E-Health and Digital Transformation in Increasing Accessibility of Health Services
Article Sidebar
-
Accessibility, E-Health, Digital Transformation
Abstract
In many countries, accessibility of healthcare remains a major challenge, especially in remote rural and urban areas. This research is relevant because of the push towards the application of technology in the healthcare sector to improve healthcare accessibility worldwide. The objectives of this study are to evaluate the role of e-Health in improving healthcare accessibility, explore the impact of digital transformation in improving the quality and coverage of healthcare services, and to draw conclusions about the implications of e-Health implementation and digital transformation in the context of improving healthcare accessibility. This research method uses a literature analysis approach to collect and analyze data from various sources of information related to e-Health and digital transformation in health services. The results of this study show that the implementation of e-Health and digital transformation has brought significant impact in improving the accessibility of health services, especially through the utilization of telemedicine, electronic medical records, and health applications. This has enabled easier access for individuals to obtain medical consultations and health information, especially for those living in remote areas. The conclusion of this study shows that e-Health and digital transformation have great potential in improving healthcare accessibility. By continuing to develop and integrate technology in the health sector, we can achieve the greater goal of providing more equitable and affordable healthcare to the global community. These steps can help reduce disparities in access to healthcare and improve people's overall quality of life.
Full text article
References
Ahmad, M., Amin, M. B., Hussain, S., Kang, B. H., Cheong, T., & Lee, S. (2016). Health Fog: A novel framework for health and wellness applications. The Journal of Supercomputing, 72(10), 3677–3695. https://doi.org/10.1007/s11227-016-1634-x
Aliffiro Naufal, M., & Muklason, A. (2022). Pengembangan Aplikasi Healthcare Intelligence System Untuk Pemantauan Kesehatan Ibu Dan Anak: Perancangan Aplikasi Frontend. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(2), 1038–1052. https://doi.org/10.35957/jatisi.v9i2.1902
Bai, Sun, Zang, Zhang, Shen, Liu, & Wei. (2019). Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China. Energies, 12(17), 3258. https://doi.org/10.3390/en12173258
Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In Artificial Intelligence in Healthcare (pp. 25–60). Elsevier. https://doi.org/10.1016/B978-0-12-818438-7.00002-2
Bull, F. C., Al-Ansari, S. S., Biddle, S., Borodulin, K., Buman, M. P., Cardon, G., Carty, C., Chaput, J.-P., Chastin, S., Chou, R., Dempsey, P. C., DiPietro, L., Ekelund, U., Firth, J., Friedenreich, C. M., Garcia, L., Gichu, M., Jago, R., Katzmarzyk, P. T., … Willumsen, J. F. (2020). World Health Organization 2020 guidelines on physical activity and sedentary behaviour. British Journal of Sports Medicine, 54(24), 1451–1462. https://doi.org/10.1136/bjsports-2020-102955
Chen, H., Yuan, X., Pei, Z., Li, M., & Li, J. (2019). Triple-Classification of Respiratory Sounds Using Optimized S-Transform and Deep Residual Networks. IEEE Access, 7, 32845–32852. https://doi.org/10.1109/ACCESS.2019.2903859
Fitriarti, E. A. (2019). URGENSI LITERASI DIGITAL DALAM MENANGKAL HOAX INFORMASI KESEHATAN DI ERA DIGITAL. Metacommunication: Journal of Communication Studies, 4(2), 219. https://doi.org/10.20527/mc.v4i2.6929
Gale, N. K., Heath, G., Cameron, E., Rashid, S., & Redwood, S. (2013). Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Medical Research Methodology, 13(1), 117. https://doi.org/10.1186/1471-2288-13-117
Haggerty, E. (2017). Healthcare and digital transformation. Network Security, 2017(8), 7–11. https://doi.org/10.1016/S1353-4858(17)30081-8
Huda, L. N. (2007). Hubungan Status Reproduksi, Status Kesehatan, Akses Pelayanan Kesehatan dengan Komplikasi Obstetri di Banda Sakti, Lhokseumawe Tahun 2005. Kesmas: National Public Health Journal, 1(6), 275. https://doi.org/10.21109/kesmas.v1i6.288
