Analysis of Receiving Surveillance Information System for Public Health Centre (SISPHEC.ID Application) using Technology Acceptance Model (TAM) at Kuningan District, Indonesia
Article Sidebar
Main Article Content
Abstract
One of the vigilance efforts against the next stage of the COVID-19 pandemic is the strengthening of surveillance information systems. The use of the Public Health Centres surveillance information system (Sisphec.id) is important in determining policies, but it needs to be tested for acceptance. The purpose of this study is to examine the use and acceptance of the Public Health Centre surveillance information system for COVID-19 with applications in Kuningan Regency. This research is a quantitative descriptive study, conducted in Kuningan Regency from October 26 to November 26, 2022. The population was 37 Public Health Centre surveillance officers and a sample of 20 respondents. Descriptive data analysis with Tableau public application and STATA 16. Result: perceived Ease of Use for applications is 85% is perceived as good, the perceived usefulness is 65% perceived as good, the social factor of users related to the application is 85% perceived, the behavioural interest variable for using the application is 90% is perceived as good, facility conditions affecting users, 90% perceived as good, usage behaviour 65% is perceived as good. The conclusion is most perceived as good for all TAM indicators namely Perceived Ease of Use, perceived usefulness, social factor variables of users, behavioural interest, and variable facility conditions affecting users and user behaviour. The recommended application sisphec.id can be used to support the Covid-19 surveillance system at Public Health Centre in Indonesia.
Downloads
Article Details
Copyright (c) 2023 Cecep Heriana
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
An, M. H., You, S. C., Park, R. W., & Lee, S. (2021). Using an extended technology acceptance model to understand the factors influencing telehealth utilization after flattening the COVID-19 curve in South Korea: Cross-sectional survey study. JMIR Medical Informatics, 9(1). https://doi.org/10.2196/25435 DOI: https://doi.org/10.2196/25435
Brooks, S. K., Webster, R. K., Smith, L. E., Woodland, L., Wessely, S., Greenberg, N., & Rubin, G. J. (2020). The Psychological Impact of Quarantine and How to Reduce It: Rapid Review of the Evidence. SSRN Electronic Journal, (January). https://doi.org/10.2139/ssrn.3532534 DOI: https://doi.org/10.2139/ssrn.3532534
Budiman, F., N, S. S., & Muslih. (2015). Design of Data Integration Between Epidemiology Databases to Support Health Data Centres Using Soa Webservice. In The 2nd SNATIF Proceeding (hal. 95–100).
Carroll, L. N., Au, A. P., Detwiler, L. T., Fu, T., Painter, I. S., & Abernethy, N. F. (2017). Visualization and AnalAytics Tools for Infectious Disease Epidemiology: A Systematic Review. Physiology & behavior, 176(3), 139–148. https://doi.org/10.1016/j.jbi.2014.04.006.Visualization
Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., … Vespignani, A. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science, 368(6489), 395–400. https://doi.org/10.1126/science.aba9757 DOI: https://doi.org/10.1126/science.aba9757
Chuenyindee, T., Ong, A. K. S., Prasetyo, Y. T., Persada, S. F., Nadlifatin, R., & Sittiwatethanasiri, T. (2022). Factors Affecting the Perceived Usability of the COVID-19 Contact-Tracing Application “Thai Chana” during the Early COVID-19 Omicron Period. International Journal of Environmental Research and Public Health, 19(7). https://doi.org/10.3390/ijerph19074383 DOI: https://doi.org/10.3390/ijerph19074383
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology.
