Chapter 131

Quality Improvement 

Sally E. Rampersad and Lynn D. Martin 

131 

CHAPTER 

INTRODUCTION 

In spite of numerous advances in medicine and technology, 

the modern health care system continues to perform far below 

acceptable levels in ensuring patient safety and addressing patient 

needs. 1 Patient safety concerns are far too common in the operative 

setting. Consider, as an example of this system failure, the 

lethal heart-lung transplantation of a ABO-incompatible organ 

into a 17-year-old girl at a well-respected U.S. medical center. 2 

How could the most technologically advanced health-care system 

in the world produce such results? The answer lies deep within 

the landmark article published in the 1999 Institute of Medicine 

(IOM) report “To Err is Human: Building a Safer Health System,” 

medical error is a failure of process. 3 This report galvanized health 

care system response and public demand for change when the U.S. 

population learned that medical errors cause 44,000 to 98,000 

deaths annually. These errors are estimated to account for more 

than $9 billion per year in lost productivity and nearly $2 billion 

per year in hospital costs. 4 

The Merriam-Webster Online dictionary defines quality assurance 

(QA) as “a program for systematic monitoring and evaluation 

of the various aspects of a project, service or facility to ensure that 

standards of quality are met.” Definitions for quality include 

“degree of excellence: grade: superiority in kind.” Improvement is 

defined as “the act or process of improving; the state of being 

improved, especially enhanced value or excellence.” 5 

IOM’s Committee on the Quality of Health Care in America 

outlined six aims to improve key dimensions in our healthcare 

systems. Health care should be: 

Safe—avoiding injuries to patients from the care intended to help 

them 

Effective—providing services based on scientific knowledge to all 

who could benefit and refraining from providing services to 

those not likely to benefit (avoiding underuse and overuse) 

Patient-centered—providing care that is respectful of and responsive 

to individual patients’ preferences, needs, and values and 

ensuring that parental expectations and values guide all clinical 

decisions 

Timely—reducing waits and sometimes harmful delays for both 

those who receive and those who provide care 

Efficient—avoiding waste, including waste of equipment, supplies, 

ideas, and energy 

Equitable—providing care that does not vary in quality because 

of personal characteristics such as gender, ethnicity, geographic 

location, or socioeconomic status 

In this chapter we will trace the history of quality improvement 

(QI) in pediatric anesthesia and will describe some of the 

methodologies used for quality improvement today. We will use a 

template to describe a pediatric case in which there were QA/QI 

issues and will demonstrate how this can be effectively applied to 

other cases, to direct QA/QI discussion away from personal fault 

finding and toward fixing broken systems. Finally, we will consider 

the evolution of error models and introduce a new 2-dimensional 

dynamic error model recently described by one of the authors (SR). 6 

HISTORY 

QA/QI programs inevitably look at morbidity and mortality as an 

important outcome measure. These are considered in Chapter 127 

but some landmark morbidity and mortality studies are con - 

sidered here for their historic significance. 

John Snow wrote two of the earliest texts on anesthesia: On the 

Inhalation of the Vapour of Ether and On Chloroform and Other 

Anaesthetics. 7,8 In these works, Snow described in detail the clinical 

effects of the inhaled anesthetic vapors, including the potential for 

respiratory depression and cardiac arrest. He followed reports of 

deaths from chloroform in the medical literature and commented 

upon them. 9 Having performed some animal experiments himself, 

he had determined that 4% was an appropriate concentration of 

chloroform to administer and cautioned against the use of higher 

concentrations. 

One of the first comprehensive reviews looking at the safety of 

anesthesia was the Beecher and Todd study in 1954. 10 In the 

conclusions of this study, the special needs of pediatric patients 

were acknowledged. “Again and again in the preceding pages we 

have pointed out specific areas worthy of ardent attack: for 

example, the question of why the anesthesia death rate is so disproportionately 

high in the first decade of life …” 

In the United Kingdom in 1987, the Confidential Enquiry into 

Perioperative Deaths (CEPOD) was another comprehensive 

examination of perioperative risks. 11 Deaths within 30 days of 

surgery were reported to CEPOD and then a detailed questionnaire 

was mailed out to those involved in care, both from surgery 

and anesthesia. Subsequently, the group looked at subsets of 

surgeries and groups of patients that seemed to be at particularly 

high risk. In an editorial in the Lancet, Lunn and Devlin reported 

that CEPOD “sought to establish facts about the delivery of surgery 

and anesthesia and thus, facilitate improvements in the 

delivery of surgical care.” They also concluded that “It is important 

that consistent protocols are created so that continuity of care is 

maintained when weekends or staff holidays are interspersed in a 

patient’s stay in hospital …. Anaesthetists have a peculiarly 

difficult role … and their position could be strengthened if defined 

guidelines were available for everyone.” 12 Thus, their goal was

2144 PART 6 ■ Specific Considerations 

quality improvement and a tool that they recommended was the 

use of “standard operating procedures” that will be discussed in 

more detail later. 

