JMIR Publications

ABSTRACT

Background:

ED crowding continues to threaten patient safety and cause poor patient outcomes. Prior models designed to predict hospital admission have had biases. Predictive models that successfully estimate the probability of patient hospital admission would be useful in reducing or preventing emergency department boarding and hospital exit block and would reduce emergency department crowding by initiating earlier hospital admission and protracted bed procurement processes.

Objective:

To develop a model to predict imminent adult patient hospital admission from the ED early in the patient visit by utilizing existing, routinely collected, clinical descriptors that were captured in the hospital’s EMR.

Methods:

This retrospective cohort study evaluated 1 year of consecutive data events of adult patients admitted to the ED to develop an algorithm that predicted which patients would require imminent hospital admission. Eight predictor variables were evaluated for their role in the outcome of the patient ED visit. Logistic regression was used to model the study data.

Results:

The 8-predictor model included age, systolic BP, diastolic BP, heart rate, respiration rate, temperature, gender, and acuity level. Our model performed well, with good agreement between observed and expected admissions, indicating a well-fitting and well-calibrated model and showing good ability to discriminate between patients who will and will not be admitted.

Conclusions:

This prediction model based on primary data identified ED patients with an increased risk of hospital admission. This actionable information can be used to improve patient care and hospital operations and especially reduce ED crowding by looking ahead to predict which patients will be admitted, providing needed information to initiate admission and bed assignment processes much earlier in the care continuum.