Leveraging Electronic Health Records (EHR) for Mental Health Care in Underserved Communities: A Systematic Literature Review
DOI:
https://doi.org/10.54536/ajmsi.v5i2.4471Keywords:
Electronic Health Record, Mental Health, Psychological Distress, Underserved CommunitiesAbstract
Mental health disorders are prevalent globally, and many patients, particularly in underrepresented populations, continue to have restricted access to high-quality care. The study aims to assess the use of EHR-based predictive models to detect and address mental health concerns in underrepresented populations. A systematic literature review was conducted in the Scopus and PubMed databases, covering articles published between 2017 and 2024, with a focus on the use of EHRs and their impact on mental health innovations among underserved populations. There is evidence that EHRs can enhance access to and diagnosis of mental health disorders. Nonetheless, the restrictions include infrastructure, financial, and data privacy issues. This enables the diagnosis of patients who exhibit potential danger signals, allowing for timely action to be taken. Mental health outcomes could be enhanced through EHR-based predictive analytics, as it can identify patients at risk early on. Nevertheless, issues still need to be addressed, such as infrastructure and clinician training, data privacy, and the need to engage underserved populations.
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Adler-Milstein, J., Holmgren, A. J., Kralovec, P., Worzala, C., Searcy, T., & Patel, V. (2017). Electronic health record adoption in US hospitals: the emergence of a digital “advanced use” divide. Journal of the American Medical Informatics Association, 24(6), 1142–1148.
Akil, M., Martucci, L., & Hoepman, J.-H. (2023). Non-interactive privacy-preserving Sybil-free authentication scheme in VANETs. In Proceedings of the Symposium on Vehicle Security and Privacy (VehicleSec) 2023 (pp. 27–35). Internet Society. https://doi.org/10.14722/vehiclesec.2023.23007
Akil, M., Martucci, L., & Hoepman, J. (2024). A secure and privacy-preserving authentication scheme with a zero-trust approach to vehicle renting in VANETs. In Proceedings of the 21st International Conference on Security and Cryptography (SECRYPT 2024) (Vol. 1, pp. 114–127). SciTePress. https://doi.org/10.5220/0012759600003767
Akil, M., Naskar, S., Martucci, L. A., & Hoepman, J. (2024). A Privacy-Preserving approach to vehicle renting and driver accountability in VANETs. IFIP Advances in Information and Communication Technology, 192–210. https://doi.org/10.1007/978-3-031-57978-3_13
Akil, M., Mancini, L., V., & Venturi, D. (2019). Multi-covert channel attack in the cloud. DIVA. https://kau.diva-portal.org/smash/record.jsf?faces-redirect=true&aq2=%5B%5B%5D%5D&af=%5B%5D&searchType=SIMPLE&sortOrder2=title_sort_
Coley, R. Y., Boggs, J. M., Beck, A., & Simon, G. E. (2021). Predicting outcomes of psychotherapy for depression with electronic health record data. Journal of Affective Disorders, 6, 100198.
Corrigan, P. W., Druss, B. G., & Perlick, D. A. (2014). The impact of mental illness stigma on seeking and participating in mental health care. Psychological Science in the Public Interest, 15(2), 37-70.
Franco, M. I., Staab, E. M., Zhu, M., Deehan, W., Moses, J., Gibbons, R., Vinci, L., Shah, S., Yohanna, D., & Beckman, N. (2024). Implementation of an EHR-integrated web-based depression assessment in primary care: PORTAL-Depression. JAMIA open, 7(3), ooae094.
Frimpong, J. A., Liu, X., Liu, L., & Zhang, R. (2023). Adoption of Electronic Health Record Among Substance Use Disorder Treatment Programs: Nationwide Cross-Sectional Survey Study. Journal of medical Internet research, 25, e45238.
Garriga, R., Mas, J., Abraha, S., Nolan, J., Harrison, O., Tadros, G., & Matic, A. (2022). Machine learning model to predict mental health crises from electronic health records. Nature medicine, 28(6), 1240-1248.
