Paediatric and perinatal epidemiology · 2025
Linkage of Electronic Health Record Data Across Two Healthcare Systems for Perinatal Health Research: A Privacy-Preserving Approach.
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Ehresmann Kirsten, Smith Claire, Vazquez-Benitez Gabriela, Seburg Elisabeth M, DeFor Terese A, Farah Asha, Sidebottom Abbey, Palmsten Kristin
Аннотация
BACKGROUND: In the United States, birthing parent-infant dyads may receive care from multiple healthcare systems. Linkage of an individual's electronic health records (EHR) across healthcare systems, in addition to birthing parent-infant linkage, may be necessary to obtain appropriate clinical data for perinatal health research.
OBJECTIVES: To develop a privacy-preserving process to link the health records of patients shared by two health systems for a perinatal health study, and to assess data enhancements associated with the linkage.
METHODS: We included pregnant patients who received care from at least one of two healthcare systems based in Minnesota, USA and their infants born between December 2020 and September 2022 who had at least one well visit. We identified infants from one health system with birthing parents who potentially received care in the second health system based on the infant's delivery hospital. We implemented a one-way matching process using an algorithm to generate unique hash values for each record at each health system. Specifically, we used four hash ID rules based on six identifiers available in the EHR at both sites plus a consistent salt.
RESULTS: One health system identified 3524 infants with birthing parents who potentially received care in the second system. The second system identified 39,321 infants delivered at the hospitals of interest during the study period. The algorithm matched 3406 (96.7%) infant records. After applying the study eligibility criteria, the birthing-parent records gained through hash matching increased the study population by 7.2% from 8100 to 8686. Overall, 13.6% of the study population had data from the second health system. Some demographic and pregnancy characteristics differed from those with data from the first system only.
CONCLUSIONS: The hash matching approach can increase study size, patient diversity, and data completeness in a privacy-preserving manner for perinatal health studies among patients that use multiple healthcare systems.
OBJECTIVES: To develop a privacy-preserving process to link the health records of patients shared by two health systems for a perinatal health study, and to assess data enhancements associated with the linkage.
METHODS: We included pregnant patients who received care from at least one of two healthcare systems based in Minnesota, USA and their infants born between December 2020 and September 2022 who had at least one well visit. We identified infants from one health system with birthing parents who potentially received care in the second health system based on the infant's delivery hospital. We implemented a one-way matching process using an algorithm to generate unique hash values for each record at each health system. Specifically, we used four hash ID rules based on six identifiers available in the EHR at both sites plus a consistent salt.
RESULTS: One health system identified 3524 infants with birthing parents who potentially received care in the second system. The second system identified 39,321 infants delivered at the hospitals of interest during the study period. The algorithm matched 3406 (96.7%) infant records. After applying the study eligibility criteria, the birthing-parent records gained through hash matching increased the study population by 7.2% from 8100 to 8686. Overall, 13.6% of the study population had data from the second health system. Some demographic and pregnancy characteristics differed from those with data from the first system only.
CONCLUSIONS: The hash matching approach can increase study size, patient diversity, and data completeness in a privacy-preserving manner for perinatal health studies among patients that use multiple healthcare systems.