"A Predictive Analytics Approach to Understanding Workload, Strees, And Mental Health Challenges Among Doctors."
Kripa D. Seth1
Student
Dept. of MBA., Sipna College of Engineering and Technology, Amravati 444607, Amravati (MS.), India
kripaseth04@gmail.com
Prof. Kasturi Kashikar 2
Asst. Professor,
Dept. of MBA., Sipna College of Engineering and Technology, Amravati 444607, Amravati (MS.), India
kdkashikar@sipnaengg.ac.in
Abstract
The medical profession is widely recognized as one of the most demanding occupations due to long working hours, heavy patient loads, high levels of responsibility, and continuous exposure to emotionally challenging situations. These occupational demands significantly increase stress among doctors, often leading to mental health challenges such as burnout, anxiety, depression, and emotional exhaustion. Poor mental well-being among doctors not only affects their personal health but also influences professional performance, patient safety, and overall healthcare quality. This study examines the relationship between workload, stress, and mental health challenges among doctors using a predictive analytics approach. A quantitative, survey-based research design was adopted, and data were collected from practicing doctors across different hospital settings. Key variables such as working hours, patient load, job stress, emotional exhaustion, and mental well-being were analyzed using statistical techniques including correlation analysis and regression modeling. The study aims to identify patterns that can help predict mental health risks associated with excessive workload and stres. The findings reveal a strong positive relationship between workload and stress and a significant negative impact of stress on doctors’ mental health. The study emphasizes the importance of predictive analytics in early identification of mental health risks and highlights the need for data-driven interventions to promote doctors’ psychological well-being and improve healthcare outcomes.
Keywords
Workload, Stress, Mental Health, Doctors, Predictive Analytics, Burnout
