Discrepancy between perceptions and acceptance of clinical decision support Systems: implementation of artificial intelligence for vancomycin dosing
Abstract Background Artificial 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. Methods T...
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| Format: | Article |
| Language: | English |
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BMC
2023-08-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-023-02254-9 |
| _version_ | 1797559294208507904 |
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| author | Xinyan Liu Erin F. Barreto Yue Dong Chang Liu Xiaolan Gao Mohammad Samie Tootooni Xuan Song Kianoush B. Kashani |
| author_facet | Xinyan Liu Erin F. Barreto Yue Dong Chang Liu Xiaolan Gao Mohammad Samie Tootooni Xuan Song Kianoush B. Kashani |
| author_sort | Xinyan Liu |
| collection | DOAJ |
| description | Abstract Background Artificial 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. Methods Thirteen 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. Results The 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. Conclusion While 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. |
| first_indexed | 2024-03-10T17:43:17Z |
| format | Article |
| id | doaj.art-ed66a0633f1143ee91915d0803eb35dd |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-03-10T17:43:17Z |
| publishDate | 2023-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-ed66a0633f1143ee91915d0803eb35dd2023-11-20T09:38:19ZengBMCBMC Medical Informatics and Decision Making1472-69472023-08-012311910.1186/s12911-023-02254-9Discrepancy between perceptions and acceptance of clinical decision support Systems: implementation of artificial intelligence for vancomycin dosingXinyan Liu0Erin F. Barreto1Yue Dong2Chang Liu3Xiaolan Gao4Mohammad Samie Tootooni5Xuan Song6Kianoush B. Kashani7Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo ClinicDepartment of Pharmacy, Mayo ClinicDepartment of Anesthesiology and Perioperative Medicine, Mayo ClinicDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo ClinicDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo ClinicHealth Informatics and Data Science. Health Sciences Campus, Loyola UniversityICU, Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo ClinicAbstract Background Artificial 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. Methods Thirteen 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. Results The 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. Conclusion While 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.https://doi.org/10.1186/s12911-023-02254-9Artificial intelligenceQualitative studyImplementation scienceAcute kidney injuryDrug dosing |
| spellingShingle | Xinyan Liu Erin F. Barreto Yue Dong Chang Liu Xiaolan Gao Mohammad Samie Tootooni Xuan Song Kianoush B. Kashani Discrepancy between perceptions and acceptance of clinical decision support Systems: implementation of artificial intelligence for vancomycin dosing BMC Medical Informatics and Decision Making Artificial intelligence Qualitative study Implementation science Acute kidney injury Drug dosing |
| title | Discrepancy between perceptions and acceptance of clinical decision support Systems: implementation of artificial intelligence for vancomycin dosing |
| title_full | Discrepancy between perceptions and acceptance of clinical decision support Systems: implementation of artificial intelligence for vancomycin dosing |
| title_fullStr | Discrepancy between perceptions and acceptance of clinical decision support Systems: implementation of artificial intelligence for vancomycin dosing |
| title_full_unstemmed | Discrepancy between perceptions and acceptance of clinical decision support Systems: implementation of artificial intelligence for vancomycin dosing |
| title_short | Discrepancy between perceptions and acceptance of clinical decision support Systems: implementation of artificial intelligence for vancomycin dosing |
| title_sort | discrepancy between perceptions and acceptance of clinical decision support systems implementation of artificial intelligence for vancomycin dosing |
| topic | Artificial intelligence Qualitative study Implementation science Acute kidney injury Drug dosing |
| url | https://doi.org/10.1186/s12911-023-02254-9 |
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