AI-Enabled Reduction of Patient Wait Times

Executive Summary

Detailed Research

Methodology

Evidence comes from observational evaluations, service audits, and scoping reviews of AI applications in emergency departments and primary-care access pathways. These studies track changes in median or mean wait times, appointment backlogs, and call volumes before and after AI implementation.

AI approaches include online symptom triage, risk-based appointment prioritization, predictive modeling of demand, and virtual triage chatbots integrated with self-scheduling.

Key Studies

NHS Smart Triage Evaluation (2024)

• Design: Service evaluation
• Sample: UK GP practice
• Findings: An NHS-funded evaluation of an AI-powered triage system at a UK GP practice reported a 73% reduction in wait times for pre-bookable appointments (from 11 to 3 days), a 47% drop in peak-hour calls, and 70% fewer repeat appointments.
• Clinical Relevance: Demonstrates substantial access improvement

AI-Assisted Mental Health Triage (2025)

• Design: Program evaluation
• Sample: Canadian mental health services
• Findings: A Canadian mental-health and addiction program introduced AI-assisted triage and reported more than 50% reduction in wait times between 2023 and 2024, while improving alignment of patients to appropriate services.
• Clinical Relevance: Mental health access improvement

AI in Emergency Departments Wait-Time Review (2024)

• Design: Scoping review
• Sample: ED AI applications
• Findings: A review of AI in EDs summarizes evidence that AI triage and patient-flow tools can reduce ED length of stay and waiting times, though study designs are heterogeneous and often non-randomized.
• Clinical Relevance: ED efficiency gains documented

Hospital Wait-Time Reduction Case Reports

• Design: Case reports
• Sample: US health systems
• Findings: Case reports from US health systems describe AI-based models that predict admissions and allocate resources, yielding reported wait-time reductions of about 30% on average and up to 50% in some ED pilots.
• Clinical Relevance: Real-world implementation success

Clinical Implications

For osteopathic practices, AI-driven triage and scheduling can help match patients to appropriate visit types (in-person vs telehealth, OMT vs evaluation) and reduce delays, especially for urgent or complex cases.

Shorter waits may support continuity, reduce no-shows, and improve patient satisfaction, enhancing the impact of hands-on osteopathic care.

Limitations & Research Gaps

Most evidence is observational, often from single sites with vendor involvement, and lacks randomized controls or long-term follow-up.

Little research specifically addresses AI wait-time interventions in small osteopathic practices or OMT-focused clinics, where visit mix and needs differ from large systems.

Osteopathic Perspective

Reducing wait times aligns with osteopathic principles by improving access to timely, hands-on care and supporting the body's self-healing capacity.

DOs can use AI triage and scheduling as tools to prioritize patients who most need structural evaluation and OMT, while ensuring that technology does not create new barriers for vulnerable populations.