Scheduled caseload proves key to capacity
Study indicates demand fluctuations may be controllable

After more than a decade of downsizing, many hospitals throughout the country are experiencing system stress in their emergency departments due to diminished capacity. Patterns of erratic patient flow with intermittent periods of extreme overload have long been familiar to anesthesiologists, intensivists and critical care providers working on busy hospital units. Overcrowding and ambulance diversion are widely recognized as public health problems and threats to emergency preparedness. This roller coaster workload often can mean delayed care, cancelled procedures, “boarding” and refused admission—as well as staff burnout and patient dissatisfaction. Typically, hospitals face only two solutions: rationing resources or continuing to add staff and beds.

But a recent study by James Mandell, MD, president and CEO of Children’s Hospital Boston, Michael McManus, MD, associate director of Children’s Medical/Surgical ICU, and several colleagues, suggests a third alternative: controlling artificial variability by smoothing elective surgery schedules. Their analysis suggests that diversion from intensive care units has more to do with scheduled caseload than with Emergency admissions. The study, published in the June 2003 issue of Anesthesiology, was the first of its kind to question the assumption that demand fluctuations are random or seasonal, and thus uncontrollable.

Investigators, who collected information on all requests for admission to Children’s 18-bed Medical/Surgical ICU over a one-year period, analyzed the peaks of demand associated with diversion of patients. These peaks were grouped into those caused by “natural” variability and those caused by “artificial” variability. “Natural” refers to variability in type of disease, its severity, and the arrival pattern of patients. This variability cannot be eliminated, but it can be controlled through operations management methodologies. “Artificial” variability is non-random and related to controllable factors. An example of this type of variability is scheduled surgical demand. Investigators found that during the hospital’s busiest periods, nearly 70 percent of all diversions were associated with variability in the scheduled surgical caseloads.

“This is an important example of how outcome studies and variability analysis can reveal causes and potential solutions to difficult health care delivery issues,” says Dr. Mandell. “The next steps will be the even more arduous ones of implementing operational changes and re-analyzing the effect those changes have on outcomes.”