The Australian Patient Safety Foundation was formed in 1987. 

It decided to set up and coordinate the Australian Incident 

Monitoring Study (AIMS). 13 An important feature of this study 

was that the reports were anonymous and voluntary. Voluntary 

reporting, however, has been shown in several studies to fall far 

short of the goal of detecting all undesirable clinical events. 14–16 

Any unintended incident which reduced, or could have 

reduced, the safety margin for a patient was reportable to AIMS. 

All reports were welcome whether or not the reporter deemed that 

the incident was preventable or that it involved human error. 

Contributing factors and factors minimizing any adverse outcome 

were considered and suggestions for corrective strategies could be 

made within the initial report. The guaranteed anonymity and 

medicolegal protection of the reporter increased the likelihood 

that details would be reported. Although the reporting of “nearmisses” 

was encouraged, there was probably a tendency, as 

previously noted by Flanagan, 17 to underreport events that were 

considered mundane and that did not result in patient harm. The 

major conclusions of the AIMS data were similar to those from a 

previous study by Cooper, 18 which showed that 83% of preventable 

incidents involved human error. 

In 1985, the Committee on Professional Liability of the 

American Society of Anesthesiologists began gathering data and 

evaluating closed anesthesia malpractice claims. Claims in the 

pediatric age group (15 years or younger) were examined as subset. 

19 A major conclusion of this comparison was that in pediatric 

closed claims there was a large prevalence of respiratory-related 

damaging events (43% in pediatric claims vs 30% in adult claims); 

mortality was greater in pediatric vs adult claims (50% vs 35%), 

and care was judged to be less than appropriate more often in the 

pediatric cases (54% in pediatric claims vs 44% in adult claims). 

Kennan et al. found that bradycardia, which is often an indicator 

of hypoxia and/or an early sign of hemodynamic instability, was 

less than half as likely to occur when a pediatric anesthesiologist 

was supervising the case. 20 Several authors have reported an 

increased incidence of perioperative cardiac arrest in infants. 21–24 

The Pediatric Perioperative Cardiac Arrest (POCA) Registry data 

also suggest that infants are at increased risk, as are children with 

severe underlying disease and those having emergency surgery. 25,26 

Such studies have led to recommendations as to who should be 

performing pediatric anesthesia and what training is acceptable. 27–35 

Matching provider and facility capabilities to the needs of pediatric 

patients, who have been identified as a particularly fragile group 

of patients, is essential if quality care is to be provided. 

QA/QI METHODOLOGIES 

Quality improvement efforts originated in the manufacturing 

settings. Many of the tools and methods developed in this setting 

have been applied successfully in health care. The most common 

QA/QI methodologies currently used in the health care setting are 

the plan-do-check-act (PDCA) cycle, Six Sigma, lean strategies, 

and Failure Modes Effects Analysis (FMEA). We will provide brief 

summaries of each method. 

The measurement of defects is a central component of quality 

improvement. The systemic measurement of a process demonstrates 

whether improvement efforts (1) lead to change in the 

primary endpoint in the desired direction, (2) contribute to 

unintended results in different part of the process, and (3) require 

additional efforts to bring the process back into acceptable ranges. 

One primary tool used by quality improvement professionals is a 

run chart. The number of successful outcomes is divided by the 

number of total opportunities for the desired variable and graphed 

versus time (Figure 131–1). The mean line can be used in the run 

chart to clarify movement of data away from the mean. Two other 

commonly calculated variables are the upper control limit and the 

lower control limit. As long as the data points remain within these 

control limits, the process is under control and further action is 

not necessary. Quality improvement efforts would expected to 

decrease the mean, upper, and lower control limits. 

