Evidence map›Paper›PMID 40066234›Full record

ArticleRisk management and healthcare policy2025

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

Mohammed Senitan, Bandar Jarallah Alzahrani

Abstract read
In one paragraph

Article in Risk management and healthcare policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Mohammed SenitanDepartment of Public Health, College of Health Sciences, Saudi Electronic University, Riyadh, Saudi Arabia.
Bandar Jarallah AlzahraniPassionate Healthcare Strategist | Change Leader & Trusted Consultant | Driving Operational Excellence & Innovation in Healthcare and Beyond at Ascend Solutions, Western Region, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Ada’a Health Programemergency department efficiencyhealthcare analyticsoperational performance Improvementpatient flow management

Identifiers

PMID40066234
PMCPMC11892489

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