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Background: We previously developed 2 complementary surveys to measure coordination of care as experienced by the specialist and the primary care provider (PCP). These Coordination of Specialty Care (CSC) surveys were developed in the Veterans Health Administration (VA), under an integrated organizational umbrella that includes a shared electronic health record (EHR).
Objective: To develop an augmented version of the CSC-Specialist in the private sector and use that version (CSC-Specialist 2.0) to examine the effect of a shared EHR on coordination.
Research Design: We administered the survey online to a national sample of clinicians from 10 internal medicine subspecialties. We used multitrait analysis and confirmatory factor analysis to evaluate the psychometric properties of the original VA-based survey and develop an augmented private sector survey (CSC-Specialist 2.0). We tested construct validity by regressing a single-item measure of overall coordination onto the 4 scales. We used analysis of variance to examine the relationship of a shared EHR to coordination.
Results: Psychometric assessment supported the 13-item, 4-scale structure of the original VA measure and the augmented 18-item, 4-scale structure of the CSC-Specialist 2.0. The CSC-Specialist 2.0 scales together explained 45% of the variance in overall coordination. A shared EHR was associated with significantly better scores for the Roles and Responsibilities and Data Transfer scales, and for overall coordination.
Conclusions: The CSC-Specialist 2.0 is a unique survey that demonstrates adequate psychometric performance and is sensitive to use of a shared EHR. It can be used alone or with the CSC-PCP to identify coordination problems, guide interventions, and measure improvements.
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http://dx.doi.org/10.1097/MLR.0000000000001402 | DOI Listing |
Alzheimers Dement
September 2025
Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
Introduction: We compared and measured alignment between the Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) standard used by electronic health records (EHRs), the Clinical Data Interchange Standards Consortium (CDISC) standards used by industry, and the Uniform Data Set (UDS) used by the Alzheimer's Disease Research Centers (ADRCs).
Methods: The ADRC UDS, consisting of 5959 data elements across eleven packets, was mapped to FHIR and CDISC standards by two independent mappers, with discrepancies adjudicated by experts.
Results: Forty-five percent of the 5959 UDS data elements mapped to the FHIR standard, indicating possible electronic obtainment from EHRs.
Health Serv Res
September 2025
Division of Clinical Informatics and Digital Transformation, Director, Center for Clinical Informatics and Improvement Research, University of California - San Francisco, San Francisco, CA, San Francisco, California, USA.
Objective: To analyze national rates of team-based ordering and evaluate changes in key outcomes following adoption.
Study Setting And Design: We conducted an observational pre-post intervention-comparison study of 249,463 ambulatory physicians across 401 organizations using the Epic EHR. Our intervention was the adoption of team-based ordering, measured as the proportion of orders involving team support.
J Imaging Inform Med
September 2025
Heart Center, Department of Geriatrics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China.
The growing heterogeneity of cardiac patient data from hospitals and wearables necessitates predictive models that are tailored, comprehensible, and safeguard privacy. This study introduces PerFed-Cardio, a lightweight and interpretable semi-federated learning (Semi-FL) system for real-time cardiovascular risk stratification utilizing multimodal data, including cardiac imaging, physiological signals, and electronic health records (EHR). In contrast to conventional federated learning, where all clients engage uniformly, our methodology employs a personalized Semi-FL approach that enables high-capacity nodes (e.
View Article and Find Full Text PDFJ Am Med Inform Assoc
September 2025
Department of Computer Science, University of British Columbia, Vancouver V6T 1Z4, Canada.
Objectives: Electronic Health Records (EHRs) sampled from different populations can introduce unwanted biases, limit individual-level data sharing, and make the data and fitted model hardly transferable across different population groups. In this context, our main goal is to design an effective method to transfer knowledge between population groups, with computable guarantees for suitability, and that can be applied to quantify treatment disparities.
Materials And Methods: For a model trained in an embedded feature space of one subgroup, our proposed framework, Optimal Transport-based Transfer Learning for EHRs (OTTEHR), combines feature embedding of the data and unbalanced optimal transport (OT) for domain adaptation to another population group.
Health Aff Sch
August 2025
Price School of Public Policy, University of Southern California, Los Angeles, CA 90089, United States.
Over the past decade, the electronic health record (EHR) market has become increasingly consolidated, with the majority of care delivery organizations now using 1 of 2 vendors -Epic and Oracle Health. This consolidation creates a "single-point-of-failure" tail risk for cybersecurity: 1 successful attack could expose millions of patients' private data and could potentially impact documentation, billing, and clinical care across thousands of sites. Moreover, dependence on other technology vendors, such as shared cloud hosts, broadens the potential attack surface beyond vendors' core firewalls.
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