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Safety Science 49 (2011) 607–615
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Safety Science
journal homepage: www.elsevier .com/locate /ssc i
Review
A review of medical error taxonomies: A human factors perspective
Ibrahim Adham Taib a,b,⇑, Andrew Stuart McIntosh a, Carlo Caponecchia c, Melissa T. Baysari d
a School of Risk and Safety Sciences, University of New South Wales, Australiab Department of Biomedical Science, Faculty of Science, International Islamic University Malaysia (IIUM), Malaysiac School of Aviation, University of New South Wales, Australiad Australian Institute of Health Innovation, University of New South Wales, Australia
a r t i c l e i n f o a b s t r a c t
Article history:Received 25 October 2010Received in revised form 8 December 2010Accepted 29 December 2010Available online 16 February 2011
Keywords:Medical errorTaxonomyHuman factorsMedical informatics
0925-7535/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.ssci.2010.12.014
⇑ Corresponding author. Address: University of Newand Safety Sciences, Kensington, NSW 2052, Australi+612 9385 6190.
E-mail address: [email protected] (I.A. Taib).
Although a large number of medical error taxonomies have been published, there is little evidence to sug-gest that these taxonomies have been systematically compared. This paper describes a study comparing26 medical error taxonomies using a human factors perspective. The taxonomies were examined todetermine if they classified systemic factors of medical errors and if they utilized theoretical error con-cepts in their classifications. Scope of classification was also examined. It was found that two-thirds of thetaxonomies classified systemic factors of medical errors and only a third utilized theoretical error con-cepts. Medical error taxonomies based on theoretical error concepts were more likely to be generic inapplicability and also more likely to classify systemic factors and psychological error mechanisms ofmedical errors. In addition to terminology, the medical error taxonomies also varied in terms ofdomain-specificity, granularity, and developmental process. Different medical error taxonomies providedifferent information; how these differences affect medical error management needs to be investigated.
� 2011 Elsevier Ltd. All rights reserved.
Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608
1.1. Human factors attributes of a medical error taxonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6091.2. Presence of human factors attributes in error taxonomies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6091.3. Current studies on medical error taxonomies and the need for a review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6092. Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6103. Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 610
3.1. Criteria for comparing medical error taxonomies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6103.2. List of medical error taxonomies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 610
4. Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 610
4.1. Domain-specificity and use of a theoretical concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6104.2. EEM, PSF, PEM, and theoretical error concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6104.3. Scope of classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6115. Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611
5.1. Classifying systemic factors of medical errors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6115.2. Classifying the causal mechanisms of medical errors based on theoretical error concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6115.3. Effect of using theoretical error concepts in medical error taxonomies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6125.4. Caveats of using medical error taxonomies that classify systemic factors or with underlying theoretical error concepts . . . . . . . . . . . . 6125.5. Variation in terms of domain-specificity and granularity of categories. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6125.6. Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6126. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613Appendix A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615
ll rights reserved.
South Wales, School of Riska. Tel.: +612 9385 5002; fax:
608 I.A. Taib et al. / Safety Science 49 (2011) 607–615
1. Introduction
Errors are planned activities that fail to achieve their goal, andwhen such failures are not due to chance alone (Reason, 1990). Er-rors are not unique to gender, profession, age, or experience.Depending on the context of the error, errors may lead to harmor property damage or cause no harm at all (Strauch, 2002).
Errors in healthcare, or medical errors, have led to a large num-ber of iatrogenic injuries or patient deaths. Studies in various coun-tries indicated that the median overall incidence of in-hospitaladverse events was 9.2% while the median percentage of adverseevents leading to deaths was 7.4% (de Vries et al., 2008). In additionto iatrogenic injuries and deaths, medical errors also burden theeconomy by causing additional healthcare costs and loss of income(Institute of Medicine, 2000).
