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An Electronic Health Record Tool Designed to Improve Pediatric Hospital Discharge has Low Predictive Utility for Readmissions

Journal of Hospital Medicine 13(11). 2018 November;:779-782. Published online first August 29, 2018 | 10.12788/jhm.3043

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.

We modeled the concept of a paper-based Early Screen for Discharge Planning – Child Version, which identifies children with multiple medical conditions, home nursing-care needs, tube feedings, presence of intravenous lines or drains, or post hospital care that requires coordination.1We opted for an electronic tool to automate screening and increase visibilty of patients’ care transitional needs via the electronic health record (EHR).

The tool was designed by our QI team to address weaknesses in our discharge process (eg, discharge instructions that are not translated appropriately) and causes of preventable readmission at our institution (eg, discharge teaching, home care, and medication-related problems).2,3 The tool’s selected components comprised those that might complicate or delay discharge care and included indicators of home health, polypharmacy, and caregiver language preference. Additional features were considered but withheld from the initial tool for several reasons noted in the methods.

We describe the development and implementation of this electronic tool. Given the paucity of pediatric risk models, we conducted an analysis of the tool’s potential to predict readmissions. We anticipated good predictive performance because the tool includes measures previously associated with readmission (eg, technology dependence, polypharmacy, and language barrier).4-7 If successful in distinguishing readmission, this embedded discharge planning tool could also serve as a pediatric readmission risk score.

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.