Introduction: Preventing the occurrence of hospital readmissions is needed to
improve quality of care and foster population health across the care continuum.
Hospitals are being held accountable for improving transitions of care to avert
unnecessary readmissions. Advocate Health Care in Chicago and Cerner (ACC)
collaborated to develop all-cause, 30-day hospital readmission risk prediction
models to identify patients that need interventional resources. Ideally,
prediction models should encompass several qualities: they should have high
predictive ability; use reliable and clinically relevant data; use vigorous
performance metrics to assess the models; be validated in populations where they
are applied; and be scalable in heterogeneous populations. However, a systematic
review of prediction models for hospital readmission risk determined that most
performed poorly (average C-statistic of 0.66) and efforts to improve their
performance are needed for widespread usage.Methods: The ACC team incorporated electronic health record data, utilized a
mixed-method approach to evaluate risk factors, and externally validated their
prediction models for generalizability. Inclusion and exclusion criteria were
applied on the patient cohort and then split for derivation and internal
validation. Stepwise logistic regression was performed to develop two predictive
models: one for admission and one for discharge. The prediction models were
assessed for discrimination ability, calibration, overall performance, and then
externally validated.Results: The ACC Admission and Discharge Models demonstrated modest
discrimination ability during derivation, internal and external validation
post-recalibration (C-statistic of 0.76 and 0.78, respectively), and reasonable
model fit during external validation for utility in heterogeneous
populations.Conclusions: The ACC Admission and Discharge Models embody the design qualities
of ideal prediction models. The ACC plans to continue its partnership to further
improve and develop valuable clinical models.