A Decision Support Tool for Allocating Hospital Bed Resources and Determining Required Acuity of Care

Limitations in health care funding require physicians and hospitals to find effective ways to utilize resources. Neural networks provide a method for predicting resource utilization of costly resources used for prolonged periods of time. Injury severity knowledge is used to determine the acuity of care required for each patient and length of stay is used to determine duration of inpatient hospitalization. Neural networks perform well on these medical domain problems, predicting total length of stay within 3 days for pediatric trauma (population mean and S.D. 4.37±45.12) and within 4 days for acute pancreatitis patients (7.75±79.19)

Use of an Artificial Neural Network to Predict Length of Stay in Acute Pancreatitis

Length of stay (LOS) predictions in acute pancreatitis could be used to stratify patients with severe acute pancreatitis, make treatment and resource allocation decisions, and for quality assurance. Artificial neural networks have been used to predict LOS in other conditions but not acute pancreatitis. The hypothesis of this study was that a neural network could predict LOS in patients with acute pancreatitis. The medical records of 195 patients admitted with acute pancreatitis were reviewed. A backpropagation neural network was developed to predict LOS \u3e 7 days. The network was trained on 156 randomly selected cases and tested on the remaining 39 cases. The neural network had the highest sensitivity (75%) for predicting LOS \u3e 7 days. Ranson criteria had the highest specificity (94%) for making this prediction. All methods incorrectly predicted LOS in two patients with severe acute pancreatitis who died early in their hospital course. An artificial neural network can predict LOS \u3e 7 days. The network and traditional prognostic indices were least accurate for predicting LOS in patients with severe acute pancreatitis who died early in their hospital course. The neural network has the advantage of making this prediction using admission data