Prediction of High Risk of Deviations in Home Care Deliveries

Anders L. Madsen, Kristian G. Olesen, Heidi Lynge Løvschall, Nicolaj Søndberg-Jeppesen, Frank Jensen, Morten Lindblad, Mads Lause Mogensen, Trine Søby Christensen

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review


This paper presents a real-world application of Bayesian networks to support existing home care quality supervision. In Denmark home care is delivered by municipalities, where the individual citizen is free to select the service provider, private or public. The aim of our work is to support the home care control process by identifying significant deviations automatically, pointing to reasons for a significant deviation and identifying future home care deliveries where there is a high probability of deviation between granted and delivered care to the individual citizen. Home care is granted as packages of time measured in minutes and we define a too high delivery rate as larger than 150. In the municipality under study in this work (municipality of Hj{ø}rring), the supervision of home care delivery is a manual and time consuming process prone to human error. This paper presents the results of efforts to automate parts of the supervision using Bayesian network modelling and data analysis. The results of the pilot study shows significant potential in applying Bayesian network modelling and data analysis to this challenge for the benefit of the municipality, the employees and the citizens.
TitelProceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR
RedaktørerManfred Jaeger, Thomas Dyhre Nielsen
Antal sider12
ForlagML Research Press
StatusUdgivet - 2020
Begivenhed10th International Conference on Probabilistic Graphical Models - Hotel Comwell Rebild Bakker, Skørping, Danmark
Varighed: 23 sep. 202025 sep. 2020


Konference10th International Conference on Probabilistic Graphical Models
LokationHotel Comwell Rebild Bakker
NavnThe Proceedings of Machine Learning Research


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