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Before using an AI forecast to plan flu staffing or beds, a hospital should define exactly what the model predicts, test it on local data against a simple baseline, examine uncertainty and failure cases, and agree on monitoring and fallback actions. A strong average score is not enough: forecasts can miss rapid rises or falls precisely when operational plans are under pressure.
Start by defining the forecast and the decision
Write an intended-use specification before comparing models. It should make clear what decision the forecast will inform and what counts as a successful prediction. A statewide weekly admission total, for example, is not automatically a forecast of one hospital’s admissions, catchment-area demand, or staffed-bed need.
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- Outcome: Define which admissions count, including the population, facility or facilities, and any relevant coding or reporting rules.
- Forecast origin and cutoff: Record when each forecast is issued and the latest data it is allowed to use.
- Horizon and cadence: State how often forecasts arrive and how far ahead they extend. CDC’s FluSight evaluates weekly influenza hospital admissions for the current week and up to three weeks ahead across U.S. jurisdictions.
- Geography and unit: Specify whether the target is a hospital, hospital group, county, state, or another catchment.
- Operational use: Identify the intended user and the action that could change, such as staffing, bed capacity, or contingency planning.
- Uncertainty and fallback: Decide how users should respond to a wide or unavailable forecast, stale inputs, or a data-quality warning.
Keep aggregate operational forecasting distinct from patient-level clinical prediction. They answer different questions and require different validation; jurisdiction-level results do not establish that a patient-level model is fair or useful.
Request enough information to reproduce and assess the model
Ask the developer for a written account of the model and its data, not just a headline accuracy figure. CDC required model metadata, including method information, from FluSight teams before accepting submissions. A hospital can use a comparable checklist:
- Model family, version, intended population, and any exclusions or conditions where the model should not be used.
- Training and validation periods, target definitions, data sources, and any changes to the model over time.
- Data latency, revision practices, missing-data handling, and what happens when a feed is delayed or unavailable.
- Whether the output is a point estimate, a probability distribution, or intervals—and how to interpret those outputs.
- Update frequency, change documentation, known limitations, and the information needed to reproduce an evaluation.
Check that the information available at each historical forecast date matches what the model would actually have had in operation. Using later revisions or information from after the forecast date can make retrospective performance look better than it would have been in practice.
Validate on the population and workflow where it will be used
Use time-ordered evaluation: produce or obtain forecasts using only information available at each forecast origin, then compare them with later finalized observations. Keep the data used to select or tune a model separate from the final evaluation period. Where feasible, include more than one flu season and run the model prospectively in silent mode using the intended production feeds and workflow before letting it influence operations.
Report results separately by lead time and by the facility or geography that matters to the decision. A national, state, or other hospital’s score is not proof of local performance. CDC’s 2025–2026 FluSight evaluation found differences by jurisdiction, reinforcing the need for local validation rather than assuming a broader result transfers to a particular hospital. These validation designs are practical recommendations, not a single split method mandated by CDC.
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For a probabilistic forecast, assess whether its stated uncertainty is useful and calibrated—not only whether its central estimate is close. CDC uses relative weighted interval score (WIS) to evaluate probabilistic intervals against a baseline that carries forward the previous week’s admissions. A relative WIS below 1 indicates better performance than that baseline. CDC calculates relative WIS from paired model comparisons over shared targets and scales the result against the baseline.
Also measure interval coverage: how often the observed value falls within intervals at their stated nominal levels. For example, a nominal 95% interval should be assessed by whether it contains observations at a rate consistent with that level over the evaluated cases. Break coverage and scores out by horizon; an apparently good all-horizon average can conceal weak performance at the lead time planners need.
| Evaluation question | What to report | Why it matters |
|---|---|---|
| Does the model beat a simple alternative? | Relative WIS or another preselected score against a transparent baseline, such as the prior period’s observed count. | A complex model is not useful merely because it produces a forecast; it should improve on a reasonable reference. |
| Are the uncertainty intervals reliable? | Coverage at each nominal interval and calibration by forecast horizon. | Planners need to know whether an interval is a credible range for contingency planning, not just a visual decoration. |
| Does it perform when decisions are tight? | Frequency and size of underestimates relative to staffing or bed thresholds, plus how long misses persist. | Average error can obscure operationally costly shortfalls. |
| Does performance vary across settings? | Scores by lead time, site or geography, and relevant operating conditions. | Performance at one scale or location may not transfer to another. |
Choose any additional decision thresholds before examining evaluation results. The sources cited here do not establish a universal acceptable miss size; each hospital must set tolerances based on the consequences of under- or over-planning.