Isernia, S., Pagliari, C., Jonsdottir, J., Castiglioni, C., Gindri, P., Gramigna, C., Palumbo, G., Salza, M., Molteni, F., Baglio, F., & HEAD study group. (2019). Efficiency and Patient-Reported Outcome Measures From Clinic to Home: The Human Empowerment Aging and Disability Program for Digital-Health Rehabilitation. Frontiers in Neurology, 10, 1206. https://doi.org/10.3389/fneur.2019.01206
Janz, N. K., & Becker, M. H. (1984). The Health Belief Model: A Decade Later. Health Education Quarterly, 11(1), 1–47. https://doi.org/10.1177/109019818401100101
Juwaini, J. (2023). Revitalisasi Peran Filsafat sebagai Proses Transformasi Masyarakat Multikultural. Jurnal Sosiologi Agama Indonesia (JSAI), 4(1), 102–118. https://doi.org/10.22373/jsai.v4i1.2690
Lee, J., Azamfar, M., Singh, J., & Siahpour, S. (2020). Integration of digital twin and deep learning in cyber?physical systems: Towards smart manufacturing. IET Collaborative Intelligent Manufacturing, 2(1), 34–36. https://doi.org/10.1049/iet-cim.2020.0009
Malik, H., Chaudhary, G., & Srivastava, S. (2022). Digital transformation through advances in artificial intelligence and machine learning. Journal of Intelligent & Fuzzy Systems, 42(2), 615–622. https://doi.org/10.3233/JIFS-189787
Marques, I. C. P., & Ferreira, J. J. M. (2020). Digital transformation in the area of health: Systematic review of 45 years of evolution. Health and Technology, 10(3), 575–586. https://doi.org/10.1007/s12553-019-00402-8
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984). Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology, 34(7), 939–939. https://doi.org/10.1212/WNL.34.7.939
Mulyasari, D., Noer, R. M., Sari, N., Ermawaty, E., Triharyadi, F., Tampubolon, D., & Catherine, S. (2023). Improving Health Status in The Elderly Through Health Checks and Education at Nuriah Nursing Homes in Karimun. Pengabdian: Jurnal Abdimas, 1(2), 75–81. https://doi.org/10.55849/abdimas.v1i2.183
Nantabah, Z. K., A, Z. A., & Laksono, A. D. (2019). Gambaran Akses Pelayanan Kesehatan pada Balita di Indonesia. Buletin Penelitian Sistem Kesehatan, 22(1), 54–61. https://doi.org/10.22435/hsr.v22i1.439
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
Preti, E., Di Mattei, V., Perego, G., Ferrari, F., Mazzetti, M., Taranto, P., Di Pierro, R., Madeddu, F., & Calati, R. (2020). The Psychological Impact of Epidemic and Pandemic Outbreaks on Healthcare Workers: Rapid Review of the Evidence. Current Psychiatry Reports, 22(8), 43. https://doi.org/10.1007/s11920-020-01166-z
Qiu, H., Qiu, M., Liu, M., & Memmi, G. (2020). Secure Health Data Sharing for Medical Cyber-Physical Systems for the Healthcare 4.0. IEEE Journal of Biomedical and Health Informatics, 24(9), 2499–2505. https://doi.org/10.1109/JBHI.2020.2973467
Rachmani, E., Dewi, F., Haikal, H., & Setiono, O. (2023). Pengukuran Literasi Kesehatan Digital Kader Kesehatan Desa Penadaran Menggunakan SI-Cerdik: Digital Health Literacy Competencies of Community Health Workers in Penadaran Village, Grobogan. Indonesian Journal of Health Information Management Services, 3(1), 7–10. https://doi.org/10.33560/ijhims.v3i1.57
Sodhro, A., Sangaiah, A., Sodhro, G., Lohano, S., & Pirbhulal, S. (2018). An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications. Sensors, 18(3), 923. https://doi.org/10.3390/s18030923
Till, C. (2014). Exercise as Labour: Quantified Self and the Transformation of Exercise into Labour. Societies, 4(3), 446–462. https://doi.org/10.3390/soc4030446
Yassine, A., Singh, S., & Alamri, A. (2017). Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications. IEEE Access, 5, 13131–13141. https://doi.org/10.1109/ACCESS.2017.2719921
Yusuf, S., Amal, A. A., & Sumarmi. (2023). EKSPLORASI SIKAP PERAWAT LULUSAN BARU TERHADAP KOMPUTER DALAM PELAYANAN KESEHATAN: Exploration of the Newly Graduated Nurses’ Attitude towards Computers in Healthcare. Jurnal Ilmiah Keperawatan (Scientific Journal of Nursing), 9(1), 189–195. https://doi.org/10.33023/jikep.v9i1.1454
Zeadally, S., Isaac, J. T., & Baig, Z. (2016). Security Attacks and Solutions in Electronic Health (E-health) Systems. Journal of Medical Systems, 40(12), 263. https://doi.org/10.1007/s10916-016-0597-z
Zhou, X., Liang, W., Wang, K. I.-K., Wang, H., Yang, L. T., & Jin, Q. (2020). Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things. IEEE Internet of Things Journal, 7(7), 6429–6438. https://doi.org/10.1109/JIOT.2020.2985082
Authors
Copyright (c) 2023 loso judijanto, Hendra Nusa Putra, Benny Novico Zani, Dadang Muhammad Hasyim, Muntasir Muntasir
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
