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Predicting the number of patient encounters and transports during mass gatherings can be challenging. The nature of these events necessitates that proper resources are available to meet the needs that arise. Several prediction models to assist event planners in forecasting medical utilization have been proposed in the literature.
The objective of this study was to determine the accuracy of the Arbon and Hartman models in predicting the number of patient encounters and transportations from the Baltimore Grand Prix (BGP), held in 2011 and 2012. It was hypothesized that the Arbon method, which utilizes regression model-derived equations to estimate, would be more accurate than the Hartman model, which categorizes events into only three discreet severity types.
This retrospective analysis of the BGP utilized data collected from an electronic patient tracker system. The actual number of patients evaluated and transported at the BGP was tabulated and compared to the numbers predicted by the two studied models. Several environmental features including weather, crowd attendance, and presence of alcohol were used in the Arbon and Hartman models.
Approximately 130,000 spectators attended the first event, and approximately 131,000 attended the second. The number of patient encounters per day ranged from 19 to 57 in 2011, and the number of transports from the scene ranged from two to nine. In 2012, the number of patients ranged from 19 to 44 per day, and the number of transports to emergency departments ranged from four to nine. With the exception of one day in 2011, the Arbon model overpredicted the number of encounters. For both events, the Hartman model overpredicted the number of patient encounters. In regard to hospital transports, the Arbon model underpredicted the actual numbers whereas the Hartman model both overpredicted and underpredicted the number of transports from both events, varying by day.
These findings call attention to the need for the development of a versatile and accurate model that can more accurately predict the number of patient encounters and transports associated with mass-gathering events so that medical needs can be anticipated and sufficient resources can be provided.
NableJV, MargolisAM, LawnerBJ, HirshonJM, PerriconeAJ, GalvagnoSM, LeeD, MillinMG, BissellRA, AlcortaRL. Comparison of Prediction Models for Use of Medical Resources at Urban Auto-racing Events. Prehosp Disaster Med. 2014;29(6):1-6.
Central line-associated bloodstream infection (CLABSI) rates are gaining importance as they become publicly reported metrics and potential pay-for-performance indicators. However, the current conventional method by which they are calculated may be misleading and unfairly penalize high-acuity care settings, where patients often have multiple consurrent central venous catheters (CVCs).
We compared the conventional method of calculating CLABSI rates, in which the number of catheter-days is used (1 patient with n catheters for 1 day has 1 catheter-day), with a new method that accounts for multiple concurrent catheters (1 patient with n catheters for 1 day has n catheter-days), to determine whether the difference appreciably changes the estimated CLABSI rate.
Academic, tertiary care hospital.
Adult patients who were consecutively admitted from June 10 through July 9, 2009, to a cardiac-surgical intensive care unit and a surgical intensive and surgical intermediate care unit.
Using the conventional method, we counted 485 catheter-days throughout the study period, with a daily mean of 18.6 catheter-days (95% confidence interval, 17.2-20.0 catheter-days) in the 2 intensive care units. In contrast, the new method identified 745 catheter-days, with a daily mean of 27.5 catheter-days (95% confidence interval, 25.6-30.3) in the 2 intensive care units. The difference was statistically significant (P < .001). The new method that accounted for multiple concurrent CVCs resulted in a 53.6% increase in the number of catheter-days; this increased denominator decreases the calculated CLABSI rate by 36%.
The undercounting of catheter-days for patients with multiple concurrent CVCs that occurs when the conventional method of calculating CLABSI rates is used inflates the CLABSI rate for care settings that have a high CVC burden and may not adjust for underlying medical illness. Additional research is needed to validate and generalize our findings.