The purpose of this study was to develop a prediction model to identify individuals and populations with a high risk of being hospitalized due to an ambulatory care-sensitive condition who might benefit from preventative actions or tailored treatment options to avoid subsequent hospital admission. A rate of 4.8 % of all individuals observed had an ambulatory care-sensitive hospitalization in 2019 and 6389.3 hospital cases per 100,000 individuals could be observed. Based on real-world claims data, the predictive performance was compared between a machine learning model (Random Forest) and a statistical logistic regression model.
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Big Data Analytics to Reduce Preventable Hospitalizations — Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions.
The purpose of this study was to develop a prediction model to identify individuals and populations with a high risk of being hospitalized due to an ambulatory care-sensitive condition who might benefit from preventative actions or tailored treatment options to avoid subsequent hospital admission. A rate of 4.8 % of all individuals observed had an […]
Autoren: Schulte, Timo, Tillmann Wurz, Oliver Groene, Sabine Bohnet-Joschko
In: In: International Journal of Environmental Research and Public Health
Literaturangabe: Schulte, T., Wurz, T., Groene, O., Bohnet-Joschko, S. (2023). Big Data Analytics to Reduce Preventable Hospitalizations — Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions. In: International Journal of Environmental Research and Public Health 2023, 20(6). https://doi.org/10.3390/ijerph20064693
