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Guest Essays

FMEA vs. FRACAS vs. RCA By Jan Krouwer F M E ( C ) A – Failure mode effects (and criticality) analysis F R A C A S – Failure reporting and corrective action system R C A – Root cause analysis Many people who work in hospitals have never heard of the reliability tools that are commonplace in manufacturing environments. Those who have—often representatives on patient safety committees—more likely know of FMEA and RCA, but not FRACAS. This essay explains the differences among these techniques, with emphasis on the perspective of the hospital worker. Background The Joint Commission on Accreditation of Healthcare Organizations (JCAHO) requires: •

FMEA to be performed once a year

•

RCA to be performed for sentinel events and near misses Hospital use of RCA has been critiqued by Berwick (1), who suggests that RCA seeks to find a single cause. I have responded (2), as in my experience I have found RCA is not limited to seeking single causes. A more important limitation of RCA as practiced by hospitals is that it often restricts attention, as implied by JCAHO policy, to sentinel events and near misses. This focus leaves the many less severe events out of the picture. FRACAS (3-4) is essentially the same as RCA, although FRACAS is often combined with other tools such as reliability growth management, which is based on learning curve theory. Moreover, in FRACAS, all observed error events are analyzed, and in this way FRACAS is similar to FMEA.

ASQ’s Healthcare Update Newsletter, February 2007

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The term FRACAS will be used here instead of RCA. Table 1 shows a comparison of some attributes of FMEA and FRACAS. Table 1 Attributes of FMEA and FRACAS for a process FMEA

FRACAS

General

“Proactive”

“Reactive”

Purpose

Affect the design before launch

Correct problems after launch*

Errors

May occur – the potential errors must be enumerated

Have occurred – observed errors are simply counted

Error rate

Assumed

Measured

Issues with technique

Is it complete? Models can be wrong.

All errors counted? Culture inhibits reporting errors.

Can be combined with

Fault trees

Fault trees

Evaluate quality of the technique

Difficult – completeness, reasonableness of mitigations is qualitative

Simple – measure error rate

*For a product FRACAS, problems can be observed after design but before launch FMEA and FRACAS Can Inform Fault Trees Both FRACAS and FMEA can be combined with fault trees, which offer a “top-down” structured way of representing causes for an undesirable event. The graphical structure imposed by a fault tree increases the likelihood that a FMEA will be more complete, since a FMEA is basically an unordered list in a table. Fault trees allow multiple causes for an event and use “and” and “or” gates to distinguish between error types. Because fault trees can contain both potential and observed errors, they are ideal for containing the knowledge expressed in both a FMEA and FRACAS. That is, when a process is designed, the ways it m i g h t fail are captured in a fault tree (and FMEA). After the process is launched, the ways in which the process h a s failed are captured through FRACAS, and this knowledge is used to update the fault tree. In both the FMEA and FRACAS, the fault tree is also updated when a mitigation is implemented, since this represents a design change to the process (see Figure 1). ASQ’s Healthcare Update Newsletter, February 2007

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Figure 1 Use of FMEA, FRACAS, and Fault trees to prevent errors in processes

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Don’t Neglect FRACAS Both FMEA and FRACAS are useful. Yet, the JCAHO requirement focuses on FMEA. In a sense, this is logical because FMEA is more encompassing than FRACAS. FMEA addresses potential errors but can also accommodate observed errors, whereas FRACAS is intended only for observed errors. The problem is that with 98,000 deaths due to medical errors each year, the huge number of observed errors introduces the possibility of paying insufficient attention to potential errors if one performs only FMEA. Consider two error events for a hypothetical FMEA for a transplant service: 1. Patient infection after surgery – an observed error 2. Organ selected with incorrect blood type – a potential error If one takes into account the entire service, the number of observed error events will likely cause a ranking problem. Ranking is important because the service will have limited funds to apply to mitigations. So even though selection of an organ with the wrong blood type may have never occurred, the selection process could possibly be flawed and could benefit from mitigations. Yet, another possibility is that this will not occur because the focus is on observed errors. Hence, one should perform both FMEA and FRACAS, as indicated in Figure 1. The combination of the two tools reduces the likelihood of ranking problems since the FMEA will focus on potential problems and the FRACAS will focus on observed problems. A Challenge with FMEAs As indicated in Table 1 under “purpose,” use of FMEA is intended to affect the design of a process. In the medical diagnostics industry, however, an FDA-required hazard analysis for instrument systems (a fault tree/FMEA for hazards) was at times merely a documentation of an existing design. The same issue exists for FMEAs in hospitals, since many FMEAs will be performed for existing processes.

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References 1. D. M. Berwick, “Errors Today and Errors Tomorrow,” New England Journal of

Medicine 348 (2003): 2570-2572.

2. J. S. Krouwer, “There Is Nothing Wrong with the Concept of a Root Cause,”

International Journal for Quality in Health Care 16 (2004): 263. 3. Department of Defense Handbook: Failure Reporting, Analysis and Corrective Action Taken, Mil-Std-2155 (Washington, DC: Department of Defense, 1995). 4. J. S. Krouwer, “Using a Learning Curve Approach to Reduce Laboratory Errors,” Accreditation and Quality Assurance Journal 7 (2002): 461-467, available at http://krouwerconsulting.com/KrouwerLearningCurve.pdf. About the Author Jan Krouwer, Ph.D., has more than 20 years of experience in the medical diagnostics industry. For most of the time, Dr. Krouwer worked for Bayer Diagnostics and its previously acquired companies of Chiron Diagnostics, Ciba Corning, and Corning Medical. He also was the executive director of the Evaluations and Reliability Department, an internal consulting group. He earned his Ph.D. in synthetic organic chemistry from M.I.T. and completed postdoctoral work in enzymology from New England Medical Center. Dr. Krouwer is the principal owner of Krouwer Consulting (http://krouwerconsulting.com/).

Copyright © 2007, Jan Krouwer. Used with permission. “Guest Essays” is a feature of ASQ’s Healthcare Update newsletter. If you would like to submit an essay for consideration, e-mail [email protected]. To subscribe to the newsletter, visit http://www.asq.org/healthcare/update_info.html.

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