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Nair M, Svedberg P, Larsson I, Nygren JM. A comprehensive overview of barriers and strategies for AI implementation in healthcare: Mixed-method design. PLoS One 2024; 19:e0305949. [PMID: 39121051 DOI: 10.1371/journal.pone.0305949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 06/07/2024] [Indexed: 08/11/2024] Open
Abstract
Implementation of artificial intelligence systems for healthcare is challenging. Understanding the barriers and implementation strategies can impact their adoption and allows for better anticipation and planning. This study's objective was to create a detailed inventory of barriers to and strategies for AI implementation in healthcare to support advancements in methods and implementation processes in healthcare. A sequential explanatory mixed method design was used. Firstly, scoping reviews and systematic literature reviews were identified using PubMed. Selected studies included empirical cases of AI implementation and use in clinical practice. As the reviews were deemed insufficient to fulfil the aim of the study, data collection shifted to the primary studies included in those reviews. The primary studies were screened by title and abstract, and thereafter read in full text. Then, data on barriers to and strategies for AI implementation were extracted from the included articles, thematically coded by inductive analysis, and summarized. Subsequently, a direct qualitative content analysis of 69 interviews with healthcare leaders and healthcare professionals confirmed and added results from the literature review. Thirty-eight empirical cases from the six identified scoping and literature reviews met the inclusion and exclusion criteria. Barriers to and strategies for AI implementation were grouped under three phases of implementation (planning, implementing, and sustaining the use) and were categorized into eleven concepts; Leadership, Buy-in, Change management, Engagement, Workflow, Finance and human resources, Legal, Training, Data, Evaluation and monitoring, Maintenance. Ethics emerged as a twelfth concept through qualitative analysis of the interviews. This study illustrates the inherent challenges and useful strategies in implementing AI in healthcare practice. Future research should explore various aspects of leadership, collaboration and contracts among key stakeholders, legal strategies surrounding clinicians' liability, solutions to ethical dilemmas, infrastructure for efficient integration of AI in workflows, and define decision points in the implementation process.
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Affiliation(s)
- Monika Nair
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Petra Svedberg
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Ingrid Larsson
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens M Nygren
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
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Sendak MP, Liu VX, Beecy A, Vidal DE, Shaw K, Lifson MA, Tobey D, Valladares A, Loufek B, Mogri M, Balu S. Strengthening the use of artificial intelligence within healthcare delivery organizations: balancing regulatory compliance and patient safety. J Am Med Inform Assoc 2024; 31:1622-1627. [PMID: 38767890 PMCID: PMC11187419 DOI: 10.1093/jamia/ocae119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/17/2024] [Accepted: 05/06/2024] [Indexed: 05/22/2024] Open
Abstract
OBJECTIVES Surface the urgent dilemma that healthcare delivery organizations (HDOs) face navigating the US Food and Drug Administration (FDA) final guidance on the use of clinical decision support (CDS) software. MATERIALS AND METHODS We use sepsis as a case study to highlight the patient safety and regulatory compliance tradeoffs that 6129 hospitals in the United States must navigate. RESULTS Sepsis CDS remains in broad, routine use. There is no commercially available sepsis CDS system that is FDA cleared as a medical device. There is no public disclosure of an HDO turning off sepsis CDS due to regulatory compliance concerns. And there is no public disclosure of FDA enforcement action against an HDO for using sepsis CDS that is not cleared as a medical device. DISCUSSION AND CONCLUSION We present multiple policy interventions that would relieve the current tension to enable HDOs to utilize artificial intelligence to improve patient care while also addressing FDA concerns about product safety, efficacy, and equity.
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Affiliation(s)
- Mark P Sendak
- Duke Institute for Health Innovation, Duke University, Durham, NC 27701, United States
| | - Vincent X Liu
- Division of Research, Kaiser Permanente, Oakland, CA 94612, United States
| | - Ashley Beecy
- Division of Cardiology, Department of Medicine, Weill Cornell Medicine and NewYork-Presbyterian Hospital, New York, NY 10021, United States
| | - David E Vidal
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, United States
| | - Keo Shaw
- DLA Piper, Washington, DC 20004, United States
| | - Mark A Lifson
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, United States
| | - Danny Tobey
- DLA Piper, Washington, DC 20004, United States
| | - Alexandra Valladares
- Duke Institute for Health Innovation, Duke University, Durham, NC 27701, United States
| | - Brenna Loufek
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, United States
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, United Kingdom
| | - Murtaza Mogri
- Division of Research, Kaiser Permanente, Oakland, CA 94612, United States
| | - Suresh Balu
- Duke Institute for Health Innovation, Duke University, Durham, NC 27701, United States
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Scott IA, van der Vegt A, Lane P, McPhail S, Magrabi F. Achieving large-scale clinician adoption of AI-enabled decision support. BMJ Health Care Inform 2024; 31:e100971. [PMID: 38816209 PMCID: PMC11141172 DOI: 10.1136/bmjhci-2023-100971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 05/15/2024] [Indexed: 06/01/2024] Open
Abstract
Computerised decision support (CDS) tools enabled by artificial intelligence (AI) seek to enhance accuracy and efficiency of clinician decision-making at the point of care. Statistical models developed using machine learning (ML) underpin most current tools. However, despite thousands of models and hundreds of regulator-approved tools internationally, large-scale uptake into routine clinical practice has proved elusive. While underdeveloped system readiness and investment in AI/ML within Australia and perhaps other countries are impediments, clinician ambivalence towards adopting these tools at scale could be a major inhibitor. We propose a set of principles and several strategic enablers for obtaining broad clinician acceptance of AI/ML-enabled CDS tools.
