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Predicting missed health care visits during the COVID-19 pandemic using machine learning methods: evidence from 55,500 individuals from 28 European Countries

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Background The COVID-19 pandemic has led many individuals to miss essential care. Machine-learning models that predict which patients are at greatest risk of missing care visits can help health administrators prioritize retentions efforts towards patients with the most need. Such approaches may be e...

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Bibliographic Details
Main Authors: Reuter, Anna (Author) , Smolić, Šime (Author) , Bärnighausen, Till (Author) , Sudharsanan, Nikkil (Author)
Format: Article (Journal) Chapter/Article
Language:English
Published: March 04, 2022
Edition:Preprint
In: medRxiv
Year: 2022, Pages: 1-21
DOI:10.1101/2022.03.01.22271611
Online Access:Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.1101/2022.03.01.22271611
Verlag, lizenzpflichtig, Volltext: http://medrxiv.org/lookup/doi/10.1101/2022.03.01.22271611
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Author Notes:Anna Reuter, Prof. Šime Smolić, Prof. Dr. Till Bärnighausen, Prof. Dr. Nikkil Sudharsanan
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Summary:Background The COVID-19 pandemic has led many individuals to miss essential care. Machine-learning models that predict which patients are at greatest risk of missing care visits can help health administrators prioritize retentions efforts towards patients with the most need. Such approaches may be especially useful for efficiently targeting interventions for health systems overburdened by the COVID-19 pandemic.
Item Description:This version: February 28, 2022
Gesehen am 04.08.2022
Physical Description:Online Resource
DOI:10.1101/2022.03.01.22271611

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