MODEL AI PREDIKSI BURNOUT BERBASIS WORKLOAD STAF KLINIS
DOI:
https://doi.org/10.51977/af7yfj23Keywords:
Burnout, Staf Klinis, Machine Learning, Electronic Health Record (EHR)Abstract
Burnout pada tenaga kesehatan merupakan tantangan sistemik yang berdampak pada kualitas layanan dan keselamatan pasien. Beban kerja klinis, pola shift yang intens, serta tuntutan administratif dari penggunaan Electronic Health Record (EHR) menjadi faktor yang berpotensi meningkatkan stres kerja. Meskipun data operasional tersedia dalam Sistem Informasi Rumah Sakit (SIRS), pemanfaatannya untuk deteksi dini risiko burnout staf masih terbatas. Penelitian ini bertujuan mengembangkan dan mengevaluasi model supervised machine learning untuk memprediksi risiko burnout staf klinis berdasarkan data perilaku dan beban kerja dari SIRS dan EHR. Penelitian kuantitatif dengan desain prediktif melibatkan 120 staf klinis yang terdiri atas dokter dan perawat. Data primer tingkat burnout diperoleh melalui instrumen terstandar, sedangkan data sekunder mencakup aktivitas EHR, jumlah pasien, jam kerja, dan pola shift. Tiga algoritma dibandingkan, yaitu Logistic Regression, Random Forest, dan Gradient Boosting, menggunakan metrik akurasi dan Area Under the Curve (AUC). Hasil menunjukkan bahwa perawat dan staf dengan pola kerja shift memiliki proporsi risiko burnout sedang hingga tinggi yang lebih besar. Beban kerja, terutama volume pasien dan jam kerja, serta intensitas aktivitas EHR menjadi faktor penting yang berkaitan dengan risiko burnout. Gradient Boosting menghasilkan kinerja terbaik dengan akurasi 0,87 dan AUC 0,91. Model tersebut berpotensi digunakan sebagai instrumen deteksi dini untuk mendukung redistribusi beban kerja, optimalisasi jadwal shift, dan intervensi preventif berbasis data dalam manajemen SDM rumah sakit.
References
Bates, David W, David Levine, Ania Syrowatka, Masha Kuznetsova, Kelly Jean Thomas Craig, Angela Rui, Gretchen Purcell Jackson, and Kyu Rhee. 2021. “The Potential of Artificial Intelligence to Improve Patient Safety: A Scoping Review.” NPJ digital medicine 4(1): 54. doi:10.1038/s41746-021-00423-6.
Canfell, Oliver J, Leanna Woods, Yasaman Meshkat, and Jenna Krivit. 2024. “The Impact of Digital Hospitals on Patient and Clinician Experience : Systematic Review and Qualitative Evidence Synthesis Corresponding Author :” 26: 1–24. doi:10.2196/47715.
Dyrbye, Lotte, Tait Shanafelt, Christine Sinsky, Pamela Cipriano, Jay Bhatt, Alexander Ommaya, Colin West, and David Meyers. 2024. “Burnout Among Health Care Professionals: A Call to Explore and Address This Underrecognized Threat to Safe, High-Quality Care.” NAM Perspectives 7. doi:10.31478/201707b.
Fang, Yilu, Betina Idnay, Yingcheng Sun, Hao Liu, Zhehuan Chen, Karen Marder, Hua Xu, Rebecca Schnall, and Chunhua Weng. 2022. “Combining Human and Machine Intelligence for Clinical Trial Eligibility Querying.” Journal of the American Medical Informatics Association 29(7): 1161–71. doi:10.1093/jamia/ocac051.
Gardner, Rebekah L, Emily Cooper, Jacqueline Haskell, Daniel A Harris, Sara Poplau, Philip J Kroth, and Mark Linzer. 2021. “Physician Stress and Burnout: The Impact of Health Information Technology.” Journal of the American Medical Informatics Association : JAMIA 26(2): 106–14. doi:10.1093/jamia/ocy145.
Ghassemi, Marzyeh, Luke Oakden-Rayner, and Andrew L Beam. 2021. “The False Hope of Current Approaches to Explainable Artificial Intelligence in Health Care.” The Lancet Digital Health 3(11): e745–50. doi:10.1016/S2589-7500(21)00208-9.
Kannampallil, Thomas, Joanna Abraham, Sunny Lou, and Philip Payne. 2020. “Conceptual Considerations for Using EHR-Based Activity Logs to Measure Clinician Burnout and Its Effects.” Journal of the American Medical Informatics Association 28. doi:10.1093/jamia/ocaa305.