Hodgkinson, S., Godoy, L., Beers, L. S., & Lewin, A. (2017). Improving mental health access for low-income children and families in the primary care setting. Pediatrics, 139(1).
Islam, M. M., Hassan, S., Akter, S., Jibon, F. A., & Sahidullah, M. (2024). A comprehensive review of predictive analytics models for mental illness using machine learning algorithms. Healthcare Analytics, 100350.
Jacob, K. (2017). Mental health services in low-income and middle-income countries. The Lancet Psychiatry, 4(2), 87-89.
Kirkbride, J. B., Anglin, D. M., Colman, I., Dykxhoorn, J., Jones, P. B., Patalay, P., Pitman, A., Soneson, E., Steare, T., & Wright, T. (2024). The social determinants of mental health and disorder: evidence, prevention and recommendations. World psychiatry, 23(1), 58.
Matthews, E. B., & Stanhope, V. (2020). A help or a hindrance: the role of electronic health records in implementing new practices in community mental health clinics. Psychiatric Services, 71(10), 1065-1068.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM computing surveys (CSUR), 54(6), 1-35.
Modi, H., Orgera, K., & Grover, A. (2022). Exploring Barriers to Mental Health Care in the US. Research and Action Institute, 10.
Morales, D. A., Barksdale, C. L., & Beckel-Mitchener, A. C. (2020). A call to action to address rural mental health disparities. Journal of clinical and translational science, 4(5), 463-467.
Nemesure, M. D., Heinz, M. V., Huang, R., & Jacobson, N. C. (2021). Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence. Scientific reports, 11(1), 1980.
Ondogan, A. G., Sargin, M., & Canoz, K. (2023). Use of electronic medical records in the digital healthcare system and its role in communication and medical information sharing among healthcare professionals. Informatics in Medicine Unlocked, 42, 101373.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., & Brennan, S. E. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. bmj, 372.
Prall, S., Scelza, B., & Davis, H. E. (2024). Medical mistrust, discrimination and healthcare experiences in a rural Namibian community. Global public health, 19(1), 2346207.
Rapfogel, N. (2022). The behavioral health Care affordability problem. Center for American Progress. https://www. americanprogress. org/article/the-behavioral-health-careaffordability-problem.
Smith, K. A., Blease, C., Faurholt-Jepsen, M., Firth, J., Van Daele, T., Moreno, C., Carlbring, P., Ebner-Priemer, U. W., Koutsouleris, N., & Riper, H. (2023). Digital mental health: challenges and next steps. BMJ Ment Health, 26(1).
Subu, M. A., Holmes, D., Arumugam, A., Al-Yateem, N., Maria Dias, J., Rahman, S. A., Waluyo, I., Ahmed, F. R., & Abraham, M. S. (2022). Traditional, religious, and cultural perspectives on mental illness: A qualitative study on causal beliefs and treatment use. International journal of qualitative studies on health and well-being, 17(1), 2123090.
Syed, S. T., Gerber, B. S., & Sharp, L. K. (2013). Traveling towards disease: transportation barriers to health care access. Journal of community health, 38, 976-993.
WHO, W. H. O. (2022). Mental disorders. https://www.who.int/news-room/fact-sheets/detail/mental-disorders#:~:text=In%202019%2C%201%20in%20every,of%20the%20COVID%2D19%20pandemic.
Williams, D. R., Lawrence, J. A., & Davis, B. A. (2019). Racism and health: evidence and needed research. Annual review of public health, 40(1), 105-125.
Yadav, N., Pandey, S., Gupta, A., Dudani, P., Gupta, S., & Rangarajan, K. (2023). Data privacy in healthcare: In the era of Artificial Intelligence. Indian Dermatology Online Journal, 14(6), 788-792.
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Copyright (c) 2026 Alexander W. Blidi, Dasantila Sherifi, Isaac Olorunisola, Farah Kattab
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