Recovering ICU capacity that turned away one in eight emergencies

The problem

Where the time was going

This facility is a Level I trauma center and a major transplant referral center, with nearly 450 beds and almost 50,000 emergency-department visits a year. Even at that size, a staggering twelve percent of the emergencies that needed intensive care had to be turned away for lack of capacity.

The scene was stark. An ambulance carrying a critical patient would call ahead, and nearly twelve percent of the time the hospital had to direct it to another facility, knowing it could not admit the patient to the ICU even though it was the best trauma center in the region. Beyond the lost revenue, every diversion was a black eye for a hospital that patients and clinicians wanted as their first choice.

A Six Sigma analyst was assigned to find why the diversions were happening and how to reduce them. He built a model covering the ICU, surgical capability, and scheduling from both emergency arrivals and elective surgeries, and it surfaced something the hospital had not seen: doctors controlled elective-surgery scheduling with no view of how the rest of the hospital was affected.

What we modeled

Mapping the process, then testing the fix

The model showed that elective surgeries stacked up randomly on certain days, filling the ICU to meet surgical demand, while on other days the ICU was barely touched by elective work. With the unit loaded that unevenly, the hospital was sometimes unable to handle emergency patients who needed intensive care, and ambulances were turned away.

Using the simulation, the analyst showed doctors and administrators the real cause of the diversions, then ran experiments to find an acceptable level of elective surgeries per day that still left room for emergency ICU care. The striking part is that the hospital would perform the same total number of elective surgeries as before; by capping how many ran on any given day, it could also absorb the emergency cases that needed the ICU.

The result

The proof, and the payoff

Both doctors and administrators agreed with the solution and began putting it in place. The added emergency cases the hospital could now accept brought a revenue increase in the millions of dollars, but the greater gain was the improvement in quality of care, which in turn strengthened the hospital's reputation.

The same approach carried into other parts of the hospital. ProcessModel went on to be used to shorten emergency-department length of stay, to optimize staffing and procedures in the gastroenterology lab, and to study how to cut patient waiting time in the eye institute.