Clinical decision-making in benzodiazepine deprescribing by healthcare providers vs. AI-assisted approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11110%2F24%3A10487615" target="_blank" >RIV/00216208:11110/24:10487615 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11160/24:10487615

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=5kFY6XZxZf" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=5kFY6XZxZf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/bcp.15963" target="_blank" >10.1111/bcp.15963</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Clinical decision-making in benzodiazepine deprescribing by healthcare providers vs. AI-assisted approach

Popis výsledku v původním jazyce

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&apos;s kappa. Results: Both HPC and ChatGPT identified patients for benzodiazepine deprescribing (96.1% and 89.6%, respectively), showing an agreement rate of 95% (kappa = .200, P = .012). Agreement on four deprescribing criteria ranged from 74.7% to 91.3% (lack of indication kappa = .352, P &lt; .001; prolonged use kappa = .088, P = .280; safety concerns kappa = .123, P = .006; incorrect dosage kappa = .264, P = .001). Important limitations of GPT-4 responses were identified, including 22.1% ambiguous outputs, generic answers and inaccuracies, posing inappropriate decision-making risks. Conclusions: While AI-HCP agreement is substantial, sole AI reliance poses a risk for unsuitable clinical decision-making. This study&apos;s findings reveal both strengths and areas for enhancement of ChatGPT-4 in the deprescribing recommendations within a real-world sample. Our study underscores the need for additional research on chatbot functionality in patient therapy decision-making, further fostering the advancement of AI for optimal performance.

Název v anglickém jazyce

Clinical decision-making in benzodiazepine deprescribing by healthcare providers vs. AI-assisted approach

Popis výsledku anglicky

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&apos;s kappa. Results: Both HPC and ChatGPT identified patients for benzodiazepine deprescribing (96.1% and 89.6%, respectively), showing an agreement rate of 95% (kappa = .200, P = .012). Agreement on four deprescribing criteria ranged from 74.7% to 91.3% (lack of indication kappa = .352, P &lt; .001; prolonged use kappa = .088, P = .280; safety concerns kappa = .123, P = .006; incorrect dosage kappa = .264, P = .001). Important limitations of GPT-4 responses were identified, including 22.1% ambiguous outputs, generic answers and inaccuracies, posing inappropriate decision-making risks. Conclusions: While AI-HCP agreement is substantial, sole AI reliance poses a risk for unsuitable clinical decision-making. This study&apos;s findings reveal both strengths and areas for enhancement of ChatGPT-4 in the deprescribing recommendations within a real-world sample. Our study underscores the need for additional research on chatbot functionality in patient therapy decision-making, further fostering the advancement of AI for optimal performance.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30104 - Pharmacology and pharmacy

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

British Journal of Clinical Pharmacology

ISSN

0306-5251

e-ISSN

1365-2125

Svazek periodika

90

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

662-674

Kód UT WoS článku

001113011000001

EID výsledku v databázi Scopus

2-s2.0-85178459514