Name
Abstract | ||
|---|---|---|
High utilizers of emergency departments account for a disproportionate number of visits, often for nonemergency conditions. This study aims to identify these high users prospectively. Routinely recorded registration data from the Indiana Public Health Emergency Surveillance System was used to predict whether patients would revisit the Emergency Department within one month, three months, and six months of an index visit. Separate models were trained for each outcome period, and several predictive models were tested. Random Forest models had good performance and calibration for all outcome periods, with area under the receiver operating characteristic curve of at least 0.96. This high performance was found to be due to non-linear interactions among variables in the data. The ability to predict repeat emergency visits may provide an opportunity to establish, prioritize, and target interventions to ensure that patients have access to the care they require outside an emergency department setting. |
Year | Venue | DocType |
|---|---|---|
2016 | CRI | Conference |
Volume | Citations | PageRank |
2016 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarah F. Poole | 1 | 0 | 0.34 |
| Shaun J. Grannis | 2 | 187 | 39.47 |
| Nigam Shah | 3 | 212 | 20.11 |