Table 5 Most relevant explanatory variables of ED crowding

Input

 Share of vulnerable population (age >  = 75 years) per day

The study confirms the evidence found in other studies that patient characteristics do have an impact on ED crowding.

The study proves also that the variability of ED arrivals does have an impact on ED operations.

Separate ED patient flows based on scores that consider different patient characteristics such as age, severity, comorbidities.

 Number of admissions

Work on scheduling and capacity planning to better match demand and supply in the busiest periods.

Process

 Number of nurses per admission

The paper confirms the results of other studies that sustain the presence of an ED capacity problem.

In particular, the model stresses the relevance of nurse shortages.

Hire more nurses.

Improve solutions enabling the saving of nursing time.

 Skill mix

ED crowding is found to be positively correlated with the physician-to-nurse ratio

Tailor solutions to the specific ED context, mission and goals.

Output

 Number of ED hospital admissions

The study shows the statistical relevance of specific variables that better operationalize the coordination between ED and bed management concerning the ward issue.

Streamline the discharge process: (i) discharge room or (ii) re-engineering wards’ operations.

 “In and out” rate

 Bed manager

Set up of an office in charge of coordinating bed management.

 ED hospital admissions/Emergency ward

Set-up an emergency ward or an admissions unit as a buffer area between the ED and hospital wards.