The healthcare system is vulnerable to errors because it is acomplex and high-risk system (Braithwaite et al., 2009; Nolan,2000; Wilson et al., 1999). A number of factors contribute towardsthe system’s complexity. First, healthcare involves complicatedprocedures and equipment. Second, healthcare involves intercon-necting and interdependent components, for example differentpersonnel from different departments are needed for a particulartreatment. Any error committed by one component may affectother components as well. The effect of such errors is usuallyunpredictable; especially if the affected component is distant fromthe where the error originally occurred. Third, the system’s compo-nents are tightly coupled, so that an error in one component doesnot take very long to propagate to other components. Fourth, thehealthcare system was designed and is operated by human beings,and human beings cannot predict all the possible effects of deci-sions or actions within the system (Christofferson and Woods,1999).
Errors cannot be fully prevented. It is crucial that when medicalerrors occur, they do not lead to harm. To achieve this goal, thehealthcare system has to be robust or error tolerant (Christoffersonand Woods, 1999). Robust or error tolerant healthcare systems aredesigned to expect and be prepared for medical errors to occur.For example, because medications could get mixed up before
Fig. 1. The role of an error taxonomy in integrating
administration, in a robust or error tolerant healthcare system,the medications are checked by more than one individual prior toadministration. That way, even if an individual chooses the wrongmedication for administration, another individual may detect andcorrect the error before it causes harm. Such systems attempt toprevent errors, but at the same time expect some errors to occurand have measures to reduce or prevent the impact of those errors.
To design a robust or error tolerant healthcare system, knowl-edge about potential medical errors within the system is required.Such knowledge can be obtained by collating information aboutmedical errors that have occurred within the system. The informa-tion can then be aggregated and integrated to form a database,which can then reveal patterns in how medical errors occur inthe system (Wallace and Ross, 2006). Once the patterns in howmedical errors occur within that system are known, the systemcan be designed to expect and prepare for those medical errors.
Forming a database on medical errors requires an error taxon-omy to organize systematically and classify collated information(Sanders and McCormick, 1993). The collated information mayvary by terminology, origin, content, and form. Before the informa-tion can be integrated, either within a unit, organization, or acrossorganizations, the terminology must first be standardized. An errortaxonomy standardizes the information by providing a finite num-ber of categories to represent the information (see Fig. 1). Thisstandardization process is akin to translating – the differentterminologies used by the collated information is translated intoa similar language consisting of the taxonomy’s categories,enabling information from different sources to be compared forintegration. During the standardization process, the error taxon-omy also acts as a ‘filter’, where only relevant information, as iden-tified based on an error taxonomy’s list of categories andclassification system, is extracted from the collated informationto be integrated. Based on the taxonomy’s categories, the standard-ization process enables the collated information to be analysed,and this can aid in interventions.
The information classified by an error taxonomy depends heav-ily on the taxonomy’s categories. The finite number of categories ineach medical error taxonomy means that the type and amount of
and analysing information on medical errors.
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information each taxonomy can classify is limited. Different med-ical error taxonomies have different categories; therefore, thechoice of medical error taxonomy used will affect what informa-tion is categorized and what information is left out. For example,two different error taxonomies for rail incidents and accidents, Hu-man Factors and Analysis Classification System (HFACS) and Tech-nique for the Retrospective and Predictive Analysis of CognitiveErrors (TRACEr-rail version), were compared in terms of their scopeof classification (Baysari et al., 2009). It was found that HFACS per-formed well at categorizing the ‘organizational context’ of errorswhile TRACEr-rail performed well at categorizing the ‘immediatecontext surrounding errors’ – indicating that taxonomies with dif-ferent categories allowed different information to be classified. Be-cause information affects how we perceive and respond to errors,taxonomies with different categories may lead to different inter-ventions. For example, in the study by Baysari et al. (2009), TRA-CEr-rail lacked categories to classify the organizational factorsassociated train driving errors while HFACS lacked categories toclassify the immediate context surrounding train driving errors.Interventions based on the classifications which emerge from aTRACEr-rail analysis may not target the organizational level of asystem while interventions based on HFACS may not be directedtowards the operator’s tasks and his/her immediate environment.It is critical that the taxonomies we utilize contain categories thatencompass the entire range of contributing factors associated withan incident and provide information deemed crucial to managingerrors. Incomplete taxonomies result in a limited understandingof the incident and so limit the recommendations that can bemade.