Considered by many the first medical quality improvement 

leader, Avedis Donabedian described quality design in relationship 

to structure, process and outcomes. 36 Structural measures assess 

the availability and quality of resources, management systems, and 

policy guidelines and are often critical to sustaining processes 

over time. Although commonly used for licensing and hospital 

accreditation, an example in health care would be the decision to 

use intensivists in the intensive care unit to decrease mortality. 37 

Process measures use the actual process of health care delivery as 

the indicator of quality by analyzing the activities of physicians or 

other health care professionals to determine whether medicine is 

practiced according to guidelines. A current example of a process 

measure would be the proportion of diabetic patients who undergo 

an annual retinal examination. Outcomes measures evaluate the 

result of health care and often depend not only on medical care 

but also on genetic, environmental, and behavioral factors. Outcomes 

are usually based on group results rather than individual 

cases and thus, are not an indicator of quality of care delivered to 

an individual patient. Examples of outcome measures include 

mortality and patient satisfaction data. 

Historically (Table 131–1), health care has focused on quality 

assurance (i.e., a system for evaluating the delivery of services or 

the quality of products) and quality control (i.e., a system for 

verifying and maintaining a desired level of quality). Used alone, 

these methods are not sufficient to enhance outcomes. Checking 

for defects and recommending changes without understanding 

the causes and recognizing the effects of these changes on other 

parts of the organization may improve one process but harm 

others. Therefore, most operating rooms are combining quality 

assurance and proactive quality improvement. 

Continuous QA/QI is based on the principle that opportunity 

for improvement exists in every process on every occasion. 38 The 

continuous QA/QI model underscores the view of health care as a 

process and focuses on the system rather than the individual when 

considering improvement opportunities. Continuous QA/QI re - 

quires a commitment to constantly improve operations, processes, 

and activities commonly using statistical methods to meet patient 

needs in a cost-effective, efficient, and reliable manner. 

The choice of methodology depends on the nature of 

the improvement project. Within most methodologies, users will 

find similar techniques. Most methodologies typically include 

iterative testing of ideas and redesign of process or technology 

based on lessons learned commonly using statistical methods. 

More recently, some experts have begun using principles 

from different methodologies for the same project (i.e., the use of 

“lean-sigma” methodology), thereby making distinctions less 

relevant.

CHAPTER 131 ■ Quality Improvement 2145 

Figure 131-1. Run chart—department of pediatric anesthesia. 

PDCA Cycle 

The PDCA cycle is the most commonly utilized approach for rapid 

cycle improvement in health care. This method involves a trial and 

learning approach in which a hypothesis or suggested solution for 

improvement is made and testing is carried out on a small scale 

before any changes are made to a whole system. 39 A logical 

sequence of four repetitive steps is carried out over a course of 

small cycles, which eventually leads to exponential improvements 

(see figure 2). During the “plan” phase, ideas for improvement are 

defined, tasks assigned and expectations confirmed. Measures of 

improvement (metrics) are then selected. In the “do” phase, the 

plan is implemented and any deviation (defects) from the plan 

documented. The defects are analyzed in the “check” phase. In this 

phase the results from the test cycle are studied and questions 

are asked regarding what went wrong and what will be changed 

in the next test cycle. In the final “act” phase, lessons learned from 

the check phase are incorporated into the test of change and a 

TABLE 131-1. Evolution of Quality Improvement Efforts in Hospitals 

Era Quality Effort Name Strategy 

1950s 

1980s 

1990s 

2000s 

Quality Assurance 

Quality Improvement 

Quality Management 

Industrial Methods 

Quality (PDCA) Cycle 

Six Sigma (Motorola) 

Lean (Toyota) 

Failure Modes and 

Effects Analysis 

Identify outliers in clinical care to eliminate these outliers from within the organization 

Decrease variation to reduce error as well as improve clinical and nonclinical processes 

Use managerial concepts centered on quality improvement to achieve technical quality and 

customer satisfaction 

Trial and learning approach in which a hypothesis or suggestion for improvement is made 

and tested on a small scale before changes are made to a whole system 

Achieve defect-free processes and reduce variance through Six Sigma improvement projects 

Identify customer demands and improve process by removing non–value added steps 

(waste) from the system 

Evaluate processes for possible failures and effects; prevent failure by correcting the 

processes proactively rather than reacting to adverse events


test a hypothesis. In the improve step, creative solutions and 

implementation plans are developed. In the final control step, the 

process is controlled by implementing policies, guidelines, errorproofing 

strategies, and monitoring with control charts. 

Figure 131-2. Plan-Do-Check-Act (PDCA) CYCLE. 

decision is made in continuation of the test cycles. Then the entire 

cycle is repeated again. 

