⚠ Official Notice: www.ijisrt.com is the official website of the International Journal of Innovative Science and Research Technology (IJISRT) Journal for research paper submission and publication. Please beware of fake or duplicate websites using the IJISRT name.



USSD-Based Digital Health in Rural Africa: A Machine Learning Research Direction for Low-Resource Health Signal Processing


Authors : Ayomide H. Agbaje

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/5e2dnuyz

Scribd : https://tinyurl.com/4jt3duwj

DOI : https://doi.org/10.38124/ijisrt/26apr2523

PlumX Metrics

Semantic Scholar

ResearchGate

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Background: Sub-Saharan Africa (SSA) bears 25% of the global disease burden, yet accounts for only 3% of the world's health workforce [1]. Smartphone-dependent digital health platforms have failed to reach most rural populations in SSA, and the 2023 collapse of Babyl Rwanda demonstrated the structural fragility of externally owned digital health infrastructure [2].  Objective: To evaluate the feasibility, acceptability, and 90-day user retention of HealthDrive, a USSD-based telehealth platform with community health worker (CHW) integration, in a pilot study conducted in two rural SSA communities.  Methods: A mixed-methods pilot implementation study (n=50 enrolled patients, 12 CHWs) conducted August 2024 to March 2026, applying the Consolidated Framework for Implementation Research (CFIR) [3], the Technology Acceptance Model for Resource-Limited Settings (TAM-RLS) [4], and the RE-AIM evaluation framework [5]. USSD interaction logs (1,247 sessions across four short codes), CHW follow-up records, and structured satisfaction interviews were analysed.  Results: Three-month user retention was 78% (95% CI: 64–88%), exceeding SSA mHealth benchmarks (45–65%). Elderly user satisfaction reached 85%. Emergency triage sessions achieved 71% completion. Total platform expenditure was $2,580 over 19 months at $125/month.  Conclusions: USSD-based telehealth with CHW integration is feasible and acceptable in rural SSA. Five open machine learning and signal processing challenges are identified as critical barriers to scaling this model to population-scale voice-based health triage.

Keywords : USSD; Digital Health; mHealth; Community Health Workers; sub-Saharan Africa; Machine Learning; Speech Processing; Implementation Science.

References :

