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Patient Safety: Achieving a New Standard for Care (2004)

Chapter:Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel

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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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F
Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel

Commissioned Paper by John E. McDonough, Milbank Memorial Fund Ronni Solomon and Luke Petosa, ECRI

ABSTRACT

Health care leaders seeking to improve quality and prevent harm to patients have an array of tools to help them in this task. We propose dividing these tools into two categories: (1) quality improvement tools—including Continuous Quality Improvement, Six Sigma, and Toyota Production System—can be applied to many organizational challenges, including but not limited to safety concerns; and (2) proactive hazard analysis tools—including Health Care Failure Mode and Effect Analysis, Hazard Analysis and Critical Control Point, Hazard and Operability Studies, Proactive Risk Analysis—are designed specifically to identify hazards and to prevent harm. Each tool has common ancestry in the application of the scientific method to process analysis pioneered by Shewhart and Deming; each has unique attributes and advantages. This report explains each model in the context of patient safety. We recommend establishment of a clearinghouse to enable physicians and other practitioners to learn from experimentation with these models and to establish a common analytic framework. We also recommend use of models for personal health information as a methodology for medical specialties to address patient safety concerns.

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

I. INTRODUCTION

Two Institute of Medicine (IOM) reports, To Err Is Human (1999) and Crossing the Quality Chasm (2001), moved public and health care industry concerns about quality, patient safety, and hazard analysis to greater visibility. As patient safety and hazard analysis concerns rise, health industry leaders have sought tools to address these challenges more effectively. Many tools exist; the quality improvement and hazard analysis models that offer methodologies to make medicine safer include Six Sigma, Hazard Analysis and Critical Control Points (HACCP), Failure Mode and Effect Analysis/ Healthcare Failure Mode and Effect Analysis (FMEA/HFMEA™), Toyota Production System (TPS), Hazard and Operability Studies (HAZOP), Total Quality Management/Continuous Quality Improvement (TQM/CQI), Root Cause Analysis (RCA), and Probabilistic Risk Assessment (PRA).

Each approach has champions, supported by consultants ready to train managers and frontline workers in the rollout of each. Competing terms, acronyms, symbols, and techniques suggest a Tower of Babel—health leaders speaking different languages and using tools that do not resemble each other. As demands for improvements in patient safety escalate, the IOM’s Patient Safety Data Standards Committee seeks a framework to understand these approaches to identify principles necessary for any quality improvement (QI) or proactive hazard analysis (PHA) methodology to succeed.

This paper provides an overview of key features of prominent methodologies, offers a framework to understand each, and shows how each relates to others. We outline principles to create effective hazard analysis in health care organizations, and we identify conceptual and methodological considerations in design and evaluation of risk/hazard identification. We relate hazard analysis to adverse event prevention and discuss strategies to apply this approach to health care. Finally, we discuss data requirements and measurement tools to support this approach.

As a caveat, we recall the words of Avedis Donabedian, who devised our modern framework for understanding quality in health care: “If we are truly committed to quality, almost any mechanism will work. If we are not, the most elegantly constructed of mechanisms will fail.” While today’s quality leaders dispute the first sentence, all affirm the validity of the second. While QI and PHA tools can assist any health care organization’s commitment to making health care safer, none will succeed in the absence of deep and sustained leadership commitment.

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

II. OVERVIEW OF EXISTING QUALITY IMPROVEMENT/ HAZARD PREVENTION MODELS

Essential Features of Health Care Quality

The Chasm report identifies six attributes for a quality health care system: (1) safe, (2) effective, (3) patient centered, (4) timely, (5) efficient, and (6) equitable.1 Safety is a preeminent feature of health care quality, first on the list, though not the only one. Health care quality may be thought of as a circle, with each of the six essential features forming smaller overlapping circles within the larger whole.

Over the past 60 years, many models have been developed to help organizations improve quality and enhance safety. Among the methodologies discussed here, we distinguish between tools that address all six aspects of quality versus tools with an explicit focus on safety and hazard analysis. General QI tools can be used to improve timeliness, efficiency, and other goals in addition to safety. PHA tools are more prescriptive and require more steps, including documentation; in cases where a tool is applied to an ongoing service operation (i.e., HACCP), it becomes a part of a firm’s daily functioning.2 This distinction provides the framework for discussion in this paper of the various methodologies:

Quality Improvement Tools (QI)

Proactive Hazard Analysis Tools (PHA)

Total Quality Management—TQM

Failure Mode and Effect Analysis—FMEA

Continuous Quality Improvement—CQI

Healthcare Failure Mode and Effect Analysis—HFMEA™

Toyota Production System

Hazard Analysis and Critical Control Points—HACCP

Six Sigma

Hazard and Operability Studies—HAZOP

Probabilistic Risk Assessment—PRA

For comparative purposes, we also include discussion of Root Cause Analysis under PHA tools. Following is a brief outline of each approach, describing purpose and features, a thumbnail history, and key applications. Tables D–1 and D–2 summarize key points.

1. Quality Improvement Tools: TQM/CQI, Toyota Production System, Six Sigma

The three approaches we will describe can be used to improve all aspects of quality and are not targeted specifically at hazard prevention. Still,

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

TABLE D-1 Quality Improvement Approaches

Continuous Quality Improvement (Total Quality Management)

Origin

TQM: Japanese and U.S. manufacturing, 1950s/1980s

CQI: Berwick and Bataldan, 1980s

Purpose

Continuously improve quality by relentless focus on customer satisfaction

Core methodology

1. Plan a process improvement.

2. Do the intervention.

3. Study the results from the intervention.

4. Act on the results—if favorable by institutionalizing; if unfavorable by testing another intervention.

Key example

Ford Motor Company

Health care example

Institute for Healthcare Improvement; JCAHO accreditation requirement

Strength

Most widely dispersed and recognized improvement methodology

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

Toyota—Lean Production

Six Sigma

Toyota

Motorola in 1984

Lean production; endlessly reduce costs and lead time through elimination of waste

Achieve near zero defects (3.4 per million opportunities)

Rule 1. All work highly specified as to content, sequence, timing, and outcome.

Rule 2. Every customer-supplier connection is direct, with unambiguous yes-or-no way to send requests and receive responses.

Rule 3. The pathway for every product and service must be simple and direct.

Rule 4. Improvement must be made in accord with scientific method, under guidance of a teacher, at the lowest possible level in organization.

1. Define: Identify problems, clarify scope, define goals

2. Measure performance to requirements, gather data, refine goals

3. Analyze: Develop hypotheses, identify root causes, analyze best practices

4. Improve: Conduct experiments to remove root cause, test solution, measure results, standardize solutions, implement new process

5. Control: Establish standard measures to maintain performance and correct problems as needed

Toyota, Alcoa

General Electric

Pittsburgh Regional Healthcare Initiative

University of Virginia Health System; Virtua Health, New Jersey

Focus on elimination of waste, empowerment of frontline workers

Focus on near zero defects and control of gains once achieved

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

TABLE D-2 Proactive Hazard Analysis Approaches

 

Healthcare Failure Mode and Effect Analysis (adapted from FMEA)

Hazard Analysis and Critical Control Points

Origin

U.S. military, 1949, and NASA, 1960s

Pillsbury for NASA, 1959, to ensure safe food for astronauts

Purpose

To evaluate potential failures and their causes, pointing to actions to eliminate or reduce them

A systematic approach to the identification, assessment, and control of hazards

Core methodology

1. Define HFMEA™ topic.

2. Assemble HFMEA™ team.

3. Describe the process.

4. Conduct failure analysis.

5. Evaluate actions and outcome measures.

1. Conduct a hazard analysis.

2. Identify critical control points.

3. Establish critical limits for each CCP.

4. Establish monitoring requirements.

5. Establish correction actions when a CCP deviation occurs.

6. Establish ongoing verification procedures.

7. Establish record-keeping procedures.

Key example

U.S. auto manufacturing (FMEA)

Food manufacturing and services

Health care example

VHA

Medical device manufacturing

Strength

Adapted specifically for health care; model for JCAHO proactive risk assessment requirement

International standard in food sector; close interface with public-sector regulation; empirical evidence of effectiveness

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

Hazard and Operability Studies

Probabilistic Risk Assessment

United Kingdom—chemical industry, 1960s

Aviation industry

A team-based, systematic, and qualitative method to identify hazards (or deviations in design) in process industries

A tool to assess the contribution of multiple failures and combinations that may lead to catastrophic occurrences

1. Will someone be harmed? Who? In which way? How severely?

2. Will processes performance be reduced? In which way? How severely? What will impact be?

3. Will costs increase? If so, by how much?

4. Will there be cascading effects where deviation leads to other deviations? If so, what are they?

1. Development of a fault tree to visualize risk. Three elements: basic events, “AND” gates, “OR” gates.

2. Probability predictions are added to fault trees.

Chemical industry

Aviation, nuclear power

Telemedicine in European Union

Environmental health risk assessment

Compels parties to assess potential difficulties and devise mutually agreeable solutions

Models all combinations of failures that may lead to adverse events

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

advocates of each approach have examples where each has been used to achieve safety improvements. TQM is the earliest of the three approaches; the other two, Toyota Production System and Six Sigma, acknowledge their debts to TQM/CQI principles and techniques.

