Purpose: The aim of this study was to examine the impact of electronic health record (EHR) adoption on hospital financial performance.
Methodology/approach: We constructed a longitudinal panel using data from the three secondary sources: (a) the 2007-2010 American Hospital Association (AHA) Annual Survey, (b) the 2007-2010 AHA Annual Survey Information Technology Supplement, and (c) the 2007-2011 Medicare Cost Reports from Centers for Medicare and Medicaid Services. Because potential financial benefits attributable to EHR adoption may take some time to accrue, we ran regressions with lags of 1 and 2 years that included hospital and year fixed effects to examine the relationship between the level of EHR adoption and three hospital financial performance measures.
Findings: A change in the level of EHR adoption was not associated with changes in operating margin or return on assets within hospitals. However, total margin was significantly improved, after 2 years, in hospitals that moved from no EHR to having a comprehensive EHR in all areas of their hospital (β = 0.030, p < .034). On the other hand, hospitals that increased their level of EHR adoption but did not achieve hospital-wide comprehensive adoption did not experience changes in any financial performance measures examined.
Practice Implications: The improvements in total margin, as opposed to operating margin, are likely due to hospital incentive payments under the Health Information Technology for Economic and Clinical Health Act that are reflected in nonpatient revenues and therefore show up in total margin calculations. Thus, after 2 years of EHR adoption, hospital financial performance is observed to improve based only on meaningful use incentive payments. More research will be needed to determine whether EHR adoption impacts financial performance on a longer time horizon.
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http://dx.doi.org/10.1097/HMR.0000000000000068 | DOI Listing |
Background: Ambient artificial intelligence offers promise for improving documentation efficiency and reducing provider burden through clinical note generation. However, challenges persist in workflow integration, compliance, and widespread adoption. This study leveraged a Learning Health System (LHS) framework to align research and operations using a hybrid effectiveness-implementation protocol, embedded as pragmatic trial operations within the electronic health record (EHR).
View Article and Find Full Text PDFJ Am Pharm Assoc (2003)
December 2024
Endocrinologist, Senior Medical Director, Duke PHMO, Durham, NC; Professor of Medicine, Professor in Family Medicine and Community Health, Division of Endocrinology, Metabolism, Nutrition, Department of Medicine, Duke University School of Medicine, Durham, NC.
Background: Use of sodium-glucose co-transporter 2 inhibitors (SGLT-2 inhibitors) falls short of their cardiorenal protective benefits. Patient and provider-level barriers hinder the adoption of these life-saving medications. Innovative practices to provide primary care providers (PCP) with added clinical-decision support via a dedicated remote interdisciplinary diabetes rounds (IDR) team could promote SGLT-2 inhibitor selection.
View Article and Find Full Text PDFSci Rep
December 2024
Fidson Health Care PLC, Ibadan, Oyo State, Nigeria.
This study assessed the factors militating against the effective implementation of electronic health records (EHR) in Nigeria, the computerization of patients' health records with a lot of benefits including improved patients' satisfaction, improved care processes, reduction of patients' waiting time, and medication errors. Despite these benefits, healthcare organizations are slow to adopt the EHR system. Therefore, the study assessed the factors militating against the effective implementation of the EHR system, the level of awareness of EHR, and the utilization of electronic health records; it also investigated the factors militating against the effective implementation of EHR.
View Article and Find Full Text PDFJ Am Med Inform Assoc
December 2024
Statistical Modeling, Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riβ 88400, Germany.
Background: Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research has so far adopted a limited view of family relations, essentially treating patients as independent samples in the analysis.
Methods: To address this gap, we present ALIGATEHR, which models inferred family relations in a graph attention network augmented with an attention-based medical ontology representation, thus accounting for the complex influence of genetics, shared environmental exposures, and disease dependencies.
Cureus
December 2024
Quality and Health Data Integrity, Arrowhead Regional Medical Center, Colton, USA.
Introduction The patient-centered care model emphasizes patient autonomy in recovery, acknowledging each individual's unique journey. Despite challenges in the healthcare system, this model has gained traction nationwide. Advances in healthcare technology have highlighted obstacles to independent decision-making.
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