Predicting Emergency Department Visits Based on Cancer Patient Types

Purpose. This study evaluates the predictive ability of patient types (clusters of similar patients) in identifying cancer patients at high risk for emergency department (ED) visits within one year (365 days) following their index date. A descriptive and retrospective cohort study of 45,356 unique cancer patients with only one primary cancer type and at least one ED visit was done using linked administrative sources of health care data. Methods. Three outcomes were investigated in this study. First, the time of ED visit following an index date was predicted using multiple linear regression. Second, those patients who visited an ED within seven days of their index date were detected using logistic regression. In addition to predicting an emergency department visit, vital status of patients was also predicted using logistic regression. We implemented the linear/logistic regression first on unclustered raw data and then on clustered data. The results of these two analyses were then compared. Conclusion. Clustering was found to contribute to a modest improvement in prediction accuracy for all three outcome variables. The results are discussed in terms of the predictive ability of patient types in development of clinical support tools with respect to privacy of patients and their implications for better allocation of resources to cancer patients.