Penggunaan Model Penerimaan Teknologi (TAM) untuk Sistem Catatan Kesehatan Pribadi Oleh Pasien (PHRS)
Keywords:
e-health, Personal, Keamanan Teknologi Aplikasi
Abstract
Personal Health Record System (PHRS) adalah aplikasi elektronik yang memungkinkan individu mengakses, mengelola, dan berbagi informasi kesehatan mereka secara aman dan pribadi. Tujuan dari literature review ini adalah untuk mengeksplorasi faktor-faktor yang mempengaruhi adopsi Personal Health Records (PHRs) oleh individu. Dengan menggunakan Model Penerimaan Teknologi yang diperluas (TAM), pencarian data base literature review ini yang digunakan termasuk SciVerse ScienceDirect, Scopus, Pubmed dan Google scholar. Kata kunci yang digunakan dalam pencarian artikel adalah Hospital Information System, Health Information Technology, EMR Implementation, Patient Portal Technology dengan mendapatkan 749 artikel dan yang digunakan hanya 24 artikel yang sesuai melalui analisis tujuan, kesesuaian topik, metode penelitian yang digunakan, ukuran sampel, etik penelitian, hasil dari setiap artikel, serta keterbatasan yang terjadi.Terdapat hasil yang efektif dalam pelaksanaan promosi kesehatan penggunaan Sistem Catatan Kesehatan Pribadi Oleh Pasien (PHRS).References
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Derecho, K. C., Cafino, R., Aquino-Cafino, S. L., Isla, A., Esencia, J. A., Lactuan, N. J., Maranda, J. A. G., & Velasco, L. C. P. (2024). Technology adoption of electronic medical records in developing economies: A systematic review on physicians’ perspective. Digital Health, 10(2). https://doi.org/10.1177/20552076231224605
Gagnon, M. P., Simonyan, D., Ghandour, E. K., Godin, G., Labrecque, M., Ouimet, M., & Rousseau, M. (2016). Factors influencing electronic health record adoption by physicians: A multilevel analysis. International Journal of Information Management, 36(3), 258–270. https://doi.org/10.1016/j.ijinfomgt.2015.12.002
Harris, M. T., & Rogers, W. A. (2023). Developing a Healthcare Technology Acceptance Model (H-TAM) for Older Adults with Hypertension. Ageing and Society, 43(4), 814–834. https://doi.org/10.1017/S0144686X21001069
Negro-Calduch, E., Azzopardi-Muscat, N., Krishnamurthy, R. S., & Novillo-Ortiz, D. (2021). Technological progress in electronic health record system optimization: Systematic review of systematic literature reviews. International Journal of Medical Informatics, 152, 104507. https://doi.org/10.1016/j.ijmedinf.2021.104507
Upadhyay, S., & Hu, H. F. (2022). A Qualitative Analysis of the Impact of Electronic Health Records (EHR) on Healthcare Quality and Safety: Clinicians’ Lived Experiences. Health Services Insights, 15. https://doi.org/10.1177/11786329211070722
Walle, A. D., Ferede, T. A., Baykemagn, N. D., Shimie, A. W., Kebede, S. D., Tegegne, M. D., Wubante, S. M., Yehula, C. M., Demsash, A. W., Melaku, M. S., & Mengistie, M. B. (2023). Predicting healthcare professionals’ acceptance towards electronic personal health record systems in a resource-limited setting: using modified technology acceptance model. BMJ Health and Care Informatics, 30(1), 1–8. https://doi.org/10.1136/bmjhci-2022-100707
Yousef, C. C., Salgado, T. M., Farooq, A., Burnett, K., McClelland, L. E., Thomas, A., Alenazi, A. O., Esba, L. C. A., AlAzmi, A., Alhameed, A. F., Hattan, A., Elgadi, S., Almekhloof, S., AlShammary, M. A., Alanezi, N. A., Alhamdan, H. S., Khoshhal, S., & DeShazo, J. P. (2021). Predicting patients’ intention to use a personal health record using an adapted unified theory of acceptance and use of technology model: Secondary data analysis. JMIR Medical Informatics, 9(8). https://doi.org/10.2196/30214
Derecho, K. C., Cafino, R., Aquino-Cafino, S. L., Isla, A., Esencia, J. A., Lactuan, N. J., Maranda, J. A. G., & Velasco, L. C. P. (2024). Technology adoption of electronic medical records in developing economies: A systematic review on physicians’ perspective. Digital Health, 10(2). https://doi.org/10.1177/20552076231224605
Gagnon, M. P., Simonyan, D., Ghandour, E. K., Godin, G., Labrecque, M., Ouimet, M., & Rousseau, M. (2016). Factors influencing electronic health record adoption by physicians: A multilevel analysis. International Journal of Information Management, 36(3), 258–270. https://doi.org/10.1016/j.ijinfomgt.2015.12.002
Harris, M. T., & Rogers, W. A. (2023). Developing a Healthcare Technology Acceptance Model (H-TAM) for Older Adults with Hypertension. Ageing and Society, 43(4), 814–834. https://doi.org/10.1017/S0144686X21001069
Negro-Calduch, E., Azzopardi-Muscat, N., Krishnamurthy, R. S., & Novillo-Ortiz, D. (2021). Technological progress in electronic health record system optimization: Systematic review of systematic literature reviews. International Journal of Medical Informatics, 152, 104507. https://doi.org/10.1016/j.ijmedinf.2021.104507
Upadhyay, S., & Hu, H. F. (2022). A Qualitative Analysis of the Impact of Electronic Health Records (EHR) on Healthcare Quality and Safety: Clinicians’ Lived Experiences. Health Services Insights, 15. https://doi.org/10.1177/11786329211070722
Walle, A. D., Ferede, T. A., Baykemagn, N. D., Shimie, A. W., Kebede, S. D., Tegegne, M. D., Wubante, S. M., Yehula, C. M., Demsash, A. W., Melaku, M. S., & Mengistie, M. B. (2023). Predicting healthcare professionals’ acceptance towards electronic personal health record systems in a resource-limited setting: using modified technology acceptance model. BMJ Health and Care Informatics, 30(1), 1–8. https://doi.org/10.1136/bmjhci-2022-100707
Yousef, C. C., Salgado, T. M., Farooq, A., Burnett, K., McClelland, L. E., Thomas, A., Alenazi, A. O., Esba, L. C. A., AlAzmi, A., Alhameed, A. F., Hattan, A., Elgadi, S., Almekhloof, S., AlShammary, M. A., Alanezi, N. A., Alhamdan, H. S., Khoshhal, S., & DeShazo, J. P. (2021). Predicting patients’ intention to use a personal health record using an adapted unified theory of acceptance and use of technology model: Secondary data analysis. JMIR Medical Informatics, 9(8). https://doi.org/10.2196/30214
Published
2024-09-30
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