Total Quality Management/Continuous Quality Improvement

TQM/CQI is the earliest application of the scientific method to process improvement. TQM techniques have been applied widely in U.S. and Japanese manufacturing and in other organizations facing competitive challenges in a disciplined approach to enhance customer satisfaction. CQI is TQM applied within health care.

The method requires organizational leaders to establish improvement goals and to choose projects that can achieve specific improvements. Cross-functional teams devise a flow chart of a process under study and use data to understand variations from quality. The methodology regards errors as products of poorly designed systems, not as the fault of individual workers or “bad apples.” Once teams have developed a sophisticated understanding of a process, they start a four-step practice:

  1. Plan an intervention/experiment to improve the process.

  2. Do the hypothesized intervention.

  3. Study the results from the intervention.

  4. Act on the results—if favorable, by institutionalizing the intervention; if unfavorable, by testing another intervention.3

Organizations with robust CQI programs have many improvement teams working at all times. TQM was introduced to Japanese manufacturers in the 1950s by Deming, Juran, and others and to U.S. manufacturers in the late 1970s and 1980s. Berwick and others proposed TQM as an alternative to traditional quality assurance under the term “Continuous Quality Improvement.” CQI may be used to improve many organizational features beyond clinical quality, including patient satisfaction, error rates, waste, unit production costs, productivity, market share, and more. In the early 1990s, the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) embraced this new paradigm and included CQI activities in its requirements for accredited institutions.

Fifteen years after its introduction, however, CQI has not lived up to its promise to “cure health care.” Reviewing CQI’s history in 1998, Blumenthal and Kilo found accomplishments and disappointments.4 Among the former is a changed mind-set from assurance to continuous improvement, aban-

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

doning blame by focusing on system defects, creating a customer focus, motivating improvement projects across the nation, and educating thousands of health care workers in improvement techniques. Among its shortcomings has been an inability to identify a dramatically changed health care institution despite manufacturing examples such as Toyota; other problems include the failure to make deep inroads into clinical quality and a scant literature documenting sustained improvement.

CQI has been eclipsed by other methodologies, including the Toyota Production System, Six Sigma, and reengineering. Still, CQI remains the predominant quality improvement philosophy and methodology in the health care industry today.

Toyota Production System

Lean production focuses on elimination of waste—of materials, time, idle equipment, and inventory—to improve productivity and profits by improving material handling, inventory, quality, scheduling, personnel, and customer satisfaction. The core methodology as applied at Toyota is captured in four rules:

Rule 1: All work is highly specific as to content, sequence, timing, and outcome.

Rule 2: Every customer-supplier connection must be direct, and there must be an unambiguous yes-or-no way to send requests and receive responses.

Rule 3: The pathway for every product and service must be simple and direct.

Rule 4: Any improvement must be made in accordance with the scientific method, under the guidance of a teacher, at the lowest possible level of responsibility.

A key feature is the empowerment of line workers to implement design changes and to halt a process to avoid errors—turning workers into problem solvers. Although some initially thought Toyota’s success was tied to cultural differences between Japan and the United States, the company’s success in implementing the strategy in its North American plants neutralized that criticism.

Alcoa used the process to achieve one of the safest manufacturing sites for workers in the nation. Its head, former U.S. Treasury Secretary Paul O’Neill, helped establish the Pittsburgh Regional Healthcare Initiative in 1998, bringing stakeholders together to pursue perfecting the region’s health

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

care system—using the Toyota Production System (see case studies section later in this appendix). The initiative focuses on three goals:

  1. Patient safety: Reducing hospital-acquired infections and medication errors to zero.

  2. Clinical initiatives: Achieving breakthrough performance in cardiac surgery, depression, diabetes, orthopedics, and obstetrics.

  3. Perfecting patient care: Redesigning organizations to allow everyone to learn from errors and problems.5

Six Sigma

Six Sigma is a quality program that seeks to improve processes so that no more than 3.4 mistakes occur per million opportunities. One commentator describes its approach as “much like that of Total Quality Management, perhaps with a more aggressive goal.” Proponents suggest that the relentless focus on error reduction provides a structure and focus missing from other QI techniques. Six Sigma has a five-step improvement cycle corresponding to the acronym DMAIC with the aim to continuously reduce defects:

  1. Define by identifying problems, clarifying scope, defining goals.

  2. Measure performance against requirements, gather data, refine problems/goals.

  3. Analyze by developing hypotheses, identifying root causes, analyzing best practices.

  4. Improve by conducting experiments to remove root cause, testing solutions, measuring results, standardizing solutions, implementing new processes.

  5. Control by establishing standard measures to maintain performance and correcting problems as needed.

In 1984, Motorola engineers invented Six Sigma, named for a statistical measure of variation (1 Sigma reflects 690,000 defects per million opportunities; 2 equals 308,000; 3 reflects 66,800; 4 reflects 6,210; 5 reflects 230, and 6 reflects 3.4). The strategy achieved prominence in the 1980s at IBM and became widely known in the 1990s at General Electric, which claims dramatic error reduction and savings from its Six Sigma program. GE has applied Six Sigma to its medical device manufacturing division and to its employee health benefits program. GE also initiated its own Six Sigma health care consulting organization.6 The University of Virginia Health System and Virtua Health in New Jersey are two examples of health care adapters.

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

Chassin provides examples where medical care performs below one sigma, as in the documented 79 percent—or 790,000 out of 1 million—of eligible heart attack survivors not receiving beta-blockers. He also notes how the anesthesia community reduced deaths from rates of 50 per million in the 1980s to as few as 5 today. A proposed advantage of Six Sigma over TQM is the former’s focus on defects from perfect versus the latter’s focus on improvement from variation in a mean.7

2. Proactive Hazard Analysis Tools: FMEA/HFMEA™, HACCP, HAZOPS, PRA, RCA

PHA tools tend to be more prescriptive and to have more record-keeping and other requirements than QI tools. These requirements, justifiable when the objective is safety, are more onerous than needed for nonsafety improvement projects. Root Cause Analysis, though not explicitly proactive, is described here for comparative purposes.

Failure Mode and Effect Analysis/Healthcare Failure Mode and Effect Analysis

FMEA is a tool used in manufacturing to evaluate potential failures and their causes and to prioritize potential failures according to risk, pointing to actions to eliminate or reduce the likelihood of occurrence. The Veterans Health Administration (VHA) pioneered the adaptation of FMEA and other industrial process control tools to patient safety, developing the HFMEA™ for use in health care settings. This section describes HFMEA™ more than FMEA. Five steps are involved in an HFMEA™ analysis:

  1. Define the HFMEAtopic, including a clear definition of the process to be studied.

  2. Assemble the HFMEAteam, which should be multidisciplinary and include subject matter experts and an adviser.

  3. Graphically describe the process with a flow diagram, numbering each step, identifying the area on which to focus, and identifying all subprocesses.

  4. Conduct a failure analysis listing all possible failure modes, determining the severity and probability of each, using a decision tree to determine if the failure mode warrants further action, and listing all failure modes where the decision is made to proceed.

  5. Evaluate actions and outcome measures determining which failure modes to eliminate, control, or accept; identifying an action for each failure

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

mode to be controlled or eliminated; identifying outcome measures to test the redesigned process; and identifying an individual to complete the action.

FMEA was developed in the U.S. military in 1949 to determine the effect of system and equipment failures8 and was used by the National Aeronautics and Space Administration (NASA) in the 1960s to predict failures, plan preventive measures, estimate the cost of failures, and plan redundant systems in the Apollo space program. In the 1970s, U.S. manufacturers began using the tool in automotive and other plants, and automakers established industrywide FMEA standards in 1993. In 1998, the VHA established the National Center for Patient Safety to create a culture of safety in its hospital system. In collaboration with Tenet HealthSystem, VHA leaders developed the HFMEA™ as “a systematic approach to identify and prevent product and process problems before they occur.”

In July 2001, JCAHO implemented a new standard requiring all accredited hospitals to complete at least one “proactive risk assessment” of a high-risk process per year. The standard (LD.5.2) requires eight actions by hospitals:

  1. Select at least one high-risk process.

  2. Identify steps where failure modes may occur.

  3. Identify possible effects on patients.

  4. Conduct a Root Cause Analysis to determine why failures may occur.

  5. Redesign the process to minimize the risk to patients.

  6. Test and implement the redesigned process.

  7. Monitor the effectiveness of the new process.

  8. Implement a strategy to maintain the process.

VHA hospitals have proceeded the furthest in using HFMEA™ although many hospitals across the nation are now using this tool in meeting the new JCAHO standards.

