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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 |
Dan Med J
August 2025
Department of Cardiology, Copenhagen University Hospital - Herlev and Gentofte Hospital.
Introduction: Long-term cardiac monitoring has become more accessible with the advent of consumer-oriented wearable devices. Smartwatches (SWs) hold promise for extended rhythm monitoring owing to their availability and direct electronic health record (EHR) integration. We studied the clinical consequences of SW implementation in patients with palpitations.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
There is a lack of longitudinal data on type 2 diabetes (T2D) in low- and middle-income countries. We leveraged the electronic health records (EHR) system of a publicly funded academic institution to establish a retrospective cohort with longitudinal data to facilitate benchmarking, surveillance, and resource planning of a multi-ethnic T2D population in Malaysia. This cohort included 15,702 adults aged ≥ 18 years with T2D who received outpatient care (January 2002-December 2020) from Universiti Malaya Medical Centre (UMMC), Kuala Lumpur, Malaysia.
View Article and Find Full Text PDFAnn Intern Med
September 2025
Department of Medicine, Johns Hopkins University School of Medicine, and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.B.S.).
Electronic health record (EHR) data are increasingly used to develop prediction models that guide clinical decision making at the point of care. These include algorithms that use high-frequency data, like in sepsis prediction, as well as simpler equations, such as the Pooled Cohort Equations for cardiovascular outcome prediction. Although EHR data used in prediction models are often highly granular and more current than other data, there is systematic and nonsystematic missingness in EHR data as there is with most data.
View Article and Find Full Text PDFFront Med (Lausanne)
August 2025
Department of Oncology, Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background: This study evaluates how AI enhances EHR efficiency by comparing a lung cancer-specific LLM with general-purpose models (DeepSeek, GPT-3.5) and clinicians across expertise levels, assessing accuracy and completeness in complex lung cancer pathology documentation and task load changes pre-/post-AI implementation.
Methods: This study analyzed 300 lung cancer cases (Shanghai Chest Hospital) and 60 TCGA cases, split into training/validation/test sets.
Curr Opin Nephrol Hypertens
September 2025
Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, College of Medicine.
Purpose Of Review: Artificial intelligence is continuously and rapidly evolving. Artificial intelligence has the potential to address several clinical challenges associated with the prevention, detection, and management of acute kidney injury (AKI). This review provides an overview of the state of artificial intelligence for AKI decision-making, highlighting key recent developments, trends, and innovations towards real-world bedside deployment.
View Article and Find Full Text PDF