Six Sigma 

Bill Smith, a reliability engineer at Motorola in 1986, is known as 

the “Father of Six Sigma.” Six Sigma is a business management 

strategy based on rigorous statistical measurement designed to 

reduce cost, decrease process variation, and eliminate defects. 40 

However, Six Sigma took off as a significant quality movement in 

the mid-1990s when Jack Welch, chief executive officer of General 

Electric, launched Six Sigma, calling it the most ambitious task the 

company had ever taken on. 41 “Sigma” is used to present the 

standard deviation of a population or given process. For normally 

distributed data, the Six Sigma level defines a process has having 

3.4 defects per million opportunities (DPMO) and is virtually 

error free (99.9997%) (Table 131–2). Once DPMO has been 

calculated, sigma values can be looked up in tables or software 

packages. Teams can then identify process capabilities and the 

level of intended magnitude of improvement. 

Six Sigma improvements are achieved through a series of steps: 

define, measure, analyze, improve, and control (DMAIC). The 

define step entails the creation of a chart that defines the customer’s 

needs, project scope, goals, success criteria, team members, and 

project deadlines. In the measurement step, a data collection plan 

for the process developed and data are collected from several 

sources to determine the depth of defects or errors in the system. 

Control charts are commonly utilized to further control the 

process. In the analyze step, data analysis occurs, deviation from 

standard is identified, and sources of process variation are used to 

Lean Strategy 

Building on the work of several pioneers (Henry Ford at the Ford 

Motor Company and W. Edward Deming, the originator of the 

concept of total quality management), Taiichi Ohno, a Toyota 

Motor Corporation engineer, revolutionized thinking regarding 

process inefficiency or “waste” in the early 1950s, leading to the 

development of the Toyota Production System (TPS). 42 Building 

on the Deming Cycle as a systematic approach to problem solving, 

a continuous quality-improvement model consisting of a logical 

sequence of four repetitive steps, Toyota has been able to use the 

TPS to become the largest and most profitable automobile 

manufacturer in the world. James Womack and Daniel T. Jones 

first characterized TPS as “lean thinking” because world-class 

companies—those with the best production systems—require less 

of everything to produce higher-quality products. 43 As noted in 

their book, lean means: 

● 

● 

● 

● 

● 

● 

● 

● 

Half the space 

Half the investment in tools 

Half the human effort in the factory 

Half the time to produce the product 

At least half the inventory on hand 

Greater flexibility to produce a variety of products based on customer 

demand 

Fewer defects 

Lower cost 

Lean philosophy is driven by the desire to identify needs of the 

customer and aims to improve processes and thus, quality by 

removing non–value added activities (Figure 131–3). Non-value 

TABLE 131-2. Sigma Levels 

Defects per Million 

Sigma Level Accuracy, % Opportunities 

1 30.85 690,000 

2 69.15 308,537 

3 93.32 66,807 

4 99.38 6,210 

5 99.977 233 

6 99.9997 3.4 

Figure 131-3. Lean philosophy—quality.


Figure 131-4. Lean philosophy—waste types. 

added activities, more commonly know as waste, do not add to the 

business margin or the customer’s experience, and the customer is 

often not willing to pay for them. Eight different types of waste have 

been described (Figure 131–4). Lean tools and methods are 

designed to maximize value-added steps in the best possible 

sequence to deliver continuous flow. Services are delivered where, 

when, and how the customer needs them. To create and maintain 

an organized, cost-efficient workplace that has clear (visual) work 

processes and standards, lean experts use five S tools (Figure 131– 

5). The degree of organization of each of the five Ss can also be 

quantified (Figure 131–6). Daily maintenance and weekly auditing 

are necessary to sustain the organization of the workplace. 

One of the most important tools in lean methodology is called 

value stream mapping (VSM). This tool graphically displays the 

process of services or product delivery with use of inputs, 

throughputs, and outputs. A current VSM is typically done at the


Figure 131-5. Lean philosophy—5 Ss. 

beginning of a project and opportunities for improvement are 

highlighted. A future state VSM is also created to depict an 

idealized (future) process. Thereafter, front-line staff members 

generate ideas for improvement. The improvement team is 

expected to test their ideas using highly structured, rapid-change 

events called rapid process improvement workshops in which 

improvement ideas are expeditiously tested and implemented. 

