Shaun J Grannis1,2, Huiping Xu1,3,4, Joshua R Vest1,5, Suranga Kasthurirathne1,6, Na Bo3, Ben Moscovitch7, Rita Torkzadeh7, Josh Rising7. 1. Regenstrief Institute, Inc, Center for Biomedical Informatics, Indianapolis, Indiana, USA. 2. School of Medicine, Department of Family Medicine, Indiana University, Indianapolis, Indiana, USA. 3. School of Medicine, Department of Biostatistics, Indiana University, Indianapolis, Indiana, USA. 4. Richard M. Fairbanks School of Public Health, Department of Biostatistics, Indiana University, Indianapolis, Indiana, USA. 5. Richard M. Fairbanks School of Public Health, Department of Health Policy and Management, Indiana University, Indianapolis, Indiana, USA. 6. School of Informatics and Computing, Department of BioHealth Informatics, Indiana University, Indianapolis, Indiana, USA. 7. The Pew Charitable Trusts, Washington DC, USA.
Abstract
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.
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.
Authors: Susana Martinez Diaz; Naeem Bhojani; Dean Elterman; Kevin Zorn; Steven A Kaplan; Tobias S Kohler; Lori B Lerner; Kevin T McVary; Matthew P Rutman; Charles Welliver; Alexis E Te; Art Sedrakyan; Bilal Chughtai Journal: World J Urol Date: 2022-10-11 Impact factor: 3.661
Authors: Claudia Medina Coeli; Valeria Saraceni; Paulo Mota Medeiros; Helena Pereira da Silva Santos; Luis Carlos Torres Guillen; Luís Guilherme Santos Buteri Alves; Thomas Hone; Christopher Millett; Anete Trajman; Betina Durovni Journal: BMC Med Inform Decis Mak Date: 2021-06-15 Impact factor: 2.796