Hazard Analysis and Critical Control Points

HACCP is a systematic approach to the identification, assessment, and control of hazards. While some definitions directly refer to food—reflecting the near exclusive use to date of HACCP in food production and service—the process is usable in the manufacturing, distribution, and use of any product or service that may experience safety problems. A “critical control point”

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

is a point, step, or procedure at which control can be exercised to prevent, eliminate, or minimize a hazard. Seven steps form the core of the HACCP approach:

  1. Conduct a hazard analysis preparing a list of steps in a process where significant hazards occur and identifying preventive measures.

  2. Identify critical control points—steps at which controls can be applied to prevent, eliminate, or reduce a safety hazard to acceptable levels.

  3. Establish critical limits for preventive measures associated with each identified critical control point.

  4. Establish monitoring requirements for each critical control point and procedures to monitor results to adjust the process and maintain control.

  5. Establish corrective actions to be taken when a critical limit deviation occurs.

  6. Establish procedures to verify on an ongoing basis that the HACCP system is working correctly.

  7. Establish record-keeping procedures to document the HACCP system.

HACCP was developed in 1959 by the Pillsbury Company to ensure the safety of food in the new U.S. space program. In 1973, the U.S. Food and Drug Administration (FDA) mandated the first use of HACCP by regulation for all low-acid canned foods after a public outcry over a botulism outbreak in canned soups. Following other foodborne illness outbreaks in the early 1990s, FDA expanded HACCP requirements for seafood and for fruit and vegetable beverages and is now considering HACCP for all foods under its jurisdiction. In 1997, the U.S. Department of Agriculture began implementation of HACCP in all meat and poultry operations under its jurisdiction. HACCP has also become the international food production and service safety standard.

Empirical research has demonstrated the effectiveness of HACCP in reducing levels of foodborne pathogens in food production and service in a wide array of settings. HACCP differs from the other QI/PHA approaches because of its significant and longstanding interface with public-sector regulation. The broad use of the tool results from government mandates more than voluntary compliance. Studies also have revealed weaknesses and gaps in HACCP implementation in the United States, demonstrating that effective implementation requires sufficient resources for regulatory authorities.

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×
Hazard and Operability Study

HAZOP is a team-based, systematic, qualitative method to identify hazards (or deviations in design intent) in process industries. The study begins with the team considering all ways a process might deviate from desired performance using guide words such as more, less, none, part of, and other than to ensure consistency and reliability. A team—typically one whose members designed and operate a facility—considers the consequences of each deviation from operating conditions by asking key questions:

  • Will someone be harmed? Who? In which way? How severely?

  • Will the performance of the processes be reduced? In which way? How severely? What will the impact be?

  • Will costs increase? If so, by how much?

  • Will there be any cascading effects where this deviation leads to other deviations? If so, what are they?

After this process, the team develops an action plan to eliminate or minimize deviations and their consequences. The technique seems to work because key parties to the process are present—designers and operators, as well as builders and maintainers. The HAZOP approach has been helpful in avoiding breakdowns in contractual relationships arising from lack of understanding of what elements are truly important and susceptible to unrecognized threats; it compels both parties to assess potential difficulties and devise mutually agreeable solutions. Another advantage is that it encourages the team to consider less obvious ways in which a deviation may occur. The HAZOP process was developed by ICI Ltd. in the United Kingdom in the 1960s to assess potential hazards of chemical plants to their operators and the public. Its use has been adapted for a range of other industries, including water.

Probabilistic Risk Assessment

The PHA tools already described are designed to eliminate or mitigate potential hazards or failures emanating from a single cause. Reliability and safety analysts in the aviation and other high-risk industries realized a need for a tool to understand multiple failures or combinations of failures that could lead to catastrophic occurrences. PRA was identified as an analysis tool that allows risks to be visualized in ways not possible with FMEA, HACCP, or HAZOP by adding two additional benefits: hierarchical model-

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

ing through fault trees and the assignment of probabilities. The fault tree framework graphically illustrates risk and reliability, and the assignment of probabilities allows regulators to establish threshold limits of risk and to specify safety and reliability standards.9 Unlike the other tools presented herein, PRA does not include process steps to operationalize its use for managers and workers. This tool, instead, may be used in combination with other approaches as a powerful addition to identify elements of a process most critical for safety improvement.

PRA has been used most frequently to assess risks in catastrophic, low-probability events such as nuclear power plant meltdowns, space shuttle accidents, and earthquakes. In recent years, analysts have begun to explore its applicability to estimating environmental health risks.10 More recently, analysts have begun using PRA to model high-impact, low-frequency iatrogenic injury events in medical care. Marx and Slonim suggest FMEA and Root Cause Analysis (to be described) “are limited in their focus to a single failure or single event” and “are not designed to assess the combinations of risk that, for example, may occur in the medication delivery process between the physician, the pharmacist, the pharmacy technician, the unit clerk and the dispensing nurse…. PRA, by comparison, would identify all combinations of failures including the initial profiling error and the failure of safety nets that might otherwise prevent the adverse event.”11

Few specific examples are available of PRA used for patient safety. The most extensive is an application to anesthesia patient risk described later in this appendix.

Root Cause Analysis

RCA is a qualitative, retrospective approach to error analysis that is widely applied to major industrial accidents. Root Cause Analyses search out latent or system failures that underlie adverse events or near misses. In 1997, JCAHO mandated use of RCA in investigation of sentinel events in its accredited hospitals. Key steps in an RCA include formation of interdisciplinary team, data collection, data analysis—establishing how and why the event happened through identification of latent and active failures, and identification of administrative and systems problems for redesign. Although regarded as retrospective, effective RCAs point toward correction of systems problems to prevent future errors or near misses. Because RCAs are uncontrolled case studies, they may be tainted by hindsight bias.

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

III. KEY COMMON PRINCIPLES AND ATTRIBUTES OF QI/PHA METHODOLOGIES

Literature review and interviews with quality experts lead to the conclusion that the QI and PHA approaches described here have more similarities to than differences from each other. The essential feature of each is the application of the scientific method to process analysis, management, and improvement. This approach is rooted in the statistical analytic tools developed by Shewhart in the 1930s, applied in war production in the 1940s, introduced to Japanese manufacturing by Deming and Juran in the 1950s, and adopted by U.S. manufacturers beginning in the 1970s and 1980s.

Each tool has attributes distinguishing it from the others. These features merit study and consideration by potential users. No empirical literature has proven any single approach to be “the best,” though HACCP has undergone more rigorous scrutiny than any other methodology.12 Each can be—and most have been—used in health care settings to perform hazard analysis. QI and PHA tools also may be used in combination; for example, at Virtua Health in New Jersey, FMEA is used for planning purposes to identify a high-risk, hazardous procedure on which to use Six Sigma to implement and sustain a process improvement.13 Tools developed outside of medical care must be adapted to fit the requirements of this sector. Any QI/PHA tool will undergo some adaptation to fit into an organization’s culture, structure, and individual requirements.

Key features found in all or at least one of the methodologies are identified as follows:

Features Common to All QI/PHA Approaches
  • Scientific approach to process analysis/system improvement

  • Decision making driven by data

  • Process focus: Use of flow diagrams

  • Improvement focus rather than reliance on external standards

  • Preventive orientation: Fixing quality problems or hazards before errors are committed

  • Interdisciplinary team focus

Features Common to All QI Tools
  • Customer focus (internal and/or external) as determinant of quality

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×
Features Unique to Specific QI Tools
  • Waste reduction focus: lean production

  • Empowerment of frontline workers: lean production

  • Reducing errors to near zero: Six Sigma

  • Focus on control phase to maintain improvement: Six Sigma

  • Companion methodology to overcome organizational resistance (change acceleration process): Six Sigma

Features Common to PHI Tools
  • Proactive identification of potential safety hazards and control mechanisms

Features Unique to Specific PHA Tools
  • Development of hazard score matrix: HFMEA

  • Significant interface with public-sector regulation: HACCP

  • Identification of Critical Control Points for ongoing hazard measurement and prevention: HACCP

  • Tool develops status as international standard: HACCP

  • Asking leading/open-ended questions to identify hard-to-identify hazards: HAZOP

  • Ability to analyze multiple or combinations of failures: PRA

  • Hierarchical modeling through fault trees: PRA

  • Assignment of probabilities: PRA

In researching this report, the authors were struck by the extent of experimentation in the medical community—in the United States and beyond—in adapting and applying these QI and PHA approaches to patient safety problems. We were unable to identify any central “clearinghouse” to enable health professionals to become familiar with these different approaches and to learn lessons from their adaptation through case studies or other methods. We suggest that the health care community—especially professionals and institutions interested in patient safety and harm reduction—would benefit from the existence of a central resource or clearinghouse on experimentation using various structured improvement tools and methodologies.

