Open AccessA process to deduplicate individuals for regional chronic disease prevalence estimates using a distributed data network of electronic health records
Author(s) -
Scott Kenneth A.,
Davies Sara Deakyne,
Zucker Rachel,
Ong Toan,
Kraus Emily McCormick,
Kahn Michael G,
Bondy Jessica,
Daley Matt F.,
Horle Kate,
Bacon Emily,
Schilling Lisa,
Crume Tessa,
HasnainWynia Romana,
Foldy Seth,
Budney Gregory,
Davidson Arthur J.
Publication year - 2022
Publication title -
learning health systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.501
H-Index - 9
ISSN - 2379-6146
DOI - 10.1002/lrh2.10297
Subject(s) - concordance , data deduplication , generalizability theory , disease , computer science , medicine , statistics , biology , bioinformatics , mathematics , database , pathology
Learning health systems can help estimate chronic disease prevalence through distributed data networks (DDNs). Concerns remain about bias introduced to DDN prevalence estimates when individuals seeking care across systems are counted multiple times. This paper describes a process to deduplicate individuals for DDN prevalence estimates. Methods We operationalized a two‐step deduplication process, leveraging health information exchange (HIE)‐assigned network identifiers, within the Colorado Health Observation Regional Data Service (CHORDS) DDN. We generated prevalence estimates for type 1 and type 2 diabetes among pediatric patients (0‐17 years) with at least one 2017 encounter in one of two geographically‐proximate DDN partners. We assessed the extent of cross‐system duplication and its effect on prevalence estimates. Results We identified 218 437 unique pediatric patients seen across systems during 2017, including 7628 (3.5%) seen in both. We found no measurable difference in prevalence after deduplication. The number of cases we identified differed slightly by data reconciliation strategy. Concordance of linked patients' demographic attributes varied by attribute. Conclusions We implemented an HIE‐dependent, extensible process that deduplicates individuals for less biased prevalence estimates in a DDN. Our null pilot findings have limited generalizability. Overlap was small and likely insufficient to influence prevalence estimates. Other factors, including the number and size of partners, the matching algorithm, and the electronic phenotype may influence the degree of deduplication bias. Additional use cases may help improve understanding of duplication bias and reveal other principles and insights. This study informed how DDNs could support learning health systems' response to public health challenges and improve regional health.