Checklist

Short-Term Demand Forecast Pilot Checklist

Interactive pilot intake and safety checklist for short-term demand forecasting and predictive triage. Captures data readiness, model choices, safety checks, action rules, success metrics, governance, and observations so pilots run safely and produce measurable evidence before scaling.

Interactive Tool

Short-Term Demand Forecast Pilot Checklist

Run a small, safe, measurable pilot that tests whether short-term demand forecasts and predictive triage can reliably reduce unplanned waits without creating unsafe false positives or clinician burden. Use this interactive checklist to capture pilot parameters, required safety checks, success metrics, and lessons so results are repeatable and auditable.

Short descriptive name for this pilot (site/clinic/team + focus).
Person or team accountable for pilot decisions, escalation, and results.
Which sites, patient cohorts, shifts, or queues are included. Note any exclusions.
How far ahead the model will predict (e.g., 1 day, 3 days). Shorter horizons usually demand less complex models.
Examples: bookings, appointment cancellations, EMS calls, referral counts, inventory of beds, historical arrivals, public events. List data sources and owners.
Amount of past data available to train/validate models. More is better, but short pilots can still work with limited windows.
Is the historical and live data accessible, consistent, and auditable for the pilot?
Choose a simple, explainable approach for an initial pilot unless strong justification exists for more complex models.
Select an evaluation approach that matches the forecast horizon and seasonality.
Average number of events (arrivals/requests) per day in the pilot scope. Useful for statistical power planning.
Action rules turn a forecast into operational steps (staffing changes, routing, patient messaging).
Describe what will happen when the model predicts a surge or shortage, who will act, and how clinicians can override.
At least one clinician must approve safety checks before live pilot actions occur.
Name(s) responsible for model updates, performance monitoring, and risk management.
Pick metrics that reflect both model accuracy and patient/operational impact.
Measured over a recent representative period. Needed to estimate impact.
A realistic, evidence-based target to judge pilot success.
Be specific: metric thresholds, acceptable false-alert rate, staff burden constraints, patient experience goals, and governance approval conditions.
YYYY-MM-DD or approximate date.
Allow enough time to collect meaningful outcome data and observe variation.
Include who will authorize rollback, how to notify stakeholders, and how to resume safe operations.
Note potential for biased predictions, unequal impact across groups, consent or privacy concerns.
Use this field to record findings, clinician feedback, and recommended next steps after pilot completion.
You can explore this tool now. Sign in or create an account to save your responses and return to them later.
Sign In to Save Create an Account
Make this tool part of your work

Save a personal copy, bring it to your team, or tailor the questions and workflow to fit what you are hungry to improve.

Member customization and team collaboration are coming soon.
Discuss Ask the Librarian

Discussion

Comments and conversation will live here.