Children’s Hospital and Regional Medical Center in Seattle, 

Washington, has been using lean methods for quality improve- 

ment in the operative setting since 2002. Adapting these methods 

to fit the health care (service) setting, these methods are called 

continuous performance improvement (CPI). The CPI philosophy 

is to focus on patients and families using quality, cost, delivery, 

safety, and engagement as metrics (Figure 131–7). This philosophy 

highlights differences between traditional and lean quality improvement. 

First, it eliminates waste, complexity and variation to 

enhance quality and reduce cost rather than eliminate labor. 

Second, quality, cost, and cycle are addressed concurrently as 

related rather than as competing priorities. Finally, focus is on 

whole system rather than subsystem improvements. 

The initial 2 years were focused on point improvements within 

the operating rooms. However, during the last 3 years, attention 

has been focused on improvements in the operative services value 

stream using the full array of lean tools, e.g., Just in Time service 

and Built in Quality (Figure 131–8). Just in Time is one pillar of the 

CPI management system. Simply put, Just in Time delivers the 

right items at the right time in the right amount. The power of Just 

in Time is that is allows staff to be responsive to the day-to-day 

shifts in customer demand. Products that move continuously 

through the processing steps with minimal waiting time in 

between and the shortest distance traveled will be produced with 

the highest efficiency. Sustaining continuous flow also serves to 

surface any problem that would inhibit that flow. In essence, the 

creation of flow forces the correction of problems, resulting in 

reduced waste. The other pillar is Built in Quality. This pillar 

requires making problems visible, never letting a defect pass along 

to the next step in the process, and stopping when there is a quality 

Figure 131-6. Lean philosophy—5s levels of achievement.


Figure 131-7. Continuous performance improvement (CPI) 

philosophy. 

problem. The key to developing effective mistake proofing lies in 

understanding how or why the mistake occurred. 

The quality of a system can be defined by five separate levels, 

as shown in Table 131–3. The distinction between error and defect 

is important in this system. An error is, as the name implies, an 

error in the process at the point of creation. A defect is defined as 

an error that is passed on down to the next step in the process or 

ultimately to the customer. The highest level of quality is to 

prevent the occurrence of the error completely. If it is not possible 

to completely prevent the error, then try to detect the error as it 

occurs. In any case, it is important to prevent any defective items 

(or mistakes) from affecting the customer. “Continuous flow” and 

“pull” are fundamental elements that help us achieve Just in Time 

and Built in Quality. Continuous flow is producing and moving 

one item at a time through a series of steps, with each step making 

just what is requested by the next step; pull dictates when material 

is moved. Nothing is produced by the upstream supplier process 

until the downstream customer process signals a need, often via a 

Kanban card (used as a signaling system to trigger action), about 

what, when, and where it is needed. Toyota describes standardized 

work as a “foundation for kaizen (impro vement).” If the work is 

not standardized and it is different each time, there is “no basis 

for evaluation,” meaning no reference point from which to 

compare or improve. 

A degree of stability is needed in three areas before moving to 

standardized work: (1) The work task must be repeatable. If the 

work is described in “If, then” terms, it will not be possible to 

standardize unless these are just a few very simple rules. (2) The 

Figure 131-8. Value stream improvement through waste 

reduction. 

line and equipment must be reliable and downtime should be 

minimal. It is not possible to standardize if the work is constantly 

interrupted and the person doing the work is sidetracked. 

(3) Quality issues must be minimal so that the person is not 

constantly correcting or struggling with poor quality. 

Over the last 5 years using CPI as the primary methodology for 

quality improvement, our institution has been able to achieve and 

sustain several important improvements in our operating rooms. A 

good example is the enhancement in admission process that has 

improved the quality (compliance) of the required preoperative 

documentation and reduced the admission work time such that we 

have decreased our scheduled arrival time before surgery to 75 

minutes, greatly improving patient and parental satisfaction. 

Failure Modes Effects Analysis 

Failure Modes and Effects Analysis (FMEA) was developed in the 

industrial setting and is now being used in health care to assess 

TABLE 131-3. Toyota (“Lean”) Levels of Quality 

Quality Level Site of Action Classification 

1 Customer inspection Check for defects 

2 Company inspection 

3 Unit inspection 

4 Self-inspection Detect errors 

5 Mistake proof Prevent errors


risk of failure and harm in processes and to identify the most 

important areas for process improvements. Teams use FMEA to 

evaluate processes for possible failures and to prevent them by 

correcting the processes proactively rather than reacting to adverse 

events after failures have occurred. This emphasis on prevention 

may reduce risk of harm to both patients and staff. FMEA is 

particularly useful in evaluating a new process before implementation 

and in assessing the impact of a proposed change to an 

existing process. FMEA includes review of the following: 

● 

● 

● 

● 

Steps in the process 

Failure modes (What could go wrong?) 

