An Electronic Health Record Tool Designed to Improve Pediatric Hospital Discharge has Low Predictive Utility for Readmissions
We developed an electronic health record tool to improve pediatric hospital discharge. This tool flags children with three components that might complicate discharge: home health, polypharmacy (≥6 medications), or non-English speaking caregiver. The tool tallies components and displays them as a composite score of 0-3 points. We describe the tool’s development, implementation, and an evaluation of its predictive utility for 30-day unplanned readmissions in 29,542 discharged children. Of these children, 28% had a composite score of 1, 8% a score ≥2, and 4% were readmitted. The odds of readmission was significantly higher in children with composite score of 1 versus 0 (odds ratio [OR]: 1.7; 95% CI, 1.5-2) and ≥2 versus 0 (OR 4.2; 95% CI 3.6-4.9). The C-statistic for this model was 0.62. Despite the positive association of the score with readmission, the tool’s discriminatory performance is low. Additional research is needed to evaluate its practical benefit for improving the quality of hospital discharge.
© 2018 Society of Hospital Medicine
As hospitalized children become more medically complex, hospital-to-home care transitions will become increasingly challenging. During a quality improvement (QI) initiative, we developed an electronic tool to improve the quality of our hospital discharge process.
METHODS
Setting
This work was conducted at the Children’s Hospital Colorado as part of a national QI collaborative. The hospital’s EHR is Epic (Verona, Wisconsin). The project was approved as QI by the Children’s Hospital Organizational Research Risk and Quality Improvement Review Panel, precluding review from the Colorado Multiple Institutional Review Board.
Tool Design, Implementation, and Use
A team of clinicians, nurse–family educators, case managers, social workers, and informatics experts helped design the instrument between 2014 and 2015. In addition to the selected features (number of discharge medications, presence of home health, and language preference), we considered adding the number of consulting specialists but had previously improved our process for scheduling follow-up appointments. Diagnoses were not systematically or discretely documented to be reliably extracted in real time. We excluded known readmission predictor variables (such as length of stay [LOS] and prior hospitalizations) from the initial model to maintain emphasis on the modifiable discharge processes. Additional considerations, such as health literacy and social determinants, were not systematically measured to be operationally usable.