Infrastructure for Wait Time Prediction & Communication

Wait time prediction requires documented definitions of what constitutes a 'wait'—door-to-triage, triage-to-room, room-to-provider—and documented throughput benchmarks by care setting. Scheduling templates and patient access policies are explicitly documented, providing the baseline structure. However, the rules governing how patient acuity affects throughput estimates, how surge thresholds trigger communication, and how predicted vs. actual times are evaluated remain largely undocumented. Core scheduling context exists; prediction logic does not.

Wait time prediction depends on systematic capture of patient flow timestamps: arrival, triage, room assignment, provider contact, discharge. EHR/PM systems capture these through required check-in and discharge workflows, generating consistent event logs by care setting. Historical throughput by time-of-day, day-of-week, and acuity level requires complete timestamp records from every patient encounter. This systematic event capture provides the training data for the prediction model and the real-time census data for live wait time calculations.

Wait time modeling requires consistent schema across care settings: patient acuity categories (ESI levels in ED, urgency codes in clinic), appointment types standardized, care setting codes uniform, and staffing levels typed as structured fields. The baseline confirms appointment types are categorized and provider schedules are templated. These consistent schema elements allow the AI to stratify wait time predictions by acuity and setting, and generate patient notifications with accurate delay estimates rather than generic approximations.

Real-time wait time prediction requires API access to current patient census (active patients in queue), real-time acuity data, current staffing levels, and appointment schedule for expected arrivals. The EHR/PM scheduling module and ED tracking systems must be programmatically queryable—not just accessible via staff portal. API-based access enables the model to refresh predictions every few minutes as patient flow changes, and to push delay notifications via patient communication platforms when predicted wait exceeds threshold.

Wait time prediction models must recalibrate when throughput patterns shift—staffing changes, new provider onboarding, service line expansions, or seasonal volume changes alter baseline throughput rates. Event-triggered model updates ensure that when a new triage protocol launches or a clinic adds afternoon sessions, the prediction baseline recalibrates rather than continuing to apply stale historical averages. Prediction accuracy degrades measurably when model inputs reflect outdated throughput assumptions.

Wait time prediction requires integration between the ED/clinic patient tracking system (census and acuity), scheduling system (expected arrivals and appointment types), staffing system (current provider availability), and patient communication platform (text/portal notifications). API-based connections allow the model to assemble real-time input across these systems and push accurate, timely wait estimates to patients without manual intervention. Without this connected data flow, predictions cannot incorporate both scheduled demand and walk-in volume simultaneously.