Predicting Patient Visits to an Urgent Care Clinic Using Calendar Variables

Abstract

Abstract. Objective: To develop a prediction equation for the number of patients seeking urgent care. Methods: In the first phase, daily patient volume from February 1998 to January 1999 was matched with calendar and weather variables, and stepwise linear regression analysis was performed. This model was used to match staffing to patient volume. The effects were measured through patient complaint and ''left without being seen'' rates. The second phase was undertaken to develop a model to account for the continual yearly increase in patient volume. The model predicted the patient volume in the validation set within Ϯ11%. When the first-phase model was used to predict patient volume and thus staffing, the percentage of patients who left without being seen decreased by 18.5% and the number of patient complaints dropped by 30%. Conclusions: Use of a prediction equation allowed for improved accuracy in staffing patterns with associated improvement in measures of patient satisfaction.

Cardiac Mortality Is Higher Around Christmas and New Year’s Than at Any Other Time

“…Earlier studies [3][4][5][6][7] suggested that some patients delay seeking treatment until after the holidays. These studies did not investigate whether such delays produced additional deaths.…”

“…Previous studies [3][4][5][6][7] show that admissions to urgent care facilities drop on holidays and spike immediately thereafter. This phenomenon may occur because some patients inappropriately delay seeking medical services to avoid disrupting their holidays.…”

“…This phenomenon may occur because some patients inappropriately delay seeking medical services to avoid disrupting their holidays. [3][4][5][6][7] Any holiday-induced delays in seeking medical care should affect not only cardiac deaths but also other deaths. Thus, given the delay-in-seeking-care hypothesis, natural noncardiac deaths also should display a holiday peak.…”

Forecasting Hourly Patient Visits in the Emergency Department to Counteract Crowding

“…We made all our models sensitive to the hour of the day and the day of the week because previous studies have found that ED arrivals vary significantly across these two calendar variables [2 -7]. In addition, we included the month of the year in one of our models because several studies have found that ED arrivals also vary with this calendar variable [7,8,11,12]. Our main interest was to forecast hourly arrivals and hourly occupancy but to assess the effect of the length of the forecasting interval on the accuracy of the models we made models for intervals of 1, 2, 4, 8, and 24 hours.…”

Development and implementation of a real time statistical control method to identify the start and end of the winter surge in demand for paediatric intensive care

“…Previous approaches to the winter surge in emergency demand in adult intensive care have involved using disease surveillance (Hiller et al, 2013;Moriña et al, 2011;Nguyen et al, 2016), and/or weather and seasonal information (Batal et al, 2001;Boyle et al, 2012;Diehl et al, 1981;Jones et al, 2002;Marcilio et al, 2013;Shiue et al, 2016), and/or previous demand (Abraham et al, 2009;Jones, 2007;Jones et al, 2008;Proudlove et al, 2003), using a range of techniques including 4 regression, stochastic Markov models and time series analysis methods such as Autoregressive Integrated Moving Average (ARIMA) models. In general, while emergency demand was universally found to be strongly seasonal and autoregressive it was also extremely variable, with its stochastic nature making accurate forecasts beyond seasonal or monthly means difficult.…”