Artificial Intelligence -Based Apps to Manage Occupational Stress and Burnout : Scoping Review
DOI:
https://doi.org/10.62951/ijhm.v2i1.216Keywords:
burnout, stress, healthcare workerAbstract
Stress and burnout among healthcare workers represent a global crisis with significant implications for psychological and physical health, job performance, and interpersonal skills. These conditions are linked to anxiety, depression, suicidal ideation, substance use, poor quality of life, digestive disorders, and cardiovascular diseases. Burnout is characterized by emotional fatigue, depersonalization, and reduced personal accomplishment, often caused by chronic workplace stress. Factors such as demographics, fatigue, and resilience influence its development and severity. Traditional stress management interventions, such as counselling and leave, often prove insufficient in addressing these challenges. Recent advancements in Artificial Intelligence (AI) provide innovative tools for stress and burnout management, including mobile applications offering mindfulness, meditation, and self-care resources. AI systems like IBM Watson and Google DeepMind are being tested to enhance accessibility and effectiveness in stress management. Additionally, Stress Inoculation Training (SIT), involving methods such as meditation, yoga, cognitive-behavioural therapy, and biofeedback, has been recognized as a proactive approach to mitigating stress. This review explores the factors contributing to stress and burnout in healthcare workers and evaluates interventions aimed at improving well-being and productivity, emphasizing the potential of AI and SIT in preventing and managing these conditions.
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Afulani, P. A., Ongeri, L., Kinyua, J., Temmerman, M., Mendes, W. B., & Weiss, S. J. (2021). Psychological and physiological stress and burnout among maternity providers in a rural county in Kenya: Individual and situational predictors. BMC Public Health, 21(1), 1–16. https://doi.org/10.1186/s12889-021-10453-0
Balakrishnan, S. (2023). The AI yoga trainer using artificial intelligence and machine learning. 11(1), 319–322.
Botha, N. N., Ansah, E. W., Segbedzi, C. E., Dumahasi, V. K., Maneen, S., Kodom, R. V., Tsedze, I. S., Akoto, L. A., & Atsu, F. S. (2024). Artificial intelligent tools: Evidence-mapping on the perceived positive effects on patient-care and confidentiality. BMC Digital Health, 2(1). https://doi.org/10.1186/s44247-024-00091-y
De La Fuente-Solana, E. I., Suleiman-Martos, N., Pradas-Hernández, L., Gomez-Urquiza, J. L., Cañadas-De La Fuente, G. A., & Albendín-García, L. (2019). Prevalence, related factors, and levels of burnout syndrome among nurses working in gynecology and obstetrics services: A systematic review and meta-analysis. International Journal of Environmental Research and Public Health, 16(14). https://doi.org/10.3390/ijerph16142585
Ghahramani, S., Lankarani, K. B., Yousefi, M., Heydari, K., Shahabi, S., & Azmand, S. (2021). A systematic review and meta-analysis of burnout among healthcare workers during COVID-19. Frontiers in Psychiatry, 12(November), 8–12. https://doi.org/10.3389/fpsyt.2021.758849
John, J., Id, N., Aborigo, R. A., Okiring, J., Kuwolamo, I., Id, B. K. D., Getahun, M., Mendes, W. B., & Afulani, P. A. (2022). Individual and situational predictors of psychological and physiological stress and burnout among maternity providers in Northern Ghana. PLOS ONE, 1–16. https://doi.org/10.1371/journal.pone.0278457
Konlan, K. D., Asampong, E., Gyeke, P. D., & Glozah, F. N. (2022). Burnout and allostatic load among health workers engaged in human-resourced constrained hospitals in Accra, Ghana. BMC Health Services Research, 1–12. https://doi.org/10.1186/s12913-022-08539-5
Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023). Artificial intelligence in disease diagnosis: A systematic literature review, synthesizing framework, and future research agenda. Journal of Ambient Intelligence and Humanized Computing, 14(7), 8459–8486. https://doi.org/10.1007/s12652-021-03612-z
Lee, H. F., Hsu, H. C., Efendi, F., Ramoo, V., & Susanti, I. A. (2023). Burnout, resilience, and empowerment among COVID-19 survivor nurses in Indonesia. PLOS ONE, 18(10), 1–13. https://doi.org/10.1371/journal.pone.0291073
Loureiro, S. M. C., Bilro, R. G., & Neto, D. (2023). Working with AI: Can stress bring happiness? Service Business, 17(1), 233–255. https://doi.org/10.1007/s11628-022-00514-8
Meduri, K., Nadella, G. S., Gonaygunta, H., Kumar, D., Addula, S. R., Satish, S., Maturi, M. H., & Rehman, S. U. (2024). Human-centered AI for personalized workload management: A multimodal approach to preventing employee burnout. Journal of Infrastructure, Policy and Development, 8(9), 6918. https://doi.org/10.24294/jipd.v8i9.6918
Mollart, L., Skinner, V. M., Newing, C., & Foureur, M. (2013). Factors that may influence midwives' work-related stress and burnout. Women and Birth, 26(1), 26–32. https://doi.org/10.1016/j.wombi.2011.08.002
Ploug, T., & Holm, S. (2020). The right to refuse diagnostics and treatment planning by artificial intelligence. Medicine, Health Care and Philosophy, 23(1), 107–114. https://doi.org/10.1007/s11019-019-09912-8
Salam, A., & Abhinesh, N. (2024). Revolutionizing dermatology: The role of artificial intelligence in clinical practice. IP Indian Journal of Clinical and Experimental Dermatology, 10(2), 107–112. https://doi.org/10.18231/j.ijced.2024.021
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21(1), 1–23. https://doi.org/10.1186/s12911-021-01488-9
Taureng, H., Harith, S., Lin, L. P., Shafie, Z. M., Sultan, U., Abidin, Z., & Terengganu, K. (2020). Health administration service system in Tanralili Polyclinic Maros Regency, South Sulawesi Province. Middle-East Journal of Scientific Research, 28(1), 54–57. https://doi.org/10.5829/idosi.mejsr.2020.54.57
Wilton, A. R., Sheffield, K., Wilkes, Q., Chesak, S., Pacyna, J., Sharp, R., Croarkin, P. E., Chauhan, M., Dyrbye, L. N., Bobo, W. V., & Athreya, A. P. (2024). The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: A decentralized digital health protocol to predict burnout in registered nurses. BMC Nursing, 23(1), 1–14. https://doi.org/10.1186/s12912-024-01711-8
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