Technology Acceptance Model (TAM) of TELE-ECG to Screen Cardiovascular Disease in Jayapura Regency, Papua - Oktaviono, Yudi Her; Isfandiari, Muhammad Atoillah; Dharmadjati, Budi Baktijasa; Ramadhan, Muhammad; Prabowo, Emil; Ludfy Avianto, Achmad Tri; Muhammad, Raditya Rizki; Tri Saputra, Pandit Bagus (Cardiovascular and Cardiometabolic Journal (CCJ), 2025)
Background: The cardiovascular mortality rate in Papua is still relatively high. This study aims to assess the readiness and benefits of telescreens with tele-ECG on high-risk population based on the Technology Acceptance Model (TAM) theory in Jayapura Regency, Papua. Material and Methods: This cross-sectional study was made based on the Technology Acceptance Model (TAM) questionnaire which was distributed to health workers using a validated Likert scale and analyzed using the Structural Equation Model with LISREL. Result: The results show that the total internet connection is 71% good or very good. Around 77% of respondents felt that telescreening for high-risk cardiovascular disease would be beneficial, with 76% of respondents having the intention to commit to using the telescreening. Conclusion: Internet access in the Jayapura Regency area is quite good and is not an obstacle to providing telescreening with with tele-ECG services. This TAM can be the basis for the development of telescreening for the early detection of high-risk populations so that cardiovascular death can be prevented. - Highlights: This study demonstrates high readiness among healthcare workers in Jayapura Regency to adopt tele-ECG–based cardiovascular screening, with adequate internet connectivity reported in most primary health centers. A majority of respondents perceived tele-ECG as useful, easy to use, and relevant to their clinical practice. Technology Acceptance Model analysis indicates strong intention to use tele-ECG for screening high-risk cardiovascular populations. These findings support tele-ECG telescreening as a feasible and scalable approach for early cardiovascular disease detection in geographically remote and underserved regions