Impact of Digital Health & AI on Job Satisfaction of Doctors in Private Tertiary Care Hospitals in Thiruvananthapuram District in Kerala, India - Mohamed, Azarudeen (Digital Repository of Theses - SSBM Geneva, 2025)
Introduction : The healthcare landscape in the state of Kerala in India is undergoing a significant transformation with the increasing adoption of digital health technologies and AI. Research conducted in different parts of the state shows doctors in Kerala are facing job stress due to the growing work pressure, administrative demands, and evolving healthcare environment. Since digital health and AI technologies are progressing in an unprecedented manner in healthcare, there are possibilities that work satisfaction issues could be made worse by the advent of these technologies, or they could also present chances to increase job satisfaction. Objective : This research aims to systematically investigate and analyze the impact of digital health and AI on job satisfaction of doctors working in private tertiary care hospitals in Thiruvananthapuram district in Kerala and to draw conclusions from the study with ultimate objective of improving strategies for policy makers, regulators, providers and technology developers for improving job satisfaction of doctors, ultimately improving the working environment resulting in better patient care. Methodology : This cross sectional study included 110 doctors as samples selected using convenience sampling method from 5 private tertiary care hospitals in Thiruvananthapuram district in the state of Kerala in India. A modified version of TAM questionnaire was used as the research instrument for primary data collection. For data analysis, sorting of data and data visualization, SPSS software and Microsoft Excel were utilized. Regression modeling, hypothesis testing, and principal component analysis were used to arrive at the conclusion. Results: Out of the selected sample 76.4% of doctors expressed over all job satisfaction which however varied across gender and age. Hypothesis testing reveals that differences across the age and gender are not statistically significant. Through the regression modelling the major factors that were affecting the job satisfaction includes work performance ( exp (β) 6.665, P value- 0.008, β = 1.897), Effectiveness at work (exp (β) 11.208, P value- 0.002, β = 2.417), Ease of use (exp(β ) 6.665, P value- 0.008, β = 1.897), Variety of purposes (exp(β) 1.735, P value- 0.000.047, β = 1.735) Conclusion: The widespread adoption of digital health and AI technologies among doctors working in tertiary care hospitals in this study is evidenced by the fact that over three-quarter of them expressed job satisfaction using digital health and AI technologies. However, compared to young doctors and male doctors, older and female doctors were less inclined to the usage of digital health technologies. It is anticipated that the findings of this study will assist policymakers, regulators, healthcare providers, planners, and technology developers in creating solutions for improving doctors job satisfaction that will enhance the efficacy and efficiency of healthcare delivery for the benefit of human life. Recommendation: Based on the findings, healthcare providers should invest in digital health technology and AI to improve job satisfaction of doctors. However, considering the fact that still a good number of all doctors especially old and female doctors tends to be less receptive to new technologies, while introduction of new technologies, it is highly recommended that the users gets trained before implementation of new technologies in healthcare facilities. Future research should look on the long-term impact of digital health technology and AI on doctors. Policymakers and practitioners can utilize these findings to help establish policies and interventions that encourage the successful use of digital health technologies. Limitations: This cross- sectional study may not have fully represented the intricacy of the investigated phenomenon because it was based on a specific theoretical framework. To improve the findings' generalizability, future research could try to enlist a bigger and more varied sample and mixed-methods technique and also investing more time to give more understanding of the phenomenon being studied. Data was collected over a period of 4 months and the short timeframe for this study may have affected the scope and depth of the investigation. Key words: Digital health, AI (Artificial Intelligence), tertiary care hospitals, job satisfaction, TAM questionnaire, healthcare, doctors, quantitative research, regression analysis, hypothesis testing, principal component analysis, SPSS