and promising advancement in modern healthcare delivery has failed so disastrously as to be considered unsalvageable. But how did we get here? The answer to this question reveals insight into how similar mistakes can be avoided in the development of AI. Novel technology—like a snowball rolling downhill—can start so innocently small yet gather size and momentum so swiftly that it overpowers any attempt to modify its course. Driven by healthcare administrators, private industry, and other nonphysicians, the EHR haphazardly consumed everything in its path (billing, legal documentation, scheduling, prescription services) before most physicians
