Predicting Patient Mortality for Earlier Palliative Care Identification in Medicare Advantage Plans: Features of a Machine Learning Model

Abstract

Background: Machine learning (ML) can offer greater precision and sensitivity in predicting Medicare patient end of life and potential need for palliative services compared to provider recommendations alone. However, earlier ML research on older community dwelling Medicare beneficiaries has provided insufficient exploration of key model feature impacts and the role of the social determinants of health.

Objective: This study describes the development of a binary classification ML model predicting 1-year mortality among Medicare Advantage plan members aged ≥65 years (N=318,774) and further examines the top features of the predictive model.

Methods: A light gradient-boosted trees model configuration was selected based on 5-fold cross-validation. The model was trained with 80% of cases (n=255,020) using randomized feature generation periods, with 20% (n=63,754) reserved as a holdout for validation. The final algorithm used 907 feature inputs extracted primarily from claims and administrative data capturing patient diagnoses, service utilization, demographics, and census tract-based social determinants index measures.

Results: The total sample had an actual mortality prevalence of 3.9% in the 2018 outcome period. The final model correctly predicted 44.2% of patient expirations among the top 1% of highest risk members (AUC=0.84; 95% CI 0.83-0.85) versus 24.0% predicted by the model iteration using only age, gender, and select high-risk utilization features (AUC=0.74; 95% CI 0.73-0.74). The most important algorithm features included patient demographics, diagnoses, pharmacy utilization, mean costs, and certain social determinants of health.

Conclusions: The final ML model better predicts Medicare Advantage member end of life using a variety of routinely collected data and supports earlier patient identification for palliative care.

Keywords: Medicare; Medicare Advantage; algorithm; machine learning; mortality; older adult; palliative; palliative care; predict; social determinants.

Figure 1

Comparison of Model 1 (M1), Model 2 (M2), and Model 3 (M3) using (A) receiver operating characteristic curves and (B) precision recall curves. AP: average precision; AUC: area under the receiver operating characteristic curve.

Figure 2

Model mortality outcomes for patients in the top decile of the highest predicted risk. M1: Model 1; M2: Model 2; M3: Model 3.

Figure 3

Absolute feature importance in Model 3 (M3). CT: computed tomography; DEM: demographics; DNX: diagnoses; LAB: laboratory utilization; MED: medical utilization; PHA: pharmacy utilization; SDI: social determinants index.