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

Ippoliti R, Falavigna G, Zanelli C, Bellini R, Numico G (2021)
Cost Effectiveness and Resource Allocation 19(1): 67.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Autor*in
Ippoliti, RobertoUniBi; Falavigna, Greta; Zanelli, Cristian; Bellini, Roberta; Numico, Gianmauro
Abstract / Bemerkung
**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.
Erscheinungsjahr
2021
Zeitschriftentitel
Cost Effectiveness and Resource Allocation
Band
19
Ausgabe
1
Art.-Nr.
67
eISSN
1478-7547
Page URI
https://pub.uni-bielefeld.de/record/2958190

Zitieren

Ippoliti R, Falavigna G, Zanelli C, Bellini R, Numico G. Neural networks and hospital length of stay: an application to support healthcare management with national benchmarks and thresholds. Cost Effectiveness and Resource Allocation. 2021;19(1): 67.
Ippoliti, R., Falavigna, G., Zanelli, C., Bellini, R., & Numico, G. (2021). Neural networks and hospital length of stay: an application to support healthcare management with national benchmarks and thresholds. Cost Effectiveness and Resource Allocation, 19(1), 67. https://doi.org/10.1186/s12962-021-00322-3
Ippoliti, R., Falavigna, G., Zanelli, C., Bellini, R., and Numico, G. (2021). Neural networks and hospital length of stay: an application to support healthcare management with national benchmarks and thresholds. Cost Effectiveness and Resource Allocation 19:67.
Ippoliti, R., et al., 2021. Neural networks and hospital length of stay: an application to support healthcare management with national benchmarks and thresholds. Cost Effectiveness and Resource Allocation, 19(1): 67.
R. Ippoliti, et al., “Neural networks and hospital length of stay: an application to support healthcare management with national benchmarks and thresholds”, Cost Effectiveness and Resource Allocation, vol. 19, 2021, : 67.
Ippoliti, R., Falavigna, G., Zanelli, C., Bellini, R., Numico, G.: Neural networks and hospital length of stay: an application to support healthcare management with national benchmarks and thresholds. Cost Effectiveness and Resource Allocation. 19, : 67 (2021).
Ippoliti, Roberto, Falavigna, Greta, Zanelli, Cristian, Bellini, Roberta, and Numico, Gianmauro. “Neural networks and hospital length of stay: an application to support healthcare management with national benchmarks and thresholds”. Cost Effectiveness and Resource Allocation 19.1 (2021): 67.

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®

Quellen

PMID: 34627288
PubMed | Europe PMC

Suchen in

Google Scholar