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×
Key Principles to Identify Critical Control Points: Conceptual and Methodological Considerations in Design and Evaluation of Risk/Hazard Identification, Assessment, and Management Strategies in Health Care

Central to all QI/PHA approaches is the use of the scientific method in the analysis, management, and improvement of work processes. Research by Ackoff suggests that in any system a small set of subprocesses accounts for the “identity of the system.” He defines a system as a whole that contains two or more parts satisfying five conditions:

  1. The whole has one or more defining functions.

  2. Each part in the set can affect the behavior or properties of the whole.

  3. There is a subset of parts that is sufficient in one or more environments for carrying out the defining function of the whole; each part is separately necessary to carry out the defining function.

  4. The way the behavior or properties of each part affects its behavior or properties depends on a behavior or property of at least one other part.

  5. The effect of any subset of parts on the system depends on the behavior of at least one other subset.14

The third condition posits that each system consists of a small number of essential processes without which the system itself cannot function.

Research by Batalden and Mohr applies this insight to medical care, demonstrating that medicine also consists of distinct and identifiable “core processes” that can be mapped, analyzed, and improved.15 Moreover, in many clinical specialties and subspecialties, the Pareto Principle holds: A small number of core processes account for a high proportion of total work performed within each specialty. James observed that in respiratory therapy five key processes (such as oxygen therapy) account for as much as 90 percent of total work; in physical therapy, four key processes account for the same overall volume of activity.16

Examining medical care through the lens of key processes provides a helpful way to consider systemic improvement. One potentially fruitful way to do this is through medical specialties. For example, anesthesiology is acknowledged as the leading medical specialty worldwide in addressing patient safety. A medical malpractice crisis in the 1970s galvanized anesthesiologists at all levels, including grassroots clinicians, to address patient safety by incorporating new technologies, standards, and guidelines and to confront problems relating to human factors and systems issues. As part of this effort, in 1985 the profession established the Anesthesia Patient Safety Foun-

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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dation, the first such initiative in organized medicine.17 As a result of their improvements, some have pegged the death rate from errors in anesthesiology at about 5 deaths per 1 million opportunities, which now approaches Six Sigma level. A recent literature review casts doubts on that level of success,18 though there is broad agreement on dramatic improvements in safety that have led to dramatic decreases in malpractice premiums.19

A core requirement in applying any QI/PHA tool effectively to improve patient safety is to build data, measurement, and control systems around key processes. HACCP demonstrates that every process contains multiple Critical Control Points that will vary, process to process. Two preconditions are necessary to identify Critical Control Points in medical care: first, the identification of core processes, and second, the availability and accessibility of data. Then it becomes more feasible to identify Critical Control Points and to eliminate or minimize hazards.

Since the publication of To Err Is Human, most interventions to enhance patient safety have focused at the institutional level—hospitals, nursing homes, and clinics. As institutions seek to incorporate patient safety initiatives, a key challenge is to win the attention and support of physicians. The identification and control of Critical Control Points in medical care, along with the striking example of anesthesiology, suggest that a parallel—and potentially more successful—approach to rigorous PHA may be through medical specialties and subspecialties in addition to institutional strategies. One clear advantage, demonstrated by the anesthesiology experience, is the potential application of systems improvements on a global basis.

Recent developments in organized medicine support this direction. Brennan reports that the European Federation of Internal Medicine, the American Board of Internal Medicine, and the American College of Physicians/American Society of Internal Medicine have recently outlined a draft physician charter with new major principles and professional responsibilities. The third draft responsibility suggests a new commitment to improve the quality of care not just for individuals but for all patients collectively, a notion Brennan refers to as a new “civic responsibility.” He writes: “The failure of the quality measurement/improvement movement to reach its full potential may reflect the relative failure of the profession to undertake, as a civic activity, the effort to ensure the quality of care defined broadly…. Civic professionalism suggests that the professional should be leading the way, not being brought along by regulations…. For this step, we must likely turn to the various specialty societies…. If we are to be serious about educating practicing physicians about professionalism and quality, we must rely on a strong confederation of specialty societies and groups.”20

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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IV. APPLICABILITY OF HAZARD ANALYSIS AND SYSTEMS APPROACH TO ADVERSE EVENT PREVENTION

Croteau and Schyve21 identify essential steps in Proactive Hazard Analysis to prevent adverse events:

  1. Identify a high-risk process.

    1. History of adverse outcomes;

    2. Identified in the literature as high risk;

    3. Has several characteristics of a high-risk process;

    4. New process;

    5. Proposed redesign (such as in response to a sentinel event).

  1. Create a flowchart of the process as designed.

  2. Assess the actual implementation of the process (e.g., different locations, shifts).

  3. Identify where there is, or may be, variation in the implementation of the process; that is, what are the failure modes?

  4. For each identified failure mode, what are the possible effects?

  5. Assess the seriousness (i.e., the “criticality”) of the possible effects (e.g., delay in treatment, temporary loss of function, patient death).

  6. For the most critical effects, conduct an RCA to determine why the variation (the failure mode) leading to that effect occurs.

  7. Redesign the process and/or underlying systems to minimize risk of that failure mode or to protect the patient from the effects of that failure mode.

  8. Conduct a PHA on the redesigned process with special attention to how the redesigned steps will affect other steps in the process and whether they will continue to achieve the beneficial things that the previous design could do.

  9. Consider simulation testing of the redesigned process.

  10. Consider a pilot test of the redesigned process.

  11. Identify and implement measures of the effectiveness of the redesigned process.

  12. Implement a strategy for maintaining the effectiveness of the redesigned process over time.

The identification of failure modes and quality management deficiencies must lead to the development and institution of reasonable interventions to prevent adverse events. Multidisciplinary teams composed of an equitable mix of frontline health care workers (e.g., clinicians, safety/facility

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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personnel, environment services) and mid- and upper-level management must promote a pervasive, patient-centered safety culture of adverse event prevention, not individual blame. In keeping with James Reason’s “swiss cheese model of error,” PHA and quality management programs must identify “latent” errors as well as the more apparent “active” errors. Systems redesign to prevent all such errors should then be based on a balanced utilization of evidence-based technology, training, ongoing education, and consensus standard operating procedures (SOPs) and “best practices,” keeping in mind each human’s inherent cognitive (e.g., memory recall) and physical (e.g., fatigue) limitations.

Lastly, health care must recognize that adverse event prevention is an ongoing process. Each new system intervention brings with it a whole new set of potential failure modes and contributing factors that should be similarly proactively analyzed and prioritized for intervention. This, combined with the ever-widening scope of system complexities due to an aging patient population, increased numbers of the immune compromised, and the need to “fast track” new and more effective technological advances in medicine, raises the need to handle health care’s current “patient safety paradox” with an organized, proactive collective consciousness.

V. DATA REQUIREMENTS AND MEASUREMENT TOOLS TO SUPPORT EACH METHODOLOGY

How can data be employed to do prospective identification of risk points without waiting for a near miss? Pareto charts (histograms), run charts, control charts, and scatter grams are among the more widely used tools to exemplify performance data. Despite the inherent strong points and weaknesses of a respective tool, the reliability, defensibility, and reproducibility of the underlying performance data must be paramount. To maximize the accuracy and precision of such data and to facilitate standardized use throughout all health care, performance measures must be the result of a well-thought-out process to maximize efforts to exceed customer expectation and consistent error and failure definition.22

To facilitate and standardize measurement, Chang proffers an error taxonomy consisting of four subclassifications of error: impact, type, domain, and cause. The “impact” classification deals with the outcome or effect of the error; the “type” concerns the visible process that was in error; the “domain” is where the error occurred and who was involved; and the “cause” is the factors and agents that bring about error. Establishing subclasses for respective errors can not only help in defining and standardizing perfor-

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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mance measures, but more importantly, it significantly facilitates the identification of corresponding failure modes and consequently the use of PHA and QI methods. Consistently (i.e., globally) defined errors across an entire health care setting enable a collective patient safety consciousness to address potential errors proactively, rather than retrospectively. Retrospective analysis, although necessary and insightful at times, is still retrospective—relying on performance measures of accidents/incidents or near misses, which themselves are the products of, or promote, hindsight bias and a host of other potential unwanted consequences.

VI. CASE STUDIES

Continuous Quality Improvement

Two studies illustrate the promises and shortcoming of CQI.

