Can Patient Variables Measured on Arrival to the Emergency Department Predict Disposition in Medium-acuity Patients?

Real-time prediction of patient disposition and the impact of reporter confidence on mid-level triage accuracies: an observational study in Israel

AIM

The emergency department (ED) is the first port-of-call for most patients receiving hospital care and as such acts as a gatekeeper to the wards, directing patient flow through the hospital. ED overcrowding is a well-researched field and negatively affects patient outcome, staff well-being and hospital reputation. An accurate, real-time model capable of predicting ED overcrowding has obvious merit in a world becoming increasingly computational, although the complicated dynamics of the department have hindered international efforts to design such a model. Triage nurses' assessments have been shown to be accurate predictors of patient disposition and could, therefore, be useful input for overcrowding and patient flow models.

METHODS

In this study, we assess the prediction capabilities of triage nurses in a level 1 urban hospital in central Israeli. ED settings included both acute and ambulatory wings. Nurses were asked to predict admission or discharge for each patient over a 3-month period as well as exact admission destination. Prediction confidence was used as an optimisation variable.

RESULT

Triage nurses accurately predicted whether the patient would be admitted or discharged in 77% of patients in the acute wing, rising to 88% when their prediction certainty was high. Accuracies were higher still for patients in the ambulatory wing. In particular, negative predictive values for admission were highly accurate at 90%, irrespective of area or certainty levels.

CONCLUSION

Nurses prediction of disposition should be considered for input for real-time ED models.

Assessment of patient mobility improves the risk stratification of triage with the Emergency Severity Index: a prospective cohort study

BACKGROUND AND IMPORTANCE

Formal triage may assign a low acuity to patients at high risk of deterioration and mortality. A patient's mobility can be easily assessed at triage.

OBJECTIVE

To investigate if a simple assessment of mobility at triage can improve the Emergency Severity Index's (ESI) prediction of adverse outcomes.

DESIGN, SETTING AND PARTICIPANTS

Prospective observational study of all patients attending the emergency department (ED) of a single academic hospital in Switzerland over a period of 3 weeks.

OUTCOME MEASURES AND ANALYSIS

Triage clinicians classified participants as having normal or impaired mobility at triage. Impaired mobility was defined as the lack of a stable independent gait, regardless of its cause or duration (e.g. any patient who needed help to walk). The primary outcome was 30-day mortality. We performed a survival analysis stratified by mobility and ESI level. We compared the performance of regression models including the ESI alone or in combination with mobility as predictors of mortality using the Bayesian information criterion (BIC).

MAIN RESULTS

2523 patients were included in the study and 880 (34.9%) had impaired mobility. Patients with impaired mobility had a lower median 30-day survival in ESI levels 1-3. Survival of patients with normal mobility was similar regardless of their ESI level.

CONCLUSION

The assessment of mobility at triage improves the ESI algorithm's risk stratification.

Disposition Decision Support by Laboratory Based Outcome Prediction

Disposition is one of the main tasks in the emergency department. However, there is a lack of objective and reliable disposition criteria, and diagnosis-based risk prediction is not feasible at early time points. The aim was to derive a risk score (TRIAL) based on routinely collected baseline (TRIage level and Age) and Laboratory data—supporting disposition decisions by risk stratification based on mortality. We prospectively included consecutive patients presenting to the emergency department over 18 weeks. Data sets of routinely collected baseline (triage level and age) and laboratory data were used for multivariable logistic regression to develop the TRIAL risk score predicting mortality. Routine laboratory variables and disposition cut-offs were chosen beforehand by expert consensus. Risk stratification was based on low risk (<1%), intermediate risk (1–10%), and high risk (>10%) of in-hospital mortality. In total, 8687 data sets were analyzed. Variables identified to develop the TRIAL risk score were triage level (Emergency Severity Index), age, lactate dehydrogenase, creatinine, albumin, bilirubin, and leukocyte count. The area under the ROC curve for in-hospital mortality was 0.93. Stratification according to the TRIAL score showed that 67.5% of all patients were in the low-risk category. Mortality was 0.1% in low-risk, 3.5% in intermediate-risk, and 26.2% in high-risk patients. The TRIAL risk score based on routinely available baseline and laboratory data provides prognostic information for disposition decisions. TRIAL could be used to minimize admission in low-risk and to maximize observation in high-risk patients.

An exhaustive review and analysis on applications of statistical forecasting in hospital emergency departments

Emergency departments (EDs) provide medical treatment for a broad spectrum of illnesses and injuries to patients who arrive at all hours of the day. The quality and efficient delivery of health care in EDs are associated with a number of factors, such as patient overall length of stay (LOS) and admission, prompt ambulance diversion, quick and accurate triage, nurse and physician assessment, diagnostic and laboratory services, consultations and treatment. One of the most important ways to plan the healthcare delivery efficiently is to make forecasts of ED processes. The aim this study is thus to provide an exhaustive review for ED stakeholders interested in applying forecasting methods to their ED processes. A categorisation, analysis and interpretation of 102 papers is performed for review. This exhaustive review provides an insight for researchers and practitioners about forecasting in EDs in terms of showing current state and potential areas for future attempts.