Faculty Lead(s)
Project Summary
Bronchiolitis is the most common illness leading to hospitalization in young children. For children under age two, bronchiolitis incurs an annual total inpatient cost of $1.73 billion. Each year in the U.S., 287,000 emergency department (ED) visits occur because of bronchiolitis, with a hospital admission rate of 32-40%. Due to a lack of evidence and objective criteria for managing bronchiolitis, ED disposition decisions (hospital admission or discharge to home) are often made subjectively resulting in significant practice variation. Studies reviewing admission need suggest that up to 29% of admissions from the ED are unnecessary. About 6% of ED discharges for bronchiolitis result in ED returns with admission. These inappropriate dispositions waste limited healthcare resources, increase patient and parental distress, expose patients to iatrogenic risks, and worsen outcomes.
Clinical guidelines are designed to reduce practice variation and improve clinicians’ decision making. Existing guidelines for bronchiolitis offer limited improvement in patient outcomes. Methodological shortcomings include that the guidelines provide no specific thresholds for ED decisions to admit or to discharge, have an insufficient level of detail, and do not account for differences in patient and illness characteristics including co-morbidities.
Predictive models are frequently used to complement clinical guidelines, reduce practice variation, and improve clinicians’ decision making. Used in real time, predictive models can present objective criteria supported by historical data for an individualized disease management plan and guide admission decisions. However, existing predictive models for bronchiolitis patients in the ED have limitations, including low accuracy and the assumption that the actual ED disposition decision was appropriate. To date, no operational definition of appropriate admission exists. No model has been built based on appropriate admissions, which include both actual admissions that were necessary and actual ED discharges that were unsafe.
To fill the gap, the proposed project will: (1) Develop an operational definition of appropriate hospital admission for bronchiolitis patients in the ED. (2) Develop and test the accuracy of a new model to predict appropriate hospital admission for a bronchiolitis patient in the ED. (3) Conduct simulations to estimate the impact of using the model on bronchiolitis outcomes. The project will produce a new predictive model that can be operationalized to guide and improve disposition decisions for bronchiolitis patients in the ED. Broad use of the model would reduce iatrogenic risk, patient and parental distress, healthcare use, and costs and improve outcomes for bronchiolitis patients. If the model proves to be accurate and associated with improved outcomes, future study will test the impact of using it in a randomized controlled trial following its implementation into an existing electronic medical record to facilitate real-time decision making.
Project Keywords: Decision Support, Forecasting, Machine Learning