Khatatbeh, Haitham, Annamária Pakai, Tariq Al-Dwaikat, David Onchonga, Faten Amer, Viktória Prémusz, and András Oláh. 2022. “Nurses’ Burnout and Quality of Life: A Systematic Review and Critical Analysis of Measures Used.” Nursing open 9(3): 1564–74. doi:10.1002/nop2.936.
Liu, Chao, Yen-Ching Chuang, Lifen Qin, Lijie Ren, Ching-Wen Chien, and Tao-Hsin Tung. 2025. “Machine-Learning-Based Model for Analysing and Accurately Predicting Factors Related to Burnout in Healthcare Workers.” BMJ public health 3(2): e000777. doi:10.1136/bmjph-2023-000777.
Mohr, David C, Shereef Elnahal, Maureen L Marks, Ryan Derickson, and Katerine Osatuke. 2025. “Burnout Trends Among US Health Care Workers.” JAMA network open 8(4): e255954. doi:10.1001/jamanetworkopen.2025.5954.
Putra, V H Cahaya, A Rachmat Raharja, G Kanugrahan, C Tho, and M Al-Husaini. 2025. “Advanced Machine Learning Models for Predicting Irrigation Water Quality: A Comparative Study of LSTM, SVM, and XGBoost Algorithms in Rice Cultivation.” In 2025 International Conference on Information Management and Technology (ICIMTech), , 234–39. doi:10.1109/ICIMTech67074.2025.11265474.
Raharja, A R, V H C Putra, G Kanugrahan, and C Tho. 2024. “Indonesian Sign Language (SIBI) Detection: A Landmark-Based Approach with Scikit-Learn Utilizing Random Forest Classifier.” In 2024 Ninth International Conference on Informatics and Computing (ICIC), , 1–8. doi:10.1109/ICIC64337.2024.10956917.
Rajkomar, Alvin, Michaela Hardt, Michael D Howell, Greg Corrado, and Marshall H Chin. 2023. “Ensuring Fairness in Machine Learning to Advance Health Equity.” Annals of internal medicine 169(12): 866–72. doi:10.7326/M18-1990.
Shanafelt, Tait D, Christine Sinsky, Lotte N Dyrbye, Mickey Trockel, and Colin P West. 2023. “Burnout Among Physicians Compared With Individuals With a Professional or Doctoral Degree in a Field Outside of Medicine.” Mayo Clinic proceedings 94(3): 549–51. doi:10.1016/j.mayocp.2018.11.035.
Sharma, Videha, Angela Davies, and John Ainsworth. 2021. “Clinical Risk Prediction Models: The Canary in the Coalmine for Artificial Intelligence in Healthcare?” BMJ Health & Care Informatics 28(1): e100421. doi:10.1136/bmjhci-2021-100421.
Tawfik, Daniel, Mohsen Bayati, Jessica Liu, Liem Nguyen, Amrita Sinha, Thomas Kannampallil, Tait Shanafelt, and Jochen Profit. 2024. “Predicting Primary Care Physician Burnout From Electronic Health Record Use Measures.” Mayo Clinic Proceedings 99(9): 1411–21. doi:10.1016/j.mayocp.2024.01.005.
Topol, Eric J. 2019. “High-Performance Medicine: The Convergence of Human and Artificial Intelligence.” Nature medicine 25(1): 44–56. doi:10.1038/s41591-018-0300-7.
West, Colin, Liselotte Dyrbye, and Tait Shanafelt. 2022. “Physician Burnout: Contributors, Consequences, and Solutions.” Journal of Internal Medicine 283. doi:10.1111/joim.12752.
WHO. 2025. Ethics and Governance of Artificial Intelligence for Health.
Wosny, Marie, Livia Maria Strasser, and Janna Hastings. 2023. “Experience of Health Care Professionals Using Digital Tools in the Hospital : Qualitative Systematic Review Corresponding Author :” 10: 1–21. doi:10.2196/50357.
Wu, Yuxuan, Mingyue Wu, Changyu Wang, Jie Lin, Jialin Liu, and Siru Liu. 2024. “Evaluating the Prevalence of Burnout Among Health Care Professionals Related to Electronic Health Record Use: Systematic Review and Meta-Analysis.” JMIR Med Inform 12: e54811. doi:10.2196/54811.
Van Zyl-Cillié, Maria Magdalena, Jacoba H Bührmann, Alwiena J Blignaut, Derya Demirtas, and Siedine K Coetzee. 2024. “A Machine Learning Model to Predict the Risk Factors Causing Feelings of Burnout and Emotional Exhaustion amongst Nursing Staff in South Africa.” BMC Health Services Research 24(1): 1665. doi:10.1186/s12913-024-12184-5.
