Bridging the Digital Divide in Disaster Nursing: A Systematic Review of AI and Telehealth Adoption in Low-Resource Settings
(1) Sekolah Tinggi Ilmu Kesehatan Columbia Asia
(2) Universitas Haji Sumatera Utara
(3) Babcock University
(4) Inha University
(*) Corresponding Author
Abstract
The integration of Artificial Intelligence (AI) and telehealth has significantly transformed disaster response capabil- ities. Nonetheless, a pronounced "digital divide" poses a risk of exacerbating health inequities, particularly in Low- and Middle-Income Countries (LMICs), where disaster vulnerability is most pronounced. Objective: This systematic review seeks to examine the adoption of digital health technologies in disaster nursing, identifying socio-technical barriers and facilitators through the application of the NASSS (Non-adoption, Abandonment, Scale-up, Spread, and Sustainability) framework. Methods: In accordance with PRISMA 2020 guidelines, a systematic search was conducted across Scopus, Web of Science, PubMed, and CINAHL for articles published between 2020 and 2025. Studies focusing on nursing roles in disaster contexts were included. The quality of the studies was assessed using the Mixed Methods Appraisal Tool (MMAT). Results: A total of 42 studies were synthesized. The review revealed a stark dichotomy: High-Income Countries (HICs) prioritized AI-driven predictive modeling and data privacy, whereas LMICs concentrated on basic connectivity and mHealth solutions. Key barriers in low-resource settings included infrastructural deficits (unstable power/internet), lack of digital literacy among frontline nurses, and unsustainable pilot projects. Conclusion: While digital health holds immense potential, its current implementa- tion is inequitable. To bridge the digital divide, future interventions must prioritize "frugal innovation" resilient, offline-capable technologies designed for resource-constrained environments rather than uncritically importing complex systems from developed nations. Policy frameworks must also address the foundational digital literacy of the nursing workforce.
Keywords
Full Text:
PDFReferences
Jedwab R, Dobroff N, Lopresti K, Langtree T, Bester M, Duncan R, et al. Implementing a National Nursing and Midwifery Digital Health Capability Framework: exploring knowledge, experience in informatics and barriers and enablers. Studies in health technology and informatics. 2024;318:12-7. Available from: http://dx.doi.org/10.3233/SHTI240884.
Esmaeilzadeh P, Mirzaei T, et al. Using electronic health records to mitigate workplace burnout among clinicians during the COVID-19 pandemic: field study in Iran. JMIR Medical Informatics. 2021;9(6):e28497. Available from: https://doi.org/10.2196/28497.
Lee S, Kang WS, Kim DW, Seo SH, Kim J, Jeong ST, et al. An artificial intelligence model for predicting trauma mortality among emergency department patients in South Korea: retrospective cohort study. Journal of medical internet research. 2023;25:e49283. Available from: https://doi.org/10.2196/ 49283.
Chandra S, Hertz C, Khurana H, Doerfler ME. Collaboration between tele-ICU programs has the potential to rapidly increase the availability of critical care physicians—our experience was during coronavirus disease 2019 nomenclature. Critical care explorations. 2021;3(3):e0363. Available from: https://doi.org/10.1097/CCE.0000000000000363.
Byrsell F, Claesson A, Ringh M, Svensson L, Jonsson M, Nordberg P, et al. Machine learning can support dispatchers to better and faster recognize out-of-hospital cardiac arrest during emergency calls: a retrospective study. Resuscitation. 2021;162:218-26. Available from: https://doi.org/10.1016/j. resuscitation.2021.02.041.
Al Meslamani AZ. Technical and regulatory challenges of digital health implementation in developing countries. Journal of Medical Economics. 2023;26(1):1057-60. Available from: https://doi.org/10. 1080/13696998.2023.2249757.
McKenna L, Efendi F, Sommers C. Digital health in Indonesian nursing: A scoping review. CIN: Computers, Informatics, Nursing. 2026;44(1):e01326. Available from: https://doi.org/10.1097/ CIN.0000000000001326.
