TY  - JOUR
AU  - Bužančić, Iva
AU  - Belec, Dora
AU  - Držaić, Margita
AU  - Kummer, Ingrid
AU  - Brkić, Jovana
AU  - Fialová, Daniela
AU  - Ortner Hadžiabdić, Maja
PY  - 2024
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/5344
AB  - Aims: The aim of this study was to compare the clinical decision-making for benzodiazepine deprescribing between a healthcare provider (HCP) and an artificial intelligence (AI) chatbot GPT4 (ChatGPT-4). Methods: We analysed real-world data from a Croatian cohort of community-dwelling benzodiazepine patients (n = 154) within the EuroAgeism H2020 ESR 7 project. HCPs evaluated the data using pre-established deprescribing criteria to assess benzodiazepine discontinuation potential. The research team devised and tested AI prompts to ensure consistency with HCP judgements. An independent researcher employed ChatGPT-4 with predetermined prompts to simulate clinical decisions for each patient case. Data derived from human-HCP and ChatGPT-4 decisions were compared for agreement rates and Cohen's kappa. 
Results : Both HPC and ChatGPT identified patients for benzodiazepine deprescribing (96.1% and 89.6%, respectively), showing an agreement rate of 95% (κ=.200,P=.012). Agreement on four deprescribing criteria ranged from 74.7% to 91.3%(lack of indicationκ=.352,P< .001; prolonged useκ=.088,P=.280; safety concernsκ=.123,P=.006; incorrect dosageκ=.264,P=.001). Important limitationsof GPT-4 responses were identified, including 22.1% ambiguous outputs, genericanswers and inaccuracies, posing inappropriate decision-making risks.Conclusions : While AI-HCP agreement is substantial, sole AI reliance poses a risk forunsuitable clinical decision-making. This study's findings reveal both strengths andareas for enhancement of ChatGPT-4 in the deprescribing recommendations within areal-world sample. Our study underscores the need for additional research on chatbot functionality in patient therapy decision-making, further fostering the advance-ment of AI for optimal performance.
PB  - John Wiley and Sons Inc
T2  - British Journal of Clinical Pharmacology
T1  - Clinical decision-making in benzodiazepine deprescribing by healthcare providers vs. AI-assisted approach
VL  - 90
IS  - 3
SP  - 662
EP  - 674
DO  - 10.1111/bcp.15963
ER  - 

@article{
author = "Bužančić, Iva and Belec, Dora and Držaić, Margita and Kummer, Ingrid and Brkić, Jovana and Fialová, Daniela and Ortner Hadžiabdić, Maja",
year = "2024",
abstract = "Aims: The aim of this study was to compare the clinical decision-making for benzodiazepine deprescribing between a healthcare provider (HCP) and an artificial intelligence (AI) chatbot GPT4 (ChatGPT-4). Methods: We analysed real-world data from a Croatian cohort of community-dwelling benzodiazepine patients (n = 154) within the EuroAgeism H2020 ESR 7 project. HCPs evaluated the data using pre-established deprescribing criteria to assess benzodiazepine discontinuation potential. The research team devised and tested AI prompts to ensure consistency with HCP judgements. An independent researcher employed ChatGPT-4 with predetermined prompts to simulate clinical decisions for each patient case. Data derived from human-HCP and ChatGPT-4 decisions were compared for agreement rates and Cohen's kappa. 
Results : Both HPC and ChatGPT identified patients for benzodiazepine deprescribing (96.1% and 89.6%, respectively), showing an agreement rate of 95% (κ=.200,P=.012). Agreement on four deprescribing criteria ranged from 74.7% to 91.3%(lack of indicationκ=.352,P< .001; prolonged useκ=.088,P=.280; safety concernsκ=.123,P=.006; incorrect dosageκ=.264,P=.001). Important limitationsof GPT-4 responses were identified, including 22.1% ambiguous outputs, genericanswers and inaccuracies, posing inappropriate decision-making risks.Conclusions : While AI-HCP agreement is substantial, sole AI reliance poses a risk forunsuitable clinical decision-making. This study's findings reveal both strengths andareas for enhancement of ChatGPT-4 in the deprescribing recommendations within areal-world sample. Our study underscores the need for additional research on chatbot functionality in patient therapy decision-making, further fostering the advance-ment of AI for optimal performance.",
publisher = "John Wiley and Sons Inc",
journal = "British Journal of Clinical Pharmacology",
title = "Clinical decision-making in benzodiazepine deprescribing by healthcare providers vs. AI-assisted approach",
volume = "90",
number = "3",
pages = "662-674",
doi = "10.1111/bcp.15963"
}

Bužančić, I., Belec, D., Držaić, M., Kummer, I., Brkić, J., Fialová, D.,& Ortner Hadžiabdić, M.. (2024). Clinical decision-making in benzodiazepine deprescribing by healthcare providers vs. AI-assisted approach. in British Journal of Clinical Pharmacology
John Wiley and Sons Inc., 90(3), 662-674.
https://doi.org/10.1111/bcp.15963

Bužančić I, Belec D, Držaić M, Kummer I, Brkić J, Fialová D, Ortner Hadžiabdić M. Clinical decision-making in benzodiazepine deprescribing by healthcare providers vs. AI-assisted approach. in British Journal of Clinical Pharmacology. 2024;90(3):662-674.
doi:10.1111/bcp.15963 .

Bužančić, Iva, Belec, Dora, Držaić, Margita, Kummer, Ingrid, Brkić, Jovana, Fialová, Daniela, Ortner Hadžiabdić, Maja, "Clinical decision-making in benzodiazepine deprescribing by healthcare providers vs. AI-assisted approach" in British Journal of Clinical Pharmacology, 90, no. 3 (2024):662-674,
https://doi.org/10.1111/bcp.15963 . .