Four Lessons In The Adoption Of Machine Learning In Health Care

The March issue of Health Affairs demonstrates the potential of health care delivery system innovation to improve value for both patients and clinicians. Technology innovations such as machine learning and artificial intelligence systems are promising breakthroughs to improve diagnostic accuracy, tailor treatments, and even eventually replace work performed by clinicians, especially that of radiologists and pathologists. Machine-learning systems infer patterns, relationships, and rules directly from large volumes of data in ways that can far exceed human cognitive capacities. As the computational underpinning of tools such as e-mail spam filters, product and content recommendations, targeted advertisements, and, more recently, autonomous vehicles, machine learning is already ubiquitous in many economic sectors. Yet, machine-learning applications are still used sparingly today in the delivery of care.

Electronic health records (EHR) systems, and the digitization of health data more broadly, have promised to transform health care to be more intelligent, safe, efficient, and cost-effective. While machine learning can be a key enabler of this promise, most EHR vendors do not provide robust machine learning, natural language processing, cognitive computing, and artificial intelligence solutions to process internally generated or imported health data, which come in a variety formats (for example, text, images, claims, genomics, and so forth). More general limitations of machine learning, such as the difficulty in interpreting results and describing to clinician users how algorithms arrive at particular outcomes, have further hindered adoption in health care.

While we believe machine learning holds great promise, it is far from clear how it will transform health and health care in the short to mid-term. Today, policy makers and industry executives face decisions about when and how to invest in machine learning to optimize organizational effectiveness and efficiency without wasting capital funds on premature or nonvalue-adding technologies. For the past several years, we have worked on multiple projects to build, test, and deploy machine-learning solutions to improve safety, quality, and patient outcomes, while reducing costs in health care. In our experience, machine-learning solutions demonstrate high potential for ancillary and support services in health care but often fail to deliver compelling impact for frontline clinicians. We share four key lessons about how to generate value today from EHR systems and machine-learning applications...