Sanz-Martos S, Lopez-Franco MD, Alvarez-Garcia C, Granero-Moya N, Lopez-Hens JM, Camara-Anguita S, et al. Drone applications for emergency and urgent care: a systematic review. Prehospital and disaster medicine. 2022;37(4):502-8. Available from: https://doi.org/10.1017/S1049023X22000887.
Malik M, Abdallah S, Ala’raj M. Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review. Annals of Operations Research. 2018;270(1):287-312. Available from: https://doi.org/10.1007/s10479-016-2393-z.
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. bmj. 2021;372. Available from: https://doi.org/10.1136/bmj.n71.
Hong QN, Pluye P, Fàbregues S, Bartlett G, Boardman F, Cargo M, et al.. Mixed methods appraisal tool (MMAT), version 2018. McGill University; 2018. Available from: https://doi.org/10.1136/ bmjopen-2020-039246.
Abdolkhani R, et al. Digital health capability framework for nursing and midwifery: A narrative review. Collegian. 2024.
O’Connor S, et al. Artificial intelligence in nursing and midwifery: A systematic review. Journal of Clinical Nursing. 2023;32(13-14):2951-68. Available from: https://doi.org/10.1111/jocn.16478.
Aprianto K, Ibrahim A. Determinants of User Acceptance of the Halodoc Application: An Analysis of User Experience and User Satisfaction. Journal Medical Informatics Technology. 2025. Available from: https://doi.org/10.37034/medinftech.v3i2.41.
Nisingizwe MP, et al. Effect of an unmanned aerial vehicle delivery system on blood product delivery in Rwanda: a mixed-methods study. The Lancet Global Health. 2022;10(4):e576-86. Available from: https://doi.org/10.1016/S2214-109X(22)00048-1.
Gunasekeran DV, et al. Digital health during COVID-19: lessons from operationalising new models of care in Asia. The Lancet Digital Health. 2021;3(2):e124-34. Available from: https://doi.org/10. 1016/S2589-7500(20)30287-9.
Kurniawati ND, et al. Disaster preparedness and its associated factors among Indonesian nurses: A cross-sectional study. International Journal of Disaster Risk Reduction. 2023. (Context: Real Indonesian Data).
Soriano GP, Yasuhara Y, Ito H, Matsumoto K, Osaka K, Kai Y, et al.; MDPI. Robots and robotics in nursing. Healthcare. 2022;10(8):1571. Available from: https://doi.org/10.3390/healthcare10081571.
Golinelli D, Boetto E, Carullo G, Nuzzolese AG, Landini MP, Fantini MP. Adoption of digital technologies in health care during the COVID-19 pandemic: systematic review of early scientific literature. Journal of medical Internet research. 2020;22(11):e22280. Available from: https://doi.org/10.2196/22280.
Whitelaw S, et al. Applications of digital technology in COVID-19 pandemic planning and re- sponse. The Lancet Digital Health. 2020;2(8):e435-40. Available from: https://doi.org/10.1016/ S2589-7500(20)30142-4.
Bahamondes-Rosado ME, Cerdá-Suárez LM, Dodero Ortiz de Zevallos GF, Espinosa-Cristia JF. Technos- tress at work during the COVID-19 lockdown phase (2020–2021): a systematic review of the literature. Frontiers in psychology. 2023;14:1173425. Available from: https://doi.org/10.3389/fpsyg.2023. 1173425.
Fagherazzi G, Fischer A, Ismael M, Despotovic V. Voice for health: the use of vocal biomarkers from research to clinical practice. Digital biomarkers. 2021;5(1):78-88. Available from: https://doi.org/ 10.1159/000515346.
Ohneberg C, Stöbich N, Warmbein A, Rathgeber I, Mehler-Klamt AC, Fischer U, et al. Assistive robotic systems in nursing care: a scoping review. BMC nursing. 2023;22(1):72. Available from: https: //doi.org/10.1186/s12912-023-01230-y.
