A new study from researchers at the Regenstrief Institute and Indiana University found that machine learning models trained using statewide health information exchange data can predict a patient's likelihood of being hospitalized with COVID-19. They noted that the model was particularly accurate for predicting one-week hospitalization and for identifying the patients who were not in need of care.
Patient age, chronic obstructive pulmonary disease status, smoking, diabetes, indication of neurological diseases, mental disorders, residence type (meaning urban versus rural) and income-level all influenced the prediction. Such utilization prediction models may be used for population health management programs in health systems, to identify high-risk populations to monitor or screen, as well as predicting resource needs in crisis situations, such as future spikes in pandemic activity or outbreaks.
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