Degeling, C., Chen, G., Gilbert, G. L., Brookes, V., Thai, T., Wilson, A., & Johnson, J. (2020). Changes in public preferences for technologically enhanced surveillance following the COVID-19 pandemic: A discrete choice experiment. BMJ Open, 10(11), 1–9. https://doi.org/10.1136/bmjopen-2020-041592 DOI: https://doi.org/10.1136/bmjopen-2020-041592
Heriana, C., Faridah, M. A., & Rana, S. (2021). Evaluation of the COVID-19 Surveillance Indicators at The Peak of The First Wave in January-February 2021 in a District of West Java Province, Indonesia. In Iyus Yosep (Ed.), Nursing Symposium, Faculty of Nursing Padjadjaran University (hal. 128). Bandung: Padjadjaran University.
Kaspar, K. (2020). Motivations for social distancing and app use as complementary measures to combat the COVID-19 pandemic: Quantitative survey study. Journal of Medical Internet Research, 22(8). https://doi.org/10.2196/21613 DOI: https://doi.org/10.2196/21613
MoH. (2020). Presenting COVID-19 Daily Reports Through Online System for COVID-19 Daily Reporting until 20 September 2020 14.00 WIB. Jakarta.
Negari, N., & Eryando, T. (2021). Silacak application is a useful media for COVID- 19 recording and reporting. This application is easy to learn but it still needs improvement and development on several sides. Journal od BIKFOKES, 1(3). DOI: https://doi.org/10.51181/bikfokes.v1i3.5297
Srinivasa Rao, A. S. R., & Vazquez, J. A. (2020). Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey when cities and towns are under quarantine. Infection Control and Hospital Epidemiology, 41(7), 826–830. https://doi.org/10.1017/ice.2020.61 DOI: https://doi.org/10.1017/ice.2020.61
Syairaji, M., & Santoso, D. B. (2019). Input Indicator for Dengue Hemorrhagic Fever (DHF) Surveillance System in Yogyakarta City. Jurnal Manajemen Informasi Kesehatan Indonesia, 7(1), 70. https://doi.org/10.33560/jmiki.v7i1.221 DOI: https://doi.org/10.33560/jmiki.v7i1.221
Task Force Team. (2021). Executive Summary : COVID-19 Contact Tracing Activities in 11 Priority Province. Jakarta.
Terry, D. L., & Buntoro, S. P. (2021). Perceived Usefulness of Telehealth Among Rural Medical Providers: Barriers to Use and Associations with Provider Confidence. Journal of Technology in Behavioral Science, 6(4), 567–571. https://doi.org/10.1007/s41347-021-00215-5 DOI: https://doi.org/10.1007/s41347-021-00215-5
Vahdat, A., Alizadeh, A., Quach, S., & Hamelin, N. (2021). Would you like to shop via mobile app technology? The technology acceptance model, social factors and purchase intention. Australasian Marketing Journal, 29(2), 187–197. https://doi.org/10.1016/j.ausmj.2020.01.002 DOI: https://doi.org/10.1016/j.ausmj.2020.01.002
Velicia-Martin, F., Cabrera-Sanchez, J. P., Gil-Cordero, E., & Palos-Sanchez, P. R. (2021). Researching COVID-19 tracing app acceptance: incorporating theory from the technological acceptance model. PeerJ Computer Science, 7(December 2019), 1–20. https://doi.org/10.7717/peerj-cs.316 DOI: https://doi.org/10.7717/peerj-cs.316
Walrave, M., Waeterloos, C., & Ponnet, K. (2020). Adoption of a contact tracing app for containing COVID-19: A health belief model approach. JMIR Public Health and Surveillance, 6(3), 1–10. https://doi.org/10.2196/20572 DOI: https://doi.org/10.2196/20572
Wang, Q., Su, M., Zhang, M., & Li, R. (2021). Integrating digital technologies and public health to fight covid-19 pandemic: Key technologies, applications, challenges and outlook of digital healthcare. International Journal of Environmental Research and Public Health, 18(11). https://doi.org/10.3390/ijerph18116053 DOI: https://doi.org/10.3390/ijerph18116053
WHO. (2023). WHO Coronavirus (COVID-19) Dashboard. Diambil 1 April 2023, dari https://covid19.who.int/