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Anesthesiology:
doi: 10.1097/ALN.0b013e31825d2d5f
Correspondence

The Devil in the Details

Tsai, Mitchell H. M.D., M.M.M.*; Black, Ian H. M.D.

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“First we shape our buildings; thereafter, they shape us.” - —Winston Churchill
To the Editor:
In the recent article by Wijeysundera et al., the authors demonstrated a significant level of variability in the preoperative testing patterns at different hospitals in Ontario, Canada.1 Their statistical analyses show that the testing patterns were not explained by the type of surgery, hospital, or patient. However, the authors did not characterize the types of preoperative evaluation processes (e.g., physician-based, nurse-telephone, web-based intake, on-site clinic, etc.). This is important because multiple preoperative assessment systems have been developed; it would not be surprising to find a myriad of systems in one Canadian province. Historically, these clinics were developed because of financial pressures and a desire to improve perioperative outcomes. Large variability is also seen in U.S. perioperative screening. Katz et al. showed that anesthesiologists and surgeons cannot agree on the number of appropriate preoperative tests.2 This variability, as Wijeysundera et al. point out, exists at a local, regional, and national level, despite the proliferation of clinical testing algorithms and consensus guidelines. Even anesthesiologists within the same group cannot agree; this results in canceled surgeries because the preoperative anesthesiologist is not the anesthesiologist on the day of surgery.
The goal of reducing variability in the delivery of health care is a worthy goal in itself, even if outcome data are not initially available. In 1989, Laffel and Blumenthal pointed out that modern industrial quality science (e.g., statistical process control) “may well make important advances in the quality of care and service through the application of rigorous principles and techniques.”3 Inherent in statistical process control is that every complex system, which health care most certainly is, has a certain level of variability. Continuous process improvement must involve the reduction of system variability. Reducing variability in the system has numerous advantages. Less variable systems are easier to study and need smaller sample sizes to prove a hypothesis. It is easier to introduce new guidelines in less variable systems. Although it is true that it will be difficult to produce “objective research” that includes “risk-adjustment and outcome measures,” such as “clinical results, financial costs, and process efficiency,” it should not deter us from improving the system or missing clinical opportunities.4
There will always be a level of variability in preoperative consultations, not only in Ontario, but also in the rest of the world. Some disagreement is inevitable because guidelines are not rules, and medicine is as much craft as science. This leads to different decisions when an anesthesiologist evaluates a moderate-risk patient for a moderate-risk surgery even with the publication of guidelines. Moreover, most guidelines are based on expert opinion and extrapolation, and we still do not know if many of these guidelines are “correct.” However, we should recognize that many unnecessary tests and consults could be reduced by simply using clinical decision support programs that reduce variability. For instance, anesthetists in Europe have used web-based preoperative systems to minimize variability in preoperative testing patterns for years.5 These programs, or other tools that reduce variability, may allow us to direct our ever-shrinking healthcare resources to where they are most needed.
Although additional research is needed, we believe that Wijeysundera et al. present a unique opportunity to improve the preoperative evaluation system in Ontario without further research. The data clearly show that the government has the opportunity to refine preoperative consultations simply by reducing variability in the patient population that had the most preoperative testing and examining the testing patterns at institutions where the percentages of consults is greater than the average for all the institutions. The majority of the preoperative consults were ordered for total knee and hip replacement surgeries and for patients with hypertension and diabetes. We believe that the government of Ontario should develop new processes to reduce these preoperative consults and drive the percentage down to the average. U.S. anesthesiologists should do the same. More importantly, these steps should be done in parallel with a prospective database that tracks patients' perioperative outcomes from the beginning to end. Ultimately, reduced systemic variability allows us to study these questions in a more focused, cost-effective, rational manner. Simply put, improving the system and additional research need not be mutually exclusive.
Mitchell H. Tsai, M.D., M.M.M.,* Ian H. Black, M.D. *University of Vermont College of Medicine, Burlington, Vermont. mitchell.tsai@vtmednet.org
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References

1. Wijeysundera DN, Austin PC, Beattie S, Hux JE, Laupacis A: Variation in the practice of preoperative medical consultation for major elective noncardiac surgery. ANESTHESIOLOGY 2012; 116:25–34

2. Katz RI, Dexter F, Rosenfeld K, Wolfe L, Redmond V, Agarwal D, Salik I, Goldsteen K, Goodman M, Glass PS: Survey study of anesthesiologists' and surgeons' ordering of unnecessary preoperative laboratory tests. Anesth Analg 2011; 112:207–12

3. Laffel G, Blumenthal D: The case for using industrial quality management science in health care organizations. JAMA 1989; 262:2869–73

4. Kheterpal S: Random clinical decisions: Identifying variation in perioperative care. ANESTHESIOLOGY 2012; 116:3–5

5. Zuidema X, Tromp Meesters RC, Siccama I, Houweling PL: Computerized model for preoperative risk assessment. Br J Anaesth 2011; 107:180–5

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