Browsing by Subject "Health information interoperability"

Now showing 1 - 5 of 5
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    Clinical, technical, and implementation characteristics of real-world health applications using FHIR
    (Oxford University Press, 2022-10-12) Griffin, Ashley C.; He, Lu; Sunjaya, Anthony P.; King, Andrew J.; Khan, Zubin; Nwadiugwu, Martin; Douthit, Brian; Subbian, Vignesh; Nguyen, Viet; Braunstein, Mark; Jaffe, Charles; Schleyer, Titus; Medicine, School of Medicine
    Objective: Understanding the current state of real-world Fast Healthcare Interoperability Resources (FHIR) applications (apps) will benefit biomedical research and clinical care and facilitate advancement of the standard. This study aimed to provide a preliminary assessment of these apps' clinical, technical, and implementation characteristics. Materials and methods: We searched public repositories for potentially eligible FHIR apps and surveyed app implementers and other stakeholders. Results: Of the 112 apps surveyed, most focused on clinical care (74) or research (45); were implemented across multiple sites (56); and used SMART-on-FHIR (55) and FHIR version R4 (69). Apps were primarily stand-alone web-based (67) or electronic health record (EHR)-embedded (51), although 49 were not listed in an EHR app gallery. Discussion: Though limited in scope, our results show FHIR apps encompass various domains and characteristics. Conclusion: As FHIR use expands, this study-one of the first to characterize FHIR apps at large-highlights the need for systematic, comprehensive methods to assess their characteristics.
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    Corrigendum to: Practice and market factors associated with provider volume of health information exchange
    (Oxford University Press, 2021) Apathy, Nate C.; Vest, Joshua R.; Adler-Milstein, Julia; Blackburn, Justin; Dixon, Brian E.; Harle, Christopher A.; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Journal of the American Medical Informatics Association, doi: 10.1093/jamia/ocab024 The author name “Julia Adler-Milstein” was incorrectly given as “Julia Adler-Milstien”. This has been corrected online.
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    Enhancing the nation’s public health information infrastructure: a report from the ACMI symposium
    (Oxford University Press, 2023) Dixon, Brian E.; Staes, Catherine; Acharya, Jessica; Allen, Katie S.; Hartsell, Joel; Cullen, Theresa; Lenert, Leslie; Rucker, Donald W.; Lehmann, Harold; Community and Global Health, Richard M. Fairbanks School of Public Health
    The COVID-19 pandemic exposed multiple weaknesses in the nation's public health system. Therefore, the American College of Medical Informatics selected "Rebuilding the Nation's Public Health Informatics Infrastructure" as the theme for its annual symposium. Experts in biomedical informatics and public health discussed strategies to strengthen the US public health information infrastructure through policy, education, research, and development. This article summarizes policy recommendations for the biomedical informatics community postpandemic. First, the nation must perceive the health data infrastructure to be a matter of national security. The nation must further invest significantly more in its health data infrastructure. Investments should include the education and training of the public health workforce as informaticians in this domain are currently limited. Finally, investments should strengthen and expand health data utilities that increasingly play a critical role in exchanging information across public health and healthcare organizations.
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    Practice and market factors associated with provider volume of health information exchange
    (Oxford University Press, 2021) Apathy, Nate C.; Vest, Joshua R.; Adler-Milstein, Julia; Blackburn, Justin; Dixon, Brian E.; Harle, Christopher A.; Health Policy and Management, School of Public Health
    Objective: To assess the practice- and market-level factors associated with the amount of provider health information exchange (HIE) use. Materials and methods: Provider and practice-level data was drawn from the Meaningful Use Stage 2 Public Use Files from the Centers for Medicare and Medicaid Services, the Physician Compare National Downloadable File, and the Compendium of US Health Systems, among other sources. We analyzed the relationship between provider HIE use and practice and market factors using multivariable linear regression and compared primary care providers (PCPs) to non-PCPs. Provider volume of HIE use is measured as the percentage of referrals sent with electronic summaries of care (eSCR) reported by eligible providers attesting to the Meaningful Use electronic health record (EHR) incentive program in 2016. Results: Providers used HIE in 49% of referrals; PCPs used HIE in fewer referrals (43%) than non-PCPs (57%). Provider use of products from EHR vendors was negatively related to HIE use, while use of Athenahealth and Greenway Health products were positively related to HIE use. Providers treating, on average, older patients and greater proportions of patients with diabetes used HIE for more referrals. Health system membership, market concentration, and state HIE consent policy were unrelated to provider HIE use. Discussion: HIE use during referrals is low among office-based providers with the capability for exchange, especially PCPs. Practice-level factors were more commonly associated with greater levels of HIE use than market-level factors. Conclusion: This furthers the understanding that market forces, like competition, may be related to HIE adoption decisions but are less important for use once adoption has occurred.
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    Predicting pharmacotherapeutic outcomes for type 2 diabetes: An evaluation of three approaches to leveraging electronic health record data from multiple sources
    (Elsevier, 2022-05) Tarumi, Shinji; Takeuchi, Wataru; Qi, Rong; Ning, Xia; Ruppert, Laura; Ban, Hideyuki; Robertson, Daniel H.; Schleyer, Titus; Kawamoto, Kensaku; Medicine, School of Medicine
    Electronic health record (EHR) data are increasingly used to develop prediction models to support clinical care, including the care of patients with common chronic conditions. A key challenge for individual healthcare systems in developing such models is that they may not be able to achieve the desired degree of robustness using only their own data. A potential solution—combining data from multiple sources—faces barriers such as the need for data normalization and concerns about sharing patient information across institutions. To address these challenges, we evaluated three alternative approaches to using EHR data from multiple healthcare systems in predicting the outcome of pharmacotherapy for type 2 diabetes mellitus (T2DM). Two of the three approaches, named Selecting Better (SB) and Weighted Average (WA), allowed the data to remain within institutional boundaries by using pre-built prediction models; the third, named Combining Data (CD), aggregated raw patient data into a single dataset. The prediction performance and prediction coverage of the resulting models were compared to single-institution models to help judge the relative value of adding external data and to determine the best method to generate optimal models for clinical decision support. The results showed that models using WA and CD achieved higher prediction performance than single-institution models for common treatment patterns. CD outperformed the other two approaches in prediction coverage, which we defined as the number of treatment patterns predicted with an Area Under Curve of 0.70 or more. We concluded that 1) WA is an effective option for improving prediction performance for common treatment patterns when data cannot be shared across institutional boundaries and 2) CD is the most effective approach when such sharing is possible, especially for increasing the range of treatment patterns that can be predicted to support clinical decision making.