Implementation qualitative studies of artificial intelligence based on NASSS framework in the real perioperative world: a systematic review_Chinese Journal of Evidence-Based Medicine

Authors：

CAI Jianwen ^1,2 , ZHU Tao ^1,3 ,  LI Weimin ^4,5,6 ,  LI Peiyi ^1,2,3

1. Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
3. The Research Units of West China (2018RU012)-Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
4. Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
5. Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
6. State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

LI Weimin, Email: weimi003@scu.edu.cn; LI Peiyi, Email: lipeiyi@wchscu.cn

Keywords：

Artificial intelligence; Perioperative period; Qualitative research; Systematic review

DOI：

10.7507/1672-2531.202405012

Video：

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Objective To systematically review the qualitative research on the obstacles and promoting factors of artificial intelligence implementation in the real perioperative world. Methods Computer searches were conducted on PubMed, CINAHL, Scopus, Web of Science, ACM Digital Library, Cochrane Library, CNKI, WanFang Data, and VIP databases to collect perioperative studies related to the clinical application of artificial intelligence. The search period was from database establishment until December 31, 2023. Based on the SPIDER model, the quality of the included literature was evaluated using the JBI Epidemiological Scale. The NASSS framework was used to integrate and analyze the qualitative factors discovered during the implementation of the perioperative artificial intelligence system, and a problem item pool was established. Results A total of 22 articles were included, and perioperative stakeholders mainly focused on perioperative artificial intelligence technology users such as anesthesiologists, anesthesiologists, and surgeons. The field of perioperative artificial intelligence services mainly focused on robot surgery. The JBI evaluation score was 4-8 points. The NASSS implementation factor framework consisted of 7 core themes and 27 secondary items. Conclusion It is undeniable that perioperative artificial intelligence has a positive impact on the prognosis, medical quality, and efficiency of surgical patients. However, its clinical application will face influences from adopters, organizational structures, social culture, and other aspects, which will ultimately affect its implementation effect. The existing qualitative research on the influencing factors of perioperative artificial intelligence systems in clinical implementation has problems such as limited quantity, moderate quality, and lack of scientific research based on a systematic implementation factor framework. Conducting scientific and standardized application research will have a guiding effect on the future use of perioperative artificial intelligence and is expected to improve its final service effectiveness.

Citation： CAI Jianwen, ZHU Tao, LI Weimin, LI Peiyi. Implementation qualitative studies of artificial intelligence based on NASSS framework in the real perioperative world: a systematic review. Chinese Journal of Evidence-Based Medicine, 2024, 24(11): 1284-1293. doi: 10.7507/1672-2531.202405012 Copy

