{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T00:05:25Z","timestamp":1787961925275,"version":"build-2784847793"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The escalation of medical disputes and patient complaints represents a critical challenge to healthcare systems globally, particularly in high-volume tertiary hospitals in China. While patient-related risk factors are well-documented, the contribution of healthcare professionals' (HCPs) occupational characteristics, burnout levels, and legal awareness to dispute risk remains under-explored. This study aims to identify these provider-side risk factors and develop a predictive nomogram to quantify the risk of medical complaints.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A single-center, cross-sectional retrospective study was conducted at the Fourth Affiliated Hospital of Harbin Medical University between May 2024 and August 2025. A total of 406 HCPs (187 physicians and 219 nurses) from high-risk departments (e.g., Neurosurgery, Orthopedics, Emergency Surgery) participated. Data on demographics, workload, Maslach Burnout Inventory (MBI) scores, defensive medical behaviors, and frequency of legal training were collected. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to screen variables, followed by multivariate logistic regression to identify independent predictors. A nomogram was constructed based on the final model and validated using the Concordance Index (C-index), calibration plots, and Decision Curve Analysis (DCA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of the 406 participants, 78 (19.2%) reported experiencing formal medical complaints in the past year. Multivariate analysis revealed that high emotional exhaustion (OR = 1.85, 95% CI: 1.23\u20132.78), depersonalization (OR = 2.10, 95% CI: 1.45\u20133.05), excessive night shifts (&amp;gt;5\/month) (OR = 1.65, 95% CI: 1.12\u20132.43), and low frequency of legal training (0\u20131 time\/year) (OR = 2.45, 95% CI: 1.56\u20133.89) were independent risk factors for complaints. Interestingly, defensive medical behaviors showed a U-shaped association. The developed nomogram demonstrated excellent discrimination with a C-index of 0.82 (95% CI: 0.76\u20130.88). Calibration curves showed good agreement between predicted and observed probabilities, and DCA confirmed the clinical utility of the model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The risk of medical complaints is significantly associated with HCP burnout, workload, and legal literacy. The proposed nomogram serves as an effective screening tool for hospital management to identify high-risk staff. Interventions focusing on psychological support and practical legal education are essential for mitigating medicolegal risks.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1816281","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:11:42Z","timestamp":1779379902000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Construction and validation of nomogram model for high-risk early warning of medical complaints based on occupational characteristics and workload of medical staff"],"prefix":"10.3389","volume":"14","author":[{"given":"Jiayi","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Humanities and Social Sciences, Harbin Medical University","place":["Harbin, Heilongjiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyu","family":"Gou","sequence":"additional","affiliation":[{"name":"Medical Records 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