AI Readiness, Perceived Utility, and Professional Integration among Medical Technologists in Cavite: A Socio-Technical Study
DOI:
https://doi.org/10.65339/ijsis.V1.I3.149Keywords:
Artificial Intelligence, AI Readiness, Clinical Laboratory, Medical Technologists, Perceived Utility, Philippines, Professional Integration, Socio-TechnicalAbstract
This study assessed the levels of AI readiness, perceived utility, and professional integration of artificial intelligence among medical technologists in selected clinical and hospital laboratories in Cavite and examined the relationships among these variables. Grounded in a socio-technical perspective that integrates the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology, and Socio-Technical Systems Theory, the research employed a descriptive-correlational design. One hundred licensed medical technologists were selected through purposive sampling according to predefined inclusion criteria. AI readiness was measured with the Medical Artificial Intelligence Readiness Scale for Medical Students, perceived utility with the Professional Impact subscale of the Shinners Artificial Intelligence Perception questionnaire, and professional integration with items adapted from the Unified Theory of Acceptance and Use of Technology. Data were analyzed using descriptive statistics and Spearman’s rank-order correlation. Respondents demonstrated high levels of AI readiness (mean = 3.60) and perceived utility (mean = 3.64), indicating adequate knowledge, technical skills, and positive perceptions toward AI use in laboratory practice. Professional integration was also rated high (mean = 3.61), although AI remained only moderately embedded in routine workflows and many respondents were neutral regarding the actual extent of use. Correlation analyses revealed very weak, non-significant relationships among the three constructs, indicating that individual readiness and favorable perceptions did not translate into greater professional integration. The findings suggest that organizational and contextual factors—such as institutional support, infrastructure, and policy implementation—play a more decisive role in AI adoption than individual-level attributes alone. The study recommends structured, hands-on AI training programs tailored to laboratory practice, clear institutional policies and protocols for incorporating AI outputs into diagnostic workflows, and future research employing probability sampling, multi-regional samples, and longitudinal designs. The research contributes to Sustainable Development Goal 3 by supporting improved diagnostic capacity and laboratory-enabled clinical decision-making, and to Sustainable Development Goal 9 by examining the conditions required for sustainable adoption of innovative health technologies. It advances public-health and institutional sustainability by clarifying the socio-technical determinants that enable resilient, digitally enabled laboratory services in resource-constrained settings.
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Copyright (c) 2026 Honeylet O. Arancon, Maria Teresa F. Mateo, Canary Anne B. Cantuba, Gallardo C. Dalhag, Rendix Rey A. Catindig, Dr. Jamilah Baragona (Author)
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