Data-Driven Simulation Model Helps Predict Hospital ED Interventions
June 11, 2014 in News
Emergency department and hospital managers could benefit from the use of data-driven simulation models to measure the effects of potential changes to patient flow before implementation, according to a study published in BMC Medical Informatics and Decision Making, FierceHealthIT reports.
For the study, researchers at the University of Florida developed a robust model using public data to examine two patient flow solutions in two settings:
- An average ED; and
- An average academic hospital ED (Hall, FierceHealthIT, 6/10).
Specifically, the researchers looked at:
- Door-to-event times per patient;
- Rates of patients leaving without receiving care;
- Occupancy level;
- Resource use; and
Using the data-driven model, the researchers were able to determine that the average U.S. hospital ED struggles with physician supply and could reduce wait times by using more appropriate sources of care for patients with less acute issues.
Meanwhile, the average academic hospital commonly faces issues regarding occupancy and could boost patient flow by reducing patients’ length of stay (Hurwitz, BMC Medical Informatics and Decision Making, 6/9).
In addition, the model was able to identify a point of diminishing returns for EDs. It found that adding one doctor in the average ED reduced patients’ mean length of stay by one hour, while adding a second physician offered no significant reduction (FierceHealthIT, 6/10).