Augmenting fact and date of death in electronic health records using internet media sources: a validation study from two large healthcare systems
This study evaluated death ascertainment from publicly available internet sources for patients in two large tertiary care US healthcare systems, Mass General Brigham (MGB) and Vanderbilt University Medical Center (VUMC), benchmarked against state and federal vital statistics data. Names, dates of birth, and dates of death were extracted from 8.1 million internet media records using previously developed natural language processing models. Internet records were matched to 78 848 deceased patients from MGB and VUMC on first name, last name, and date of birth. Dates of death were validated against state vital statistics databases or the National Death Index as reference standards. We calculated sensitivity and positive predicted values (PPV) of internet sources in identifying dates of death within 7 days of the reference standard. Exact matching of records between internet media and reference standards on first name, last name, and date of birth, resulted in 30 067 (38.8%) matches, which showed PPV for death identification (98.2%-MGB; 98.9%-VUMC) in internet media and increased sensitivity of death capture over EHR alone by 24% at MGB and 18% at VUMC. In conclusion, using internet sources to augment mortality data increased capture of death meaningfully over reliance on EHR records alone.
Authors
- Mohammed Ali Al-Garadi (ORCID: https://orcid.org/0000-0002-6991-2687)
- Rishi Desai (ORCID: https://orcid.org/0000-0003-0299-7273)
- Michael E. Matheny (ORCID: https://orcid.org/0000-0003-3217-4147)
- Michele L. Lenoue-Newton (ORCID: https://orcid.org/0000-0003-3469-3784)
- Shirley Wang (ORCID: https://orcid.org/0000-0001-7761-7090)
- Candace C. Fuller (ORCID: https://orcid.org/0000-0002-8902-1435)
- Kueiyu Joshua Lin (ORCID: https://orcid.org/0000-0003-4564-3715)
- Ruth Reeves (ORCID: https://orcid.org/0000-0003-4260-2707)
- Melissa L McPheeters (ORCID: https://orcid.org/0000-0002-4423-797X)
- José J. Hernández‐Muñoz (ORCID: https://orcid.org/0000-0002-2553-3159)
- Daniel Park (ORCID: https://orcid.org/0000-0001-5191-1371)
- Xi Wang (ORCID: https://orcid.org/0000-0003-0260-1160)
- David Westerman (ORCID: https://orcid.org/0000-0002-9288-9817)
- Kerry Ngan
- Haritha S. Pillai
- Aida Kuzucan (ORCID: https://orcid.org/0000-0003-0893-7028)
Institutions
- Brigham and Women's Hospital (US)
- United States Food and Drug Administration (US)
- Center for Drug Evaluation and Research (US)
- Harvard University (US)
- RTI International (US)
- Harvard Pilgrim Health Care (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- American Journal of Epidemiology
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1093/aje/kwaf258
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00