ArticleJournal of the American Medical Informatics Association : JAMIA2026
Accuracy of an XGBoost-based privacy preserving record linkage system compared with an electronic health record patient matching module in identifying patients shared between nearby academic health centers.
Article in Journal of the American Medical Informatics Association : JAMIA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesPatients often receive health care from multiple organizations. Privacy Preserving Record Linkage (PPRL) is a technology for linking patient records without releasing personally identifiable information. We compared a commercial PPRL tool that uses the XGBoost machine learning algorithm with Care Everywhere (CE), a widely used rule-based patient linkage module. MATERIALS AND
methodsWe matched the complete patient populations from Cedars-Sinai Health System and University of California, Los Angeles (UCLA) Health using the XGBoost PPRL tool at each of 3 score thresholds (98, 95, and 90), reflecting stricter vs more permissive matching. We compared PPRL matches with CE matches for the cohort of 849 157 patients who had been queried by CE from UCLA to Cedars-Sinai over 18 months. To classify proposed matches as false, uncertain or correct matches, 2 reviewers manually reviewed a random sample of 1200 patients representing each category of matches.
resultsCare Everywhere matched 18% of the cohort, whereas PPRL matched 9%, 27%, and 29% of the cohort using the 98, 95, and 90 thresholds, respectively. Projecting the false match rates from the manual review to the original populations, precision for CE was 99.6% (95% CI, 97.8%-100%). Precision for PPRL was 100% (95% CI, 99.2%-100%), 99.4% (95% CI, 97.4%-99.9%), and 98.7% (95% CI, 96.5%-99.4%) at the 3 thresholds, respectively. Using CE and PPRL matches together as a proxy gold standard, recall for CE was 61.5% (95% CI, 60.3%-61.9%) and for PPRL was 30.6% (95% CI, 30.3%-30.7%), 92.2% (95% CI, 90.2%-92.7%), and 96.8% (95% CI, 94.6%-97.5%) at each threshold, respectively.
conclusionsThe precision and recall of PPRL matching differed substantially across the available match thresholds. Compared with the rule-based system, PPRL at the 95 threshold had 50% higher recall with similar precision. Privacy Preserving Record Linkage holds promise for improving research, but users must choose the precision vs recall needed for their application.
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