Failure causes (Why would the failure happen?) 

Failure effects (What would be the consequences of each 

failure?) 

FMEA identifies the opportunities for failure, or “failure 

modes,” in each step of the process. Each failure mode gets a 

numeric score that quantifies (1) likelihood that the failure will 

occur, (2) likelihood that the failure will be detected, and (3) the 

amount of harm or damage the failure mode may cause to a person 

or to equipment. The product of these three scores is the risk 

priority number (RPN) for that failure mode. The sum of the RPNs 

for the failure modes is the overall RPN for the process. As an 

organization works to improve a process, it can anticipate and 

compare the effects of proposed changes by calculating hypothetical 

RPNs of different scenarios. The FMEA can be used in two 

separate but related ways. Teams can use FMEA to discuss and 

analyze the steps of a process, consider changes, and calculate the 

RPN of changes under consideration. They can use FMEA to 

“verbally simulate” a change and evaluate its expected impact in a 

safe environment, before testing it in a patient care area. Some 

ideas that seem like great improvements can turn out to be changes 

that would actually increase the estimated RPN of the process. In 

addition to using FMEA to help evaluate the impact of changes 

under consideration, teams can calculate the total RPN for a 

process and then track the RPN over time to see whether changes 

being made to the process are leading to improvement. Numerous 

online resources are available to assist teams with FMEA. 44 

CASE DISCUSSION 

Everyone can remember the horror of presenting at old style 

QA/QI rounds, where ABC meant “Accuse, Blame, and Criticize”— 

and you were the one who would be blamed. If we are to learn from 

our near misses, we must conduct our analysis in a way that it is 

nonpunitive and where voluntary, anonymous reporting is not only 

possible, but is encouraged. NASA and the airline industry have 

had such anonymous, voluntary systems in place for some time. 45 

Recently, a framework for analyzing the root causes of adverse 

events and for looking at the barriers that are available to prevent 

the error from reaching the patient was described by one of the 

authors. 6 This framework has been helpful in our departmental 

QA/QI discus sions to steer the discussion away from individual 

blame. We be lieve that voluntary reporting is more likely to occur 

in a situation where we always look for something to fix, not 

someone to blame. 

The following case was described by an anesthesiolgy trainee. 

“I was asked to help out with a hepatocarcinoma resection. I came in 

around 7:15 pm and the Attending anesthesiologist and I decided to 

switch to isoflurane for maintenance, as the case was likely to be 

prolonged. Desflurane and sevoflurane vaporizers were in position on 

the machine, with the sevoflurane vaporizer in use. I replaced the 

desflurane vaporizer with an isoflurane vaporizer: I locked it and 

turned on the agent. 

Ten to 15 minutes later, there was an episode of hypotension that 

required resuscitation with blood products and vasopressors. I turned 

down the vaporizer setting to an Fi isoflurane 0.4% to maintain some 

anesthesia. There was ongoing massive transfusion of blood products 

throughout this time. The end tidal agent analyzer read 0.7% and 

then 0.5% over next thirty minutes. 

I was then asked to leave the room and do another case and came 

back an hour later to help out. It was noted that the end tidal agent 

analyzer was not registering any agent, although the vaporizer was 

still switched on at about 0.4%. The Attending and resident had 

taken off the circuit briefly to see if they could smell agent and both 

had thought that they could and so attributed the reading of zero 

end-tidal agent to an analyzer error. Finally at around 20:45 pm it 

was noted that the isoflurane canister was not seated properly on the 

machine (despite the locking mechanism in the “locked” position) 

and so the end-tidal agent reading of zero was real. We reseated the 

canister, gave the patient 4 mg of midazolam for its amnestic effects, 

informed the surgeons, and continued with ongoing resuscitation 

with low level end-tidal agent for amnesia. The case ended at around 

3:00 am. He has not yet reported anything suggestive of awareness, 

but we are continuing to visit him to ask about this possibility. 

How do you prevent this from happening in the future? More 

vigilance of course, but that’s easier said than done. Perhaps a 

different locking mechanism design on the vaporizer? Is it possible to 

have an alarm system on the vaporizer if it is improperly seated? 

