This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2803) Clinical Information and Decision Making (1634) Adoption and Change Management of eHealth Systems (794) Decision Support for Health Professionals (1332) Artificial Intelligence (1702)

Published on in Vol 6, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36501, first published .
Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence–Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation

Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence–Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation

Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence–Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation

Authors of this article:

Ryo Fujimori1, 2 Author Orcid Image ;   Keibun Liu2, 3 Author Orcid Image ;   Shoko Soeno2, 4 Author Orcid Image ;   Hiromu Naraba2, 5 Author Orcid Image ;   Kentaro Ogura1, 2 Author Orcid Image ;   Konan Hara2, 6 Author Orcid Image ;   Tomohiro Sonoo2, 5 Author Orcid Image ;   Takayuki Ogura7 Author Orcid Image ;   Kensuke Nakamura5 Author Orcid Image ;   Tadahiro Goto2 Author Orcid Image
Article Authors Cited by (45) Tweetations (18) Metrics

Journals

  1. Khan N, Nwafor Okoli C, Ekpin V, Attai K, Chukwudi N, Sabi H, Akwaowo C, Osuji J, Benavente L, Uzoka F. Adoption and utilization of medical decision support systems in the diagnosis of febrile Diseases: A systematic literature review. Expert Systems with Applications 2023;220:119638 View
  2. Gould D, Dowsey M, Spelman T, Bailey J, Bunzli S, Rele S, Choong P. Established and Novel Risk Factors for 30-Day Readmission Following Total Knee Arthroplasty: A Modified Delphi and Focus Group Study to Identify Clinically Important Predictors. Journal of Clinical Medicine 2023;12(3):747 View
  3. Fröhlich P, Mirnig A, Falcioni D, Schrammel J, Diamond L, Fischer I, Tscheligi M. Effects of reliability indicators on usage, acceptance and preference of predictive process management decision support systems. Quality and User Experience 2022;7(1) View
  4. Fernando M, Abell B, Tyack Z, Donovan T, McPhail S, Naicker S. Using Theories, Models, and Frameworks to Inform Implementation Cycles of Computerized Clinical Decision Support Systems in Tertiary Health Care Settings: Scoping Review. Journal of Medical Internet Research 2023;25:e45163 View
  5. Gould D, Bailey J, Spelman T, Bunzli S, Dowsey M, Choong P. Predicting 30-day readmission following total knee arthroplasty using machine learning and clinical expertise applied to clinical administrative and research registry data in an Australian cohort. Arthroplasty 2023;5(1) View
  6. Mittermaier M, Raza M, Kvedar J. Collaborative strategies for deploying AI-based physician decision support systems: challenges and deployment approaches. npj Digital Medicine 2023;6(1) View
  7. Levy J, Madrigal E, Vaickus L. Editorial: Artificial intelligence: applications in clinical medicine. Frontiers in Medical Technology 2023;5 View
  8. Hogg H, Al-Zubaidy M, Keane P, Hughes G, Beyer F, Maniatopoulos G. Evaluating the translation of implementation science to clinical artificial intelligence: a bibliometric study of qualitative research. Frontiers in Health Services 2023;3 View
  9. Lareyre F, Nasr B, Chaudhuri A, Di Lorenzo G, Carlier M, Raffort J. Comprehensive Review of Natural Language Processing (NLP) in Vascular Surgery. EJVES Vascular Forum 2023;60:57 View
  10. Dalvi-Esfahani M, Mosharaf-Dehkordi M, Leong L, Ramayah T, Jamal Kanaan-Jebna A. Exploring the drivers of XAI-enhanced clinical decision support systems adoption: Insights from a stimulus-organism-response perspective. Technological Forecasting and Social Change 2023;195:122768 View
