Fig 1.
Study design for modeling the risk of an inpatient hospital readmission 30 days post discharge.
There were three steps in model development: 1) two independent cohorts were constructed for retrospective modeling and prospective validation; 2) the retrospective cohort was split into two subgroups with each incorporating non-overlapped care facilities. The first subgroup was further split into model training and calibration sub cohorts, and the second subgroup was used as the blind-test cohort; and 3) the model was validated using the prospective cohort. Unsupervised clustering pattern analysis that included demographic and clinical data was performed. The prospectively validated model was then deployed in production to support healthcare quality monitoring and improvement efforts.
Table 1.
The final list of features in the model after 2 rounds of feature selections.
Fig 2.
Retrospective and prospective results of the 30-day readmission risk stratification.
30-day readmission rates were measured in 10 risk bins by intervals of 10. The risk metric was divided into three regions: low (0–30), intermediate (30–70), and high (70–100).
Fig 3.
Time to event analysis on retrospective (top) and prospective cohorts.
‘Time to event’ graphic representation of the low-, intermediate-, and high-risk patients’ time to the next impending inpatient visit.
Table 2.
Comparison of our model with previous studies.
Fig 4.
The deployment of the 30-day readmission risk model.
The validated risk model was deployed via a real time provider portal that was integrated into the Maine HIE. The model and results are subject to continuous adaptation in response to EMR output on a daily basis. A screenshot: the real-time dashboard allowing for high-risk inpatient encounter identification and in support of targeted interventions is shown.