Mostly it was my fault and not a device error. I take full 

responsibility.” 

Here is the response from one of the authors (SR) in her role as 

QA/QI director for our department. 

“Errors are rarely the fault of an individual and much more often 

due to a combination of factors. Here is a template that I often use 

for analyzing errors.” 6 

Catalyst event—your patient developed hepatocarcinoma and 

needed surgery, thereby putting him in harm’s way. 

System faults—we have machines that can’t accommodate 

3 vaporizers, so you have to swap them out as needed. Our 

vaporizers do not alarm or refuse to switch on if they are not fully 

locked. The OR was busy and you were shuttled between cases, 

unable to give your full attention to a difficult case. 

Loss of situational awareness—the OR team noted the zero end-tidal 

agent, but dismissed it as an analyzer error because they were task 

overloaded due to the ongoing resuscitation. I notice the times on 

the incident were well into the evening, so there were some 

fatigued members of the OR team, this also contributes to loss of 

situational awareness. 

Human Error—the vaporizer was not locked in position correctly, no 

one noticed and acted upon the reading of zero end-tidal agent. 

Barriers for safety that can potentially trap and mitigate an error 

are:- 

Technology—technology did help you in that there was a reading of 

zero end-tidal agent but there were technology failures in that the 

improperly seated vaporizer was not flagged as a problem by our 

existing technology. At the time of the incident, the analyzer was 

set to sevoflurane, not to automatic and so it was not “looking” for


isoflurane. This made it easy to dismiss the zero end-tidal 

agent reading as an analyzer error and not real, because end-tidal 

agent readings and vaporizer settings had not matched earlier 

in the case, so you did not trust that your technology was 

working. 

Proficiency—other than delay in acting upon the low end-tidal agent, 

I see no proficiency failures here—everyone worked hard to save 

a critically ill patient. 

Standard Operating Procedures (SOP)—perhaps we need a SOP for 

changing a vaporizer—perhaps we should set mandatory checks 

on end-tidal agent for the next hour after any change of vaporizer 

to make sure subtle (and not so subtle) problems are not missed. 

In addition, we need to schedule these cases such that one team is 

able to give their full attention to the case, with additional 

dedicated help as needed. 

Judgment—I see no judgment lapses here and you have 

demonstrated good judgment in your handling of the situation 

since the problem was recognized. 

I would suggest: 

1. Please stop beating yourself up (easier said than done). 

2. Continue to visit your patient and if your patient has 

awareness—you and your Attending should discuss with 

risk management and arrange follow up for the patient. 

3. Yes, we will discuss this case at the next QA/QI conference, 

but I would like to put it in the sort of framework described 

above, rather than the “mea culpa” version. 

This can be used as valuable teaching moment for our 

department and I thank you for having the courage to disclose 

it and for the professional way in which you have handled it 

so far.” 

In follow-up, the patient did have a brief episode of awareness, 

but no pain and was not distressed about it. The vaporizer 

problem was reported to the Food and Drug Administration’s 

Medwatch. 46 

HISTORY AND EVOLUTION 

OF ERROR MODELS 

Historically, adverse outcomes are often viewed as each separate 

event being the links in a chain, which lead to an accident. 

Supposedly, breaking any of the links prevents the error from 

reaching the patient. This may be a flawed way of analyzing 

accidents, since each link contributes to the outcome, but breaking 

a link does not necessarily stop or prevent the accident. 

Another popular illustration of errors is the Swiss cheese model 

(Figure 131–9). In this model, various barriers exist that attempt 

to trap an error and to prevent it from causing harm. Only if all of 

the holes in the various barriers line up does the error get through. 

However, this model is also limited. One problem is that, in theory, 

there must be an infinite number of barriers to trap all errors. 

(Robert Caplan MD, personal communication, 2004) Also, where 

does the error come from, and where does it go when it is stopped? 

What is missing from all these models is the element of time and 

the dynamic nature of these situations. 

This reverse Volant diagram (Figure 131–10) illustrates a global 

view of a situation. Red indicates a critical situation; yellow/amber 

indicates a situation that is not perfect but may be good enough to 

allow the team to stabilize the situation and to think about how 

Figure 131-9. Swiss cheese model. 

best to resolve the problem; green indicates an optimum situation 

with all systems functioning as intended. If a catastrophic (red) 

situation arises, our tendency is to want to go back to green quickly. 