1.	严律南. 人工智能在医学领域应用的现状与展望. 中国普外基础与临床杂志, 2018, 25(5): 513-514.
2.	Nepogodiev D, Martin J, Biccard B, et al. Global burden of postoperative death. Lancet, 2019, 393(10170): 401.
3.	Hashimoto DA, Witkowski E, Gao L, et al. Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. Anesthesiology, 2020, 132(2): 379-394.
4.	Durrand JW, Moore J, Danjoux G. Prehabilitation and preparation for surgery: has the digital revolution arrived. Anaesthesia, 2022, 77(6): 635-639.
5.	徐东, 陈江芸, 蔡毅媛. 实施科学的前世今生(上篇)—起源与发展. 协和医学杂志, 2024, 15(2): 442-449.
6.	Shaw J, Rudzicz F, Jamieson T, et al. Artificial intelligence and the implementation challenge. J Med Internet Res, 2019, 21(7): e13659.
7.	van der Meijden SL, Arbous MS, Geerts BF. Possibilities and challenges for artificial intelligence and machine learning in perioperative care. BJA Educ, 2023, 23(8): 288-294.
8.	Borges do Nascimento IJ, Abdulazeem H, Vasanthan LT, et al. Barriers and facilitators to utilizing digital health technologies by healthcare professionals. NPJ Digit Med, 2023, 6(1): 161.
9.	Hamilton AB, Finley EP. Qualitative methods in implementation research: an introduction. Psychiatry Res, 2019, 280: 112516.
10.	谢润生, 徐东, 李慧, 等. 医疗卫生领域中实施科学的研究方法. 中国循证医学杂志, 2020, 20(9): 1104-1110.
11.	张静怡, 张雅婷, 盖琼艳, 等. 定性资料的系统评价方法学汇总. 中国循证心血管医学杂志, 2017, 9(5): 523-527.
12.	靳英辉, 高维杰, 李艳, 等. 质性研究证据评价及其循证转化的研究进展. 中国循证医学杂志, 2015, 15(12): 1458-1464.
13.	Hannes K, Lockwood C, Pearson A. A comparative analysis of three online appraisal instruments' ability to assess validity in qualitative research. Qual Health Res, 2010, 20(12): 1736-1743.
14.	Zeng X, Zhang Y, Kwong JS, et al. The methodological quality assessment tools for preclinical and clinical studies, systematic review and meta-analysis, and clinical practice guideline: a systematic review. J Evid Based Med, 2015, 8(1): 2-10.
15.	陈文嘉, 徐东, 李慧, 等. 实施科学理论的分类与介绍. 中国循证医学杂志, 2020, 20(8): 986-992.
16.	Gama F, Tyskbo D, Nygren J, et al. Implementation frameworks for artificial intelligence translation into health care practice: scoping review. J Med Internet Res, 2022, 24(1): e32215.
17.	Greenhalgh T, Wherton J, Papoutsi C, et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res, 2017, 19(11): e367.
18.	Abraham J, Bartek B, Meng A, et al. Integrating machine learning predictions for perioperative risk management: towards an empirical design of a flexible-standardized risk assessment tool. J Biomed Inform, 2023, 137: 104270.
19.	Abraham J, King CR, Meng A. Ascertaining design requirements for postoperative care transition interventions. Appl Clin Inform, 2021, 12(1): 107-115.
20.	Berge GT, Granmo OC, Tveit TO, et al. Machine learning-driven clinical decision support system for concept-based searching: a field trial in a Norwegian hospital. BMC Med Inform Decis Mak, 2023, 23(1): 5.
21.	Estrada Alamo CE, Diatta F, Monsell SE, et al. Artificial intelligence in anesthetic care: a survey of physician anesthesiologists. Anesth Analg, 2024, 138(5): 938-950.
22.	Cobianchi L, Verde JM, Loftus TJ, et al. Artificial intelligence and surgery: ethical dilemmas and open issues. J Am Coll Surg, 2022, 235(2): 268-275.
23.	Collins JW, Marcus HJ, Ghazi A, et al. Ethical implications of AI in robotic surgical training: a Delphi consensus statement. Eur Urol Focus, 2022, 8(2): 613-622.
24.	Greenberg JK, Otun A, Nasraddin A, et al. Electronic clinical decision support for children with minor head trauma and intracranial injuries: a sociotechnical analysis. BMC Med Inform Decis Mak, 2021, 21(1): 161.
25.	Hameed MS, Laplante S, Masino C, et al. What is the educational value and clinical utility of artificial intelligence for intraoperative and postoperative video analysis. A survey of surgeons and trainees. Surg Endosc, 2023, 37(12): 9453-9460.
