Long-term forecasting of anesthesia workload in operating rooms from changes in a hospital's local population can be inaccurate.
BACKGROUND
Anesthesia department planning depends on forecasting future demand for perioperative services. Little is known about long-range forecasting of anesthesia workload.


METHODS
We studied operating room (OR) times at Hospital A over 16 yr (1991-2006), anesthesia times at Hospital B over 26 yr (1981-2006), and cases at Hospital C over 13 yr (1994-2006). Each hospital is >100 yr old and is located in a US city with other hospitals that are >50 yr old. Hospitals A and B are the sole University hospitals in their metropolitan statistical areas (and many counties beyond). Hospital C is the sole tertiary hospital for >375 km.


RESULTS
Each hospital's choice of a measure of anesthesia work to be analyzed was likely unimportant, as the annual hours of anesthesia correlated highly both with annual numbers of cases (r = 0.98) and with American Society of Anesthesiologist's Relative Value Guide units of work (r = 0.99). Despite a 2% decline in the local population, the hours of OR time at Hospital A increased overall (Pearson r = -0.87, P < 0.001) and for children (r = -0.84). At Hospital B, there was a strong positive correlation between population and hours of anesthesia (r = 0.97, P < 0.001), but not between annual increases in population and workload (r = -0.18). At Hospital C, despite a linear increase in population, the annual numbers of cases increased, declined with opening of two outpatient surgery facilities, and then stabilized. The predictive value of local personal income was low. In contrast, the annual increases in the hours of OR time and anesthesia could be modeled using simple time series methods.


CONCLUSIONS
Although growth of the elderly population is a simple justification for building more ORs, managers should be cautious in arguing for strategic changes in capacity at individual hospitals based on future changes in the national age-adjusted population. Local population can provide little value in forecasting future anesthesia workloads at individual hospitals. In addition, anesthesia groups and hospital administrators should not focus on quarterly changes in workload, because workload can vary widely, despite consistent patterns over decades. To facilitate long-range planning, anesthesia groups and hospitals should save their billing and OR time data, display it graphically over years, and supplement with corresponding forecasting methods (e.g., staff an additional OR when an upper prediction bound of workload per OR exceeds a threshold).
Masursky
Danielle
D
Department of Anesthesiology, SUNY Upstate Medical University, USA.
Dexter
Franklin
F
O'Leary
Colleen E
CE
Applegeet
Carol
C
Nussmeier
Nancy A
NA
D000758:Anesthesia / Q000706:statistics & numerical data*; D000761:Anesthesia Department, Hospital / Q000706:statistics & numerical data*; D000776:Anesthesiology / Q000706:statistics & numerical data*; D003120:Colorado; D005544:Forecasting; D006801:Humans; D007484:Iowa; D009873:Operating Rooms / Q000706:statistics & numerical data*; D010561:Personnel Staffing and Scheduling / Q000706:statistics & numerical data*; D011156:Population Density; D044382:Population Groups; D015203:Reproducibility of Results; D016526:Workload