Eric G Poon, Andrew L Rosenberg, Adam B Landman, Tejal K Gandhi
{"title":"似曾相识?电子健康记录的经验教训如何应用于人工智能?","authors":"Eric G Poon, Andrew L Rosenberg, Adam B Landman, Tejal K Gandhi","doi":"10.1016/j.jcjq.2025.07.007","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>The ai era: </strong>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.</p><p><strong>Conclusion: </strong>By applying these lessons, organizational leaders can realize the potential of AI to improve patient outcomes and transform healthcare delivery.</p>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Déjà Vu? How Might Lessons Learned from Electronic Health Record Implementation Apply to Artificial Intelligence?\",\"authors\":\"Eric G Poon, Andrew L Rosenberg, Adam B Landman, Tejal K Gandhi\",\"doi\":\"10.1016/j.jcjq.2025.07.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>The ai era: </strong>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.</p><p><strong>Conclusion: </strong>By applying these lessons, organizational leaders can realize the potential of AI to improve patient outcomes and transform healthcare delivery.</p>\",\"PeriodicalId\":14835,\"journal\":{\"name\":\"Joint Commission journal on quality and patient safety\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Joint Commission journal on quality and patient safety\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.jcjq.2025.07.007\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Joint Commission journal on quality and patient safety","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.jcjq.2025.07.007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Déjà Vu? How Might Lessons Learned from Electronic Health Record Implementation Apply to Artificial Intelligence?
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.