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

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

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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.

Original language	English
Title of host publication	Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR
Editors	Manfred Jaeger, Thomas Dyhre Nielsen
Number of pages	12
Publisher	ML Research Press
Publication date	2020
Pages	281-292
Publication status	Published - 2020
Event	10th International Conference on Probabilistic Graphical Models - Hotel Comwell Rebild Bakker, Skørping, Denmark Duration: 23 Sept 2020 → 25 Sept 2020 https://pgm2020.cs.aau.dk/

Conference

Conference	10th International Conference on Probabilistic Graphical Models
Location	Hotel Comwell Rebild Bakker
Country/Territory	Denmark
City	Skørping
Period	23/09/2020 → 25/09/2020
Internet address	https://pgm2020.cs.aau.dk/

Series	The Proceedings of Machine Learning Research
Volume	138
ISSN	2640-3498

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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. In M. Jaeger, & T. Dyhre Nielsen (Eds.), Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR (pp. 281-292). ML Research Press. http://proceedings.mlr.press/v138/madsen20a/madsen20a.pdf

Madsen, Anders L. ; Olesen, Kristian G. ; Løvschall, Heidi Lynge et al. / Prediction of High Risk of Deviations in Home Care Deliveries. Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR. editor / Manfred Jaeger ; Thomas Dyhre Nielsen. ML Research Press, 2020. pp. 281-292 (The Proceedings of Machine Learning Research, Vol. 138).

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title = "Prediction of High Risk of Deviations in Home Care Deliveries",

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{{\o}}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.",

author = "Madsen, {Anders L.} and Olesen, {Kristian G.} and L{\o}vschall, {Heidi Lynge} and Nicolaj S{\o}ndberg-Jeppesen and Frank Jensen and Morten Lindblad and Mogensen, {Mads Lause} and Christensen, {Trine S{\o}by}",

year = "2020",

language = "English",

series = "The Proceedings of Machine Learning Research",

pages = "281--292",

editor = "Manfred Jaeger and {Dyhre Nielsen}, Thomas",

booktitle = "Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR",

publisher = "ML Research Press",

note = "10th International Conference on Probabilistic Graphical Models ; Conference date: 23-09-2020 Through 25-09-2020",

url = "https://pgm2020.cs.aau.dk/",

}

Madsen, AL , Olesen, KG, Løvschall, HL, Søndberg-Jeppesen, N, Jensen, F, Lindblad, M, Mogensen, ML & Christensen, TS 2020, Prediction of High Risk of Deviations in Home Care Deliveries. in M Jaeger & T Dyhre Nielsen (eds), Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR. ML Research Press, The Proceedings of Machine Learning Research, vol. 138, pp. 281-292, 10th International Conference on Probabilistic Graphical Models, Skørping, Denmark, 23/09/2020. <http://proceedings.mlr.press/v138/madsen20a/madsen20a.pdf>

Prediction of High Risk of Deviations in Home Care Deliveries. / Madsen, Anders L.; Olesen, Kristian G.; Løvschall, Heidi Lynge et al.
Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR. ed. / Manfred Jaeger; Thomas Dyhre Nielsen. ML Research Press, 2020. p. 281-292 (The Proceedings of Machine Learning Research, Vol. 138).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Prediction of High Risk of Deviations in Home Care Deliveries

AU - Madsen, Anders L.

AU - Olesen, Kristian G.

AU - Løvschall, Heidi Lynge

AU - Søndberg-Jeppesen, Nicolaj

AU - Jensen, Frank

AU - Lindblad, Morten

AU - Mogensen, Mads Lause

AU - Christensen, Trine Søby

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

M3 - Article in proceeding

T3 - The Proceedings of Machine Learning Research

SP - 281

EP - 292

BT - Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR

A2 - Jaeger, Manfred

A2 - Dyhre Nielsen, Thomas

PB - ML Research Press

T2 - 10th International Conference on Probabilistic Graphical Models

Y2 - 23 September 2020 through 25 September 2020

ER -

Madsen AL , Olesen KG, Løvschall HL, Søndberg-Jeppesen N, Jensen F, Lindblad M et al. Prediction of High Risk of Deviations in Home Care Deliveries. In Jaeger M, Dyhre Nielsen T, editors, Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR. ML Research Press. 2020. p. 281-292. (The Proceedings of Machine Learning Research, Vol. 138).

Prediction of High Risk of Deviations in Home Care Deliveries

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