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

Anna Reuter, Šime Smolić, Till Bärnighausen, Nikkil Sudharsanan
2022 medRxiv   pre-print
AbstractBackgroundThe 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.MethodsWe compare the performance of four machine learning algorithms to
more » ... t missed health care visits based on common patient characteristics available to most health care providers. We use data from 55,500 respondents of the Survey of Health, Ageing and Retirement in Europe (SHARE) COVID-19 survey (June – September 2020) in conjunction with longitudinal data from waves 1-8 (April 2004 – March 2020). We use stepwise selection, group lasso, random forest and neural network algorithms and employ 5-fold cross-validation to test the prediction accuracy, sensitivity, and specificity of the selected models.FindingsWithin our sample, 15.5% of the respondents reported any missed essential health care visit due to the COVID-19 pandemic. All four machine learning methods perform similarly in their predictive power. When classifying all individuals with a predicted probability for missed care above 17% as at risk of a missed visit, they correctly identify between 41% and 53% of the respondents at risk, while correctly identifying between 74% and 64% of the individuals not at risk. We find that the sensitivity and specificity of the models are strongly related to the risk threshold used to classify individuals; thus, the models can be calibrated depending on users' resource constraints and targeting approach. All models had an area under the curve around 0.62, indicating that they outperform random prediction.InterpretationPandemics such as COVID-19 require rapid and efficient responses to reduce disruptions in health care. Based on characteristics available to health insurance providers, machine learning algorithms can be used to efficiently target efforts to reduce missed essential care.FundingResearch in this article is a part of the European Union's H2020 SHARE-COVID19 project (Grant Agreement No. 101015924).
doi:10.1101/2022.03.01.22271611 fatcat:muhvxj2u6jcwxkp2sn624pcvru