  1. Asamani JA, Bediakon KSB, Boniol M, Munga'tu JK, et al. Projected health workforce requirements and shortage for addressing the disease burden in the WHO Africa Region, 2022–2030: a needs-based modelling study. BMJ Glob Health. 2024 Oct 22;7(Suppl 1):e015972. doi:10.1136/bmjgh-2024-015972. PMC11789529.
  2. Rubuga FK, et al. Telemedicine implementation and healthcare utilisation in Rwanda: interrupted time series of Babyl Digital Health Services from 2015 to 2024. BMC Prim Care. 2026;27:45. doi:10.1186/s12875-026-03179-8. PMC12879403.
  3. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50. doi:10.1186/1748-5908-4-50.
  4. Campbell JI, Aturinda I, Mwesigwa E, et al. The Technology Acceptance Model for Resource-Limited Settings (TAM-RLS): a novel framework for mobile health interventions targeted to low-literacy end-users in resource-limited settings. AIDS Behav. 2017;21(11):3129–3140. doi:10.1007/s10461-017-1765-0. PMC5763567.
  5. Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health. 1999;89(9):1322–1327. doi:10.2105/AJPH.89.9.1322.
  6. Ahmat A, Okoroafor SC, Kazanga I, Asamani JA, et al. The health workforce status in the WHO African Region: findings of a cross-sectional study. BMJ Glob Health. 2022;7(Suppl 1):e008317. doi:10.1136/bmjgh-2021-008317. PMID:35675966.
  7. Ministry of Health, Republic of Rwanda. Rwanda Health Sector Performance Report 2017–2019. Kigali: MOH; 2020.
  8. WHO. Republic of Rwanda Ministry of Health Health Labour Market Analysis Report. Geneva: WHO; 2020.
  9. Condo J, Mugeni C, Naughton B, et al. Rwanda's evolving community health worker system: a qualitative assessment of client and provider perspectives. Hum Resour Health. 2014;12:71. doi:10.1186/1478-4491-12-71.
  10. Rwanda Utilities Regulatory Authority (RURA). Mobile-Cellular Telephone Subscriptions Report September 2024. Kigali: RURA; 2024.
  11. Maliwichi P, Chigona W, Sowon K. Appropriation of mHealth interventions for maternal health care in Sub-Saharan Africa: hermeneutic review. JMIR mHealth uHealth. 2021;9(10):e22653. doi:10.2196/22653.
  12. McKinsey & Company. Overcoming the health workforce paradox in sub-Saharan Africa. McKinsey Global Institute; 2024.
  13. Eze P, Lawani LO, Agu UJ, Acharya Y. Catastrophic health expenditure in sub-Saharan Africa: a systematic review and meta-analysis. Bull World Health Organ. 2022;100(5):337–351. doi:10.2471/BLT.21.287673. PMC9047424.
  14. Owoyemi A, Owoyemi J, Osiyemi A, Boyd A. Digital solutions for community and primary health workers: lessons from implementations in Africa. Front Digit Health. 2022;4:876957. doi:10.3389/fdgth.2022.876957.
  15. National Department of Health, South Africa. MomConnect 10th anniversary programme overview. Pretoria: NDoH; 2024. Reported: IOL Saturday Star, 28 Sep 2024.
  16. Huisman L, van Duijn SMC, Silva N, et al. A digital mobile health platform increasing efficiency and transparency towards universal health coverage in LMICs. Digit Health. 2022;8:20552076221092213. doi:10.1177/20552076221092213.
  17. Nkurunziza T, et al. Digital Primary Health in Rwanda: Qualitative Study of User Experiences and Implementation Lessons From Babyl's Telemedicine Platform. J Med Internet Res. 2026;28(1):e84832. doi:10.2196/84832.
  18. ICTworks. Babyl Paradox: When Evidence-Based Digital Success Cannot Beat Corporate Failure. 2024. Available: https://www.ictworks.org
  19. Means AR, Kemp CG, Gwayi-Chore MC, et al. Evaluating and optimising the Consolidated Framework for Implementation Research (CFIR) for use in low- and middle-income countries: a systematic review. Implement Sci. 2020;15:17. doi:10.1186/s13012-020-0977-0.
  20. Eysenbach G. The law of attrition. J Med Internet Res. 2005 mar 31;7(1):e11. doi:10.2196/jmir.7.1.e11. PMID:15829473. PMCID:1550631.
  21. Yang S, Wu Q, Huang Z, et al. Understanding inequalities in mobile health utilisation across phases: systematic review and meta-analysis. J Med Internet Res. 2025;27:e71349. doi:10.2196/71349.
  22. Kachimanga C, Sinyiza F, Msosa A, et al. Evaluating the adoption of mHealth technologies by community health workers to improve the use of maternal health services in Sub-Saharan Africa: systematic review. JMIR mHealth uHealth. 2024;12:e55819. doi:10.2196/55819. PMID:39316427.
  23. Palinkas LA, Horwitz SM, Weisz JR, et al. Mixed method designs in implementation research. Admin Policy Mental Health. 2011;38(1):44–53. doi:10.1007/s10488-010-0314-z.
  24. Pinnock H, Barwick M, Carpenter CR, et al. Standards for Reporting Implementation Studies (StaRI) Statement. BMJ. 2017;356:i6795. doi:10.1136/bmj.i6795. PMID:28264797.
  25. Hoffmann TC, Glasziou PP, Boutron I, et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ. 2014;348:g1687. doi:10.1136/bmj.g1687. PMID:24609605.
  26. Julious SA. A sample size of 12 per group is the rule of thumb for a pilot study. Pharm Stat. 2005;4(4):287–291. doi:10.1002/pst.185.
  27. Hertzog MA. Considerations in determining sample size for pilot studies. Res Nurs Health. 2008;31(2):180–191. doi:10.1002/nur.20247.
  28. Eldridge SM, Chan CL, Campbell MJ, et al. CONSORT 2010 statement: extension to randomised pilot and feasibility trials. BMJ. 2016;355:i5239. doi:10.1136/bmj.i5239. PMID:27777223.
  29. Bertolazzi A, Quaglia V, Bongelli R. Barriers and facilitators to health technology adoption by older adults with chronic diseases: integrative systematic review. BMC Public Health. 2024;24:506. doi:10.1186/s12889-024-18036-5.
  30. Kruk ME, Gage AD, Arsenault C, et al. High-quality health systems in the Sustainable Development Goals era: time for a revolution. Lancet Glob Health. 2018;6(11):e1196–e1252. doi:10.1016/S2214-109X(18)30386-3.
  31. WHO. Global Strategy on Digital Health 2020–2025. Geneva: WHO; 2021. Available: apps.who.int/iris/handle/10665/344249.
  32. Shah MA, Raj B. Speech Robust Bench: A Robustness Benchmark For Speech Recognition. International Conference on Learning Representations (ICLR). 2025.
  33. Waheed A, Kadaoui K, Raj B, Abdul-Mageed M. uDistil-Whisper: Label-Free Data Filtering for Knowledge Distillation in Low-Data Regimes. In: Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025:5750–5767. Albuquerque, New Mexico: ACL.
  34. Teixeira F, Abad A, Raj B, Trancoso I. Privacy-Preserving Automatic Speaker Diarization. Proc ICASSP 2023. doi:10.1109/ICASSP49357.2023.10094969.
  35. Teixeira F, Abad A, Raj B, Trancoso I. Privacy-oriented manipulation of speaker representations. arXiv:2310.06652. 2023.
  36. Alvarado E, Grádeda N, Luzanto A, et al. Automatic Detection of Dyspnea in Real Human–Robot Interaction Scenarios. Sensors. 2023;23(3):1257. doi:10.3390/s23031257.
  37. Alvarado E, Grádeda N, Luzanto A, et al. Respiratory Distress Estimation in Human-Robot Interaction Scenario. Proc Interspeech. Dublin, 2023.
  38. Alharthi D, Zamani M, Raj B, Singh R. Tessellated Linear Model for Age Prediction from Voice. Proc ICASSP. 2025:1–4.
  39. Wang Y, Chen H, Fan Y, Sun W, Tao R, Hou W, et al. USB: A Unified Semi-Supervised Learning Benchmark for Classification. Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Datasets and Benchmarks Track. doi:10.48550/arXiv.2208.07204.
  40. Shah A, Tang F, Ye Z, Singh R, Raj B. Importance of Negative Sampling in Weak Label Learning. Proc ICASSP 2024.
  41. Atwany H, Waheed A, Singh R, Choudhury M, Raj B. Lost in Transcription, Found in Distribution Shift: Demystifying Hallucination in Speech Foundation Models. In Findings of the Association for Computational Linguistics: ACL 2025. 2025:23181–23203. Vienna, Austria. doi:10.18653/v1/2025.findings-acl.1190.
  42. GBD 2019 Human Resources for Health Collaborators. Measuring the availability of human resources for health. Lancet. 2022;399(10341):2129–2154. doi:10.1016/S0140-6736(22)00532-3.
  43. National Institute of Statistics of Rwanda (NISR). Integrated Household Living Conditions Survey 7 (EICV7) 2023–2024. Kigali: NISR; 2025. [Health insurance coverage: 85.3%].
  44. Damschroder LJ, Reardon CM, Widerquist MAO, Lowery J. The updated CFIR is based on user feedback. Implement Sci. 2022;17:75. doi:10.1186/s13012-022-01245-0.