1.1. Human factors attributes of a medical error taxonomy
Different people may have different views on the essential attri-butes of a medical error taxonomy. Here, we present a human fac-tors perspective on two essential attributes of a medical errortaxonomy. Human factors is the ‘theoretical and fundamentalunderstanding of human behaviour and performance in purposefulinteracting sociotechnical systems. . . and the application of thatunderstanding to optimize human well-being and overall systemperformance’ (International Ergonomics Association, 2000; Wilson,2000). Human factors, like the open systems theory, acknowledgesthat systemic factors, such as workers, machines, and the environ-ment, affect the occurrence of medical errors (Schutz et al., 2007).For example, communication, medication procedures, and workingenvironment were identified as systemic factors for medication er-rors (Carayon, 2007) while low morale was identified as a systemicfactor for violations in medication administration (Fogarty andMcKeon, 2006). According to Rasmussen, the study of error mustinclude the ‘structure of the task and the environment’ (Rasmus-sen, 1988). An approach that examines such systemic factors willprevent adverse events (Catchpole et al., 2006). Our understandingof such factors will affect our ability to predict medical errors(Reason, 1990). Because the ability to predict errors is crucial todesigning a robust or error tolerant system, it is essential thatmedical error taxonomies are able to classify systemic factors ofmedical errors.
Human factors also uses theoretical and fundamental under-standings of human behaviour to prevent errors (Wilson, 2000).This approach should be present in how medical error taxonomiesclassify the causal mechanisms of medical errors. Using theoreticalconcepts to classify errors is more useful than not using theoreticalconcepts, as described below. According to Reason (1990), threelevels of error classification exist: behavioural, contextual, andconceptual. Behavioural level error classification is the most super-ficial of the three levels and classifies errors based on the observa-ble features of errors, such as the actions involved or consequences
of the error. Errors that share the same behavioural classificationmay have different causal mechanisms. Contextual level error clas-sification classifies errors based on the conditions under which theerrors occurred. This level of error classification identifies the situ-ational factors that influence error occurrence. However, some sit-uational factors may not consistently affect error occurrence,which is the weakness of this level of error classification. Concep-tual level error classification utilizes knowledge of cognitive mech-anisms and theoretical concepts to determine the underlyingcauses of errors. Conceptual level error classification is thereforedeemed the most useful of the three levels of error classification(Reason, 1990). It is essential for medical error taxonomies to havea conceptual level error classification, which is based on theoreticalerror concepts.
1.2. Presence of human factors attributes in error taxonomies
In a study by Kirwan (1992), non-medical human error taxono-mies were examined to determine if they had three different clas-ses of categories that were similar to the three levels of errorclassification proposed by Reason (1990). The classes were externalerror modes (EEM), psychological error mechanisms (PEM), or per-formance shaping factors (PSF). EEMs are concerned with errorsthat are visibly observable, such as a healthcare worker adminis-tering the wrong medication. EEMs are equivalent to Reason’sbehavioural level error classification. PEMs are concerned withthe psychological mechanism of an error’s occurrence, for examplememory failure may have led to a failure to check the dose of themedication administered. This information is useful because er-rors, regardless of domain, have some psychological basis behindthem (Kostopoulou, 2006). PEMs are equivalent to Reason’s con-ceptual level error classification. PSFs relate to how componentsof the system affected the occurrence of the error, for example timepressure may have affected a healthcare worker’s ability to admin-ister medication safely. PSFs are equivalent to Reason’s contextuallevel error classification. Among the human error taxonomiesexamined in Kirwan’s study were the Technique for Human ErrorRate Prediction (THERP) and the Systematic Human Error Reduc-tion and Prediction Approach (SHERPA) (Kirwan, 1992). Kirwanfound that some of the taxonomies had all three classes of catego-ries while some had just one class of categories (Kirwan, 1992).These findings support the idea that different error taxonomiescan provide different information. Kirwan’s study also demon-strated that error taxonomies could be compared to determine ifthey classified systemic factors and psychologically-based causalmechanisms by using the PSF and PEM categories respectively.
