Predicting potential palliative care beneficiaries for health plans: A generalized machine learning pipeline

Recognizing that palliative care improves the care quality and reduces the healthcare costs for individuals in their end of life, health plan providers strive to better enroll the appropriate target population for palliative care. Current research has not adequately addressed challenges related to proactively select potential palliative care beneficiaries from a population health perspective. This study presents a Generalized Machine Learning Pipeline (GMLP) to predict palliative needs in patients using administrative claims data. The GMLP has five steps: data cohort creation, feature engineering, predictive modeling, scoring beneficiaries, and model maintenance. It encapsulates principles of population health management, business domain knowledge, and machine learning (ML) process knowledge with an innovative data pull strategy. The GMLP was applied in a regional health plan using a data cohort of 17,197 patients. Multiple ML models were turned and evaluated against a custom performance metric based on the business requirement. The best model was an AdaBoost model with a precision of 71.43% and a recall of 67.98%. The post-implementation evaluation of the GMLP showed that it increased the recall of high mortality risk patients, improved their quality of life, and reduced the overall cost. The GMLP is a novel approach that can be applied agnostically to the data and specific ML algorithms. To the best of our knowledge, it is the first attempt to continuously score palliative care beneficiaries using administrative data. The GMLP and its use case example presented in the paper can serve as a methodological guide for different health plans and healthcare policymakers to apply ML in solving real-world clinical challenges, such as palliative care management and other similar risk-stratified care management workflows.