Inspect turning points, disruptions, and failure modes
Review the forecast through onset, peak, sharp declines, unusual local outbreaks, reporting backlogs, and changes in testing or admission definitions. Ask how delayed, missing, revised, or out-of-distribution inputs affect both the prediction and the uncertainty shown to users. Define a visible warning and an operational fallback for cases where the model should not be trusted.
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The CDC’s 2025–2026 season evaluation illustrates why this matters. Thirty-four teams submitted 53 unique flu-admission forecasting models; 39 met the evaluation’s inclusion criteria. The FluSight ensemble ranked seventh of those 39 on average relative WIS, and 33 of the 39 models performed better than the baseline. The ensemble was one of 12 models that consistently outperformed the baseline in all jurisdictions. Yet its 50% and 95% forecast intervals did not anticipate the late-December increase and mid-January decrease. For the ensemble’s two-week horizon, fewer than 25% of prediction intervals across jurisdictions contained observations around the week ending December 27, 2025; coverage stabilized near 95% beginning in February 2026.
Those results describe a particular U.S. season and jurisdiction-level evaluation, not an individual hospital’s expected performance. They show that favorable overall ranking and baseline comparisons can coexist with poor uncertainty coverage during rapid change. Review such episodes separately rather than allowing average scores to stand in for resilience during the weeks that matter most.
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Assess bias and differences between groups or sites
For a facility-level model, examine errors and interval coverage across relevant sites and operating conditions. For a patient-level model, choose groups relevant to that clinical task and supported by available data, then compare errors, coverage, and failure rates. Check whether data availability, coding, or reporting practices differ between groups, and document limitations where sample sizes are small.
Do not treat aggregate jurisdiction-level forecast results as evidence of patient-level fairness. ASTP’s 2024 survey found that 74% of hospitals evaluated predictive AI for bias, but the survey covered predictive AI broadly and does not prescribe one fairness measure for flu admission forecasting.
Assign ownership and monitor after deployment
Give the evaluation named owners: a clinical sponsor and operational lead, with analytics or data engineering, IT and security, quality and safety, and governance or compliance involved as appropriate. Agree in advance on approval criteria, who can pause use, how updates are reviewed, how incidents are reported, and how often results are reviewed.
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After deployment, monitor data freshness and missingness, forecast scores and interval coverage as outcomes arrive, performance across sites or relevant groups, changes following model updates, and use of fallback procedures. Set local investigation or suspension triggers around the risks of the planning decision rather than relying on a universal threshold.
ASTP’s 2025 report, based on the 2023–2024 AHA IT Supplement, provides context for hospital practice but not flu-forecast-specific results. In 2024, 71% of U.S. non-federal acute care hospitals reported predictive AI integrated into their EHR, up from 66% in 2023. Among hospitals, 82% reported evaluating predictive AI for accuracy, 74% for bias, and 79% conducting post-implementation evaluation or monitoring. Also in 2024, 74% said multiple entities were accountable for evaluating predictive AI; an AI committee or task force was reported by 66%, and division or department leaders by 60%. These are self-reported survey figures for predictive AI generally, not measures of flu forecast quality.
Compare candidate models on the same terms
When several models are available, evaluate them on shared forecast targets and the same time periods. A compact comparison should include:
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- Interval score and coverage or calibration at each horizon.
- Performance by lead time and site or geography.
- Behavior during rapid rises, declines, outbreaks, and data disruptions.
- Data latency, missingness tolerance, and how uncertainty or data-quality warnings are communicated.
- Subgroup and site differences, reproducibility, update transparency, and support for ongoing monitoring.
CDC’s evaluation of 39 included models found meaningful variation across models and jurisdictions. That makes a like-for-like comparison useful, but the season-specific rankings do not establish which model will work best at an individual hospital.
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