The anesthetizing sites supervised to anesthesiologist ratio is an invalid surrogate for group productivity in academic anesthesia departments when used without consideration of the corresponding managerial decisions

When the anesthesiologist does not individually perform the anesthesia care, then to make valid comparisons among US anesthesia departments, one must consider the staffing ratio (i.e., how many cases each anesthesiologist supervises when working with Certified Registered Nurse Anesthetists [CRNAs] or Certified Anesthesiologist Assistants [CAA]). The staffing ratio also must be considered when accurately measuring group productivity. In this narrative review, we consider anesthesia departments with non-physician anesthesia providers and anesthesiology residents. We investigate the validity of such departments assessing the overall ratio of anesthetizing sites supervised per anesthesiologist as a surrogate for group clinical productivity. The sites/anesthesiologist ratio can be estimated accurately using the arithmetic mean calculated by anesthesiologist, the harmonic mean calculated by case, or the harmonic mean calculated by CRNA or CAA, but not by the arithmetic mean ratio by case. However, there is lack of validity to benchmarking the percentage time that anesthesiologists are supervising the maximum possible number of CRNAs or CAAs when some of the anesthesiologists also are supervising resident physicians. Assignments can differ in the total number anesthesiologists needed while every anesthesiologist is supervising as many sites as possible. Similarly, there is lack of validity to limiting assessment to the anesthesiologists supervising only CRNAs or CAAs. There also is lack of validity to limiting assessment only to cases performed by supervised CRNAs or CAAs. When cases can be assigned to anesthesiology residents or CRNAs or CAAs, increasing sites/anesthesiologist while limiting consideration to the CRNAs or CAAs creates incentive for the CRNAs or CAAs to be assigned cases, even when lesser productivity is the outcome. Decisions also can increase sites/anesthesiologist without increasing productivity (e.g., when one anesthesiologist relieves another before the end of the regular workday). A suitable alternative approach to fallaciously treating the sites/anesthesiologist ratio as a surrogate for productivity is that, when a teaching hospital supplies financial support, a responsibility of the anesthesia department is to explain annually the principal factors affecting productivity at each facility it manages and to show annually that decisions were made that maximized productivity, subject to the facilities' constraints

Use of Operating Room Information System Data to Predict the Impact of Reducing Turnover Times on Staffing Costs

Potential benefits to reducing turnover times are both quantitative (e.g., complete more cases and reduce staffing costs) and qualitative (e.g., improve professional satisfaction). Analyses have shown the quantitative arguments to be unsound except for reducing staffing costs. We describe a methodology by which each surgical suite can use its own numbers to calculate its individual potential reduction in staffing costs from reducing its turnover times. Calculations estimate optimal allocated operating room (OR) time (based on maximizing OR efficiency) before and after reducing the maximum and average turnover times. At four academic tertiary hospitals, reductions in average turnover times of 3 to 9 min would result in 0.8% to 1.8% reductions in staffing cost. Reductions in average turnover times of 10 to 19 min would result in 2.5% to 4.0% reductions in staffing costs. These reductions in staffing cost are achieved predominantly by reducing allocated OR time, not by reducing the hours that staff work late. Heads of anesthesiology groups often serve on OR committees that are fixated on turnover times. Rather than having to argue based on scientific studies, this methodology provides the ability to show the specific quantitative effects (small decreases in staffing costs and allocated OR time) of reducing turnover time using a surgical suite’s own data