Concerns about the quality of health care in France led to the creation, in 1991, of a national agency for health care quality, Agence National pour le Développement de l’Evaluation Médicale (ANDEM). In 1997, ANDEM became Agence Nationale pour l’Accréditation et l’Evaluation en Santé (ANAES). Between 1995 and 1998, ANAES sought to increase hospital management’s awareness of CQI and to study its implementation in public hospitals. In 1995, a call was issued for projects on patient safety concerns such as nosocomial infections and incidents after anesthesia and blood transfusions. A second call for projects issued in 1996 was open for all project types. Selected projects received a financial incentive of between $10,000 and $80,000. Juries were composed of 12 to 14 individuals with experience in quality selected projects.

From 260 first-round project applications, 29 were selected and 26 were evaluated. Nine projects addressed prevention of nosocomial infections, five addressed medical records management, five addressed anesthesia safety, four addressed blood transfusion safety, four addressed drug dispensing safety, and two addressed controlling violence in psychiatric units. At evaluation, 38 months after initiation, 61 percent of the patient safety projects had met their objectives, and more than 50 percent of participating hospitals had established new CQI projects following the initial one. Half of the project team leaders considered that, at the time of the final evaluation, their main performance indicator (e.g., number of falls, number of nosocomial infections) had begun to evolve satisfactorily. Overall evaluation of this project is limited by the noncomparative nature of the study, which was

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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managed by highly motivated individuals in institutions that voluntarily applied to participate.23

By contrast, a randomized controlled trial called Improving Prevention through Organization, Vision and Empowerment (IMPROVE) was conducted by two competing health maintenance organizations (HMOs) in the Minneapolis–St. Paul area to test the hypothesis that an HMO can use CQI to stimulate private primary care clinics to develop better delivery systems for eight clinical preventive services: blood pressure monitoring, Pap smear, cholesterol monitoring, tobacco use cessation, breast examination, mammography, influenza vaccine, and pneumococcal vaccine. Forty-four clinics were randomized with follow up involving 3,136 patients from the control clinics and 3,295 from the intervention clinics. The intervention was conducted between September 1994 and June 1996.

All 22 intervention clinics established improvement teams, and all training sessions received excellent evaluations. At the end, 94 percent of the 114 clinic team members reported being very satisfied or satisfied with their experience. Results showed no significant difference between intervention and control clinics on any of the clinical measures except for blood pressure. Except for two small differences between the intervention and control clinics, CQI failed to produce any significantly greater improvement in the intervention clinics during the trial. “Our study raises questions about whether CQI is the right model for making these changes.”24

Hazard Analysis and Critical Control Points25

Morrison Management Specialists, a member of the Compass Group, is a leading provider of food service expertise to the health care industry. Morrison services approximately 500 health care facilities nationwide, including hospitals, long-term care facilities, and senior dining communities. Mary Ivins, Director, Dining on Call, is responsible for Morrison’s program that provides patients with room service–style food delivery and for implementing and overseeing Morrison’s food safety policies and procedures.

Morrison’s use of HACCP is pivotal to minimizing foodborne illness risks and maximizing quality service. She sees the results from using HACCP as favorable but admits that Morrison’s biggest challenges are ongoing education and training of employees in proper food service procedures and simplifying the HACCP process.

Because many food preparation processes and subprocesses are similar, Morrison uses HACCP to identify as many hazards as possible throughout

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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the entire food production process—from receiving food products from vendors, through holding, storage, cooking, and preparation, through service to the patients/residents, visitors, and workers. Ivins points to a number of types of entrees in which HACCP has made a significant impact in reducing the risk of foodborne illness. She believes HACCP has been used to safely prepare ground meat and chicken in a way that prevents diseases, including E. coli, salmonella, and campylobacter; to use luncheon meats safely; and to develop methods to ensure the safe cold storage of hot foods.

To keep the program on track, Morrison uses a comprehensive system of internal and third-party auditing with customer service feedback. Critical Control Points and corresponding acceptable physical constraints are established for each part of the process. Temperature, holding/storage time, storage location (e.g., raw food stored below cooked foods), labeling criteria (e.g., name and expiration date), as well as strict guidelines for cleanliness, disinfection, and hygiene of both the facility and food service workers are all important criteria within Morrison’s HACCP compliance process. Morrison also audits its vendors by checking and documenting the temperature of a minimum of 10 percent of all potentially hazardous foods at the time of delivery by the vendor.

Morrison’s regional directors of operations are responsible for ensuring that each account is monitoring and documenting compliance with HACCP guidelines. Quality assurance management plays a key role, including keeping a policies and procedures manual readily available, posting food safety signage and professional information, and providing a certified food safety manager at each site. All monitoring and documenting are vital to complying with current HACCP guidelines and in determining if the processes and HACCP should be modified to mitigate variations of existing or newly identified hazards.

Ivins sees the long-term gains from Morrison’s use of HACCP as the comfort of knowing the company is serving quality, safe food to patients/ residents, clients, and employees and a well-educated supervisory and service staff.

Hazard Analysis and Operability Studies

HAZOP has been successful in identifying security threats in certain safety-critical information and communication technology systems.26 CORAS27 (risk assessment of security critical systems) has used HAZOP for information security risk analysis involving medical databases and telemedicine. Areas include (1) authentication procedures (e.g., password poli

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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cies, authentication mechanisms); (2) threats related to unauthorized change of information while produced, transmitted, or stored; (3) threats related to availability of service and information; and (4) threats related to network availability.

Whether used with or without other PHA techniques, HAZOP is an integral part of the CORAS risk management process, specifically the identification of threats involving confidentiality, integrity, and availability for a Web-based telecollaboration service. In at least one proactive risk analysis, CORAS used both fault tree analysis (FTA) and HAZOP. After identifying the threats using HAZOP, the threats were inserted into three fault trees (confidentiality, integrity, and availability) to better visualize the interrelationships among the respective threats.

Prior to conducting a HAZOP, CORAS identified areas of relevance on which the risk assessment and specific security aspects should focus, as well as worst-case threat scenarios. These “targets” and aspects—as well as the experiences of previous assessments—facilitated selection of guidewords for the HAZOP. CORAS’s process also uses diagrams (use-case diagrams, sequence diagrams, collaboration diagrams, activity diagrams) of the most important issues. The HAZOP is run as a structured brainstorming session with participants who include developers, providers, and end users (e.g., hospital/medical staff). The diagrams and the HAZOP table are shown on side-by-side screens as the HAZOP is conducted. This enables the team to focus on each threat to be assessed. Although the HAZOP session was used to identify threats, consequences and frequencies also were assessed.

Eva Skipenes, Security Adviser, Norwegian Centre for Telemedicine comments:

“The HAZOP method is very useful to identify and document threats and unwanted incidents, and to gather as much information as possible from different participants. It is easy for nontechnologists to follow this method, but it requires good planning (choice of guidewords, choice of which aspects to focus on, and which detail level to use). A good result also depends on the availability of important stakeholders, like the users of the system/service, and the providers (both technical and, for example, medical service providers). A HAZOP often identifies threats at very different levels of detail. The use of fault trees afterwards to identify the relationships among the identified threats was very useful.”

Skipenes adds that CORAS and NTS will use HAZOP again. NTS is using it for risk assessment of telemedicine services and information security at primary health care centers in North Norway.28

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Failure Mode and Effect Analysis

The Detroit Medical Center (DMC) in Michigan has successfully used FMEA to identify and mitigate a number of patient safety issues throughout its health system. One such endeavor was initiated as a result of a nationally publicized event concerning “gaps” in the recall process involving Olympus bronchoscopes at a major teaching hospital. The failure of a recall notice to be delivered from a teaching hospital’s loading dock area to the clinical areas where the bronchoscopes were used on patients led to contaminated bronchoscope use in patients and subsequent nosocomial infection. Prompted by this report and the potential for use of recalled equipment and drugs in patients if the recall process fails, Tammy S. Lundstrom, M.D., and associated staff at DMC prioritized their current recall process for FMEA. DMC’s “recall-FMEA” is based on internal near-miss data and/or events that have been reported in the media from throughout the United States or from other event databases, such as JCAHO Sentinel Event Alerts or Institute for Safe Medication Practices.

DMC’s recall-FMEA team is composed of staff from stakeholder departments, including logistics, pharmacy, operating room, invasive procedure areas, materials management, environment of care, epidemiology, purchasing, and respiratory therapy. A core group was chosen to perform the actual FMEA with input from affected areas. Criteria considered for inclusion in the recall-FMEA team included:

  • People who have experience with the recall process;

  • People who regularly perform steps in the recall process;

  • People who have no experience with the process (a reality check);

  • A subject matter expert (procurement personnel in charge of recall process);

  • Quality department facilitator.

DMC gathered relevant information related to the recall process, including current internal procedures/policies/guidelines related to recalls, a search for any external or professional society guidelines and best practices, development of a professional organization resource list, and interviews of key staff and departments regarding the current process.