1.3. Current studies on medical error taxonomies and the need for areview
The literature about medical error taxonomies is focused on theissue of non-standardized ‘terminology’ of the categories found inmedical error taxonomies. With error taxonomies utilizing differ-ent terms or names as categories to describe a particular incident,it is difficult to determine if the resulting classified data are similarenough to be grouped together, or whether they are different en-ough to remain separated. The International Classification for Pa-tient Safety (ICPS) is being developed by the World Alliance forPatient Safety Alliance of the World Health Organization (WHO)to produce a standardized terminology. By performing a Delphisurvey, the group has proposed a number of key concepts with pre-ferred terms or names to be used within the ICPS (Runciman et al.,2009; Sherman et al., 2009; Thomson et al., 2009).
While standardizing ‘language’ is important for interactions be-tween organizations, from a human factors perspective it is alsocritical that medical error taxonomies provide useful information
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by classifying the system’s role in medical errors and using theo-retical error concepts as the basis for their classifications. To date,no systematic comparison has been performed on medical errortaxonomies to determine if these two attributes are present inthe taxonomies.
2. Objective
Medical error taxonomies have not been previously reviewed todetermine if the taxonomies classified the system’s role in medicalerrors or used theoretical error concepts as a basis for their classi-fications. We carried out a review of the literature to determinewhat published medical error taxonomies possessed both attri-butes. We also discussed differences and similarities between themedical error taxonomies in terms of the two attributes.
3. Method
3.1. Criteria for comparing medical error taxonomies
To determine if published medical error taxonomies have thetwo attributes (i.e. they classified systemic factors of medical er-rors and they classified medical errors based on underlying theo-retical error concepts), we adopted Kirwan’s method of groupingerror taxonomy categories into EEM, PEM, or PSF. As mentionedabove, EEM, PEM, and PSF are equivalent to Reason’s three levelsof classification; therefore this method was chosen for the compar-ison. Taxonomies that have categories grouped into EEM are taxo-nomies that classify the observable features of medical errors.Taxonomies that have categories grouped into PSF are taxonomiesthat classify systemic factors of medical errors. Taxonomies thathave categories grouped into PEM are taxonomies that classifymedical errors based on underlying cognitive mechanisms. We alsodetermined if the medical error taxonomies utilized any theoreti-cal error concepts for error classification. This was determined bynoting any reported usage of human error-related theories orclassification systems derived from such theories during thetaxonomy’s development or the presence of these theories in itsstructure.
In addition to determining whether the taxonomies possessedthe two human factors attributes, we looked at the contextualinformation each medical error taxonomy categorized. This wasthe scope of the taxonomy, which included different informationon an error’s occurrence, e.g. why it occurred, where it occurred,what procedure was involved, and what was done to interveneor mitigate. This information complements a human factors ap-proach in tackling medical errors by providing information onthe medicine aspect of a medical error (Woods et al., 2007). Basedon a published medical error taxonomy that utilized different hu-man-error-related theories in its development (Chang et al., 2005),the following categories were adopted to determine a medical er-ror taxonomy’s scope: type, setting, cause, impact, and prevention/mitigation. ‘Type’ referred to the healthcare processes that failed.This information would be useful to understand what went wrongduring the healthcare delivery process. ‘Setting’ referred to wherethe event occurred and who was involved. This information isimportant to understand the context of the medical error. ‘Cause’referred to factors and agents that contributed to the error, whichis important as it identifies what needs to be done to reduce errors.‘Impact’ referred to the consequences of the error, which is usefulto differentiate between those that led to an adverse event andthose that lead to a near miss. ‘Prevention/mitigation’ referred tosteps taken to prevent errors or reduce the effect of an error. Thisinformation is useful to understand how errors are being managed
– valuable when used prospectively to identify which interven-tions were effective and also to evaluate existing barriers.
