Quality Improvement in Otolaryngology

Quality Improvement in Otolaryngology

Rahul K. Shah

Jean Brereton

David W. Roberson

The otolaryngologist may sometimes feel that the word “quality” is used by everyone, in every situation, to address every issue and to mean whatever the speaker or writer wishes it to mean. The busy physician may be pardoned if he/she concludes that the term has no fixed meaning and that it is safe to tune out of any conversation where it comes up.

This chapter starts by trying not to define quality in medicine but to at least characterize it. We then make the case that, while the term is overused, there is a core set of principles and behaviors that can substantially improve the life of the otolaryngologist and his/her patients. Quality in medicine refers to two very different, but complementary, things:

  • The rigorous measurement of actual outcomes and the use of that data to drive improvement

  • A set of principles and behaviors that have been shown to bring about improvements in a wide variety of diverse complex systems, including medicine and surgery

There is a large body of literature and practical experience about each of these domains. Much is known about how to measure real-world outcomes of complex processes, including medical and surgical care. Much is also known about how one can successfully bring about improvement in a complex organization (such as a physician practice, a hospital, or an operating room) (1).

Although the skills and practices needed for each domain are very different, it should be obvious that the two processes are complementary. If one sets out to improve a complex organization, it will be helpful to know whether the resulting outcomes are better, worse, or the same. Similarly, there is little if any value to measuring outcomes rigorously unless one has tools with which to improve low-performing organizations.

In order to provide a context for the quality improvement (QI) efforts within medicine, this chapter reviews the historical background for quality in surgery and medicine and the specific data in otolaryngology. Next, we describe the role of measurement for QI and discuss the organizations that are trying to create valid measures in medicine today and some of the current and future external standards for quality. We then discuss the performance of complex systems and how some organizations achieve high performance in complex systems and conclude with a very basic primer on how to engage in local QI.


The concept of QI in medicine is not novel; some of our current-day practices were espoused by surgeons decades ago (2,3). Dr. Ernest Avery Codman (1869 to 1940) conceived of what he called “The End Result Idea” in Boston in the first decade of the 20th century (2,3). By rigorously following outcomes, he demonstrated that some surgeons far outperformed others and justified the need for specialization and extra training in specific surgical domains (2,3). He was essentially forced out of organized medicine and converted his home to a surgical hospital (2,3). It is impossible to know to what extent he was forced out because his ideas were threatening and to what extent his undiplomatic approach contributed. One high point of his notso-subtle approach was distributing, at a meeting of the Boston county surgical society, a caricature of the Boston medical system that included (among other things) a caricature of the president of Harvard, musing “Could my clinical professors make a living without humbug?” (2,3).

Codman’s work was later validated in many venues. He was one of the first surgeons to advocate for a surgical morbidity and mortality conference, now almost universal. He later chaired the committee for Standardization of Hospital for the American College of Surgeons—which ultimately morphed into the Joint Commission. Dr. Codman might be impressed with our surgical virtuosity and the technical
advances within our hospitals, but he would surely be extremely disappointed that most of us still do not follow the “End Result System” (2,3).

Much as the Flexner report in 1910 revolutionized and codified the medical education process, the Institute of Medicine’s (IOM) 2000 report, To Err is Human, is seen by many as a watershed moment in our awareness of patient safety and quality (4). This report did not present any new data, but it did organize and synthesize the vast body of existing data on medical error and called it out publically and prominently. While some argued that the estimate of over 100,000 preventable deaths annually from extrapolated data from one state was unfair, subsequent research has generally indicated that the IOM report did “size” the problem reasonably accurately (4). The result has been a decade of tremendous contributions in the literature and actual improvement in outcomes for patients and hospitals. The initiatives have been broad based—from pediatrics to geriatrics and from private practices toward health systems.


As noted above, some have doubted the conclusion of the IOM report, To Err is Human, about the number of errors and adverse events in American medicine. Specialtyspecific data within otolaryngology have been generally confirmatory (5,6). A study of self-reports in a survey methodology revealed that approximately 2,500 preventable deaths occur annually in otolaryngology! (5). Aside from the mortalities, the study highlighted zones of risk for surgery. Specific areas where errors could be potentially ameliorated include zones of high risk, such as sinus surgery, surgery around major cranial nerves, use of concentrated epinephrine, and the immediate perioperative period (5). Targeted research has been performed to specifically address each of these areas and have further supported these areas as zones of risk.


There are two fundamental differences in the way that measurement is performed for QI purposes, compared to the measurement that is done for basic or clinical research (Tables 206.1 and 206.2):

  • For local improvement efforts, the overriding concern is that measurement should be inexpensive. Perfect accuracy and statistical significance are secondary considerations.

  • To compare physicians, hospitals, or health care systems, one needs not just measures but metrics—defined by Webster as “standards for measurement.” All players need to measure something the same way in order for comparisons to be fair.

Most of us are trained to perform basic and/or clinical research. Extremely rigorous measurement, and an insistence on statistical validity, is the norm. In local clinical QI processes, the best measures are the cheapest ones—because you can afford to make them and afford to repeat them. When your practice introduces a new “check-in” form, you do not have the time, personnel, or money to study its effect with scientific rigor. Instead, you will use some type of “quick and dirty” measure of success. Often the measure will be as simple as asking your receptionist if it is working well. If it’s not obvious there has been a change, you might go so far as to track check-in times for a couple of weeks. It would be foolish and wasteful to try to prove that the new form is better or worse with statistical significance. For ongoing improvement efforts, the major measurement consideration is opportunity cost, and one should strive for the simplest, roughest measure that will provide useful (even if imperfect) information. One of the biggest obstacles to local improvement is the insistence on “scientific quality” data when “quick and dirty” measures should be used.


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May 24, 2016 | Posted by in OTOLARYNGOLOGY | Comments Off on Quality Improvement in Otolaryngology

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Measurement for Local QI



Measurement accuracy

“Get it right”

“Spend wisely”

Goal is perfect accuracy.

The resources for quality measurement are finite, and there are many important things to measure.

Measuring something perfectly will usually take resources away from measurement of other important things.

We should spend the least amount of money and human resources possible to get reasonably accurate information.

Data-based decision-making

“Is P < 0.05?”

“What do we think is the best decision?”

Clinical practice will not typically alter unless convincing statistical evidence builds up that a new treatment/process is better.

Quality is more like clinical medicine. Just as we must often make a clinical decision for a patient without perfect data, in quality, we often have to make decisions about our department and our processes without perfect data.

If data are suggestive but not statistically significant that, for example, there is a problem or an area for improvement, the important question is not “Is P < 0.05?” but “Do we believe that this is concerning and we should do something about it?”


“Next case”