Next, DMC developed a process flow chart. Because the recall process itself is such a huge undertaking, the team narrowed the scope of the FMEA to include only that part of the process related to internal departmental responses to recall notices, with the understanding that once this FMEA was completed, the scope would be expanded. Likely failure modes were identi-

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

fied and scored for criticality by assigning a hazard score related to severity of the failure mode and probability of the failure mode (each using a 1 to 4 rating). Subprocesses with a total hazard score of 8 or greater were chosen for redesign. A total of 15 subprocesses were identified, of which 11 had a hazard score of more than 8. Six of these involved the internal departmental response and therefore were chosen for improvement efforts. For example, internal processes involve delivery of DMC recall notices via e-mail notification. The failure mode included involved departments failing to receive the e-mail notification; the likely cause identified was failure to include the appropriate department/individual on the department’s procurement e-mail notification list. The effect was that the involved department/individual would not know of the recall, and faulty equipment/drugs would be used on patients. The solution was to identify a point person in purchasing who would be responsible for maintaining the notification list. That procurement staff member was responsible for contacting department heads at all facilities to verify names of point people in each department with the ultimate responsibility of responding to the e-mail recall notice. Redundancy is built in by including department heads, and list maintenance is performed on an ongoing basis to ensure accuracy.

DMC’s ability to identify multiple processes and subprocesses as likely fail points in its current recall process, and therefore potential unexpected clinical events involving patients, was decisive in DMC considering the FMEA to be a success. This success was facilitated by DMC’s decision to narrow the scope of the FMEA to include only a portion of the recall process, with the goal to expand the scope once the initial subprocess was rectified. The first phase of the recall-FMEA was started on October 9, 2002, and completed on December 10, 2002. Action items were scheduled to be closed out by the end of April 2003, and the next phase of the recall process will be targeted for improvement.

The FMEA has resulted in DMC improving the timeliness and accuracy of its targeted recall notification. The response rates to recalls (e.g., whether a product was not used, returned to logistics, or pulled for pickup) increased by a factor of three over the previous recall notification process. Efforts are ongoing to refine the process further in order to have response rates of 100 percent.

Dr. Lundstrom considers the recall-FMEA a success. She stresses that “although the FMEA process is time consuming, prioritization of improvements through narrowed scope and hazard scoring focuses improvement efforts on the critical elements.” Dr. Lundstrom and her staff would use the FMEA process again.

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Healthcare Failure Mode and Effect Analysis

Failure mode and effects analysis helps to anticipate what can go wrong with a high-risk health care process and to apply measures to prevent the error. Industries such as aviation, aerospace, and automotive manufacturing have long used failure mode and effects analysis to prevent accidents from occurring, but there is only one model specific to health care. That model, called Healthcare Failure Mode and Effects Analysis, was developed by the Department of Veterans Affairs (VA) National Center for Patient Safety and first put into practice in 2001.

The VA’s HFMEA™ model is a five-step process that involves selecting a topic for analysis, selecting a team to do the analysis, mapping a flow chart of the high-risk process, identifying failure modes within the process, and, if necessary, redesigning the process.

In its first application of HFMEA™ the VA asked its 163 medical centers to use HFMEA™ to analyze their contingency plans for their computerized, bar-code medication administration systems in the event of a power failure or other interruption to the system. The process was a valuable exercise, VA officials say, and revealed vulnerabilities to facilities’ contingency plans and prompted facilities to make changes to prevent problems from occurring.

For example, some facilities learned that they wrongly assumed that data backups of their computerized bar-code systems were performed more frequently than every 24 hours. In the event of a power failure, newly entered data such as a change in a patient’s medication may not have been included in the data backup, and the patient could be at risk of receiving an incorrect medication order. HFMEA™ teams recommended redesigning the process by requiring more frequent data backups of their facilities’ electronic medication records and providing a mechanism to let staff know when the backup is completed.

The HFMEA™ process helped the teams identify other gaps in the contingency plans by asking the following questions:

  • Do caregivers know how to access and use their contingency plans for the medication administration system?

  • Is a process in place to stop new referrals to a unit, if necessary, when the electronic medication administration system is unavailable?

  • Is there a procedure to request additional staff if necessary to help implement the contingency measures?

  • What process is in place to ensure that once the electronic system is restored, any information about a patient’s medications that is recorded manually during a power failure is available to caregivers?

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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  • How are new medication orders recorded while the electronic system is unavailable, and how are they entered into the system when it is restored?

  • How much data from the patient’s medication history should be provided when paper backup records are needed?

  • Without some parameters on the amount of information needed from paper backup records, several facilities realized they could end up with complete paper records of 100 or more pages for some patients.

Although the HFMEA™ teams addressed the same topic, each designed its own solutions to the questions raised by the analysis. VA facilities are now on their own to select topics for a proactive risk assessment in 2003. Topics selected include reporting of laboratory or radiology results, patient identification procedures, and patient backlogs for procedures. The VA’s first experience with HFMEA™ also provided the agency with additional lessons to improve the process for proactive risk analysis. Some of the lessons learned from the VA’s first application of HFMEA™ include the following:

  • Assign an HFMEA™ team member the task of mapping the flow diagram before the team’s first meeting. This ensures that the team moves in the right direction from the start.

  • Ensure that the steps to a process are numbered and the subprocesses are lettered. These simple measures help to keep the HFMEA™ team organized and prevent the team from overlooking potential failure modes.

  • Limit the flow diagram of the process to no more than 10 to 12 steps; otherwise the diagram gets too large.

  • Make testing of proposed changes a formal part of the HFMEA™ process. Testing can evaluate whether any of the proposed changes introduce unintended consequences.

Additional information and tools for HFMEA™ are available from the VA National Center for Patient Safety Web site (http://www.patientsafety.gov).

Probabilistic Risk Assessment

In the only published study of Probabilistic Risk Assessment and patient safety that we could identify, Dr. Elisabeth Paté-Cornell extended PRA—called “engineering risk analysis”—to the study of anesthesia patient risk to show how this tool can incorporate human and organizational factors to support patient safety decisions before complete datasets can be gathered and in cases where key factors are not directly observable.29

In assessing the risk of severe anesthesia accidents, technical failures

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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such as machine malfunctions can be easily identified and corrected. Indeed, most of the progress in improving anesthesia safety over the past 25 years has been attributed to identifying and correcting technical risks. Risks attributable to human errors are more difficult to detect, characterize, and anticipate because statistical samples are seldom available, such as the risk of injury due to substance abuse by the anesthesiologist. Gathering these data is difficult.

Accidents are divided into scenarios formed of “basic events,” and a Bayesian approach is used to assess probabilities and consequences of each type. Probabilities are developed from three sources: existing datasets, analysis of basic engineering properties of the systems, and expert opinions. Expert opinions, when well defined and encoded, provide essential information that could not be obtained in time to support urgent decisions.

Seven initiating events were identified: breathing circuit disconnect, esophageal intubation, nonventilation, malignant hyperthermia, inhaled anesthetic overdose, anaphylactic reaction, and severe hemorrhage. Probabilities per operation were assessed. Experts identified types of problems that could affect the performance of anesthesiologists and the rates of occurrence. Analysts then recomputed the probability of each patient accident for each problem type:

  • Problem-free: 0.53

  • Fatigue: 0.10

  • Cognitive problems: 0.04

  • Personality problems: 0.04

  • Severe distraction: 0.03

  • Drug abuse: 0.03

  • Alcohol abuse: 0.04

  • Aging/neurological problems: 0.03

  • Lack of training: 0.12

  • Lack of supervision: 0.04

Figures show estimated probability of the state of the anesthesiologist per operation.

Experts identified policies to decrease the probability of each problem. The distribution of practitioner problems was then used to compute the anticipated benefits from each measure. Whereas alcohol and drug problems had been at the forefront of concerns at the outset of the study, the more immediate and less visible problems were supervision of residents and

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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problems of incompetence. The results were “interesting” because they did not correspond to the initial motivation of the sponsors (the Anesthesia Patient Safety Foundation), who were concerned about drug abuse and behavioral problems. “The major contributors to the problems are much closer to home and the most beneficial measures are mundane, such as better supervision of residents and periodic retraining of all practitioners so that they get familiar again with situations that they may have forgotten because they only rarely occur.”

Root Cause Analysis

A recent article in the Quality Grand Rounds series as presented in the September 3, 2002, issue of the Annals of Internal Medicine, deals with a patient who suffered multiple adverse events consistent with cascade iatrogenesis. This case raises two important quality issues: Can health care improve the reliability and accuracy of interpretations of diagnostic tests, and should health care regulate the introduction and use of new technologies? It also brings to light limitations to routine use of RCA to identify remediable errors or to better prevent those system errors when the causal pathway to an apparent adverse medical outcome has not been definitively established. In this case there is a question as to whether RCA would yield improved systems for patient care. Despite multiple opportunities to identify errors in the patient’s care, the decisions or circumstances associated with these adverse events contributed to the outcomes in uncertain ways and are not easily classified as clear-cut errors. If the recommendations of such an ill-conceived RCA are based on unreliable assessment of causality, a Root Cause Analysis can do more harm than good.

