Evaluation of California Non-Comprehensive Death File Against National Death Index.

The National Death Index (NDI) by the Centers for Disease Control and Prevention and Death Master File (DMF) by Social Security Administration are the two most broadly utilized data files for mortality outcomes in clinical research. NDI's high costs and the elimination of protected death records from California in DMF calls for alternative death files. The recently emerged California Non-Comprehensive Death File (CNDF) serves as an alternative source for vital statistics. This study aims to evaluate the sensitivity and specificity of CNDF compared to NDI. Of 40,724 consented subjects in the Cedars-Sinai Cardiac Imaging Research Registry, 25,836 eligible subjects were queried through the NDI and the CDNF. After exclusion of death records to establish the same temporal and geographic availability of data, NDI identified 5,707 exact matches, while CNDF identified 6,051 death records. CNDF had a sensitivity of 94.3% and specificity of 96.4% compared to NDI exact matches. NDI also produced 581 close matches: all were verified as deaths by CNDF through matching death date and patient identifiers. Combining all NDI death records, CNDF had a sensitivity of 94.8% and specificity of 99.5%. CNDF is a reliable source for obtaining mortality outcomes and providing additional mortality validation. The use of CNDF can aid and replace the use of NDI in the state of California.

Accuracy of an Electronic Health Record Patient Linkage Module Evaluated between Neighboring Academic Health Care Centers.

BACKGROUND: Patients often seek medical treatment among different health care organizations, which can lead to redundant tests and treatments. One electronic health record (EHR) platform, Epic Systems, uses a patient linkage tool called Care Everywhere (CE), to match patients across institutions. To the extent that such linkages accurately identify shared patients across organizations, they would hold potential for improving care. OBJECTIVE: This study aimed to understand how accurate the CE tool with default settings is to identify identical patients between two neighboring academic health care systems in Southern California, The University of California Los Angeles (UCLA) and Cedars-Sinai Medical Center. METHODS: We studied CE patient linkage queries received at UCLA from Cedars-Sinai between November 1, 2016, and April 30, 2017. We constructed datasets comprised of linkages ("successful" queries), as well as nonlinkages ("unsuccessful" queries) during this time period. To identify false positive linkages, we screened the "successful" linkages for potential errors and then manually reviewed all that screened positive. To identify false-negative linkages, we applied our own patient matching algorithm to the "unsuccessful" queries and then manually reviewed a sample to identify missed patient linkages. RESULTS: During the 6-month study period, Cedars-Sinai attempted to link 181,567 unique patient identities to records at UCLA. CE made 22,923 "successful" linkages and returned 158,644 "unsuccessful" queries among these patients. Manual review of the screened "successful" linkages between the two institutions determined there were no false positives. Manual review of a sample of the "unsuccessful" queries (n = 623), demonstrated an extrapolated false-negative rate of 2.97% (95% confidence interval [CI]: 1.6-4.4%). CONCLUSION: We found that CE provided very reliable patient matching across institutions. The system missed a few linkages, but the false-negative rate was low and there were no false-positive matches over 6 months of use between two nearby institutions.