Evaluating the effect of data standardization and validation on patient matching accuracy
| Autor: | Na Bo, Suranga N. Kasthurirathne, Joshua R. Vest, Ben Moscovitch, Rita Torkzadeh, Josh Rising, Huiping Xu, Shaun J. Grannis |
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| Rok vydání: | 2019 |
| Předmět: |
Matching (statistics)
Health Information Exchange Standardization Computer science Interoperability Datasets as Topic Health Informatics 030204 cardiovascular system & hematology Research and Applications computer.software_genre Social Security 03 medical and health sciences Neonatal Screening 0302 clinical medicine Telephone number Humans Registries 030212 general & internal medicine Data Management Demography Health Information Interoperability Infant Newborn Health information exchange United States Social Security number Master file Public Health Data mining computer Record linkage |
| Zdroj: | J Am Med Inform Assoc |
| ISSN: | 1527-974X |
| DOI: | 10.1093/jamia/ocy191 |
| Popis: | Objective This study evaluated the degree to which recommendations for demographic data standardization improve patient matching accuracy using real-world datasets. Materials and Methods We used 4 manually reviewed datasets, containing a random selection of matches and nonmatches. Matching datasets included health information exchange (HIE) records, public health registry records, Social Security Death Master File records, and newborn screening records. Standardized fields including last name, telephone number, social security number, date of birth, and address. Matching performance was evaluated using 4 metrics: sensitivity, specificity, positive predictive value, and accuracy. Results Standardizing address was independently associated with improved matching sensitivities for both the public health and HIE datasets of approximately 0.6% and 4.5%. Overall accuracy was unchanged for both datasets due to reduced match specificity. We observed no similar impact for address standardization in the death master file dataset. Standardizing last name yielded improved matching sensitivity of 0.6% for the HIE dataset, while overall accuracy remained the same due to a decrease in match specificity. We noted no similar impact for other datasets. Standardizing other individual fields (telephone, date of birth, or social security number) showed no matching improvements. As standardizing address and last name improved matching sensitivity, we examined the combined effect of address and last name standardization, which showed that standardization improved sensitivity from 81.3% to 91.6% for the HIE dataset. Conclusions Data standardization can improve match rates, thus ensuring that patients and clinicians have better data on which to make decisions to enhance care quality and safety. |
| Databáze: | OpenAIRE |
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