Utilization of patient acuity data for predicting staffing needs

The LDS Hospital in Salt Lake City, Utah wishes to utilize the computerized patient acuity data to determine the staffing requirement of the nursing units. Acuity items are the nursing care procedures provided the patient during a shift; a standard time is assigned to each procedure. A model was designed which assumed the total hours of nursing care received by the patients during the previous 24 hours would be an adequate estimate of the total hours of care that would be needed in the next 24 hour period. The purpose of this study was to evaluate this current model and to develop an additional model for comparison. Two separate criteria were applied to the current model; one that looked at the overall prediction error and the other that focused on the prediction error per nurse. Both units could not meet the overall criterion a high percentage of the time but the error per nurse was significantly less on the larger unit than on the smaller unit. In addition, the following four assumptions of the current model were tested: (1) the acuity data is accurate (2) the workload distributions among the shifts are relatively stable, (3) census fluctuation will not significantly affect the model, and (4) the model serves the units equally well. Only the first assumption proved to be correct. Linear regression models were formulated to predict staffing needs on two medical/surgical units, one a 46 bed unit and the other a 32 bed unit. The units were studied for close to three months. Patient acuity data were utilized in the formulations of these models along with the census and the net change in census. The linear models were compared to the model currently being used by the hospital. The smaller unit showed an improvement in the degree of prediction errors when using the linear model; however, the larger unit did not. It was concluded that neither the current model nor the linear models explained enough of the variability in the acuity totals to adequately predict the staffing needs of the hospital.