Adding value to clinical data by linkage to a public death registry.
We describe the methodology and impact of merging detailed statewide mortality data into the master patient index tables of the clinical data repository (CDR) of the University of Virginia Health System (UVAHS). We employ three broadly inclusive linkage passes (designed to result in large numbers of false positives) to match the patients in the CDR to those in the statewide files using the following criteria: a) Social Security Number; b) Patient Last Name and Birth Date; c) Patient Last Name and Patient First Name. The results from these initial matches are refined by calculation and assignment of a total score comprised of partial scores depending on the quality of matching between the various identifiers. In order to validate our scoring algorithm, we used those patients known to have died at UVAHS over the eight year period as an internal control. We conclude that we are able to update our CDR with 97% of the deaths from the state source using this scheme. We illustrate the potential of the resulting system to assist caregivers in identification of at-risk patient groups by description of those patients in the CDR who were found to have committed suicide. We suggest that our approach represents an efficient and inexpensive way to enrich hospital data with important outcomes information.
Pates
R D
RD
Department of Health Evaluation Sciences, University of Virginia Medical School, Charlottesville, Virginia 22908, USA. Rpates@virginia.edu
Scully
K W
KW
Einbinder
J S
JS
Merkel
R L
RL
Stukenborg
G J
GJ
Spraggins
T A
TA
Reynolds
C
C
Hyman
R
R
Dembling
B P
BP
D003644:Death Certificates; D006751:Hospital Information Systems / Q000458:organization & administration*; D017052:Hospital Mortality; D006801:Humans; D008498:Medical Record Linkage / Q000379:methods*; D016347:Medical Records Systems, Computerized / Q000458:organization & administration; D012042:Registries; D013405:Suicide / Q000706:statistics & numerical data; D014768:Virginia / Q000453:epidemiology