Assess algorithms for linking patients across de‐identified databases without compromising confidentiality.
Data Sources/Study Setting
Hospital discharges from 11 Mayo Clinic hospitals during January 2008–September 2012 (assessment and validation data). Minnesota death certificates and hospital discharges from 2009 to 2012 for entire state (application data).
Cross‐sectional assessment of sensitivity and positive predictive value () for four linking algorithms tested by identifying readmissions and posthospital mortality on the assessment data with application to statewide data.
Data Collection/Extraction Methods
De‐identified claims included patient gender, birthdate, and zip code. Assessment records were matched with institutional sources containing unique identifiers and the last four digits of Social Security number (4).
Gender, birthdate, and five‐digit zip code identified readmissions with a sensitivity of 98.0 percent and a of 97.7 percent and identified postdischarge mortality with 84.4 percent sensitivity and 98.9 percent . Inclusion of 4 produced nearly perfect identification of readmissions and deaths. When applied statewide, regions bordering states with unavailable hospital discharge data had lower rates.
Addition of 4 to administrative data, accompanied by appropriate data use and data release policies, can enable trusted repositories to link data with nearly perfect accuracy without compromising patient confidentiality. States maintaining centralized de‐identified databases should add 4 to data specifications.
