The need for various social service referrals can be predicted with considerable accuracy using a wide range of readily available clinical and community data that measure socioeconomic and public health conditions. While the use of SDH did not result in significant performance improvements, our approach represents a novel and important application of risk predictive modeling.
Objective: This study evaluated the degree to which recommendations for demographic data standardization improve patient matching accuracy using real-world datasets. Methods: We used four manually reviewed datasets, containing a random selection of matches and non-matches. Matching datasets included Health Information Exchange (HIE) records, public health registry records, Social Security Death Master records, and newborn screening records. Standardized fields including last name (LN), telephone number (TEL), social security number (SSN), date of birth (DOB), and address (ADDR). Matching performance was evaluated using four 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, DOB, or SSN) showed no matching improvements. Since 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. Conclusion: 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.
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