OPTIMIZATION OF THE USE OF DIGITAL IMAGING TECHNOLOGY FOR APPROPRIATE DIAGNOSTICS IN CLINICS AND TEACHING HOSPITALS IN NORTH SUMATERA

Abstract

The adoption of digital imaging (DI) technology has revolutionized medical diagnostic practices, offering the potential for increased accuracy, efficiency, and accessibility compared to conventional modalities. However, optimal utilization of DI in healthcare facilities, particularly in resource-poor regions such as North Sumatra, still faces significant challenges. Global trends show a rapid increase in DI investment, but recent data in Indonesia, particularly in North Sumatra, indicates a gap between the availability of technology and its effective implementation for precision diagnostics. This gap stems from a lack of comprehensive understanding of the factors influencing DI adoption and optimization, including infrastructure readiness, human resource capabilities, and appropriateness of usage protocols, which can ultimately compromise the quality of diagnostic services. This study aims to comprehensively analyze the level of optimization of digital imaging technology use for precision diagnostics in clinics and teaching hospitals in North Sumatra and identify key factors contributing to such optimization, based on a modified Technology Acceptance Model (TAM) theoretical framework. A quantitative descriptive-analytical research design with a cross-sectional survey method was used, involving 350 respondents from 15 clinics and 5 teaching hospitals in North Sumatra who were selected through stratified random sampling. A validated and reliable structured questionnaire was used to collect data, which were then analyzed using descriptive statistics and multiple regression analysis. The results showed that the level of optimization of DI use was still moderate (mean score of 3.25 out of 5), with perceived usefulness (β = 0.42, p < 0.001) and human resource competency (β = 0.35, p < 0.001) being the strongest predictors of optimization, explaining 58% of the variance (R-squared = 0.58). Infrastructure support also contributed significantly (β = 0.21, p < 0.01), while perceived ease of use had a smaller influence (β = 0.15, p < 0.05). An unexpected finding highlighted the lack of ongoing training programs as a major barrier to competency. In conclusion, DI optimization in North Sumatra is strongly influenced by perceived usefulness, human resource competency, and infrastructure. This research contributes to the enrichment of the TAM model with contextual variables and provides practical implications in the form of the urgency of developing comprehensive training programs and improving infrastructure. Recommendations include cross-sector collaboration to design more effective DI adoption strategies in the future.