Figure 1.
Study design to develop the ED 30 day revisit predictive algorithm.
There were three main steps for model development: 1) two independent cohorts were constructed for retrospective modelling and prospective validation; 2) samples in the retrospective cohort were used to train a decision-tree-based predictive model, followed by a calibration and blind-test procedure; 3) the model integrating a risk-score metric was validated on the prospective cohort for further performance analysis.
Figure 2.
Study cohort construction (A, retrospective; B, prospective), and inclusion/exclusion criteria.
Figure 3.
A. “Time to event” analysis. The ED revisit “time-to-event curve” showed a pattern of a rapid accrual with a stable and consistent ED visit rate thereafter. The population ED revisit curves, of patients with or without past history of ED visits, decreased significantly within 30 days from the ED discharge time, indicating that a 30-day cutoff is clinically reasonable. B. Our analysis found that both the total number and the percentage of patients with future 30-day ED visits increased as a functional of either the distinct chronic diagnoses (left panel) or the ED visit counts (right) in the prior 12 months.
Figure 4.
Characterization of the discriminant features in the prospective data set.
Shrunken difference for the selected features to develop the ED risk model were graphed in order to measure the feature abilities in discriminating different classes. The x axis is the shrunken difference of each feature listed along the y axis, which is a measure of the difference between the standardized mean value of a feature within a specific class and the overall mean value of that feature. Comparing the two cohorts (case/control or the low/medium/high risk), the shrunken differences of these discriminative features were much more pronounced in the low/medium/high risk cohort, demonstrating the effectiveness of these features in prospectively differentiating the targeted outcomes.
Figure 5.
Observed rates of future 30-day ED returns versus risk scores in prospective tests.
Figure 6.
Future 30-day resource utilization analysis as a function of the ED risks.
PMP1M: per member per 1 month. Two vertical lines serves as the boundaries between low, medium, high ED revisit risks.
Figure 7.
The ED predictive algorithm effectively risk-stratified the prospective patient cohort for future 30-day ED visit.
Left panel: “Time to event” graphic representation of the low, medium and high risk patients' time to the next impending ED visit. Right panel: Unsupervised clustering of the high-risk patients identified distinct subgroups in the prospective cohort. Color-coding reflects the average cost of the high-risk patients in the next 30-day post discharge.
Table 1.
Clustering of ED-30 day high risk patients in the prospective cohort.
Figure 8.
A prospective case study on monthly ED visits and risks for a patient.