Electronic Health Records: Origination, Adoption, and Progression
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The electronic health record (EHR) represents the evolution and convergence of technology and administration of medicine. Its advent has significantly changed the landscape in which medical policy and process can be created. As vast amounts of data are gathered in the management of care and administration of health systems, these clinical big data provide opportunities to enhance public health practice. This work delineates core functionalities and primary and secondary uses of EHRs that are aligned with these practices, including uses of EHRs for healthcare, public health, and population health outcomes. From EHRs origins as simple billing and accounting systems, to its adoption as, and progression toward, full-fledged interactive records, the medical, public health, and governmental stakeholders have at times been at odds to ensure their concerns and requirements are adequately represented in implementations. Legislation to promote the adoption of EHRs capable of recording and reporting health data in a standardized, structured, and secure format, and technologies to facilitate the progress towards the goals of EHRs, such as achieving portability, have had varying results. Various milestones to EHR goals are discussed, with respect to legislation, regulation, policies, and the importance of standards and technologies. This work examines some lessons learned from EHR developments and implementations, which also exposes limitations, disparities, and unintended consequences of EHR adoption and progression globally. It concludes with forward looking perspectives on EHRs, a crucial cornerstone of public health informatics and information systems.
KeywordsAcknowledgement logic Computerized provider order entry Electronic data interchange Electronic health record Electronic prescribing Health information technology Integration engines Meaningful Use Picture archiving and communications systems Protected health information
The authors gratefully acknowledge the editorial assistance from J. A. Magnuson and Brian E. Dixon. The authors also gratefully acknowledge the guidance on this intellectual work from advisors, Charles M. Heilig and Mark L. Messonnier, as well as the comments on it from several reviewers, including Adi V. Gundlapalli, Sanjeev Tandon, Michael Paul Reid, and Fatima Coronado, at the Centers for Disease Control and Prevention (CDC). This work was supported by CDC Public Health Informatics Fellowship Program (PHIFP) Fellowships (to F.R., and to J.T.P.). The CDC Population Health Workforce Initiative (PHWI) also provided support for this work (to F.R., and to J.T.P.). This work was also supported by a Biomedical Engineering Society (BMES) Career Development Award (to F.R.). The findings and conclusions in this work are those of the authors, and do not necessarily represent the official position of the CDC, or of the Mayo Clinic. The authors declared no conflict of interest.
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