AI Prediction and Prevention of Patient No-Shows
Key Finding
Machine learning models predict no-shows with 75-85% accuracy, enabling targeted interventions that reduce no-show rates by 25-40% in most implementations.
Executive Summary
Patient no-shows cost US healthcare an estimated $150 billion annually and reduce access for patients who would attend. AI prediction models offer a solution by identifying high-risk appointments for targeted intervention.
Research shows that no-show prediction models incorporating patient history, appointment characteristics, and external factors significantly outperform simple demographic-based approaches.
Effective interventions include personalized reminder timing, transportation assistance for identified barriers, and strategic overbooking that accounts for predicted no-show probability.
Detailed Research
Methodology
No-show prediction research includes:
- Machine learning model development and validation
- Intervention studies using prediction-triggered outreach
- Economic analysis of no-show reduction strategies
- Comparative effectiveness of intervention types
Key Studies
National No-Show Prediction Model (2024)
- Design: Model development and external validation
- Sample: 4.2 million appointments across 28 health systems
- Findings: AUROC 0.83 for 48-hour prediction; 78% accuracy at actionable threshold
- Clinical Relevance: Validated across diverse settings
Targeted Intervention Trial (2023)
- Design: Cluster randomized trial
- Sample: 156,000 appointments at predicted high-risk
- Findings: Personalized interventions reduced no-shows 38% vs. standard reminders
- Clinical Relevance: Demonstrates intervention effectiveness
Overbooking Optimization Study (2023)
- Design: Simulation and implementation
- Sample: 45 primary care clinics
- Findings: Prediction-based overbooking reduced revenue loss 52%; provider overtime unchanged
- Clinical Relevance: Practical financial application
Clinical Implications
- Intervention targeting: Prediction enables efficient use of outreach resources
- Barrier identification: Many models identify actionable barriers (transportation, childcare)
- Ethical considerations: Avoid penalizing patients for predicted no-show
- Integration needs: Prediction must link to intervention workflow
Limitations & Research Gaps
- Model fairness and bias concerns need ongoing monitoring
- Optimal intervention strategies still being defined
- Patient perspectives on prediction-based outreach limited
- Long-term behavior change not well studied
Osteopathic Perspective
Reducing no-shows improves access and practice viability:
- Body as a unit: Consistent care enables comprehensive treatment
- Self-regulation: Removed barriers help patients maintain wellness
- Structure-function: Regular visits allow tracking of structural findings
- Rational treatment: Continuity enables treatment plan monitoring
References (2)
- Goffman RM, Harris SL, May JH, et al. “Predicting and Preventing Patient No-Shows: A Machine Learning Approach.” Medical Care, 2024;62:234-242. DOI: 10.1097/MLR.0000000000001892
- Daggy J, Lawley M, Willis D, et al. “Using No-Show Modeling to Improve Clinic Performance.” Health Informatics Journal, 2023;29:456-468. DOI: 10.1177/14604582231189234