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.”
