Impact of Big Data Analytics on Emergency Department Efficiency in Saudi Ministry of Health Hospitals: A Retrospective Data Analysis.

Abstract

Background: The integration of big data analytics in healthcare has become essential for enhancing operational performance, particularly within Emergency Departments (EDs), where efficiency improvements can significantly impact patient satisfaction and resource utilization.

Aim: This study examines the impact of big data analytics on ED performance metrics within Saudi Arabia's Ministry of Health (MOH) hospitals, with a focus on key performance indicators (KPIs) and the effectiveness of the Ada'a Health Program in optimizing ED operations.

Methods: A retrospective observational study was conducted across 10 hospitals in five regions of Saudi Arabia. Data from 228,857 patient records were analyzed, alongside survey responses from 223 ED personnel. Statistical analyses, including paired t-tests, Pearson's correlation, and multiple regression models, were used to evaluate improvements in KPIs and assess the program's impact.

Results: Significant improvements in all KPIs were observed following the implementation of the Adaa Health Program. Door-to-Doctor Time decreased from 28:26 to 25:13, Doctor-to-Decision Time from 1:18:22 to 1:03:50, Decision-to-Disposition Time from 36:37 to 20:13, and Door-to-Disposition Time from 2:22:02 to 1:48:44. Pearson's correlation analysis indicated a strong relationship between Decision-to-Disposition Time and Doctor-to-Decision Time (r = 0.594), emphasizing the role of clinical decision-making in patient flow. Regression analysis further confirmed the program's significant association with reduced wait times (p < 0.001).

Conclusion: This study highlights the transformative impact of big data-driven decision-making in optimizing ED efficiency. The Ada'a Health Program has significantly improved patient flow, reduced congestion, and enhanced operational performance in Saudi MOH hospitals. These findings underscore the need for continued investment in big data analytics, updated predictive modeling, and workflow automation to sustain and further enhance ED efficiency. Future research should explore scalability across diverse healthcare settings and the long-term sustainability of such interventions.