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Affiliation(s)
- Ian A Scott
- Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, Queensland, Australia
- Centre for Health Services Research, The University of Queensland Faculty of Medicine and Biomedical Sciences, Brisbane, Queensland, Australia
| | - Anton van der Vegt
- Digital Health Centre, The University of Queensland Faculty of Medicine and Biomedical Sciences, Herston, Queensland, Australia
| | - Paul Lane
- Safety, Quality and Innovation, The Prince Charles Hospital, Brisbane, Queensland, Australia
| | - Steven McPhail
- Australian Centre for Health Services Innovation, Queensland University of Technology Faculty of Health, Brisbane, Queensland, Australia
| | - Farah Magrabi
- Macquarie University, Sydney, New South Wales, Australia
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van der Vegt A, Campbell V, Zuccon G. Why clinical artificial intelligence is (almost) non-existent in Australian hospitals and how to fix it. Med J Aust 2024; 220:172-175. [PMID: 38146620 DOI: 10.5694/mja2.52195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/27/2023] [Indexed: 12/27/2023]
Affiliation(s)
- Anton van der Vegt
- Centre for Health Services Research, University of Queensland, Brisbane, QLD
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5
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Levy J, Yao K. The future of digital cytology and artificial intelligence: an editorial commentary for Digital Cytology part 1 and 2. J Am Soc Cytopathol 2024; 13:81-85. [PMID: 38267293 DOI: 10.1016/j.jasc.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 12/20/2023] [Indexed: 01/26/2024]
Affiliation(s)
- Joshua Levy
- Department of Pathology & Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Keluo Yao
- Department of Pathology & Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California; Enterprise Information Services, Cedars-Sinai, Los Angeles, California.
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van der Vegt AH, Campbell V, Mitchell I, Malycha J, Simpson J, Flenady T, Flabouris A, Lane PJ, Mehta N, Kalke VR, Decoyna JA, Es’haghi N, Liu CH, Scott IA. Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals: what is known and what remains uncertain. J Am Med Inform Assoc 2024; 31:509-524. [PMID: 37964688 PMCID: PMC10797271 DOI: 10.1093/jamia/ocad220] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/27/2023] [Accepted: 10/31/2023] [Indexed: 11/16/2023] Open
Abstract
OBJECTIVE To identify factors influencing implementation of machine learning algorithms (MLAs) that predict clinical deterioration in hospitalized adult patients and relate these to a validated implementation framework. MATERIALS AND METHODS A systematic review of studies of implemented or trialed real-time clinical deterioration prediction MLAs was undertaken, which identified: how MLA implementation was measured; impact of MLAs on clinical processes and patient outcomes; and barriers, enablers and uncertainties within the implementation process. Review findings were then mapped to the SALIENT end-to-end implementation framework to identify the implementation stages at which these factors applied. RESULTS Thirty-seven articles relating to 14 groups of MLAs were identified, each trialing or implementing a bespoke algorithm. One hundred and seven distinct implementation evaluation metrics were identified. Four groups reported decreased hospital mortality, 1 significantly. We identified 24 barriers, 40 enablers, and 14 uncertainties and mapped these to the 5 stages of the SALIENT implementation framework. DISCUSSION Algorithm performance across implementation stages decreased between in silico and trial stages. Silent plus pilot trial inclusion was associated with decreased mortality, as was the use of logistic regression algorithms that used less than 39 variables. Mitigation of alert fatigue via alert suppression and threshold configuration was commonly employed across groups. CONCLUSIONS : There is evidence that real-world implementation of clinical deterioration prediction MLAs may improve clinical outcomes. Various factors identified as influencing success or failure of implementation can be mapped to different stages of implementation, thereby providing useful and practical guidance for implementers.
