Discrepancy between perceptions and acceptance of clinical decision support Systems: implementation of artificial intelligence for vancomycin dosing

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..

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:23

Enthalten in:

BMC medical informatics and decision making - 23(2023), 1 vom: 11. Aug.

Sprache:

Englisch

Beteiligte Personen:

Liu, Xinyan [VerfasserIn]
Barreto, Erin F. [VerfasserIn]
Dong, Yue [VerfasserIn]
Liu, Chang [VerfasserIn]
Gao, Xiaolan [VerfasserIn]
Tootooni, Mohammad Samie [VerfasserIn]
Song, Xuan [VerfasserIn]
Kashani, Kianoush B. [VerfasserIn]

Links:

Volltext [kostenfrei]

Themen:

Acute kidney injury
Artificial intelligence
Drug dosing
Implementation science
Qualitative study

Anmerkungen:

© BioMed Central Ltd., part of Springer Nature 2023

doi:

10.1186/s12911-023-02254-9

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

OLC2144976422

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