JMIR Publications

ABSTRACT

Background:

Machine learning (ML) offers greater precision and sensitivity in predicting Medicare patient end of life (EOL) and potential need for palliative services. 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+ (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 positively predicted 41.5% of patient expirations among the top 1% of highest risk members (AUC=0.84; 95% CI: 0.83-0.85), versus 22.2% predicted by the model iteration based on age, gender, and select high-risk utilization features alone (AUC=0.73; 95% CI: 0.72-0.75). 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 members approaching EOL using a variety of routinely-collected data and can support earlier patient identification for palliative care.