TY - JOUR
T1 - The Drivers of Acceptance of Artificial Intelligence-Powered Care Pathways Among Medical Professionals
T2 - Web-Based Survey Study
AU - Cornelissen, Lisa
AU - Egher, Claudia
AU - van Beek, Vincent
AU - Williamson, Latoya
AU - Hommes, Daniel
N1 - ©Lisa Cornelissen, Claudia Egher, Vincent van Beek, Latoya Williamson, Daniel Hommes. Originally published in JMIR Formative Research (https://formative.jmir.org), 21.06.2022.
PY - 2022/6/21
Y1 - 2022/6/21
N2 - BACKGROUND: The emergence of Artificial Intelligence (AI) has been proven beneficial in several health care areas. Nevertheless, the uptake of AI in health care delivery remains poor. Despite the fact that the acceptance of AI-based technologies among medical professionals is a key barrier to their implementation, knowledge about what informs such attitudes is scarce.OBJECTIVE: The aim of this study was to identify and examine factors that influence the acceptability of AI-based technologies among medical professionals.METHODS: A survey was developed based on the Unified Theory of Acceptance and Use of Technology model, which was extended by adding the predictor variables perceived trust, anxiety and innovativeness, and the moderator profession. The web-based survey was completed by 67 medical professionals in the Netherlands. The data were analyzed by performing a multiple linear regression analysis followed by a moderating analysis using the Hayes PROCESS macro (SPSS; version 26.0, IBM Corp).RESULTS: Multiple linear regression showed that the model explained 75.4% of the variance in the acceptance of AI-powered care pathways (adjusted R2=0.754; F9,0=22.548; P<.001). The variables medical performance expectancy (β=.465; P<.001), effort expectancy (β=-.215; P=.005), perceived trust (β=.221; P=.007), nonmedical performance expectancy (β=.172; P=.08), facilitating conditions (β=-.160; P=.005), and professional identity (β=.156; P=.06) were identified as significant predictors of acceptance. Social influence of patients (β=.042; P=.63), anxiety (β=.021; P=.84), and innovativeness (β=.078; P=.30) were not identified as significant predictors. A moderating effect by gender was found between the relationship of facilitating conditions and acceptance (β=-.406; P=.09).CONCLUSIONS: Medical performance expectancy was the most significant predictor of AI-powered care pathway acceptance among medical professionals. Nonmedical performance expectancy, effort expectancy, perceived trust, and professional identity were also found to significantly influence the acceptance of AI-powered care pathways. These factors should be addressed for successful implementation of AI-powered care pathways in health care delivery. The study was limited to medical professionals in the Netherlands, where uptake of AI technologies is still in an early stage. Follow-up multinational studies should further explore the predictors of acceptance of AI-powered care pathways over time, in different geographies, and with bigger samples.
AB - BACKGROUND: The emergence of Artificial Intelligence (AI) has been proven beneficial in several health care areas. Nevertheless, the uptake of AI in health care delivery remains poor. Despite the fact that the acceptance of AI-based technologies among medical professionals is a key barrier to their implementation, knowledge about what informs such attitudes is scarce.OBJECTIVE: The aim of this study was to identify and examine factors that influence the acceptability of AI-based technologies among medical professionals.METHODS: A survey was developed based on the Unified Theory of Acceptance and Use of Technology model, which was extended by adding the predictor variables perceived trust, anxiety and innovativeness, and the moderator profession. The web-based survey was completed by 67 medical professionals in the Netherlands. The data were analyzed by performing a multiple linear regression analysis followed by a moderating analysis using the Hayes PROCESS macro (SPSS; version 26.0, IBM Corp).RESULTS: Multiple linear regression showed that the model explained 75.4% of the variance in the acceptance of AI-powered care pathways (adjusted R2=0.754; F9,0=22.548; P<.001). The variables medical performance expectancy (β=.465; P<.001), effort expectancy (β=-.215; P=.005), perceived trust (β=.221; P=.007), nonmedical performance expectancy (β=.172; P=.08), facilitating conditions (β=-.160; P=.005), and professional identity (β=.156; P=.06) were identified as significant predictors of acceptance. Social influence of patients (β=.042; P=.63), anxiety (β=.021; P=.84), and innovativeness (β=.078; P=.30) were not identified as significant predictors. A moderating effect by gender was found between the relationship of facilitating conditions and acceptance (β=-.406; P=.09).CONCLUSIONS: Medical performance expectancy was the most significant predictor of AI-powered care pathway acceptance among medical professionals. Nonmedical performance expectancy, effort expectancy, perceived trust, and professional identity were also found to significantly influence the acceptance of AI-powered care pathways. These factors should be addressed for successful implementation of AI-powered care pathways in health care delivery. The study was limited to medical professionals in the Netherlands, where uptake of AI technologies is still in an early stage. Follow-up multinational studies should further explore the predictors of acceptance of AI-powered care pathways over time, in different geographies, and with bigger samples.
U2 - 10.2196/33368
DO - 10.2196/33368
M3 - Article
C2 - 35727614
SN - 2561-326X
VL - 6
JO - JMIR Formative Research
JF - JMIR Formative Research
IS - 6
M1 - e33368
ER -