ChatGPT-3.5 System Usability Scale early assessment among Healthcare Workers: Horizons of adoption in medical practice

dc.contributor.authorAljamaan, Fadi
dc.contributor.authorMalki, Khalid H.
dc.contributor.authorAlhasan, Khalid
dc.contributor.authorJamal, Amr
dc.contributor.authorAltamimi, Ibraheem
dc.contributor.authorKhayat, Afnan
dc.contributor.authorAlhaboob, Ali
dc.contributor.authorAbdulmajeed, Naif
dc.contributor.authorAlshahrani, Fatimah S.
dc.contributor.authorSaad, Khaled
dc.contributor.authorAl-Eyadhy, Ayman
dc.contributor.authorAl-Tawfiq, Jaffar A.
dc.contributor.authorTemsah, Mohamad-Hani
dc.contributor.departmentMedicine, School of Medicine
dc.date.accessioned2024-07-15T09:55:17Z
dc.date.available2024-07-15T09:55:17Z
dc.date.issued2024-04-07
dc.description.abstractArtificial intelligence (AI) chatbots, such as ChatGPT, have widely invaded all domains of human life. They have the potential to transform healthcare future. However, their effective implementation hinges on healthcare workers' (HCWs) adoption and perceptions. This study aimed to evaluate HCWs usability of ChatGPT three months post-launch in Saudi Arabia using the System Usability Scale (SUS). A total of 194 HCWs participated in the survey. Forty-seven percent were satisfied with their usage, 57 % expressed moderate to high trust in its ability to generate medical decisions. 58 % expected ChatGPT would improve patients' outcomes, even though 84 % were optimistic of its potential to improve the future of healthcare practice. They expressed possible concerns like recommending harmful medical decisions and medicolegal implications. The overall mean SUS score was 64.52, equivalent to 50 % percentile rank, indicating high marginal acceptability of the system. The strongest positive predictors of high SUS scores were participants' belief in AI chatbot's benefits in medical research, self-rated familiarity with ChatGPT and self-rated computer skills proficiency. Participants' learnability and ease of use score correlated positively but weakly. On the other hand, medical students and interns had significantly high learnability scores compared to others, while ease of use scores correlated very strongly with participants' perception of positive impact of ChatGPT on the future of healthcare practice. Our findings highlight the HCWs' perceived marginal acceptance of ChatGPT at the current stage and their optimism of its potential in supporting them in future practice, especially in the research domain, in addition to humble ambition of its potential to improve patients' outcomes particularly in regard of medical decisions. On the other end, it underscores the need for ongoing efforts to build trust and address ethical and legal concerns of AI implications in healthcare. The study contributes to the growing body of literature on AI chatbots in healthcare, especially addressing its future improvement strategies and provides insights for policymakers and healthcare providers about the potential benefits and challenges of implementing them in their practice.
dc.eprint.versionFinal published version
dc.identifier.citationAljamaan F, Malki KH, Alhasan K, et al. ChatGPT-3.5 System Usability Scale early assessment among Healthcare Workers: Horizons of adoption in medical practice. Heliyon. 2024;10(7):e28962. Published 2024 Apr 7. doi:10.1016/j.heliyon.2024.e28962
dc.identifier.urihttps://hdl.handle.net/1805/42187
dc.language.isoen_US
dc.publisherElsevier
dc.relation.isversionof10.1016/j.heliyon.2024.e28962
dc.relation.journalHeliyon
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourcePMC
dc.subjectAI in healthcare
dc.subjectArtificial Intelligence
dc.subjectChatGPT
dc.subjectChatbots in medical practice
dc.subjectDigital health
dc.subjectHealthcare technology
dc.subjectHealthcare workers usability
dc.subjectSaudi Arabia
dc.subjectSystem Usability Scale
dc.titleChatGPT-3.5 System Usability Scale early assessment among Healthcare Workers: Horizons of adoption in medical practice
dc.typeArticle
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