Estimating Hospital Admissions with a Randomized Regression Approach

Boarding or holding in the Emergency Department (ED) reduces capacity of the ED and delays patients from receiving specialized care. Estimating accurately the number of admissions from the ED can help determine appropriate level of staffing to reduce holding. We propose a randomized non-linear regression algorithm, RT-KGERS, to estimate the number of admissions a week in advance. We also devise features based on cyclical patterns found with a Fast Fourier Transform analysis on the hospital admission data. We evaluate the accuracy and efficiency of RT-KGERS and three existing algorithms in a dataset provided by a local hospital. We then compare our features with related features. Initial experimental results from RT-KGERS encouraged the hospital and us to conduct a live trial study which yielded similar levels of accuracy using RT-KGERS and the six features we devised.