Overview​

This project explored how predictive analytics can be leveraged to reduce no-show rates in outpatient clinics by identifying high-risk patients and improving their ability to attend through targeted supports Focusing on a real-world case study at a Canadian outpatient rehabilitation organization, the solution integrates a no-show prediction model within the electronic health record (EHR) system to improve attendance, continuity of care, and health equity, while generating significant cost savings.

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Background

​Missed appointments (no-shows) are a costly and common problem in healthcare. They disrupt continuity of care, contribute to poor health outcomes, especially among marginalized populations, and result in substantial financial losses. Traditional strategies such as generic reminders or punitive no-show fees often fail to address the root causes of no-show behavior. A modern, data-informed approach is needed to engage patients more effectively and equitably.​​

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Case Study Setting

• Organization: Multicenter outpatient rehab network affiliated with a large Canadian hospital

• Focus: High-cost physician specialist clinics for complex worker’s compensation cases

• Scale: 21,000 annual visits, current 7% no-show rate (~1,400 missed visits), which equates to $2.1M in annual losses

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Health Informatics Solution

The solution involves integrating Epic Systems' “Risk of Patient No-Show” predictive model directly into the clinic’s EHR. This random forest model analyzes over 20 factors (e.g., lead time, visit type, prior no-show history) to calculate no-show probability for each scheduled appointment. The predictions are used to prioritize which patients receive proactive, enhanced reminder calls from trained support staff.

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Workflow Redesign

Current State:

• Reminder method depends on patient portal registration

• Most patients receive only automated emails, regardless of actual no-show risk

Future State:

• Reminders are based on data-driven no-show risk thresholds

• Patients above the risk threshold receive enhanced phone calls from specialized staff

• Calls are personalized to help address barriers (e.g., transportation, anxiety, literacy)

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Business Case

• Target Outcome: 25% reduction in no-show rate (from 7% to 5%)

• Year 1 Investment: $202,000 (project staff, training, integration)

• Estimated Annual Savings: $610,000

• Net Savings in Year 1: $408,000

• Additional ROI: Scalable to other service lines, further increasing savings and impact

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Policy & Governance

• Equity-focused Intent: Predictions must support, not penalize, patients

• Transparency & Explainability: Model must be clear and justifiable

• Data Ethics: Avoid use of race, income, or other bias-prone variables

• Access Controls: Limit model visibility to appropriate staff

• Standardization: Define and include “late cancellations” in training data

• Bias Mitigation: Validate and monitor model performance across populations

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Conclusion

This no-show prediction model provides a scalable, data-driven approach to improving patient attendance, especially for underserved populations. By embedding predictive tools into EHR workflows and redesigning reminder processes, organizations can improve continuity of care, reduce health disparities, and realize substantial cost savings.

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Keywords

Health Informatics, Predictive Analytics, Workflow Redesign, Proces Improvement, No-Show Model, EHR Integration, Health Equity, Continuity of Care, Epic Systems, Outpatient Scheduling