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Affiliation(s)
- Anton H van der Vegt
- Centre for Health Services Research, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Victoria Campbell
- Intensive Care Unit, Sunshine Coast Hospital and Health Service, Birtynia, QLD 4575, Australia
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Imogen Mitchell
- Office of Research and Education, Canberra Health Services, Canberra, ACT 2601, Australia
| | - James Malycha
- Department of Critical Care Medicine, The Queen Elizabeth Hospital, Woodville, SA 5011, Australia
| | - Joanna Simpson
- Eastern Health Intensive Care Services, Eastern Health, Box Hill, VIC 3128, Australia
| | - Tracy Flenady
- School of Nursing, Midwifery & Social Sciences, Central Queensland University, Rockhampton, QLD 4701, Australia
| | - Arthas Flabouris
- Intensive Care Department, Royal Adelaide Hospital, Adelaide, SA 5000, Australia
- Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia
| | - Paul J Lane
- Safety Quality & Innovation, The Prince Charles Hospital, Chermside, QLD 4032, Australia
| | - Naitik Mehta
- Patient Safety and Quality, Clinical Excellence Queensland, Brisbane, QLD 4001, Australia
| | - Vikrant R Kalke
- Patient Safety and Quality, Clinical Excellence Queensland, Brisbane, QLD 4001, Australia
| | - Jovie A Decoyna
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Nicholas Es’haghi
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Chun-Huei Liu
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Ian A Scott
- Centre for Health Services Research, The University of Queensland, Brisbane, QLD 4102, Australia
- Department of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, QLD 4102, Australia
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Shah NH, Halamka JD, Saria S, Pencina M, Tazbaz T, Tripathi M, Callahan A, Hildahl H, Anderson B. A Nationwide Network of Health AI Assurance Laboratories. JAMA 2024; 331:245-249. [PMID: 38117493 DOI: 10.1001/jama.2023.26930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Importance Given the importance of rigorous development and evaluation standards needed of artificial intelligence (AI) models used in health care, nationwide accepted procedures to provide assurance that the use of AI is fair, appropriate, valid, effective, and safe are urgently needed. Observations While there are several efforts to develop standards and best practices to evaluate AI, there is a gap between having such guidance and the application of such guidance to both existing and new AI models being developed. As of now, there is no publicly available, nationwide mechanism that enables objective evaluation and ongoing assessment of the consequences of using health AI models in clinical care settings. Conclusion and Relevance The need to create a public-private partnership to support a nationwide health AI assurance labs network is outlined here. In this network, community best practices could be applied for testing health AI models to produce reports on their performance that can be widely shared for managing the lifecycle of AI models over time and across populations and sites where these models are deployed.
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Affiliation(s)
- Nigam H Shah
- Stanford Medicine, Palo Alto, California
- Coalition for Health AI, Dover, Delaware
| | - John D Halamka
- Coalition for Health AI, Dover, Delaware
- Mayo Clinic Platform, Mayo Clinic, Rochester, Minnesota
| | - Suchi Saria
- Coalition for Health AI, Dover, Delaware
- Bayesian Health, New York, New York
- Johns Hopkins University, Baltimore, Maryland
- Johns Hopkins Medicine, Baltimore, Maryland
| | - Michael Pencina
- Coalition for Health AI, Dover, Delaware
- Duke AI Health, Duke University School of Medicine, Durham, North Carolina
| | - Troy Tazbaz
- US Food and Drug Administration, Silver Spring, Maryland
| | - Micky Tripathi
- US Office of the National Coordinator for Health IT, Washington, DC
| | | | | | - Brian Anderson
- Coalition for Health AI, Dover, Delaware
- MITRE Corporation, Bedford, Massachusetts
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Leone M, Lakbar I, Vincent JL. Sepsis : Actual numbers and uncertainties. Rev Epidemiol Sante Publique 2023; 71:102176. [PMID: 37918044 DOI: 10.1016/j.respe.2023.102176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 09/21/2023] [Accepted: 09/22/2023] [Indexed: 11/04/2023] Open
Affiliation(s)
- Marc Leone
- Department of Anesthesiology and Intensive Care, Hôpital Nord, Assistance Publique Hôpitaux de Marseille, University of Aix Marseille, Marseille, France..
