Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data.

ABSTRACT: The COVID-19 pandemic has strong effects on most health care systems. Forecasting of admissions can help for the efficient organisation of hospital care. We aimed to forecast the number of admissions to psychiatric hospitals before and during the COVID-19 pandemic and we compared the performance of machine learning models and time series models. This would eventually allow to support timely resource allocation for optimal treatment of patients. We used admission data from 9 psychiatric hospitals in Germany between 2017 and 2020. We compared machine learning models with time series models in weekly, monthly and yearly forecasting before and during the COVID-19 pandemic. A total of 90,686 admissions were analysed. The models explained up to 90% of variance in hospital admissions in 2019 and

SUBMITTER: Wolff J 

PROVIDER: S-EPMC9508170 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature