Recovering missing electronic health record mortality data with a machine learning-enhanced data linkage process.

Objective: To develop a continual process for linking more comprehensive external mortality data to electronic health records (EHRs) for a large healthcare system, which can serve as a template for other healthcare systems.

Materials and methods: Monthly updates of state death records were arranged, and an automated pipeline was developed to identify matches with patients in the EHR. A machine learning classifier was used to closely match human classification performance of potential record matches.

Results: The automated linkage process achieved high performance in classifying potential record matches, with a sensitivity of 99.3% and specificity of 98.8% relative to manual classification. Only 22.4% of identified patient deaths were previously indicated in the EHR.

Discussion and conclusions: We developed a solution for recovering missing mortality data for EHR that is effective, scalable for cost and computation, and sustainable over time. These recovered mortality data now supplement the EHR data available for research purposes.