Using Machine Learning to Plan Rehabilitation for Home Care Clients: Beyond "Black-Box" Predictions

Resistance to adopting machine-learning algorithms in clinical practice may be due to a perception that these are “black-box” techniques and incompatible with decision-making based on evidence and clinical experience. We believe this resistance is unfortunate, given the increasing availability of large databases containing assessment information that could benefit from machine-learning and data-mining techniques, thereby providing a new and important source of evidence upon which to base clinical decisions. We have focused our investigation on the clinical applications of machine-learning algorithms on older persons in a home care rehabilitation setting. Data for this research were obtained from standardized client assessments using the comprehensive RAI-Home Care (RAI-HC) assessment instrument. Our work has shown that machine-learning algorithms can produce better decisions than standard clinical protocols. More importantly, we have shown that machine-learning algorithms can do much more than make “black-box” predictions; they can generate important new clinical and scientific insights. These insights can be used to make better decisions about treatment plans for patients and about resource allocation for healthcare services, resulting in better outcomes for patients, and in a more efficient and effective healthcare system.