Adoption Intentions Toward AI-based Clinical Decision Support Tools: A Tam Study on Hospital Pharmacists
The increasing prevalence of antibiotic resistance leads to an alarming challenge to global healthcare, necessitating the adoption of machine learning (ML)-driven clinical decision support systems (CDSS) to enhance antimicrobial stewardship. Despite its potential, hospital pharmacist’s adoption of...
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UUM Press
2025-07-01
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| Series: | Journal of ICT |
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| Online Access: | https://www.e-journal.uum.edu.my/index.php/jict/article/view/26905 |
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| author | Muhammad Thesa Ghozali Satibi Gerhard Fortwengel |
| author_facet | Muhammad Thesa Ghozali Satibi Gerhard Fortwengel |
| author_sort | Muhammad Thesa Ghozali |
| collection | DOAJ |
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The increasing prevalence of antibiotic resistance leads to an alarming challenge to global healthcare, necessitating the adoption of machine learning (ML)-driven clinical decision support systems (CDSS) to enhance antimicrobial stewardship. Despite its potential, hospital pharmacist’s adoption of ML-based antibiotic resistance predictors remains limited due to usability, trust, organisational support, and perceived risk concerns. This study uses an extended version of Technology Acceptance Model (TAM) to examine the primary factors influencing pharmacists’ behavioural intention (BI) to adopt the ML-driven CDSS. A cross-sectional survey was conducted among 235 hospital pharmacists across major provinces in Indonesia, using a PLS-SEM approach to test hypothesised relationships. The findings reveal that perceived usefulness (PU) (β = 0.355, p < 0.001) is the strongest predictor of BI, followed by trust in technology (TT) (β = 0.179, p = 0.017), social influence (SI) (β = 0.184, p = 0.009), and facilitating conditions (FC) (β = 0.150, p = 0.010). Perceived risk (PR) negatively affects BI (β = -0.103, p = 0.023), highlighting concerns over AI reliability. The study underlines the need for hospital administrators to enhance IT support and training, policymakers to establish AI regulatory frameworks, and professional organisations to promote AI acceptance through peer advocacy. Clinically, increased adoption of ML-driven CDSS can improve the accuracy of antibiotic prescribing, reduce resistance rates, and enhance patient safety. It provides insights into optimising AI interventions in antimicrobial stewardship programmes and guiding future implementation strategies in hospital pharmacy practice.
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| format | Article |
| id | doaj-art-551f73afd96c40e48fe36398ce6eb01f |
| institution | DOAJ |
| issn | 1675-414X 2180-3862 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | UUM Press |
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| series | Journal of ICT |
| spelling | doaj-art-551f73afd96c40e48fe36398ce6eb01f2025-08-20T03:22:48ZengUUM PressJournal of ICT1675-414X2180-38622025-07-0124310.32890/jict2025.24.3.3Adoption Intentions Toward AI-based Clinical Decision Support Tools: A Tam Study on Hospital PharmacistsMuhammad Thesa Ghozali0Satibi1Gerhard Fortwengel2Department of Pharmaceutical Management, School of Pharmacy, Faculty of Medicine and Health Sciences, Universitas Muhammadiyah Yogyakarta, IndonesiaDepartment of Pharmaceutics, Faculty of Pharmacy, Universitas Gadjah Mada, IndonesiaFakultat III, Hochschule Hannover – University of Applied Sciences and Arts, Expo Plaza, Hannover, Lower Saxony, Germany The increasing prevalence of antibiotic resistance leads to an alarming challenge to global healthcare, necessitating the adoption of machine learning (ML)-driven clinical decision support systems (CDSS) to enhance antimicrobial stewardship. Despite its potential, hospital pharmacist’s adoption of ML-based antibiotic resistance predictors remains limited due to usability, trust, organisational support, and perceived risk concerns. This study uses an extended version of Technology Acceptance Model (TAM) to examine the primary factors influencing pharmacists’ behavioural intention (BI) to adopt the ML-driven CDSS. A cross-sectional survey was conducted among 235 hospital pharmacists across major provinces in Indonesia, using a PLS-SEM approach to test hypothesised relationships. The findings reveal that perceived usefulness (PU) (β = 0.355, p < 0.001) is the strongest predictor of BI, followed by trust in technology (TT) (β = 0.179, p = 0.017), social influence (SI) (β = 0.184, p = 0.009), and facilitating conditions (FC) (β = 0.150, p = 0.010). Perceived risk (PR) negatively affects BI (β = -0.103, p = 0.023), highlighting concerns over AI reliability. The study underlines the need for hospital administrators to enhance IT support and training, policymakers to establish AI regulatory frameworks, and professional organisations to promote AI acceptance through peer advocacy. Clinically, increased adoption of ML-driven CDSS can improve the accuracy of antibiotic prescribing, reduce resistance rates, and enhance patient safety. It provides insights into optimising AI interventions in antimicrobial stewardship programmes and guiding future implementation strategies in hospital pharmacy practice. https://www.e-journal.uum.edu.my/index.php/jict/article/view/26905Artificial intelligenceclinical decision support systemmachine learningpharmacist adoptiontechnology acceptance model |
| spellingShingle | Muhammad Thesa Ghozali Satibi Gerhard Fortwengel Adoption Intentions Toward AI-based Clinical Decision Support Tools: A Tam Study on Hospital Pharmacists Journal of ICT Artificial intelligence clinical decision support system machine learning pharmacist adoption technology acceptance model |
| title | Adoption Intentions Toward AI-based Clinical Decision Support Tools: A Tam Study on Hospital Pharmacists |
| title_full | Adoption Intentions Toward AI-based Clinical Decision Support Tools: A Tam Study on Hospital Pharmacists |
| title_fullStr | Adoption Intentions Toward AI-based Clinical Decision Support Tools: A Tam Study on Hospital Pharmacists |
| title_full_unstemmed | Adoption Intentions Toward AI-based Clinical Decision Support Tools: A Tam Study on Hospital Pharmacists |
| title_short | Adoption Intentions Toward AI-based Clinical Decision Support Tools: A Tam Study on Hospital Pharmacists |
| title_sort | adoption intentions toward ai based clinical decision support tools a tam study on hospital pharmacists |
| topic | Artificial intelligence clinical decision support system machine learning pharmacist adoption technology acceptance model |
| url | https://www.e-journal.uum.edu.my/index.php/jict/article/view/26905 |
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