Abstract
Background There is a need for flexible, accurate record-linkage systems with transparent rules that work across diverse populations. Objectives We developed rules responsive to challenges in linking records for an urban safety-net health system; we calculated performance characteristics for our algorithm. Methods We evaluated encounters during January 1, 2012 through September 30, 2018. We compared our algorithm, using name (first-last), date-of-birth (DOB), and last four of social security number to our electronic health record (EHR) system's reconciliation process. We applied our algorithm to unreconciled real-time Admission-Discharge-Transfer registration data, and compared match results to reconciled identities from our enterprise data warehouse. We manually validated matches for randomly sampled discordant pairs; we calculated sensitivity/specificity. We evaluated predictors of discordance, including census tract information. Results Of 771,477 unique medical record numbers, most (95%) were concordant between systems; a substantial minority (5%) was discordant. Of 38,993 discordant pairs, most (n = 36,539; 94%) were detected by our local algorithm. The sensitivity of our algorithm was higher than the EHR process (99% vs. 81%), but with lower specificity (98.6% vs. 99.9%). Our highest-yield rules, beyond full first and last name plus complete DOB match, were first three initials of first name, transposed first-last names, and DOB offsets (+1 and +365 days). Factors associated with discordance were homelessness (adjusted odds ratio [aOR] = 2.4; 95% confidence interval [CI], 2.2–2.6) and living in a census tract with high levels of poverty (aOR = 1.4; 95% CI, 1.3–1.4). Conclusion Our algorithm had superior sensitivity compared to our EHR process. Homelessness and poverty were associated with unmatched records. Improved sensitivity was attributable to several critical input-variable processing steps useful for similar difficult-to-link populations.
Highlights
There is a need for flexible, accurate record-linkage systems with transparent rules that work across diverse populations
For most unique MRNs (78%), neither our local rules matching system nor the electronic health record (EHR) reconciliation process identified individuals who had been assigned more than one MRN; a substantial minority had multiple MRNs reconciled to a single person ID by both processes, that is, concordant matches (►Fig. 2)
Since we implemented this rule after the validation sample selection, the hyphenated name rule is not included in other results
Summary
There is a need for flexible, accurate record-linkage systems with transparent rules that work across diverse populations. Opportunities to monitor and improve the health of populations through record linkage across clinical, community, government agencies, and public health domains are abundant, but largely unrealized. E64 Record Linkage Rules in a Safety-Net Health System Trick et al. Linking data sets poses several of the following well-documented challenges: [1] privacy concerns, with large-volume aggregated data sets2; [2] accuracy in the context of a limited number of input variables, dynamic variables (e.g., name changes), and frequent data entry errors; and [3] technical challenges in linking records while retaining protected health information at local sites, among sites with limited technical proficiency. We report on the performance of these rules for our large, urban, safety net population
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