Enhancing PCORnet Clinical Research Network data completeness by integrating multistate insurance claims with electronic health records in a cloud environment aligned with CMS security and privacy requirements. (13th December 2021)

• Record Type: Journal Article
• Authors: Waitman, Lemuel R; Song, Xing; Walpitage, Dammika Lakmal; Connolly, Daniel C; Patel, Lav P; Liu, Mei; Schroeder, Mary C; VanWormer, Jeffrey J; Mosa, Abu Saleh; Anye, Ernest T; Davis, Ann M
• Is Part Of: Journal of the American Medical Informatics Association. Volume 29:Number 4(2022)
• Page Start: 660
• Page End: 670
• Publication Date: 2021-12-13
• Subjects: obesity -- electronic health records -- Centers for Medicare and Medicaid Services -- PCORnet -- Patient-Centered Outcomes Research Institute -- cloud computing -- Amazon Web Services private cloud; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocab269
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: The Greater Plains Collaborative (GPC) and other PCORnet Clinical Data Research Networks capture healthcare utilization within their health systems. Here, we describe a reusable environment (GPC Reusable Observable Unified Study Environment [GROUSE]) that integrates hospital and electronic health records (EHRs) data with state-wide Medicare and Medicaid claims and assess how claims and clinical data complement each other to identify obesity and related comorbidities in a patient sample. Materials and Methods: EHR, billing, and tumor registry data from 7 healthcare systems were integrated with Center for Medicare (2011–2016) and Medicaid (2011–2012) services insurance claims to create deidentified databases in Informatics for Integrating Biology & the Bedside and PCORnet Common Data Model formats. We describe technical details of how this federally compliant, cloud-based data environment was built. As a use case, trends in obesity rates for different age groups are reported, along with the relative contribution of claims and EHR data-to-data completeness and detecting common comorbidities. Results: GROUSE contained 73 billion observations from 24 million unique patients (12.9 million Medicare; 13.9 million Medicaid; 6.6 million GPC patients) with 1 674 134 patients crosswalked and 983 450 patients with body mass index (BMI) linked to claims. Diagnosis codes from EHR and claims sources underreport obesity by 2.56 times compared with body mass index measures. However, common comorbidities such as diabetes and sleep apnea diagnoses were more often available from claims diagnoses codes (1.6 and 1.4 times, respectively). Conclusion: GROUSE provides a unified EHR-claims environment to address health system and federal privacy concerns, which enables investigators to generalize analyses across health systems integrated with multistate insurance claims.