26.	Henckert D, Malorgio A, Schweiger G, et al. Attitudes of anesthesiologists toward artificial intelligence in anesthesia: a multicenter, mixed qualitative-quantitative study. J Clin Med, 2023, 12(6): 2096.
27.	Kang MJ, De Gagne JC, Kang HS. Perioperative nurses' work experience with robotic surgery: a focus group study. Comput Inform Nurs, 2016, 34(4): 152-158.
28.	King CR, Shambe A, Abraham J. Potential uses of AI for perioperative nursing handoffs: a qualitative study. JAMIA Open, 2023, 6(1): ooad015.
29.	Lesselroth BJ, Yang J, McConnachie J, et al. Addressing the sociotechnical drivers of quality improvement: a case study of post-operative DVT prophylaxis computerised decision support. BMJ Qual Saf, 2011, 20(5): 381-389.
30.	Li B, de Mestral C, Mamdani M, et al. Perceptions of Canadian vascular surgeons toward artificial intelligence and machine learning. J Vasc Surg Cases Innov Tech, 2022, 8(3): 466-472.
31.	Moloney R, Coffey A, Coffey C, et al. Patients' experience of robotic-assisted surgery: a qualitative study. Br J Nurs, 2023, 32(6): 298-305.
32.	Mudumbai S, Ayer F, Stefanko J. Perioperative and ICU healthcare analytics within a veterans integrated system network: a qualitative gap analysis. J Med Syst, 2017, 41(8): 118.
33.	Pecqueux M, Riediger C, Distler M, et al. The use and future perspective of artificial intelligence-a survey among German surgeons. Front Public Health, 2022, 10: 982335.
34.	Randell R, Greenhalgh J, Hindmarsh J, et al. How do team experience and relationships shape new divisions of labour in robot-assisted surgery. A realist investigation. Health (London), 2021, 25(2): 250-268.
35.	Safranek CW, Feitzinger L, Joyner AKC, et al. Visualizing opioid-use variation in a pediatric perioperative dashboard. Appl Clin Inform, 2022, 13(2): 370-379.
36.	Schuessler Z, Scott Stiles A, Mancuso P. Perceptions and experiences of perioperative nurses and nurse anaesthetists in robotic-assisted surgery. J Clin Nurs, 2020, 29(1-2): 60-74.
37.	Silveira Thomas Porto C, Catal E. A comparative study of the opinions, experiences and individual innovativeness characteristics of operating room nurses on robotic surgery. J Adv Nurs, 2021, 77(12): 4755-4767.
38.	Vedula SS, Ghazi A, Collins JW, et al. Artificial intelligence methods and artificial intelligence-enabled metrics for surgical education: a multidisciplinary consensus. J Am Coll Surg, 2022, 234(6): 1181-1192.
39.	Zhao B, Lam J, Hollandsworth HM, et al. General surgery training in the era of robotic surgery: a qualitative analysis of perceptions from resident and attending surgeons. Surg Endosc, 2020, 34(4): 1712-1721.
40.	Maheshwari K, Cywinski JB, Papay F, et al. Artificial intelligence for perioperative medicine: perioperative intelligence. Anesth Analg, 2023, 136(4): 637-645.
41.	破晓过后, 初日照林. 中国人工智能+医疗与生命科学行业研究报告. 艾瑞咨询系列研究报告, 2021, (11): 82.
42.	Jain B, Rahim FO, Thirumala PD, et al. Cost-benefit analysis of intraoperative neuromonitoring for cardiac surgery. J Stroke Cerebrovasc Dis, 2024, 33(3): 107576.
43.	Kiyasseh D, Zhu T, Clifton D. The promise of clinical decision support systems targetting low-resource settings. IEEE Rev Biomed Eng, 2022, 15: 354-371.
44.	刘永. 医院集成平台建设中的数据治理. 信息系统工程, 2023(1): 119-121.
45.	Roshanov PS, Fernandes N, Wilczynski JM, et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ, 2013, 346: f657.
46.	DeCamp M, Lindvall C. Mitigating bias in AI at the point of care. Science, 2023, 381(6654): 150-152.
47.	Ferryman K, Mackintosh M, Ghassemi M. Considering biased data as informative artifacts in AI-assisted health care. N Engl J Med, 2023, 389(9): 833-838.
48.	申红霞, 严斯静, 黎敏仪, 等. 实施科学中实施策略的介绍. 中国循证医学杂志, 2023, 23(6): 708-714.

