HEALTHCARE WORKFLOW OPTIMIZATION
A simulation framework for evaluating electronic order workflows in integrated health records
Electronic health record (EHR) systems are critical to modern healthcare delivery, yet the dynamic workflows that govern electronic order processing remain underexplored. Inefficiencies in these digital pathways can cause delays in care, repetitive workloads, and even patient harm. This study presents a discrete-event simulation framework used to reconstruct and evaluate EHR-based order workflows in a large integrated healthcare system. Using real-world data extracted from the Veterans Health Administration's Corporate Data Warehouse, the authors mapped order events to standardized state transitions and modeled their progression across different facilities of varying complexity levels. After being calibrated with empirical distributions of transition times and validated against observed time-in-system metrics, the simulation demonstrates close alignment with historical performance. Scenario analyses reveal that resource capacity constraints significantly amplify the impact of electronic order surges, which are reflected in the disproportionate growth in backlogs and processing delays. Adjustments in transition probabilities further increased recirculation and extended workflow paths. Network-based analysis identified Reserved, InProgress, and Completed as structurally critical states that function as hubs within the process network but the transitions in-between also act as major bottlenecks. These results showcased the effectiveness of simulation-based approaches in monitoring EHR order processing performance and evaluating consequences of workflow changes on healthcare network resources planning. The proposed simulation framework provides a scalable data-driven tool to support operational decision-making and improve the efficiency of electronic order management in complex healthcare environments.
Authors: Yang Chen, Haoran Niu, Olufemi A. Omitaomu, Soumendra Bhanja, Angela Laurio, Amber Trickey, Vijayalakshmi Sampath & Jonathan R. Nebeker. Publication: npj Health Systems | (2026)3:14
Executive Impact & Key Findings
This study leverages discrete-event simulation to dissect and optimize complex EHR order workflows, revealing critical insights for operational efficiency and patient care quality in large healthcare systems.
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Orders Analyzed Annually
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Potential Reduction in Delays
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Throughput Improvement Identified
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Insights on Model Validation
p > 0.1
No Statistically Significant Difference in Time-in-System (95% Confidence)
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IQR Ratio: Facility A
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IQR Ratio: Facility B
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IQR Ratio: Facility C
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IQR Ratio: Facility D
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IQR Ratio: Facility E
The simulation models accurately replicated typical processing patterns, with IQR ratios across facilities falling within a narrow ±10% range, indicating strong agreement with observed system variability.
Impact of Electronic Order Surges & Capacity Constraints
Facility D Scenario Analysis
Summary: Scenario analyses revealed that resource capacity constraints significantly amplify the impact of electronic order surges. Under increased arrival rates and limited capacity (e.g., 1000 orders concurrent processing), the average number of orders in the system surged dramatically, failing to reach stability. This led to disproportionate growth in backlogs and processing delays, indicating critical system tipping points. The InProgress and Completed queues experienced significant increases in mean waiting times, underscoring the vulnerability of the system to demand fluctuations without adequate resource allocation.
Challenge: How does the EHR system respond to increased order volume with limited processing capacity?
Solution: Discrete-event simulation modeling of order workflows under various capacity and arrival rate scenarios.
Results: Identified critical capacity thresholds beyond which order processing becomes unstable, leading to severe backlogs and delays. Provided data to predict system tipping points and inform resource planning.
Key EHR Order State Transitions
Enterprise Process Flow
Created
→
Ready
→
Reserved
→
InProgress
→
Completed
→
END
The general workflow progresses from Created to Ready, then often through Reserved and InProgress before reaching Completed. However, dynamic adjustments in transition probabilities (e.g., restricting Ready to Completed shortcut) significantly increase recirculation, leading to longer, more complex workflow paths and higher time-in-system. Abnormal states like Failed and Error also contribute to workflow complexity and potential loops. Network analysis highlights Reserved, InProgress, and Completed as critical hubs in this process.
Critical Workflow States Identified by Centrality Analysis
| State | Role in Workflow | Centrality Highlight |
|---|---|---|
| Reserved | Critical Bridge/Routing Hub | Highest Betweenness Centrality (lies on many shortest paths) |
| InProgress | Major Processing Hub | Overall high scores across centrality metrics |
| Completed | Frequent Termination Point | Dominated PageRank & Eigenvector Centralities (strong connectivity) |
| Failed | Rework/Recovery Point | Higher PageRank (connected to influential states, often subsequent) |
| Exited & Error | Terminal Abnormal States | Lower centrality scores (less integrated into core flow loops) |
Actionable Insights for Healthcare Operations & HIT Professionals
Use simulation for safe policy testing and workflow design
The framework serves as a virtual testing environment to evaluate proposed changes to workflow, policy, or resource allocation, enabling informed decisions prior to real-world implementation.
Plan for scalable surge response
Simulation results under order surge conditions highlight the importance of contingency strategies such as adaptive prioritization protocols, staffing flexibility, and elastic processing capacity.
Monitor critical states to detect emerging congestion
Centrality analysis identified critical states (e.g., Reserved, InProgress, Completed). Monitoring metrics like active order volume or dwell time in these states can enable earlier detection of congestion and support timely intervention.
Prevent rework cycles by identifying looping transitions
Automatic flagging excessive recirculation or order re-entries can help detect inefficiencies and trigger corrective workflows or alerts.
Leverage transition dynamics as early warning indicators
Shifts in transition duration or path probabilities can serve as leading indicators of workflow drift, supporting proactive operational adjustments.
Quantify Your Potential ROI
Estimate the efficiency gains and cost savings by optimizing your EHR workflows with our AI-driven simulation framework.
Estimated Annual Savings
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Hours Reclaimed Annually
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Your Journey to Optimized Workflows
Our structured approach ensures a seamless integration and measurable improvements, tailored to your enterprise's unique needs.
Phase 01: Discovery & Data Integration
Comprehensive analysis of existing EHR systems, data sources (e.g., CDW), and workflow logs. Secure and anonymize historical event data to build a foundational understanding of current processes.
Phase 02: Simulation Model Development
Design and calibrate discrete-event simulation models based on extracted data, mapping order events to standardized state transitions (OASIS). Incorporate empirical distributions of transition times and resource constraints.
Phase 03: Validation & Baseline Performance
Validate the simulation models against observed time-in-system metrics and historical performance to ensure accuracy. Establish a baseline for operational performance and identify initial bottlenecks.
Phase 04: Scenario Analysis & Optimization
Conduct 'what-if' scenario analyses, evaluating impacts of order surges, resource re-allocations, and policy changes. Identify optimal workflow configurations and resource planning strategies.
Phase 05: Deployment & Continuous Monitoring
Implement recommended changes and deploy the simulation framework for real-time monitoring. Integrate predictive models and adaptive simulation for continuous performance optimization and anomaly detection.
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