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


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Article Info

DATE

2021-10-09

AUTHORS

Roberto Ippoliti, Greta Falavigna, Cristian Zanelli, Roberta Bellini, Gianmauro Numico

ABSTRACT

BACKGROUND: The problem of correct inpatient scheduling is extremely significant 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 Artificial Neural Networks and examine risk factors for prolonged hospitalizations in 2018. With respect current alternative approaches (e.g., logistic models), Artificial Neural Networks give the opportunity to identify whether the model will maximize specificity 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 effective tool for administrators and medical professionals to predict the outcome of hospital admission and design interventions to improve hospital efficiency and effectiveness. More... »

PAGES

67

References to SciGraph publications

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  • <error retrieving object. in <ERROR RETRIEVING OBJECT
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    http://scigraph.springernature.com/pub.10.1186/s12962-021-00322-3

    DOI

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    DIMENSIONS

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/34627288


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