1. 严律南. 人工智能在医学领域应用的现状与展望. 中国普外基础与临床杂志, 2018, 25(5): 513-514.
2. Nepogodiev D, Martin J, Biccard B, et al. Global burden of postoperative death. Lancet, 2019, 393(10170): 401.
3. Hashimoto DA, Witkowski E, Gao L, et al. Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. Anesthesiology, 2020, 132(2): 379-394.
4. Durrand JW, Moore J, Danjoux G. Prehabilitation and preparation for surgery: has the digital revolution arrived. Anaesthesia, 2022, 77(6): 635-639.
5. 徐东, 陈江芸, 蔡毅媛. 实施科学的前世今生(上篇)—起源与发展. 协和医学杂志, 2024, 15(2): 442-449.
6. Shaw J, Rudzicz F, Jamieson T, et al. Artificial intelligence and the implementation challenge. J Med Internet Res, 2019, 21(7): e13659.
7. van der Meijden SL, Arbous MS, Geerts BF. Possibilities and challenges for artificial intelligence and machine learning in perioperative care. BJA Educ, 2023, 23(8): 288-294.
8. Borges do Nascimento IJ, Abdulazeem H, Vasanthan LT, et al. Barriers and facilitators to utilizing digital health technologies by healthcare professionals. NPJ Digit Med, 2023, 6(1): 161.
9. Hamilton AB, Finley EP. Qualitative methods in implementation research: an introduction. Psychiatry Res, 2019, 280: 112516.
10. 谢润生, 徐东, 李慧, 等. 医疗卫生领域中实施科学的研究方法. 中国循证医学杂志, 2020, 20(9): 1104-1110.
11. 张静怡, 张雅婷, 盖琼艳, 等. 定性资料的系统评价方法学汇总. 中国循证心血管医学杂志, 2017, 9(5): 523-527.
12. 靳英辉, 高维杰, 李艳, 等. 质性研究证据评价及其循证转化的研究进展. 中国循证医学杂志, 2015, 15(12): 1458-1464.
13. Hannes K, Lockwood C, Pearson A. A comparative analysis of three online appraisal instruments' ability to assess validity in qualitative research. Qual Health Res, 2010, 20(12): 1736-1743.
14. Zeng X, Zhang Y, Kwong JS, et al. The methodological quality assessment tools for preclinical and clinical studies, systematic review and meta-analysis, and clinical practice guideline: a systematic review. J Evid Based Med, 2015, 8(1): 2-10.
15. 陈文嘉, 徐东, 李慧, 等. 实施科学理论的分类与介绍. 中国循证医学杂志, 2020, 20(8): 986-992.
16. Gama F, Tyskbo D, Nygren J, et al. Implementation frameworks for artificial intelligence translation into health care practice: scoping review. J Med Internet Res, 2022, 24(1): e32215.
17. Greenhalgh T, Wherton J, Papoutsi C, et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res, 2017, 19(11): e367.
18. Abraham J, Bartek B, Meng A, et al. Integrating machine learning predictions for perioperative risk management: towards an empirical design of a flexible-standardized risk assessment tool. J Biomed Inform, 2023, 137: 104270.
19. Abraham J, King CR, Meng A. Ascertaining design requirements for postoperative care transition interventions. Appl Clin Inform, 2021, 12(1): 107-115.
20. Berge GT, Granmo OC, Tveit TO, et al. Machine learning-driven clinical decision support system for concept-based searching: a field trial in a Norwegian hospital. BMC Med Inform Decis Mak, 2023, 23(1): 5.
21. Estrada Alamo CE, Diatta F, Monsell SE, et al. Artificial intelligence in anesthetic care: a survey of physician anesthesiologists. Anesth Analg, 2024, 138(5): 938-950.
22. Cobianchi L, Verde JM, Loftus TJ, et al. Artificial intelligence and surgery: ethical dilemmas and open issues. J Am Coll Surg, 2022, 235(2): 268-275.
23. Collins JW, Marcus HJ, Ghazi A, et al. Ethical implications of AI in robotic surgical training: a Delphi consensus statement. Eur Urol Focus, 2022, 8(2): 613-622.
24. Greenberg JK, Otun A, Nasraddin A, et al. Electronic clinical decision support for children with minor head trauma and intracranial injuries: a sociotechnical analysis. BMC Med Inform Decis Mak, 2021, 21(1): 161.
25. Hameed MS, Laplante S, Masino C, et al. What is the educational value and clinical utility of artificial intelligence for intraoperative and postoperative video analysis. A survey of surgeons and trainees. Surg Endosc, 2023, 37(12): 9453-9460.
