In Response

We thank Tsai et al1 for their careful reading of our article2 and for their comments highlighting the case to view perioperative care through a value-based framework. We further appreciate that they point out that an important underlying strength of the Perioperative Enhancement Team (POET) initiative is to extend the scope of practice and bridge multidisciplinary decision making of preoperative care by reframing the discussion of perioperative risk assessment and management through a broader population health lens. We respect their comments concerning value-based health care accounting as they apply to the POET model. Tsai et al1 purport that time-driven activity-based costing (TDABC) was the foundation of our value argument3 and cite the argument by Dexter and Wachtel4 that perioperative care value enhancement relies on either reducing unnecessary interventions or optimizing staff productivity. Moreover, they raise a question concerning the effectiveness of the POET model to modify important change and wisely cite Kheterpal et al5 who showed that simple process implementation improvement does not necessarily translate to outcome improvement. As such, they call out the value argument for POET as nebulous without demonstrating improvement in postoperative outcomes and ask for financial transparency stating with a numerator in the value-based framework, a denominator is required. To address these comments, we would offer the following observations and clarifications. First, simple application of activity-based costing (ABC) or TDABC methodology does not work and is not solely applied in our model. The methodological framework of TDABC includes calculation of capacity cost rate, and the time variable differs from ABC in that it allows operations to be expressed by specific times spent for specific activities. As such, TDABC enables a more accurate calculation of cost than ABC. In the POET model, value is defined as direct contribution margin and cost avoidance. The mechanics of TDABC are most applicable to understanding contribution margin but are not as easily applied when calculating cost avoidance. Clearly, a baseline understanding of personal cost, capacity, and knowledge of flexible skill sets are materials in developing a financial model for a preoperative optimization program; however, they are not the only considerations. As health care moves from fee-for-service to a risk-bearing and value-based model, cost avoidance becomes increasingly more meaningful in financial modeling. In addition, the cost of transition for system readiness and customer transparency are other components of the health care value proposition to consider and are difficult to quantify with TDABC methodology. We have performed business modeling based on proforma calculations, including purported revenue generation as well as expense consideration of capacity, cost rate, and time variable assumptions for all of our POET optimization programs. These calculations are tracked and validated when sufficiently tested. A POET optimization program is launched when an acceptable return is demonstrated to the system directly or indirectly to the population that the system serves. The direct value to the system is defined as direct contribution margin as well as cost avoidance and system readiness. The latter 2 are underrepresented in a TDABC value analysis. While responsibility to demonstrate a positive reward versus risk is recognized, the single most important variable for assessing value and initiating a POET program is because it is the right thing to do for patient-centric health care delivery and population health care delivery. Second, essential to the POET initiative has been the creation of a data repository capable of tracking perioperative clinical metrics, including process outcome variables, health economic variables, clinical outcomes, and patient-reported outcomes. These data heretofore have been manually pulled from existing data repositories in our electronic medical record. Dashboards created for each program are now in the process of being autopopulated to enable tracking. Third, we acknowledge insufficient clarity in our manuscript and now offer that a significant role and responsibility (among other things) of Perioperative Optimization for Senior Health is to assess the risk versus reward of whether surgery should be scheduled once contemplated. We have come to realize (unpublished) that the answer to that question for a number of patients in their eighth and ninth decade is on average, no, approximately 3%–5% of the time. Finally, we maintain that the analysis of value in health care for an institution is ultimately predicated on bias, culture, vision, and leadership perception and not just financial transparency of a spread sheet as the proposition depends on the assumptions baked into the model and metrics measured. We believe that adaptation of a financial model is best served by understanding institutional assumptions and principals that make best sense to fit local needs. We hope that our approach to change the practice of anesthesiology and perioperative medicine will encourage others to think more carefully about where they are and where they are going to be, regardless of the denominator. Solomon Aronson, MD, MBA, FASA, FACC, FCCP, FAHA, FASEDuke University School of MedicineDurham, North Carolina