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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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Published on: January 8, 2020

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

John P Powers¹, Samyuktha Nandhakumar¹, Sofia Z Dard¹

¹North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.

Journal of the American Medical Informatics Association : JAMIA

|April 15, 2025

Summary

This study created an automated system to link external mortality data with electronic health records (EHRs). The process effectively recovers missing patient death information, enhancing data for research.

Keywords:

death certificates electronic health records machine learning medical record linkage mortality

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Area of Science:

Health Informatics
Biomedical Data Science
Public Health Surveillance

Background:

Electronic health records (EHRs) often lack comprehensive mortality data.
Accurate mortality data is crucial for clinical research and public health.
Existing methods for updating mortality information in EHRs can be manual and inefficient.

Purpose of the Study:

To establish a scalable, automated, and sustainable process for integrating external mortality data into EHRs.
To create a template adaptable for use across different healthcare systems.
To improve the completeness of mortality information within EHRs for research.

Main Methods:

Developed an automated pipeline for monthly updates of state death records linked to EHR patient data.
Utilized a machine learning classifier to automate the matching of potential patient death records.
Achieved classification performance comparable to manual review.

Main Results:

The automated linkage demonstrated high accuracy with 99.3% sensitivity and 98.8% specificity.
A significant portion of patient deaths (77.6%) were not previously recorded in the EHR.
The system successfully recovered substantial amounts of previously missing mortality data.

Conclusions:

An effective, scalable, and sustainable solution was developed for recovering missing mortality data in EHRs.
The recovered data enhances the utility of EHRs for research purposes.
This automated approach offers a template for other healthcare systems to improve mortality data completeness.