Blomberg SN, et al. Machine learning as a supportive tool for recognizing cardiac arrest in emer- gency calls. JAMA Network Open. 2019;2(1):e187232. Available from: https://doi.org/10.1001/ jamanetworkopen.2018.7232.
Firouzkouhi M, Abdollahimohammad A, Arulappan J, Nouraei T, Farzi J. Challenges and opportunities of using telenursing during COVID-19 pandemic: An integrative review. Frontiers in Health Informatics. 2021;10(1):98. Available from: http://dx.doi.org/10.30699/fhi.v10i1.332.
James HM, Papoutsi C, Wherton J, Greenhalgh T, Shaw SE. Spread, scale-up, and sustainability of video consulting in health care: systematic review and synthesis guided by the NASSS framework. Journal of medical Internet research. 2021;23(1):e23775. Available from: https://doi.org/10.2196/23775.
Buvik A, et al. Quality of care for remote orthopaedic consultations using video technology. BMC Health Services Research. 2019. [28] Dorsey ER, Topol EJ. The state of telemedicine. New England Journal of Medicine. 2020;383(2):2136. Available from: https://doi.org/10.1056/NEJFra2011384.
Wosik J, Fudim M, Cameron B, Gellad ZF, Cho A, Phinney D, et al. Telehealth transformation: COVID-19 and the rise of virtual care. Journal of the American Medical Informatics Association. 2020;27(6):957-62. Available from: https://doi.org/10.1093/jamia/ocaa067.
Secinaro S, et al. The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making. 2021. Available from: https://doi.org/10.1186/ s12911-021-01488-9.
Edjinedja K, Barakat O, Desmettre T, Marx T, Elfahim O, Bredy-Maux C; Springer. Cross Approach Between Modern Artificial Intelligence and Emergency Medicine: A Review. 2024:278-97. Available from: https://doi.org/10.1007/978-3-031-62269-4_20.
Greenhalgh T, et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. Journal of Medical Internet Research. 2017;19(11):e8775. Available from: https://doi.org/10.2196/ jmir.8775.
Glover WJ, Nissinboim N, Naveh E. Examining innovation in hospital units: a complex adaptive systems approach. BMC Health Services Research. 2020;20(1):554. Available from: https://doi.org/10. 1186/s12913-020-05403-2.
Veenema TG, et al. Call to action: The case for advancing disaster nursing education in the United States. Journal of Nursing Scholarship. 2017;49(6):688-96. Available from: https://doi.org/10.1111/jnu. 12338.
Risling T. Nursing intelligence: Artificial intelligence in the age of nursing. Canadian Journal of Nursing Research. 2017.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine. 2019;25:44-56. Available from: https://doi.org/10.1038/s41591-018-0300-7.
Dike FO, Mutabazi JC, Ubani BC, Isa AS, Ezeude C, Musa E, et al. Implementation and impact of mobile health (mHealth) in the management of diabetes mellitus in Africa: a systematic review protocol. BMJ open. 2021;11(12):e047556. Available from: https://doi.org/10.1136/bmjopen-2020-047556.
Das AV, Mididoddi S, Kammari P, Deepthi Davara N, Loomba A, Khanna R, et al. App-based tele ophthalmology: a novel method of rural eye care delivery connecting tertiary eye care center and vision centers in India. International Journal of Telemedicine and Applications. 2019;2019(1):8107064. Available from: https://doi.org/10.1155/2019/8107064DigitalObjectIdentifier(DOI).
Subhan R, Ismail WA, Musharraf S, Khan M, Hafeez R, Alam MK. Teledentistry as a supportive tool for dentists in Pakistan. BioMed Research International. 2021;2021(1):8757859. Available from: https: //doi.org/10.1155/2021/8757859.
Loutfy A, Elzeiny A, Van Belkum C, El-Monshed AH, Al-Helali A, Rashed A, et al. Digital proficiency: a self-assessment of eHealth literacy and informatics competencies among nursing students. Informatics for Health and Social Care. 2026:1-16. Available from: https://doi.org/10.1080/17538157.2025. 2605554.