26. Henckert D, Malorgio A, Schweiger G, et al. Attitudes of anesthesiologists toward artificial intelligence in anesthesia: a multicenter, mixed qualitative-quantitative study. J Clin Med, 2023, 12(6): 2096.
27. Kang MJ, De Gagne JC, Kang HS. Perioperative nurses' work experience with robotic surgery: a focus group study. Comput Inform Nurs, 2016, 34(4): 152-158.
28. King CR, Shambe A, Abraham J. Potential uses of AI for perioperative nursing handoffs: a qualitative study. JAMIA Open, 2023, 6(1): ooad015.
29. Lesselroth BJ, Yang J, McConnachie J, et al. Addressing the sociotechnical drivers of quality improvement: a case study of post-operative DVT prophylaxis computerised decision support. BMJ Qual Saf, 2011, 20(5): 381-389.
30. Li B, de Mestral C, Mamdani M, et al. Perceptions of Canadian vascular surgeons toward artificial intelligence and machine learning. J Vasc Surg Cases Innov Tech, 2022, 8(3): 466-472.
31. Moloney R, Coffey A, Coffey C, et al. Patients' experience of robotic-assisted surgery: a qualitative study. Br J Nurs, 2023, 32(6): 298-305.
32. Mudumbai S, Ayer F, Stefanko J. Perioperative and ICU healthcare analytics within a veterans integrated system network: a qualitative gap analysis. J Med Syst, 2017, 41(8): 118.
33. Pecqueux M, Riediger C, Distler M, et al. The use and future perspective of artificial intelligence-a survey among German surgeons. Front Public Health, 2022, 10: 982335.
34. Randell R, Greenhalgh J, Hindmarsh J, et al. How do team experience and relationships shape new divisions of labour in robot-assisted surgery. A realist investigation. Health (London), 2021, 25(2): 250-268.
35. Safranek CW, Feitzinger L, Joyner AKC, et al. Visualizing opioid-use variation in a pediatric perioperative dashboard. Appl Clin Inform, 2022, 13(2): 370-379.
36. Schuessler Z, Scott Stiles A, Mancuso P. Perceptions and experiences of perioperative nurses and nurse anaesthetists in robotic-assisted surgery. J Clin Nurs, 2020, 29(1-2): 60-74.
37. Silveira Thomas Porto C, Catal E. A comparative study of the opinions, experiences and individual innovativeness characteristics of operating room nurses on robotic surgery. J Adv Nurs, 2021, 77(12): 4755-4767.
38. Vedula SS, Ghazi A, Collins JW, et al. Artificial intelligence methods and artificial intelligence-enabled metrics for surgical education: a multidisciplinary consensus. J Am Coll Surg, 2022, 234(6): 1181-1192.
39. Zhao B, Lam J, Hollandsworth HM, et al. General surgery training in the era of robotic surgery: a qualitative analysis of perceptions from resident and attending surgeons. Surg Endosc, 2020, 34(4): 1712-1721.
40. Maheshwari K, Cywinski JB, Papay F, et al. Artificial intelligence for perioperative medicine: perioperative intelligence. Anesth Analg, 2023, 136(4): 637-645.
41. 破晓过后, 初日照林. 中国人工智能+医疗与生命科学行业研究报告. 艾瑞咨询系列研究报告, 2021, (11): 82.
42. Jain B, Rahim FO, Thirumala PD, et al. Cost-benefit analysis of intraoperative neuromonitoring for cardiac surgery. J Stroke Cerebrovasc Dis, 2024, 33(3): 107576.
43. Kiyasseh D, Zhu T, Clifton D. The promise of clinical decision support systems targetting low-resource settings. IEEE Rev Biomed Eng, 2022, 15: 354-371.
44. 刘永. 医院集成平台建设中的数据治理. 信息系统工程, 2023(1): 119-121.
45. Roshanov PS, Fernandes N, Wilczynski JM, et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ, 2013, 346: f657.
46. DeCamp M, Lindvall C. Mitigating bias in AI at the point of care. Science, 2023, 381(6654): 150-152.
47. Ferryman K, Mackintosh M, Ghassemi M. Considering biased data as informative artifacts in AI-assisted health care. N Engl J Med, 2023, 389(9): 833-838.
48. 申红霞, 严斯静, 黎敏仪, 等. 实施科学中实施策略的介绍. 中国循证医学杂志, 2023, 23(6): 708-714.

Chinese Journal of Evidence-Based Medicine

Implementation qualitative studies of artificial intelligence based on NASSS framework in the real perioperative world: a systematic review

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