Neural networks and hospital length of stay: an application to support healthcare management with national benchmarks and thresholds

Background: The problem of correct inpatient scheduling is extremely signifcant for healthcare management. Extended length of stay can have negative effects on the supply of healthcare treatments, reducing patient accessibility and creating missed opportunities to increase hospital revenues by means of other treatments and additional hospitalizations. Methods: Adopting available national reference values and focusing on a Department of Internal and Emergency Medicine located in the North-West of Italy, this work assesses prediction models of hospitalizations with length of stay longer than the selected benchmarks and thresholds. The prediction models investigated in this case study are based on Artifcial Neural Networks and examine risk factors for prolonged hospitalizations in 2018. With respect current alternative approaches (e.g., logistic models), Artifcial Neural Networks give the opportunity to identify whether the model will maximize specifcity or sensitivity. Results: Our sample includes administrative data extracted from the hospital database, collecting information on more than 16,000 hospitalizations between January 2018 and December 2019. Considering the overall department in 2018, 40% of the hospitalizations lasted more than the national average, and almost 3.74% were outliers (i.e., they lasted more than the threshold). According to our results, the adoption of the prediction models in 2019 could reduce the average length of stay by up to 2 days, guaranteeing more than 2000 additional hospitalizations in a year. Conclusions: The proposed models might represent an efective tool for administrators and medical professionals to predict the outcome of hospital admission and design interventions to improve hospital efciency and efectiveness. Keywords: Neural Networks, Hospital admission, Length of stay, Health services research