Artificial intelligence (A. I.) will inevitably converge with medicine, yet how and where it will yield the greatest impact remains to be seen. If wielded wisely, machine learning and other related forms of A.I. may prove to be the lifeline health care needs.
While the practice of medicine is an art and its history is storied, in today’s modern practice we permit ill-conceived electronic health records (EHRs) primarily designed for billing to enter patient rooms and detract from the doctor-patient relationship. Weighing available data stored in the EHR paints the picture of the patient, but reliance on the screen and the infinite clicks it demands creates nothing more than the illusion of patient-centered care at the expense of truly attentive, human-centered care.
By failing to tailor our plans by listening to the expectations, thoughts, and feelings of the patient in front of us, we practice cookie cutter medicine where one size fits no one. Continuing down this path will only lead to the further dehumanization of both doctor and patient. As Eric Topol describes in his new book, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, we are in the midst of a Fourth Industrial Age wherein Big Data, A.I., and robotics may be able to revolutionize health care inefficiencies, provide custom care, and maximize the latest evidence to guide treatment.
First described in 1956, A.I. has quickly become a reality with the ubiquity of advanced computing power and the newfound availability to collect and store vast quantities of data, colloquially known as Big Data. In studying this data, sophisticated algorithms may be created and improved upon to recognize patterns that aid in diagnosis or projection of value metrics.
The practice of medicine has simultaneously transformed into a value-based industry focused on the best possible patient experience at the lowest possible price point. Orthopaedic surgery, specifically the field of lower extremity arthroplasty wherein diseased joints are replaced with artificial ones made of metal and plastic, is ideally poised to evaluate the impact of A.I. on the rest of medicine.
First and foremost, joint replacement is usually elective surgery. The patient with end-stage arthritis on x-ray may be referred to a specialist for joint replacement, but the decision to operate requires shared decision-making that involves careful consideration tailored to the patient’s functional demands, medical status, quality of life, and expectations. A.I. boasts the ability to detect such nuance and anticipate the future with enough high quality patient data, which may lend to a sophisticated algorithm that predicts the risk of eventually undergoing a joint replacement, the cost and length of staying during admission, or even their post-operative recovery trajectory.
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