JMIR Publications

ABSTRACT

Background:

ED boarding and hospital exit block are known to be the main causes of ED crowding and are conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs subsequent to the patient being delayed or blocked from transitioning out of the ED by a dysfunctional transition and bed assignment process. Predictive models estimating the probability that an event will occur in the future would be valuable if they reduced or ended ED boarding and hospital exit block, and subsequently reduced ED crowding.

Objective:

Identify and appraise hospital admission prediction models that utilized prehospital patient data. Report on model predictive performance, predictor utility, model application, and model utility.

Methods:

Multiple databases were searched from January 2008 to September 30, 2019 for studies that evaluated models predicting adult patient imminent hospital admission, utilized prehospital patient data, and examined relationships with regression analysis. PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) guided the critical assessment of included studies.

Results:

Potential bias was found in most studies, suggesting model predictive performance requires further investigation. The contribution of prehospital patient data to the identification of patients requiring hospital admission was shown. Biomarker predictors may add superior value and advantages to models. No models operated electronically, in real time, nor were integrated with an information system or workflow. Several models could be performed at the site-of-care in real time without digital devices, making them suitable to low-technology or no-electricity environments. Models have potential to positively impact patient care and hospital operations.

Conclusions:

There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools contributing to model identification of patients likely to require imminent hospital admission. Information models produce can be used to justify patient earlier hospital admission and care, directly impacting morbidity and mortality. Additionally, number and duration of patients boarding and crowding the ED is reduced. Models utilizing biomarker predictors offer numerous value and advantages.