Poisson Modeling Predicts Acute Telestroke Patient Call Volume.

Abstract

Background: Predicting the frequency of calls for telestroke and emergency teleneurology consultation is essential to prepare staffing for the immediate management of time-sensitive strokes. In this study, we evaluate Poisson distribution count data using a generalized linear model that predicts the volume of hourly telestroke calls over a 24-h period. Methods: We performed an Institutional Review Board approved retrospective cohort review of patients (January 2019-December 2022) from an institutional telestroke database at a large nonprofit multihospital system in the United States. All patients ≥18 years with a telestroke activation were included. Telestroke calls were quantified in frequency per day and analyzed by multiple time and date intervals. Poisson probability mass function (PMF) and cumulative distribution function (CDF) were used to predict call probabilities. A univariable Poisson regression model was fit to predict call volumes. Results: A total of 8,499 patients at 21 hospitals met inclusion criteria, the mean calls/day were 5.82 ± 2.54, and mean calls/day within each hour increment ranged from a minimum of 0.07 from 5 a.m. to 6 a.m. to a maximum of 0.45 from 7 p.m. to 8 p.m. The Poisson distribution was the most appropriate parametric probability model for these data, confirmed by the fit of the data to the expected distributions corresponding to the calculated means. The predicted probabilities of call frequencies by hour were calculated using the Poisson PMF and CDF; the probability of two or fewer calls/day by hour ranged from 98.9% to 99.9%. Univariable Poisson regression modeled an increase of future calls/day from 6.7 calls/day in July 2023 to 7.6 calls/day in October 2025. Conclusion: Poisson modeling closely fits telestroke call volumes, predicts the future volumes, and can be applied to any health system in which the mean call volume is known, which may inform the number of physicians needed to cover calls in real-time.