| | - Ines Lakbar
- Anesthesiology and Intensive Care, Anesthesia and Critical Care Department B, Saint Eloi Teaching Hospital, PhyMedExp, University of Montpellier, INSERM U1046, 1, Montpellier, France
| | - Jean-Louis Vincent
- Department of Intensive Care, Erasme University Hospital, Université Libre de Bruxelles, Brussels, Belgium
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Kim T, Tae Y, Yeo HJ, Jang JH, Cho K, Yoo D, Lee Y, Ahn SH, Kim Y, Lee N, Cho WH. Development and Validation of Deep-Learning-Based Sepsis and Septic Shock Early Prediction System (DeepSEPS) Using Real-World ICU Data. J Clin Med 2023; 12:7156. [PMID: 38002768 PMCID: PMC10672000 DOI: 10.3390/jcm12227156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Successful sepsis treatment depends on early diagnosis. We aimed to develop and validate a system to predict sepsis and septic shock in real time using deep learning. METHODS Clinical data were retrospectively collected from electronic medical records (EMRs). Data from 2010 to 2019 were used as development data, and data from 2020 to 2021 were used as validation data. The collected EMRs consisted of eight vital signs, 13 laboratory data points, and three demographic information items. We validated the deep-learning-based sepsis and septic shock early prediction system (DeepSEPS) using the validation datasets and compared our system with other traditional early warning scoring systems, such as the national early warning score, sequential organ failure assessment (SOFA), and quick sequential organ failure assessment. RESULTS DeepSEPS achieved even higher area under receiver operating characteristic curve (AUROC) values (0.7888 and 0.8494 for sepsis and septic shock, respectively) than SOFA. The prediction performance of traditional scoring systems was enhanced because the early prediction time point was close to the onset time of sepsis; however, the DeepSEPS scoring system consistently outperformed all conventional scoring systems at all time points. Furthermore, at the time of onset of sepsis and septic shock, DeepSEPS showed the highest AUROC (0.9346). CONCLUSIONS The sepsis and septic shock early warning system developed in this study exhibited a performance that is worth considering when predicting sepsis and septic shock compared to other traditional early warning scoring systems. DeepSEPS showed better performance than existing sepsis prediction programs. This novel real-time system that simultaneously predicts sepsis and septic shock requires further validation.
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Affiliation(s)
- Taehwa Kim
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, School of Medicine, Pusan National University and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan 50612, Republic of Korea; (T.K.); (H.J.Y.); (J.H.J.)
| | - Yunwon Tae
- VUNO, Seoul 06541, Republic of Korea; (Y.T.); (K.C.); (D.Y.); (Y.L.)
| | - Hye Ju Yeo
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, School of Medicine, Pusan National University and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan 50612, Republic of Korea; (T.K.); (H.J.Y.); (J.H.J.)
- Department of Internal Medicine, School of Medicine, Pusan National University, Busan 46241, Republic of Korea
| | - Jin Ho Jang
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, School of Medicine, Pusan National University and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan 50612, Republic of Korea; (T.K.); (H.J.Y.); (J.H.J.)
| | - Kyungjae Cho
- VUNO, Seoul 06541, Republic of Korea; (Y.T.); (K.C.); (D.Y.); (Y.L.)
| | - Dongjoon Yoo
- VUNO, Seoul 06541, Republic of Korea; (Y.T.); (K.C.); (D.Y.); (Y.L.)
- Department of Critical Care Medicine and Emergency Medicine, Inha University College of Medicine, Incheon 22212, Republic of Korea
| | - Yeha Lee
- VUNO, Seoul 06541, Republic of Korea; (Y.T.); (K.C.); (D.Y.); (Y.L.)
| | - Sung-Ho Ahn
- Division of Biostatistics, Department of Neurology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan 50612, Republic of Korea;
| | - Younga Kim
- Department of Pediatrics, School of Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (Y.K.); (N.L.)
| | - Narae Lee
- Department of Pediatrics, School of Medicine, Pusan National University, Yangsan 50612, Republic of Korea; (Y.K.); (N.L.)
| | - Woo Hyun Cho
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, School of Medicine, Pusan National University and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan 50612, Republic of Korea; (T.K.); (H.J.Y.); (J.H.J.)
- Department of Internal Medicine, School of Medicine, Pusan National University, Busan 46241, Republic of Korea
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Bakken S. Perspectives on implementing models for decision support in clinical care. J Am Med Inform Assoc 2023; 30:1463-1464. [PMID: 37593902 PMCID: PMC10436137 DOI: 10.1093/jamia/ocad142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 07/10/2023] [Indexed: 08/19/2023] Open
Affiliation(s)
- Suzanne Bakken
- School of Nursing, Department of Biomedical Informatics, and Data Science Institute, Columbia University, New York, New York, USA
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Bakken S. AI in health: keeping the human in the loop. J Am Med Inform Assoc 2023; 30:1225-1226. [PMID: 37337923 PMCID: PMC10280340 DOI: 10.1093/jamia/ocad091] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 05/10/2023] [Indexed: 06/21/2023] Open
Affiliation(s)
- Suzanne Bakken
- School of Nursing, Department of Biomedical Informatics, and Data Science Institute, Columbia University, New York, New York, USA
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