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. For this phase daily patient volume from February 1998 to April 2000 was used; the patient volume from May 2000 to July 2000 was used as a validation set. RESULTS: First-phase prediction equation was: daily patient volume = 66.2 + 11.1 January + 4.56 winter + 47.2 Monday + 37.3 Tuesday + 35.6 Wednesday + 28.2 Thursday + 24.2 Friday + 7.96 Saturday + 10.1 day after a holiday. This equation accounted for 75.2% of daily patient volume (p<0.01). Inclusion of significant weather variables only minimally improved the predictive ability (r(2) = 0.786). The second-phase final model was: daily patient volume = 57.2 + 0.035 Newdate + 52.0 Monday + 44. 2 Tuesday + 39.2 Wednesday + 30.2 Thursday + 26.5 Friday + 10.9 Saturday + 12.2 February + 3.9 March, which accounted for 72.7% of the daily variation (p<0.01). The model predicted the patient volume in the validation set within +/-11%. 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.
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. For this phase daily patient volume from February 1998 to April 2000 was used; the patient volume from May 2000 to July 2000 was used as a validation set. RESULTS: First-phase prediction equation was: daily patient volume = 66.2 + 11.1 January + 4.56 winter + 47.2 Monday + 37.3 Tuesday + 35.6 Wednesday + 28.2 Thursday + 24.2 Friday + 7.96 Saturday + 10.1 day after a holiday. This equation accounted for 75.2% of daily patient volume (p<0.01). Inclusion of significant weather variables only minimally improved the predictive ability (r(2) = 0.786). The second-phase final model was: daily patient volume = 57.2 + 0.035 Newdate + 52.0 Monday + 44. 2 Tuesday + 39.2 Wednesday + 30.2 Thursday + 26.5 Friday + 10.9 Saturday + 12.2 February + 3.9 March, which accounted for 72.7% of the daily variation (p<0.01). The model predicted the patient volume in the validation set within +/-11%. 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.
Authors: Timothy Y Liu; Jason L Sanders; Fu-Chiang Tsui; Jeremy U Espino; Virginia M Dato; Joe Suyama Journal: PLoS One Date: 2013-03-21 Impact factor: 3.240