BMC Medical Informatics and Decision Making | |
Discrepancy between perceptions and acceptance of clinical decision support Systems: implementation of artificial intelligence for vancomycin dosing | |
Research Article | |
Yue Dong1  Erin F. Barreto2  Xiaolan Gao3  Chang Liu4  Kianoush B. Kashani5  Xinyan Liu6  Mohammad Samie Tootooni7  Xuan Song8  | |
[1] Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 55905, Rochester, MN, USA;Department of Pharmacy, Mayo Clinic, 55905, Rochester, MN, USA;Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, 55905, Rochester, MN, USA;Department of Critical Care Medicine, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, 230001, Hefei, Anhui, China;Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, 55905, Rochester, MN, USA;Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, 430071, Wuhan, Hubei, China;Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, 55905, Rochester, MN, USA;Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 200 First Street SW, 55905, Rochester, MN, USA;Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, 55905, Rochester, MN, USA;ICU, DongE Hospital Affiliated to Shandong First Medical University, 252200, Liaocheng, Shandong, China;Health Informatics and Data Science. Health Sciences Campus, Loyola University, 60611, Chicago, IL, USA;ICU, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 250098, Jinan, Shandong, China; | |
关键词: Artificial intelligence; Qualitative study; Implementation science; Acute kidney injury; Drug dosing; | |
DOI : 10.1186/s12911-023-02254-9 | |
received in 2022-07-17, accepted in 2023-07-31, 发布年份 2023 | |
来源: Springer | |
【 摘 要 】
BackgroundArtificial intelligence (AI) tools are more effective if accepted by clinicians. We developed an AI-based clinical decision support system (CDSS) to facilitate vancomycin dosing. This qualitative study assesses clinicians' perceptions regarding CDSS implementation.MethodsThirteen semi-structured interviews were conducted with critical care pharmacists, at Mayo Clinic (Rochester, MN), from March through April 2020. Eight clinical cases were discussed with each pharmacist (N = 104). Following initial responses, we revealed the CDSS recommendations to assess participants' reactions and feedback. Interviews were audio-recorded, transcribed, and summarized.ResultsThe participants reported considerable time and effort invested daily in individualizing vancomycin therapy for hospitalized patients. Most pharmacists agreed that such a CDSS could favorably affect (N = 8, 62%) or enhance (9, 69%) their ability to make vancomycin dosing decisions. In case-based evaluations, pharmacists' empiric doses differed from the CDSS recommendation in most cases (88/104, 85%). Following revealing the CDSS recommendations, we noted 78% (69/88) discrepant doses. In discrepant cases, pharmacists indicated they would not alter their recommendations. The reasons for declining the CDSS recommendation were general distrust of CDSS, lack of dynamic evaluation and in-depth analysis, inability to integrate all clinical data, and lack of a risk index.ConclusionWhile pharmacists acknowledged enthusiasm about the advantages of AI-based models to improve drug dosing, they were reluctant to integrate the tool into clinical practice. Additional research is necessary to determine the optimal approach to implementing CDSS at the point of care acceptable to clinicians and effective at improving patient outcomes.
【 授权许可】
CC BY
© BioMed Central Ltd., part of Springer Nature 2023
【 预 览 】
Files | Size | Format | View |
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RO202309155273644ZK.pdf | 1033KB | download | |
Table 2 | 189KB | Table | download |
MediaObjects/13046_2023_2749_MOESM7_ESM.pdf | 613KB | download |
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