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Generative Artificial Intelligence for Monthly Emergency Department Volume Forecasting and Fiscal-Year Budget Planning.
ABSTRACT
Background:
Accurate emergency department (ED) volume forecasts support staffing, capacity planning, and budgeting, but conventional time-series forecasting can require specialized analytic expertise. Consumer-facing large language model (LLM) interfaces may lower this barrier, although their forecasting accuracy, consistency, and sensitivity to data recency are uncertain.
Objective:
To compare forecasts produced by commonly available generative AI interfaces with a Holt-Winters exponential smoothing baseline and to determine whether progressively updating the historical data window (“proximal forecasting”) identifies a pragmatic recency point for fiscal-year planning.
Methods:
We conducted a two-part retrospective observational forecasting study using monthly ED visit volumes from four hospitals. In part 1, 12-month forecasts were generated from 12-, 24-, and 36-month historical windows using Holt-Winters and five LLM interfaces (ChatGPT, Claude, Copilot, Perplexity, and Gemini); LLM forecasts were repeated 10 times to characterize run-to-run variability. In part 2, ChatGPT and Gemini were evaluated using 24-month windows whose end dates were systematically reduced from 7 months before to immediately before FY2025 and were compared with an R-implemented Holt-Winters baseline. Accuracy was assessed using mean absolute percentage error (MAPE) and root mean square error (RMSE).
Results:
Across all four sites, Holt-Winters had the lowest aggregate MAPE with a 24-month historical window (3.26±0.68) compared with 12 months (4.29±0.46) and 36 months (3.43±0.73). With 24 months of history, two LLM interfaces (ChatGPT 5.5 and Gemini 3.1) produced MAPE values similar to Holt-Winters at ED A (2.17±1.22 and 2.10±1.04 vs 2.56±1.08) and ED B (2.35±1.16 and 2.21±1.21 vs 2.46±1.39), but had higher error at the pediatric ED (7.35±5.26 and 7.29±5.27 vs 5.90±2.05) and the summer-seasonal community ED (8.74±3.11 and 8.69±3.11 vs 2.12±1.35). No approach met a predefined threshold in the Ped ED and community ED. In adult EDs with typical seasonality, forecasts generated up to three months from the fiscal-year start were comparable to fiscal-year–proximal forecasts (mean MAPE difference -0.4%–1.8%). Pediatric and highly seasonal sites demonstrated higher baseline error and recency bias susceptibility.
Conclusions:
Selected LLM interfaces can produce monthly ED volume forecasts comparable to Holt-Winters in some adult ED settings when given sufficient historical context. A forecast update up to 3 months before the fiscal year may be a pragmatic planning point for sites with typical seasonality. Pediatric and strongly seasonal EDs may require local validation and tailored models.
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