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Status of AI-Enabled Clinical Decision Support Systems Implementations in ChinaFunding This work was supported by the “2019 China Medical AI Development Research Project” of the National Institute of Hospital Administration.
Background AI-enabled Clinical Decision Support Systems (AI + CDSSs) were heralded to contribute greatly to the advancement of health care services. There is an increased availability of monetary funds and technical expertise invested in projects and proposals targeting the building and implementation of such systems. Therefore, understanding the actual system implementation status in clinical practice is imperative.
Objectives The aim of the study is to understand (1) the current situation of AI + CDSSs clinical implementations in Chinese hospitals and (2) concerns regarding AI + CDSSs current and future implementations.
Methods We investigated 160 tertiary hospitals from six provinces and province-level cities. Descriptive analysis, two-sided Fisher exact test, and Mann-Whitney U-test were utilized for analysis.
Results Thirty-eight of the surveyed hospitals (23.75%) had implemented AI + CDSSs. There were statistical differences on grade, scales, and medical volume between the two groups of hospitals (implemented vs. not-implemented AI + CDSSs, p <0.05). On the 5-point Likert scale, 81.58% (31/38) of respondents rated their overall satisfaction with the systems as “just neutral” to “satisfied.” The three most common concerns were system functions improvement and integration into the clinical process, data quality and availability, and methodological bias.
Conclusion While AI + CDSSs were not yet widespread in Chinese clinical settings, professionals recognize the potential benefits and challenges regarding in-hospital AI + CDSSs.
Keywordsartificial intelligence - AI-enabled clinical decision support systems - clinical decision support systems - clinical implementation - survey
This study was submitted to and approved by the Ethics Review Committee, Children's Hospital of Shanghai/Shanghai Children's Hospital, Shanghai Jiao Tong University. The informed consent obtained from study participants was written on the front page of the electronic questionnaires.
Received: 14 February 2021
Accepted: 17 August 2021
25 October 2021 (online)
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- 1 Reddy S, Fox J, Purohit MP. Artificial intelligence-enabled healthcare delivery. J R Soc Med 2019; 112 (01) 22-28
- 2 Shortliffe EH, Davis R, Axline SG, Buchanan BG, Green CC, Cohen SN. Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Comput Biomed Res 1975; 8 (04) 303-320
- 3 Lemaire JB, Schaefer JP, Martin LA, Faris P, Ainslie MD, Hull RD. Effectiveness of the quick medical reference as a diagnostic tool. CMAJ 1999; 161 (06) 725-728
- 4 NSTC. National Artificial Intelligence Research and Development Strategic Plan. 2019 Accessed December 18, 2019 at: https://www.nitrd.gov/PUBS/national_ai_rd_strategic_plan.pdf
- 5 Hall DW, Pesenti J. Growing the artificial intelligence industry in the UK. 2019 Accessed December 18, 2019 at: https://www.gov.uk/government/publications/growing-the-artificial-intelligence-industry-in-the-uk
- 6 van Hartskamp M, Consoli S, Verhaegh W, Petković M, van de Stolpe A. Artificial intelligence in clinical health care applications: viewpoint. Interact J Med Res 2019; 8 (02) e12100
- 7 CBInsights. Healthcare Remains The Hottest AI Category For Deals. 2019 Accessed December 1, 2019 at: https://www.cbinsights.com/research/artificial-intelligence-healthcare-startups-investors/
- 8 EOIntelligence. AI Innovation of Healthcare Industry in China. Dec. 2019. Accessed December 14, 2019 at: https://www.iyiou.com/intelligence/report561.html
- 9 Ream M, Woods T., Joshi I, Da L.. Accelerating Artificial Intelligence in health and care: results from a state of the nation survey (NHS). 2018 Available at: https://www.ahsnnetwork.com/
- 10 He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med 2019; 25 (01) 30-36
- 11 Sim I, Gorman P, Greenes RA. et al. Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc 2001; 8 (06) 527-534
- 12 Haynes RB, Wilczynski NL. Computerized Clinical Decision Support System (CCDSS) Systematic Review Team. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: methods of a decision-maker-researcher partnership systematic review. Implement Sci 2010; 5 (05) 12
- 13 Grout RW, Cheng ER, Carroll AE, Bauer NS, Downs SM. A six-year repeated evaluation of computerized clinical decision support system user acceptability. Int J Med Inform 2018; 112: 74-81
- 14 Daniel G, Silcox C, Sharma I, Wright MB. Current State and Near-Term Priorities for AI-Enabled Diagnostic Support Software in Health Care. Duke-Margolis Center for Health Policy. 2019. Updated June 6. Accessed November 9, 2019 at: https://healthpolicy.duke.edu/publications/current-state-and-near-term-priorities-ai-enabled-diagnostic-support-software-health
- 15 Salem H, Attiya G, El-Fishawy N. A survey of multi-agent based intelligent decision support system for medical classification problems. Int J Comput Appl 2015; 123 (10) 20-25