In the case, a 40-year-old woman with a history of type B aortic dissection, renal insufficiency, poorly controlled hypertension, erratic adherence to prescribed treatment regimen, and cocaine use was to be evaluated for dyspnea and swelling of her left breast and arm. At initial presentation, the findings seemed consistent with deep vein thrombosis of the upper left extremity and pulmonary embolism associated with a hypercoagulable state due to possible left-sided breast cancer. In contrast to the initial read (by a radiology resident) of a spiral computed tomography (CT) scan as negative for pulmonary emboli, the attending radiologist identified segmental emboli in the lungs, chronic type B aortic dissection, and a huge pericardial effusion when reading the scan the next morning. Based on this read, the patient was treated with intravenous heparin and oral warfarin. Mammography revealed no evidence of breast cancer and ultrasonography of the left arm found no

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

deep venous thrombosis. After one week of hospitalization, another attending radiologist, one with expertise in imaging pulmonary emboli, reevaluated the original CT scan and found it to be negative for pulmonary emboli—a read consistent with the initial read by the radiology resident. The authors point to this portion of the case as highlighting the need for a general strategy to improve the reliability of radiographic interpretation and introduce new medical technologies (i.e., spiral CT scan) instead of using more well-studied, albeit more resource-intensive, diagnostics such as ventilation perfusion scanning or pulmonary arteriography. The authors see the diagnostic uncertainty regarding the use of the spiral CT scan as pointing to an apprehension, namely the appropriateness of integrating new health care technologies prior to sufficient supporting evidence.

With pulmonary embolism having been ruled out, physicians debated whether pericardiocentesis under cardiographic guidance should be performed in an effort to explain the patient’s dyspnea and arm and breast swelling. Unfortunately, the patient’s anticoagulant therapy had not been discontinued in time to permit the procedure to be performed on the more desired day, Thursday. Instead the pericardiocentesis was performed on a Friday evening by another competent cardiologist with a full complement of catheterization laboratory personnel. Because of some of the patient’s preexisting complications and the formation of a hemopneumothorax during the process, the patient went into cardiac arrest with pulseless electrical activity. The patient was successfully resuscitated after 10 minutes and a pericardial window and pleural and pericardial drains were surgically inserted.

Using RCA, one is inclined to look at the decision to perform pericardiocentesis. Was it wrong to perform the procedure? Was it wrong to perform the procedure on a Friday evening? The authors suggest the decision to go ahead with the pericardiocentesis, even if problematic in retrospect, does not suggest a clear preventive solution to the breakdown in decision making. In contrast, the failure to discontinue the anticoagulation therapy in a timely manner is an error of omission. In retrospect and knowing the outcome already, an observer could be tempted to label the pericardiocentesis an error of commission, arguing that watchful waiting would have been a more reasonable alternative because the patient’s symptoms were stable. But watchful waiting could still lead to cardiac arrest due to tamponade over the weekend, implicating an error of omission. This is a good example of an RCA influenced by hindsight bias and a case where the overall outcome of the patient may not have been improved by any intervention that would prevent the decision to conduct pericardiocentesis.

Several evenings after the patient seemingly recovered from the cardiac

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

arrest and pericardial window insertion, she developed right-sided pleuritic chest pain and relative hypotension. Two days earlier, based on the unlikelihood of recurrent pericardial effusion (with the pericardial window in place), the patient’s mediastinal drain was removed. Again considering the possibility of pulmonary embolism and in an effort to diagnose the patient, the residents initiated intravenous heparin and a repeat spiral CT scan. Later that same morning, the patient’s attending physician discontinued the anticoagulant medication. An emergency echocardiography revealed a large thrombus in the pericardium compressing the left atrium of the heart. The patient subsequently suffered a second cardiac arrest with pulseless activity while undergoing the echocardiography. An emergency sternotomy was performed; then the pericardial clot was evacuated and a laceration of the left ventricle was repaired. On the second day in the intensive care unit, the patient developed R-on-T phenomenon, followed by torsade de pointes tachycardia and subsequent pulseless ventricular tachycardia, requiring intubation, defibrillation, and amiodarone therapy. Laboratory results revealed the patient’s renal function and metabolic acidosis had worsened, requiring dialysis.

Although the authors indicate that the decision to discount tamponade and restart anticoagulation therapy may have been the worst decision of the case, it may be difficult even here to get a consensus opinion on whether the decision was an “error” and whether such a system error could be prevented under the circumstances. The authors suggest that the resident’s error is more likely from not knowing his own skill limitations and not seeking a competent supervisor to help in making the decision, which represents an important policy issue throughout health care. The patient eventually recovered and was discharged after a 27-day hospital stay, with more than $200,000 in hospital charges and the need for long-term dialysis.

Six Sigma

Virtua Health, a not-for-profit community hospital system in southern New Jersey, adopted Six Sigma in 2000 to achieve operational goals. One of its first six projects, conducted between January and June 2001, sought improvements and error reduction in anticoagulation therapy. Specifically, the hospital sought to reduce errors related to incorrect pump settings, incorrect use of pumps, delays in obtaining and reacting to activated partial thromboplastin time (aPPT), dosing errors, and mixing errors. Other QI activities, including RCA, failed to address the overall performance of the anticoagulation process in quantitative terms.

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

The improvement team used the Six Sigma DMAIC process: define the process to address, measure how the current process is performing, analyze key factors driving the process, improve the process, and control the process to sustain progress. The team defined safe and effective anticoagulation capability as the project goal:

  1. First aPTT after bolus above therapeutic threshold;

  2. aPTT in therapeutic range at 24 hours;

  3. Interval between aPTTs until two consecutive are in range;

  4. Low platelet counts noted and addressed;

  5. Low hemoglobins noted and addressed.

The team’s analysis revealed 92 steps required to reach completion of the first dose adjustment—and that system complexity hampered staff performance, with few elements in place to prevent errors by staff. The team determined that simplifying and error proofing the process were the greatest opportunities to increase safety. The following chart shows the steps taken in the improvement phase:

Six Sigma Anticoagulation Improvements: Virtua Health

Process Step

Deficiency

Intervention

Anticipated Benefit

Weighing patients

Done on admission only 48% of time

Bed scales purchased

Easier to weigh patients

Lab–pharmacy data link

No prior system to monitor efficiency

All patients on heparin included in automated review, with manual review of charts identified

Detection of otherwise silent process failures; ongoing comparison to target performance

Heparin hold for aPTT >240 seconds

Unclear definition of start time for 6-hour interval

Clarification with physicians

Decreased process variation

Physician called for aPTT >240 × 3

Unclear which physician group to call

ID of physician group responsible for heparin order on initial order sheet

Decreased miscommunication

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

Six Sigma Anticoagulation Improvements: Virtua Health

Process Step

Deficiency

Intervention

Anticipated Benefit

Preheparin lab studies

Inconsistency among nurses and physicians on holding heparin until results received

Clarification with physicians; default is do not wait for labs with hold option for physician

Decreased variation in nursing practice

Infusion pumps

Occasional incorrect setting leading to dosage error

Programmable pumps with drug personalities and maximum drip rate settings

Avoidance of extreme overdosage due to pump-setting errors

Use of unfractioned heparin

Complex process with complexity-related failures

Substitute low-molecular heparin

Fewer complexity-related errors

The control phase includes creation of visible metrics used by process owners to ensure gains are sustained. Study authors note their work “is not a research methodology, and the findings of this project should not be interpreted in the same light as a rigorous clinical research paper. The focus of this paper is to describe an approach for identifying opportunities for improvement and taking action that leads to results that matter to patients in a framework that is achievable in the typical community hospital setting.”30

Toyota Production System

The Pittsburgh Regional Healthcare Initiative (PRHI) is a collaborative effort by institutions and individuals that provide, purchase, insure, and support health care services in Southwestern Pennsylvania. The initiative aims to achieve “perfect patient care” in six counties in the Pittsburgh Metropolitan Statistical Area with the following goals:

  • Zero medication errors

  • Zero health care–acquired (nosocomial) infections

  • Perfect clinical outcomes, measured by complications, readmissions, and other patient outcomes in the following areas:

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×
  • Invasive cardiac procedures (cardiac bypass surgery, angioplasty, and diagnostic catheterization)

  • Hip and knee replacement surgery

  • Repeat cesarean sections for women with no clinical indications for them

  • Depression

  • Diabetes

The initiative calls these goals “the most aggressive and ambitious performance goals in American health care.” It seeks to redefine the patient as the “client” in health care, as opposed to the physician, the insurer, or the payer in the current environment, by reallocating resources based on each patient’s needs. “In effect, the patient ‘pulls’ the resources he or she needs. This system—derived from the Toyota Production System—is capable of adjusting to and meeting varying patient needs quickly and flawlessly.”31

A Learning Line is a small hospital unit organized around the principles of TPS. At the point of patient care, the people doing the work are the experts and focus on the shared goal of meeting patient needs, one patient at a time. When a problem hinders work, the full-time team leader takes the lead, researching the problem by first determining what happened and asking the question “why” five times to determine the root cause. As the origins become known, the workers closest to the problem design solutions immediately, testing them with scientific methods. The team leader is free to pull assistance as needed to the point of patient care from the manager, the director, the chief executive officer, even trustees. Proponents suggest this approach enables health care professionals to spend more time providing frontline caregiving by wringing inefficiency out of the system; inefficiency is estimated to consume 33 to 50 cents of every health care dollar.