  11. Liao X, Yao C, Zhang J, Liu L. Recent advancement in integrating artificial intelligence and information technology with real‐world data for clinical decision‐making in China: A scoping review. Journal of Evidence-Based Medicine 2023;16(4):534 View
  12. van der Vegt A, Scott I, Dermawan K, Schnetler R, Kalke V, Lane P. Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework. Journal of the American Medical Informatics Association 2023;30(9):1503 View
  13. He X, Zheng X, Ding H, Liu Y, Zhu H. AI-CDSS Design Guidelines and Practice Verification. International Journal of Human–Computer Interaction 2024;40(18):5469 View
  14. Stevens A, Stetson P. Theory of trust and acceptance of artificial intelligence technology (TrAAIT): An instrument to assess clinician trust and acceptance of artificial intelligence. Journal of Biomedical Informatics 2023;148:104550 View
  15. Stewart J, Freeman S, Eroglu E, Dumitrascu N, Lu J, Goudie A, Sprivulis P, Akhlaghi H, Tran V, Sanfilippo F, Celenza A, Than M, Fatovich D, Walker K, Dwivedi G. Attitudes towards artificial intelligence in emergency medicine. Emergency Medicine Australasia 2024;36(2):252 View
  16. Okada Y, Ning Y, Ong M. Explainable artificial intelligence in emergency medicine: an overview. Clinical and Experimental Emergency Medicine 2023;10(4):354 View
  17. Shevtsova D, Ahmed A, Boot I, Sanges C, Hudecek M, Jacobs J, Hort S, Vrijhoef H. Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study. JMIR Human Factors 2024;11:e47031 View
  18. Giddings R, Joseph A, Callender T, Janes S, van der Schaar M, Sheringham J, Navani N. Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review. The Lancet Digital Health 2024;6(2):e131 View
  19. Rony M, Kayesh I, Bala S, Akter F, Parvin M. Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals - A descriptive qualitative study. Heliyon 2024;10(4):e25718 View
  20. Dingel J, Kleine A, Cecil J, Sigl A, Lermer E, Gaube S. Predictors of Health Care Practitioners’ Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology. Journal of Medical Internet Research 2024;26:e57224 View
  21. Cheng R, Aggarwal A, Chakraborty A, Harish V, McGowan M, Roy A, Szulewski A, Nolan B. Implementation considerations for the adoption of artificial intelligence in the emergency department. The American Journal of Emergency Medicine 2024;82:75 View
  22. Rane N, Choudhary S, Rane J. Acceptance of artificial intelligence: key factors, challenges, and implementation strategies. SSRN Electronic Journal 2024 View
  23. Raj A, Allababidi A, Kayed H, Gerken A, Müller J, Schoenberg S, Zöllner F, Rink J. Streamlining Acute Abdominal Aortic Dissection Management—An AI-based CT Imaging Workflow. Journal of Imaging Informatics in Medicine 2024;37(6):2729 View
  24. Li B, Beaton D, Lee D, Aljabri B, Al-Omran L, Wijeysundera D, Hussain M, Rotstein O, de Mestral C, Mamdani M, Al-Omran M. Comprehensive review of virtual assistants in vascular surgery. Seminars in Vascular Surgery 2024;37(3):342 View
  25. Kamel Rahimi A, Pienaar O, Ghadimi M, Canfell O, Pole J, Shrapnel S, van der Vegt A, Sullivan C. Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers. Journal of Medical Internet Research 2024;26:e49655 View
  26. Rony M, Numan S, Akter K, Tushar H, Debnath M, Johra F, Akter F, Mondal S, Das M, Uddin M, Begum J, Parvin M. Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare. Heliyon 2024;10(17):e36702 View
  27. El Tantawi M, Ammar N, Mariño R, Uribe S, Manton D, Hugo F, Clément C, Sim C, Maret D, Kopycka-Kedzierawski D, Mbende E, Kruger E, Lan R, Doghri L, Castelaz M, Alam M, Ibiyemi O, Naidoo S, Schwarz E, Priya H, Braga M, Giraudeau N, Foláyan M. Developing the teledentistry acceptance survey for dentists – TAS-D: a Delphi study. BMC Oral Health 2024;24(1) View
