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
Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:281-292, 2020.

Abstract

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v138-madsen20a, title = {Prediction of High Risk of Deviations in Home Care Deliveries}, author = {Madsen, Anders L. and Olesen, Kristian G. and L{\o}vschall, Heidi Lynge and S{\o}ndberg-Jeppesen, Nicolaj and Jensen, Frank and Lindblad, Morten and Mogensen, Mads Lause and Christensen, Trine S{\o}by}, booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models}, pages = {281--292}, year = {2020}, editor = {Manfred Jaeger and Thomas Dyhre Nielsen}, volume = {138}, series = {Proceedings of Machine Learning Research}, month = {23--25 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v138/madsen20a/madsen20a.pdf}, url = { http://proceedings.mlr.press/v138/madsen20a.html }, abstract = { 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. } }
Endnote
%0 Conference Paper %T Prediction of High Risk of Deviations in Home Care Deliveries %A Anders L. Madsen %A Kristian G. Olesen %A Heidi Lynge Løvschall %A Nicolaj Søndberg-Jeppesen %A Frank Jensen %A Morten Lindblad %A Mads Lause Mogensen %A Trine Søby Christensen %B Proceedings of the 10th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2020 %E Manfred Jaeger %E Thomas Dyhre Nielsen %F pmlr-v138-madsen20a %I PMLR %P 281--292 %U http://proceedings.mlr.press/v138/madsen20a.html %V 138 %X 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.
APA
Madsen, A.L., Olesen, K.G., Løvschall, H.L., Søndberg-Jeppesen, N., Jensen, F., Lindblad, M., Mogensen, M.L. & Christensen, T.S.. (2020). Prediction of High Risk of Deviations in Home Care Deliveries. Proceedings of the 10th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 138:281-292 Available from http://proceedings.mlr.press/v138/madsen20a.html .

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