At the Veterans Administration Pittsburgh Healthcare System, one Learning Line team addressed the issue of antibiotic-resistant infection by attempting to increase compliance with procedures to halt the spread of infection and act on PHRI’s goal of zero nosocomial infections. In seeking to understand the root cause for infections—asking “why” five times and observing workers at close range—the team leader discovered one reason workers had trouble complying with infection control procedures: Some rooms had gowns and some did not, and stock outs occurred daily. Workers on the Learning Line established who would be responsible for restocking gloves, how often supplies would be checked (daily), and how the cupboards would be labeled so any deficiency would immediately become obvious. Within days, gloves and gowns that workers had stashed away became available as

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

the system supported the workers; glove consumption and costs dropped 15 percent as “stashes” disappeared. The unit believes it has already gained ground on the Centers for Disease Control and Prevention’s goal of improved compliance as hand hygiene compliance has risen.32

Special thanks to the following individuals for their advice and comments on this project: Judene Bartley, Paul Batalden, Donald Berwick, David Blumenthal, Mark Brulin, Mark Chassin, Richard Croteau, Edward Dunn, Karen Feinstein, Robert Galvin, Doris Hanna, Brent James, Molly Joel Coye, Lucian Leape, Tammy Lundstrom, Thomas Massero, Julie Mohr, Thomas Nolan, Elisabeth Paté-Cornell, Paul Schyve, Ethel Seljevold, Kimberly Thompson, Mark Van Kooy, Cindy Wallace, and Jonathan Wilwerding.

REFERENCES

1. Committee on Quality of Health Care in America. 2001. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press. Pp. 41–42.

2. For a discussion of HFMEA/FMEA and HACCP, see McDonough J. 2002. Proactive Hazard Analysis and Health Care Policy. New York: Milbank Fund and ECRI.

3. Nolan T. 1996. The Improvement Guide. New York: Jossey Bass.

4. Blumenthal D, Kilo C. 1998. A report card on Continuous Quality Improvement. Milbank Quarterly 76(4): 625–648.

5. See http://www.prhi.org/ [accessed March 27, 2003].

6. Welch J. 2001. Jack: Straight from the Gut. New York: Warner Business Books.

7. Chassin M. 1998. Is health care ready for Six Sigma quality? Milbank Quarterly 76(4):565–591.

8. Military Procedure MIL-P-1629. 1949 (November 9). Procedures for Performing a Failure Mode, Effects and Criticality Analysis.

9. Marx, D, Slonim A. 2003. Assessing Patient Safety Risk Before the Injury Occurs: An Introduction to Socio-Technical Probabilistic Risk Modeling in Healthcare. Qual Saf Health Care 12(Suppl2):ii33–ii38.

10. Green L, Crouch E. 1997. Probabilistic risk assessment: Lessons from four case studies. Annals of the New York Academy of Sciences 837:387–396.

11. Op. cit., p. 10.

12. See McDonough, op. cit., for detailed references.

13. Personal communication, Dr. Mark Van Kooy, M.D., Virtua Health Master Black Belt, March 26, 2003.

14. Ackoff R. 1994. The Democratic Corporation. New York: Oxford University Press. Pp. 18–21.

15. Batalden PB, Mohr JJ. 1997. Building knowledge of health care as a system. Quality Management in Health Care 5(3):1–12.

16. Personal Communication, Dr. Brent James, March 3, 2003.

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×

17. Gaba D. 2000. Anesthesiology as a model for patient safety in health care. British Medical Journal 320:785–788.

18. Lagasse R. 2002. Anesthesia safety: Model or myth? A review of the published literature and analysis of current original data. Anesthesiology 97(6):1609–1617.

19. Cooper J, Gaba D. 2002. No myth: Anesthesia is a model for addressing patient safety. Anesthesia 97(6):1335–1337.

20. Brennan T. 2002. Physicians’ professional responsibility to improve the quality of care. Academic Medicine 77:973–980.

21. Croteau R, Schyve P. 2000. Proactively error proofing health care processes. In: Spath P. editor. Error Reduction in Health Care. New York: Jossey Bass. P. 184.

22. Chang A. 2003. Joint Commission Benchmark 5(2).

23. Marguerez G, Erbault E, Terra JL, Maisonneuve H, Matillon Y. 2001. Evaluation of 60 continuous quality improvement projects in French hospitals. International Journal for Quality in Health Care 13(2):89–97.

24. Solberg L, et al. 2000. Failure of a continuous quality improvement intervention to increase the delivery of preventive services: A randomized trial. Effective Clinical Practice May/June:105–115.

25. Contributed by Mary Ivins, Morrison Management Specialists.

26. Gran, B.A., Winther, R. and Johnsen, O.A. Security Assessment of Safety Critical Systems Using HAZOPs, in Proc. of Safecomp 2001, Budapest, Hungary, September 26-28, 2001. Stolen K. A Framework for Risk Analysis of Security Critical Systems. In supplement of the 2001 International Conference on Dependable Systems and Networks. Gothenburg, Sweden, July 2-4, 2001, P. D4-D11.

27. CORAS is a European Research and Development project funded by the 5th Framework Program on Information Society Technologies by the European Commission. The project began in 2001 and will last through 2003. Eleven partners are involved: five from Norway, three from Greece, two from England, and one from Germany. One Norwegian participant is the National Centre for Telemedicine (NST), whose mission is to contribute to making effective health services available to all. Formerly the Norwegian Centre for Telemedicine, NST was designated the first World Health Organization Collaborating Center for Telemedicine in July 2002.

28. Personal communication, e-mail from Eva Skipenes, Security Adviser, Norwegian Centre for Telemedicine, to Luke Petosa, Director, ECRI’s Center for Healthcare Environmental Management. Sent March 17, 2003.

29. Paté-Cornell E. 1999. Medical application of engineering risk analysis and anesthesia patient risk illustration. American Journal of Therapeutics 6(5):245–255.

30. Van Kooy M, Edell L, Scheckner HM. 2002. Use of Six Sigma to Improve the Safety and Efficacy of Acute Anticoagulation with Heparin. Journal of Clinical Outcomes Management 9(8): 445–453.

31. Pittsburgh Regional Healthcare Initiative. 2001. PHRA Scorecard 2001–2003. [Online]. Available: http://www.prhi.org/publications/member_pubs.htm [accessed April 25, 2003].

32. Pittsburgh Regional Healthcare Initiative. 2002. On the Learning Line: Case Studies from the Perfecting Patient Care Learning Lines in Pittsburgh-Area Hospitals. [Online]. Available: http://www.prhi.org/pdfs/Learning_Line_Booklet.pdf [accessed April 25, 2003].

Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Page473
Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×
Page474
Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×
Page475
Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×
Page476
Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
×
Page477
Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Page478
Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Page479
Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Page481
Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Page482
Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Page483
Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Suggested Citation:"Appendix F: Quality Improvement and Proactive Hazard Analysis Models: Deciphering a New Tower of Babel." Institute of Medicine. 2004. Patient Safety: Achieving a New Standard for Care. Washington, DC: The National Academies Press. doi: 10.17226/10863.
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Americans should be able to count on receiving health care that is safe.

To achieve this, a new health care delivery system is needed — a system that both prevents errors from occurring, and learns from them when they do occur. The development of such a system requires a commitment by all stakeholders to a culture of safety and to the development of improved information systems for the delivery of health care. This national health information infrastructure is needed to provide immediate access to complete patient information and decision-support tools for clinicians and their patients. In addition, this infrastructure must capture patient safety information as a by-product of care and use this information to design even safer delivery systems. Health data standards are both a critical and time-sensitive building block of the national health information infrastructure.

Building on the Institute of Medicine reports To Err Is Human and Crossing the Quality Chasm, Patient Safety puts forward a road map for the development and adoption of key health care data standards to support both information exchange and the reporting and analysis of patient safety data.

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