An example of this in practice is the unanticipated difficult airway. 

Faced with a patient who is difficult to intubate, our tendency is to 

Figure 131-10. Reverse Volant diagram.


want to try to accomplish that task, especially if we did not expect 

it to be difficult. We want to be back in the green, and we hope no 

one will notice. However, repeated attempts to intubate the patient 

may worsen the situation, resulting in the feared combination 

of cannot intubate/cannot ventilate. The American Society of 

Anesthesiologists (ASA) guidelines for difficult airway manage - 

ment 47 do not recommend repeated tracheal intubation attempts, 

and yet we still have a tendency to persist in this path. A much 

safer path is to return to mask ventilation or to place a laryngeal 

mask airway, an amber/yellow situation. This buys thinking time, 

so that additional technology may be brought in (difficult airway 

equipment), additional proficiency may be added (more/different 

personnel), the American Society of Anesthesiologists guidelines 

can be referred to (standard operating procedure), and the 

judgment of the individual trying to intubate the patient can be 

enhanced by using the collective wisdom of those around him. 

TWO-DIMENSIONAL ERROR MODEL 

It is possible to combine the color progression of the Volant diagram 

together with the idea of barriers from the Swiss cheese diagram. 

The barriers are technology, proficiency, standard operating 

procedure, and judgment. Adding the element of time creates a 

dynamic component to the model. (Figures 130–11 to 130–14) 

The yellow ball represents an error that is rolling toward the 

patient, with potential for harm. (Figure 131–11). Notice how the 

error (the ball) increases in size as it moves from left to right, 

representing the increasing momentum of the error, as barriers 

fail to stop it. The error also changes color from green to yellow to 

amber to red as it moves from left to right, indicating greater 

potential for harm as it moves closer to the patient (Figure 131– 

11). In the second diagram (Figure 131–12), the axis is tilted; this 

represents a change in the underlying condition of the patient. If 

the first diagram was an ASA 1 patient, this diagram could 

represent an ASA 3 patient. The tilt of the slope means that the 

error gains momentum more easily than in the first situation and 

so the potential to reach the patient is greater; in other words, this 

patient has less physiologic reserve. In the third diagram, this same 

Figure 131-12. Snapshot of dynamic error model: tilted axis. 

With permission from Capt. Carlyle Rampersad. 

ASA 3 patient is protected from harm because the judgment 

barrier is raised and this traps the error (Figure 131–13). In the 

final diagram, the judgment barrier is raised further, reversing the 

ball and restoring the situation partially toward normal (Figure 

131–14). We do this every day, perhaps without realizing it, 

monitoring sicker patients more closely, assigning the patient to 

appropriately skilled personnel, and using standard guidelines 

where these exist. In the case discussed above the slope of the axis 

of the model was tilted due to the patient’s underlying condition 

(hepatocarcinoma) and also the time of day (resulting in fatigued 

medical providers). Raising a high standard operating procedure 

barrier could potentially have trapped the error of misaligning the 

vaporizer before it reached the patient, and the awareness could 

have been prevented if the vaporizer problem had been detected 

and corrected sooner. 

Figure 131-11. Snapshot of dynamic error model: healthy 

patient. With permission from Capt. Carlyle Rampersad. 

Figure 131-13. Snapshot of dynamic error model: error trapped 

by judgment. With permission from Capt. Carlyle Rampersad.


Figure 131-14. Snapshot of dynamic error model: error 

reversed by judgment. With permission from Capt. Carlyle 

Rampersad. 

CONCLUSIONS 

Samuel Beckett wrote “Ever tried. Ever failed. No matter. Try Again. 

Fail again. Fail better.” When considering quality improvement, 

it is important that we also consider how it is to be measured. 

Recent editorials and commentaries have made the point that the 

traditional scientific method, in which the primary goal is to 

discover and disseminate new knowledge, is not well suited to the 

measurement of quality improvement. 48–50 In our world, where 

evidence-based medicine is highly valued and the randomized 

controlled trial is the pinnacle of all studies, descriptive studies of 

quality improvement efforts that utilize local knowledge may be 

overlooked by editors in favor of more “scientific” research. It will 

be important to be sure that managers and administrators in 

healthcare understand the iterative nature of quality improvement. 

In addition, if we are to be successful in QA/QI efforts, websites 

that allow the integration of data from several sources will be 

essential and our work needs to be transparent and widely 

reported, so many may benefit from the lessons to be learned. 

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