Background: Sub-Saharan Africa (SSA) bears 25% of the global disease burden, yet accounts for only 3% of the world's health workforce [1]. Smartphone-dependent digital health platforms have failed to reach most rural populations in SSA, and the 2023 collapse of Babyl Rwanda demonstrated the structural fragility of externally owned digital health infrastructure [2].  Objective: To evaluate the feasibility, acceptability, and 90-day user retention of HealthDrive, a USSD-based telehealth platform with community health worker (CHW) integration, in a pilot study conducted in two rural SSA communities.  Methods: A mixed-methods pilot implementation study (n=50 enrolled patients, 12 CHWs) conducted August 2024 to March 2026, applying the Consolidated Framework for Implementation Research (CFIR) [3], the Technology Acceptance Model for Resource-Limited Settings (TAM-RLS) [4], and the RE-AIM evaluation framework [5]. USSD interaction logs (1,247 sessions across four short codes), CHW follow-up records, and structured satisfaction interviews were analysed.  Results: Three-month user retention was 78% (95% CI: 64–88%), exceeding SSA mHealth benchmarks (45–65%). Elderly user satisfaction reached 85%. Emergency triage sessions achieved 71% completion. Total platform expenditure was $2,580 over 19 months at $125/month.  Conclusions: USSD-based telehealth with CHW integration is feasible and acceptable in rural SSA. Five open machine learning and signal processing challenges are identified as critical barriers to scaling this model to population-scale voice-based health triage.

Keywords : USSD; Digital Health; mHealth; Community Health Workers; sub-Saharan Africa; Machine Learning; Speech Processing; Implementation Science.

Paper Submission Last Date
31 - May - 2026

SUBMIT YOUR PAPER CALL FOR PAPERS
Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe