Predicting missed health care visits during the COVID-19 pandemic using machine learning methods: evidence from 55,500 individuals from 28 European Countries
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...
Gespeichert in:
| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) Kapitel/Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
March 04, 2022
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| Ausgabe: | Preprint |
| In: |
medRxiv
Year: 2022, Pages: 1-21 |
| DOI: | 10.1101/2022.03.01.22271611 |
| Online-Zugang: | 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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| Verfasserangaben: | Anna Reuter, Prof. Šime Smolić, Prof. Dr. Till Bärnighausen, Prof. Dr. Nikkil Sudharsanan |
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| 520 | |a 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. | ||
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