Analyzing the Efficiency of Moroccan Hospital Network Regions via DEA and Tobit Regression: Assessing DEAP 2.1 Software versus Generative AI ChatGPT 3.5

Résumé

Context: Optimising hospital establishment efficiency, especially in staffing and resource utilization, is crucial for achieving SDG 3 objectives like quality healthcare services, universal health coverage, and individual well-being.

Objectif: This study aims to assess the technical efficiency of hospital networks in each health directorate region in Morocco and analyze the impact of staff personnel health on inefficiency. Method: The study uses Data Envelopment Analysis Programming (DEAP) software version 2.1 and generative Artificial Intelligent ChatGPT 3.5 to analyze 12 hospital network health directorate regions. Tobit regression was employed to analyze the impact of worker health and hospital activity on inefficiency. Results and discussion:  showed that the average technical efficiency was more inefficient in generative AI ChatGPT 3.5 than in DEAP software version 2.1. Hospital activity and nurse staffing significantly impacted inefficiency levels. Conclusion: The study concludes that inefficiency in hospital networks and staff personnel health pose challenges for managers in health directorate regions, emphasizing the need for New Public Management principles based on contractualization, accountability, and managerial practices. 

Keywords:   Hospital network; Data Envelopment Analysis; Tobit regression; DEAP software, ChatGPT 3.5.