- 16 Aljaaf AJ, Al-Jumeily D, Hussain AJ, Fergus P, Al-Jumaily M, Abdel-Aziz K. Toward an Optimal Use of Artificial Intelligence Techniques within a Clinical Decision Support System. Paper presented at: Science and Information Conference; 2015. London, UK:548–554
- 17 FDA. Digital Health Innovation Action Plan. 2019 Accessed November 9, 2019 at: https://www.fda.gov/media/106331/download
- 18 FDA. FDA permits marketing of clinical decision support software for alerting providers of a potential stroke in patients. 2021 Accessed February 13, 2018 at: https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-clinical-decision-support-software-alerting-providers-potential-stroke
- 19 FDA. FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. 2021 Accessed April 11, 2018 at: https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-artificial-intelligence-based-device-detect-certain-diabetes-related-eye
- 20 FDA. FDA Authorizes Marketing of First Device that Uses Artificial Intelligence to Help Detect Potential Signs of Colon Cancer. 2021 Accessed April 9, 2021 at: https://www.fda.gov/news-events/press-announcements/fda-authorizes-marketing-first-device-uses-artificial-intelligence-help-detect-potential-signs-colon
- 21 Eysenbach G. Improving the quality of web surveys: the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). J Med Internet Res 2004; 6 (03) e34
- 22 Kelley K, Clark B, Brown V, Sitzia J. Good practice in the conduct and reporting of survey research. Int J Qual Health Care 2003; 15 (03) 261-266
- 23 Burns KE, Duffett M, Kho ME. et al; ACCADEMY Group. A guide for the design and conduct of self-administered surveys of clinicians. CMAJ 2008; 179 (03) 245-252
- 24 Rastogi V. Software development life cycle models comparison, consequences. Int J Comput Sci Inf Technol 2015; 6 (01) 168-172
- 25 Shinners L, Aggar C, Grace S, Smith S. Exploring healthcare professionals' understanding and experiences of artificial intelligence technology use in the delivery of healthcare: an integrative review. Health Informatics J 2020; 26 (02) 1225-1236
- 26 Maassen O, Fritsch S, Palm J. et al. Future medical artificial intelligence application requirements and expectations of physicians in German University Hospitals: web-based survey. J Med Internet Res 2021; 23 (03) e26646
- 27 Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med 2016; 375 (13) 1216-1219
- 28 Lewis SJ, Gandomkar Z, Brennan PC. Artificial Intelligence in medical imaging practice: looking to the future. J Med Radiat Sci 2019; 66 (04) 292-295
- 29 Shung DL, Sung JJY. Challenges of developing artificial intelligence-assisted tools for clinical medicine. J Gastroenterol Hepatol 2021; 36 (02) 295-298
- 30 Chua IS, Gaziel-Yablowitz M, Korach ZT. et al. Artificial intelligence in oncology: path to implementation. Cancer Med 2021; 10 (12) 4138-4149
- 31 Vermeulen J, Verwey R, Hochstenbach LM, van der Weegen S, Man YP, de Witte LP. Experiences of multidisciplinary development team members during user-centered design of telecare products and services: a qualitative study. J Med Internet Res 2014; 16 (05) e124
- 32 Baslymana M, Almoaber B, Amyot D, Bouattane EM. Using goals and indicators for activity-based process integration in healthcare. Procedia Comput Sci 2017; (113) 318-325
- 33 Campbell R. The five “rights” of clinical decision support. J AHIMA 2013; 84 (10) 42-47 , quiz 48
- 34 Delvaux N, Van Thienen K, Heselmans A, de Velde SV, Ramaekers D, Aertgeerts B. The effects of computerized clinical decision support systems on laboratory test ordering: a systematic review. Arch Pathol Lab Med 2017; 141 (04) 585-595
- 35 Borum C. Barriers for hospital-based nurse practitioners utilizing clinical decision support systems: a systematic review. Comput Inform Nurs 2018; 36 (04) 177-182
- 36 Qu Z, Krauth C, Amelung VE. et al. Decision modelling for economic evaluation of liver transplantation. World J Hepatol 2018; 10 (11) 837-848
- 37 Liu H, Zhong C, Alnusair A, Islam Sr FAIXID. A framework for enhancing AI explainability of intrusion detection results using data cleaning techniques. J Netw Syst Manage 2021; 29 (04) 1-30
- 38 Liang H, Tsui BY, Ni H. et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med 2019; 25 (03) 433-438
- 39 Petersen C, Smith J, Freimuth RR. et al. Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper. J Am Med Inform Assoc 2021; 28 (04) 677-684
- 40 Zhang Y, Qiu M, Tsai C-W, Hassan MM, Alamri A. Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst J 2017; 11 (01) 88-95
- 41 Howard J. Artificial intelligence: implications for the future of work. Am J Ind Med 2019; 62 (11) 917-926
- 42 Fan W, Liu J, Zhu S, Pardalos PM. Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Ann Oper Res 2020; 294: 567-592
- 43 Magrabi F, Ammenwerth E, McNair JB. et al. Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications. Yearb Med Inform 2019; 28 (01) 128-134
- 44 Chon A, Marie-Claude B, Iris J, Richard W. Enriching our theoretical repertoire: the role of evolutionary psychology in technology acceptance. Eur J Inf Syst 2013; 22 (01) 56-75