Digital Pathways to Recovery: Developing and Testing an AI-Supported Rehabilitation Tool in Low-Resource Settings

Psychosocial rehabilitation for homeless persons with severe mental illness (SMI) in low- and middle-income countries is frequently constrained by staff shortages, high turnover, and reliance on uniform, non-individualized care. This paper reports the development and validation of PRAGATI Care (Psychosocial Rehabilitation with AI-Guided Assistance for Therapeutic Interventions), a web-based, artificial intelligence (AI)-augmented care-planning tool designed for residential rehabilitation centres in Kerala, India.

artificial intelligence; psychosocial rehabilitation; severe mental illness; homelessness

1. Ali, A., & Gajendragad, J. M. (2025). Addressing the Intersection of Homelessness and Mental Illness: A Critical Analysis of Treatment and Rehabilitation Frameworks in India. Indian Journal of Community Medicine, 50(5), 719–723. https://doi.org/10.4103/ijcm.ijcm_518_24 [Google Scholar] [Crossref]

2. Anish, K. R. (2013). An Evaluation of Psychosocial Rehabilitation Facilities for Homeless Mentally Ill in India. Artha Journal of Social Sciences, 12(2), 1–19. https://doi.org/10.12724/ajss.25.1 [Google Scholar] [Crossref]

3. Cruz-Gonzalez, P., He, A. W.-J., Lam, E. P., Ng, I. M. C., Li, M. W., Hou, R., Chan, J. N.-M., Sahni, Y., Vinas Guasch, N., Miller, T., Lau, B. W.-M., & Sánchez Vidaña, D. I. (2025). Artificial intelligence in mental health care: A systematic review of diagnosis, monitoring, and intervention applications. Psychological Medicine, 55, e18. https://doi.org/10.1017/S0033291724003295 [Google Scholar] [Crossref]

4. Farkas, M., Jansen, M. A., & Penk, W. E. (2007). Psychosocial rehabilitation: Approach of choice for those with serious mental illnesses. The Journal of Rehabilitation Research and Development, 44(6), vii. https://doi.org/10.1682/JRRD.2007.09.0143 [Google Scholar] [Crossref]

5. Fowler, P. J., Hovmand, P. S., Marcal, K. E., & Das, S. (2019). Solving Homelessness from a Complex Systems Perspective: Insights for Prevention Responses. Annual Review of Public Health, 40(1), 465–486. https://doi.org/10.1146/annurev-publhealth-040617-013553 [Google Scholar] [Crossref]

6. Gowda, G. S., Gopika, G., Manjunatha, N., Kumar, C. N., Yadav, R., Srinivas, D., Rose Dawn, B., & Math, S. B. (2017). Sociodemographic and clinical profiles of homeless mentally ill admitted in mental health institute of South India: ‘Know the Unknown’ project. International Journal of Social Psychiatry, 63(6), 525–531. https://doi.org/10.1177/0020764017714494 [Google Scholar] [Crossref]

7. Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019). Artificial Intelligence for Mental Health and Mental Illnesses: An Overview. Current Psychiatry Reports, 21(11), 116. https://doi.org/10.1007/s11920-019-1094-0 [Google Scholar] [Crossref]

8. Habtamu, K., Alem, A., Medhin, G., Fekadu, A., Dewey, M., Prince, M., & Hanlon, C. (2017). Validation of the World Health Organization Disability Assessment Schedule in people with severe mental disorders in rural Ethiopia. Health and Quality of Life Outcomes, 15(1), 64. https://doi.org/10.1186/s12955-017-0647-3 [Google Scholar] [Crossref]

9. Johnson, D., Goodman, R., Patrinely, J., Stone, C., Zimmerman, E., Donald, R., Chang, S., Berkowitz, S., Finn, A., Jahangir, E., Scoville, E., Reese, T., Friedman, D., Bastarache, J., van der Heijden, Y., Wright, J., Carter, N., Alexander, M., Choe, J., … Wheless, L. (2023). Assessing the Accuracy and Reliability of AI-Generated Medical Responses: An Evaluation of the Chat-GPT Model. Research Square, rs.3.rs-2566942. https://doi.org/10.21203/rs.3.rs-2566942/v1 [Google Scholar] [Crossref]

10. Kalyanasundaram, J., Elangovan, A., & Roy, R. (2023). Psychosocial preparedness among homeless people: A study from an urban rehabilitation center in South India. Journal of Family Medicine & Primary Care, 12(1), 62–66. asn. [Google Scholar] [Crossref]

11. Koopmans, A. B., van Hoeken, D., Clarke, D. E., Vinkers, D. J., van Harten, P. N., & Hoek, H. W. (2020). Proxy WHO Disability Assessment Schedule 2.0 Is Clinically Useful for Assessing Psychosocial Functioning in Severe Mental Illness. Frontiers in Psychiatry, 11, 303. https://doi.org/10.3389/fpsyt.2020.00303 [Google Scholar] [Crossref]

12. Kwobah, E., Jaguga, F., Robert, K., Ndolo, E., & Kariuki, J. (2021). Efforts and Challenges to Ensure Continuity of Mental Healthcare Service Delivery in a Low Resource Settings During COVID-19 Pandemic—A Case of a Kenyan Referral Hospital. Frontiers in Psychiatry, 11, 588216. https://doi.org/10.3389/fpsyt.2020.588216 [Google Scholar] [Crossref]

13. Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., & Liang, P. (2023). Lost in the Middle: How Language Models Use Long Contexts (Version 3). arXiv. https://doi.org/10.48550/ARXIV.2307.03172 [Google Scholar] [Crossref]

14. Meghrajani, V. R., Marathe, M., Sharma, R., Potdukhe, A., Wanjari, M. B., & Taksande, A. B. (2023). A Comprehensive Analysis of Mental Health Problems in India and the Role of Mental Asylums. Cureus. https://doi.org/10.7759/cureus.42559 [Google Scholar] [Crossref]

15. Olawade, D. B., Wada, O. Z., Odetayo, A., David-Olawade, A. C., Asaolu, F., & Eberhardt, J. (2024). Enhancing mental health with Artificial Intelligence: Current trends and future prospects. Journal of Medicine, Surgery, and Public Health, 3, 100099. https://doi.org/10.1016/j.glmedi.2024.100099 [Google Scholar] [Crossref]

16. Periyasamy, C., & Jangam, K. V. (2024). The Current Challenges in Psychiatric Community Rehabilitation: A Case Study. Indian Journal of Social Psychiatry, 40(2), 193. https://doi.org/10.4103/ijsp.ijsp_241_21 [Google Scholar] [Crossref]

17. Philip, S., Jadhav, P., Sharda, A., Allam, A., Singh, A., Seralathan, M., Angothu, H., Rawat, V. S., Muliyala, K. P., & Thirthalli, J. (2024). Psychiatric rehabilitation in routine Indian mental health practice: A review of social protections for persons with mental health conditions. Indian Journal of Psychiatry, 66(3), 235–246. https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_138_23 [Google Scholar] [Crossref]

18. Radhakrishnan, V. K., & Abraham, K. R. (2017). GUEST EDITORIAL. A Magna Carta in psychosocial rehabilitation: The long road traveled. Indian Journal of Psychiatry, 59(4), 407–410. asn. [Google Scholar] [Crossref]

19. Rosa, A. R., & Lima, F. (2019). Functioning and illness progression in severe mental illness. In A. R. Rosa & F. Lima, Neuroprogression in Psychiatry (pp. 147–160). Oxford University Press. https://doi.org/10.1093/med/9780198787143.003.0009 [Google Scholar] [Crossref]

20. Sahoo, K. C., Sahay, M. R., Dubey, S., Nayak, S., Negi, S., Mahapatra, P., Bhattacharya, D., Barrio, M. O. D., & Pati, S. (2023). Community engagement and involvement in managing the COVID-19 pandemic among urban poor in low-and middle-income countries: A systematic scoping review and stakeholders mapping. Global Health Action, 16(1), 1–12. [Google Scholar] [Crossref]

21. Saraswat, N., Rao, K., Subbakrishna, D. K., & Gangadhar, B. N. (2006). The Social Occupational Functioning Scale (SOFS): A brief measure of functional status in persons with schizophrenia. Schizophrenia Research, 81(2), 301–309. https://doi.org/10.1016/j.schres.2005.09.008 [Google Scholar] [Crossref]

22. Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: A scoping review of methods and applications. Psychological Medicine, 49(09), 1426–1448. https://doi.org/10.1017/S0033291719000151 [Google Scholar] [Crossref]

23. Singh, N., Koiri, P., & Shukla, S. K. (2018). Signposting invisibles: A study of the homeless population in India. Chinese Sociological Dialogue, 3(3), 179–196. https://doi.org/10.1177/2397200918763087 [Google Scholar] [Crossref]

24. Suenghataiphorn, T., Tribuddharat, N., Danpanichkul, P., & Kulthamrongsri, N. (2025). Bias in Large Language Models Across Clinical Applications: A Systematic Review (arXiv:2504.02917). arXiv. https://doi.org/10.48550/arXiv.2504.02917 [Google Scholar] [Crossref]

25. Üstün, T. B., Kostanjsek, N., Chatterji, S., & Rehm, J. (Eds.). (2010). Measuring health and disability: Manual for WHO Disability Assessment Schedule (WHODAS 2.0). World Health Organization. https://iris.who.int/handle/10665/43974 [Google Scholar] [Crossref]

26. Wang, X., Sanders, H. M., Liu, Y., Seang, K., Tran, B. X., Atanasov, A. G., Qiu, Y., Tang, S., Car, J., Wang, Y. X., Wong, T. Y., Tham, Y.-C., & Chung, K. C. (2023). ChatGPT: Promise and challenges for deployment in low- and middle-income countries. The Lancet Regional Health - Western Pacific, 41, 100905. https://doi.org/10.1016/j.lanwpc.2023.100905 [Google Scholar] [Crossref]

27. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Version 6). arXiv. https://doi.org/10.48550/ARXIV.2201.11903 [Google Scholar] [Crossref]

28. World Health Organization. (2004). The World Health Organization Quality of Life (WHOQOL)-BREF (2012 revision). https://iris.who.int/handle/10665/77773 [Google Scholar] [Crossref]

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