Mutasa LS, Iyamu T. Application of activity theory to examine the implementation of e-health in Namibia. International Journal of Healthcare Management. 2023;16(2):157-66. Available from: https: //doi.org/10.1080/20479700.2022.2083046.
Odai ED, Azodo CC, Chhabra KG. Disaster management: Knowledge, attitude, behavior, willingness, and preparedness among Nigerian dentists. Prehospital and disaster medicine. 2019;34(2):132-6. Available from: https://doi.org/10.1017/S1049023X19000074.
Choudhury A, Choudhury M, et al. Mobile for mothers mHealth intervention to augment maternal health awareness and behavior of pregnant women in tribal societies: randomized quasi-controlled study. JMIR mHealth and uHealth. 2022;10(9):e38368. Available from: https://doi.org/10.17605/ OSF.IO/9U8D5.
Labrique AB, Vasudevan L, Kochi E, Fabricant R, Mehl G. mHealth innovations as health system strengthening tools: 12 common applications and a visual framework. Global health: science and practice. 2013;1(2):160-71. Available from: https://doi.org/10.9745/GHSP-D-13-00031.
Adetunji O, Agbaza B. Evaluating the efficacy of Tele-behavioral health interventions in rural African communities: A mixed methods approach. IJBB. 2025;5(1):64-73. Available from: https://www.doi. org/10.33545/26646536.2023.v5.i1a.167.
Putra DS, et al. Factors influencing the adoption of Electronic Medical Records in Primary Health Care in Indonesia. Health Policy and Technology. 2020. Available from: https://doi.org/10.1016/j. hlpt.2020.08.001.
Sari NK, et al. Nurses’ experiences in providing tele-counseling for patients with COVID-19 in Indonesia. Belitung Nursing Journal. 2022;8(1):15-22.
Pratama AR. PeduliLindungi: The adoption of contact tracing application in Indonesia. Journal of Information Systems in Developing Countries. 2021. Available from: https://doi.org/10.1002/ isd2.12199.
Fathoni M, Sangchan H, Songwathana P. Triage knowledge and skills among emergency nurses in East Java Province, Indonesia. Australasian Emergency Nursing Journal. 2010;13(4):153. Available from: https://doi.org/10.1016/j.aenj.2010.08.304.
Condor-Camara DF, Pio-del Aguila C, Bendezu-Quispe G, González-Aguña A, Santamaría-García JM. Characterization of Nursing Informatics Courses in Latin America and the Caribbean. Healthcare Informatics Research. 2025;31(3):253-62. Available from: https://doi.org/10.4258/hir.2025. 31.3.253.
Handa S, Mehta H, Bishnoi A, Vinay K, Mahajan R, Narang T, et al. Teledermatology during the COVID- 19 pandemic: experience at a tertiary care centre in North India. Dermatologic therapy. 2021;34(4):e15022. Available from: https://doi.org/10.1111/dth.15022.
Ridout B, McKay M, Amon K, Campbell A, Wiskin AJ, Seng Du PML, et al. Social media use by young people living in conflict-affected regions of Myanmar. Cyberpsychology, Behavior, and Social Networking. 2020;23(12):876-88. Available from: https://doi.org/10.1089/cyber.2020.0131.
Scott RE, Mars M. Telehealth in the developing world: current status and future prospects. Smart Homecare Technology and TeleHealth. 2015:25-37. Available from: https://doi.org/10.2147/ SHTT.S75184.
Article Metrics
Abstract view : 28 timesPDF - 10 times
DOI: https://doi.org/10.26714/jichi.v6i2.17329
Refbacks
- There are currently no refbacks.
____________________________________________________________________________
Journal of Intelligent Computing and Health Informatics (JICHI)
ISSN 2715-6923 (print) | 2721-9186 (online)
Organized by
Department of Informatics
Faculty of Engineering
Universitas Muhammadiyah Semarang
W : https://jurnal.unimus.ac.id/index.php/ICHI
E : jichi.informatika@unimus.ac.id, ahmadilham@unimus.ac.id
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.