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Background: The US healthcare system is currently facing significant challenges in quality, affordability, and labor shortages. Artificial intelligence (AI) promises to transform healthcare delivery by making it safer, more effective, less wasteful, and more patient-centered. With more than $30 billion invested in healthcare AI companies in the past three years, the proliferation of AI solutions is expected to bring much-needed relief to the strained healthcare industry. To harness the current enthusiasm for AI in healthcare, we can draw parallels to the adoption of electronic health records (EHRs) under the HITECH Act of 2009. EHR adoption has been widespread and has contributed to significant health information technology spending, but it has also brought unintended consequences, such as clinician burnout, workarounds, and mixed impacts on patient safety and quality measures. THE EHR ERA VS.
The Ai Era: DIFFERENCES: This article grounds the discussion by first reviewing the key differences between the EHR implementation era that followed the passage of HITECH and the current AI era. The authors identified three characteristics of the AI era that distinguish it from the EHR implementation era: different regulatory and legislative context, diminished capacity of the workforce to absorb new work, and an accelerated pace of change. LESSONS FROM EHR IMPLEMENTATION TO CARRY FORWARD TO AI IMPLEMENTATION: Based on the collective experience of the authorship team and published literature on EHR and AI implementation, the authors identified five critical lessons from the EHR implementation era that organizations deploying AI must consider: (1) respect the human element, (2) build strong organizational governance, (3) adapt leadership and culture, (4) ready the workforce, and (5) build for the long term.
Conclusion: By applying these lessons, organizational leaders can realize the potential of AI to improve patient outcomes and transform healthcare delivery.
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http://dx.doi.org/10.1016/j.jcjq.2025.07.007 | DOI Listing |
Jt Comm J Qual Patient Saf
July 2025
Background: The US healthcare system is currently facing significant challenges in quality, affordability, and labor shortages. Artificial intelligence (AI) promises to transform healthcare delivery by making it safer, more effective, less wasteful, and more patient-centered. With more than $30 billion invested in healthcare AI companies in the past three years, the proliferation of AI solutions is expected to bring much-needed relief to the strained healthcare industry.
View Article and Find Full Text PDFAnn Epidemiol
September 2025
Veterans Health Administration- VA Tennessee Valley Health Care System Geriatric Research, Education and Clinical Center (GRECC), and VETWISE-LHS Center of Innovation, Nashville, TN; Vanderbilt-Ingram Cancer Center, Nashville, TN; Center for Clinical Quality and Implementation Research, Vanderbilt U
Purpose: Tobacco use is not commonly represented as computable information in the electronic health record (EHR). We developed an algorithm in the Veterans Health Administration (VHA) to identify tobacco ever-use among Veterans.
Methods: We used the VHA corporate data warehouse to develop an algorithm comprised of multiple data types (health factors [semi-structured template data entry and decision support tools], billing, orders, medication, and encounter codes) to identify tobacco ever-use (current or former) versus never use.
PLoS One
September 2025
Department of Cardiology, Yale New Haven Health System, Yale New Haven Hospital, New Haven, Connecticut, United States of America.
Background: Heart failure (HF) mortality is rising despite robust evidence-based guidelines. Hospitalization presents an opportune time to optimize care. Inpatient care pathways (CP) embedded in the electronic health record (EHR) can enhance adherence to guidelines by providing real-time decision support.
View Article and Find Full Text PDFJAMIA Open
October 2025
MCIT Department of Health Informatics, NYU Langone Health, New York, NY 10016, United States.
Objectives: Assess time impact and provider perception of AI-generated encounter summaries.
Materials And Methods: An artificial intelligence (AI) clinical note summarization tool was deployed in ambulatory practices for 22 providers. A 6-month pre-post analysis evaluated changes in EHR time, and survey feedback assessed tool utility.
J Oncol Pharm Pract
September 2025
Department of Pharmacy, University of Michigan Health - Academic Medical Center, Ann Arbor, MI, USA.
ObjectiveOncology treatment regimens require increasing information technology (IT) integration in health systems to enhance delivery and safety, however, this creates a burden on medical teams and clinical pharmacists to manage. This primer introduces the University of Michigan Health Academic Medical Center's (UMH-AMC) response to this need with the Chemotherapy Orders Team (COT).SummaryThe COT includes five clinical oncology pharmacy generalists with a split full-time equivalent (FTE) appointment in COT-based activities and staffing in infusion.
View Article and Find Full Text PDF