AI Powered Early Warning Systems for Healthcare Worker Burnout in New York City: Predictive Modeling of Absenteeism, Turnover, and Scheduling Burden

Abstract

Background--New York City healthcare professionals are experiencing rising cases of burnout because of heavy workloads, shortages, irregular working schedules, and operational stress. These issues lead to absenteeism, turnover intention, and decreased workforce stability. Developments in artificial intelligence (AI)-based early warning systems have become new technologies that can anticipate burnout and enhance workforce management, with predictive analytics and real-time monitoring.

Objective-- The objective of the study was to measure the efficacy of AI-enabled early warning systems in forecasting the occurrence of burnout among healthcare workers by analyzing the scheduling load, burnout-related symptoms, absenteeism, turnover intention, perception of AI systems, and ethical issues in healthcare settings in New York City.

Methodology-- The study used a quantitative cross-sectional research design. The structured close-ended questionnaire based on a five-point Likert scale was used to collect data on 355 healthcare professionals (nurses, physicians, technicians, and administrative staff). Data gathered were entered in SPSS software and analyzed under descriptive statistics, such as frequencies, percentages, means, standard deviations, variance analysis and reliability test using Cronbach Alpha.

Results-- The findings revealed high levels of scheduling burden, burnout, absenteeism tendencies, and turnover intention among healthcare workers. Burnout Indicators recorded a high mean score (M = 4.01), while AI System Perception achieved the highest mean value (M = 4.08), indicating strong acceptance of AI-based predictive systems. Reliability analysis demonstrated excellent internal consistency across all constructs, with Cronbach’s Alpha values ranging from 0.84 to 0.91. Respondents also expressed significant ethical concerns regarding privacy, fairness, and transparency in AI implementation.

Conclusion-- The paper concludes that AI-based early warning systems would be useful in mitigating the risk of burnout, enhancing workforce stability, and proactive management of healthcare workforce. Nonetheless, to implement it successfully, ethical leadership, transparency, data privacy, and human-centered AI practices are necessary to guarantee trust and responsible adoption in healthcare settings.