  28. Liao X, Yao C, Jin F, Zhang J, Liu L. Barriers and facilitators to implementing imaging-based diagnostic artificial intelligence-assisted decision-making software in hospitals in China: a qualitative study using the updated Consolidated Framework for Implementation Research. BMJ Open 2024;14(9):e084398 View
  29. Karakoç M, Durusoy H, Özdemir E, Bilek E. Reçete Yazımında Yapay Zekâ Kullanımı Konusunda Tıp Fakültesi Öğrencilerinin Bilgi Düzeyleri ve Beklentilerinin Araştırılması. OSMANGAZİ JOURNAL OF MEDICINE 2024;46(6) View
  30. Ramgopal S, Macy M, Hayes A, Florin T, Carroll M, Kshetrapal A. Clinician Perspectives on Decision Support and AI-based Decision Support in a Pediatric ED. Hospital Pediatrics 2024;14(10):828 View
  31. Haga S. Artificial intelligence, medications, pharmacogenomics, and ethics. Pharmacogenomics 2024;25(14-15):611 View
  32. Sobaih A, Chaibi A, Brini R, Abdelghani Ibrahim T. Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration. European Journal of Investigation in Health, Psychology and Education 2025;15(1):6 View
  33. Irgang L, Sestino A, Barth H, Holmén M. Healthcare workers' adoption of and satisfaction with artificial intelligence: The counterintuitive role of paradoxical tensions and paradoxical mindset. Technological Forecasting and Social Change 2025;212:123967 View
  34. Giebel G, Raszke P, Nowak H, Palmowski L, Adamzik M, Heinz P, Tokic M, Timmesfeld N, Brunkhorst F, Wasem J, Blase N. Problems and Barriers Related to the Use of AI-Based Clinical Decision Support Systems: Interview Study. Journal of Medical Internet Research 2025;27:e63377 View
  35. Wang J, Zhou Y, Tan K, Yu Z, Li Y. Acceptance of artificial intelligence clinical assistant decision support system to prevent and control venous thromboembolism among healthcare workers: an extend Unified Theory of Acceptance and Use of Technology Model. Frontiers in Medicine 2025;12 View
  36. O'Grady M, Harrison L, Suleiman A, Hutchison M, Kwon N, Muench F, Kapoor S. Text Messaging Interventions for Unhealthy Alcohol Use in Emergency Departments: Mixed Methods Assessment of Implementation Barriers and Facilitators. JMIR Formative Research 2025;9:e65187 View
  37. Masukawa K, Hirayama H, Tagami K, Kawashima A, Chiu S, Ito K, Matsuzaka S, Aoyama M, Mori M. Needs of bereaved families of patients with cancer towards artificial intelligence in palliative care: A web-based survey. European Journal of Oncology Nursing 2025;76:102875 View
  38. Lee A, Ramasamy R, Subbarao A. Barriers to and Facilitators of Technology Adoption in Emergency Departments: A Comprehensive Review. International Journal of Environmental Research and Public Health 2025;22(4):479 View
  39. Arends B, McCormick J, van der Harst P, Heus P, van Es R. Barriers, facilitators and strategies for the implementation of artificial intelligence‐based electrocardiogram interpretation: A mixed‐methods study. European Journal of Clinical Investigation 2025;55(S1) View
  40. Scipion C, Manchester M, Federman A, Wang Y, Arias J. Barriers to and facilitators of clinician acceptance and use of artificial intelligence in healthcare settings: a scoping review. BMJ Open 2025;15(4):e092624 View
  41. Sabbe M, Lassen A, de Groot B. Qualitative research in emergency medicine, closing the implementation gap. European Journal of Emergency Medicine 2025;32(3):151 View

Books/Policy Documents

  1. Mouloudj K, LE V, Bouarar A, Bouarar A, Asanza D, Srivastava M. The Use of Artificial Intelligence in Digital Marketing. View
  2. Katebi M, Poshdar M, Babaeian Jelodar M, Zihayat Kermani M. CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. View
  3. Katebi M, Poshdar M, Babaeian Jelodar M, Zihayat Kermani M. CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. View
  4. Pilch A, Zygała R, Gryncewicz W, Dyvak M, Melnyk A. Emerging Challenges in Intelligent Management Information Systems. View