We also determined if the taxonomies were designed for a spe-cific domain, i.e. division in healthcare such as paediatrics or sur-gery, or were generic. This was based on information included inthe articles, such as how the taxonomy was developed andwhether it was stated that the taxonomy was specific for a partic-ular domain.
3.2. List of medical error taxonomies
To compile a list of medical error taxonomies, a literaturesearch was performed using seven databases: Medline (1950–2009), Embase (1988–2009), Web of Science (1900–2009), Ergo-nomics Abstracts (1985–2009), PsycINFO (1967–2009), Sociologi-cal Abstracts (1960–2009), and Biological Abstracts (1969–2009).The keywords used were ‘patient safety’, ‘diagnostic error’, ‘medi-cal error’, ‘taxonomy’, and ‘classification’. The title and abstract ofretrieved articles were read for the first exclusion stage. Articlesnot written in English were excluded, as were those that did notaddress the classification of medical errors; the full articles werechecked if we were not sure. Next, we read the full paper ofremaining articles for the second exclusion stage. We identifiedarticles that classified medical errors using a classification systemwhere its usage was fully described and its mechanism of errorclassification visible. Only these articles were included to compilethe list of taxonomies. We excluded taxonomies restricted to a spe-cific medical task or procedure, for e.g. an echo-cardiographic pro-cedure, as their applicability was restricted to that purpose andmay not be applicable to various domains in healthcare. Relevantwebsites, such as by the World Health Organization (WHO), werealso checked for medical error taxonomies.
4. Results
Initially over 1321 articles were obtained. After the first exclu-sion stage, only 284 articles remained. Twenty-six different medi-cal error taxonomies were identified after the second exclusionstage, and these are listed in the Appendix A. Twenty (77%) ofthe medical error taxonomies classified EEM, 17 (65%) of the med-ical error taxonomies classified PSF or systemic factors of medicalerrors, and only eight (31%) of the medical error taxonomies clas-sified PEM (see Fig. 2).
4.1. Domain-specificity and use of a theoretical concept
Nineteen of the medical error taxonomies identified were de-signed for use in a particular domain, with the majority being gen-eral practice or primary care. Such domain-specific medical errortaxonomies utilized categories specific to their respective domains.Seven medical error taxonomies were generic and had categorieswidely applicable to different domains.
Of the nine medical error taxonomies that utilized theoreticalconcepts, seven were generic while two were domain-specific.Medical error taxonomies that used theoretical error conceptswere more likely to be generic than domain-specific; their catego-ries were not domain-specific because it was apparent that thetaxonomies were derived from other industries such as chemicalprocessing and aviation.
4.2. EEM, PSF, PEM, and theoretical error concepts
As shown in Fig. 2, the majority of taxonomies classified EEM,followed by PSF and PEM. When medical error taxonomies thatused theoretical error concepts were compared with those that
Fig. 2. Percentage of medical error taxonomies that classified EEM, PSF, and PEM.
I.A. Taib et al. / Safety Science 49 (2011) 607–615 611
did not, a lower percentage of taxonomies that used theoreticalconcepts classified EEM compared to those that did not use theo-retical concepts. A higher percentage of taxonomies that used the-oretical concepts classified PSF or systemic factors of medicalerrors and PEM compared to those that did not use theoretical con-cepts. The results indicated that medical error taxonomies thatused theoretical error concepts were more likely to classify PSFor systemic factors and PEM than those that did not use theoreticalconcepts; however, the opposite was true for classification of EEM.
4.3. Scope of classification
As shown in Fig. 3, the majority of the medical error taxonomiesclassified type of errors, followed by cause, impact, setting, andprevention/mitigation steps in errors. The results also indicatedthat medical error taxonomies with theoretical error conceptswere more likely to classify cause and prevention/mitigationaspects of errors compared to taxonomies without such theoreticalconcepts; however, the opposite is true for classifying type, setting,and impact of medical errors.
Fig. 3. Percentage of medical error taxonomies that classified type, cause
5. Discussion
5.1. Classifying systemic factors of medical errors
The review indicated that out of 26 medical error taxonomiesreviewed, about two-thirds classified systemic factors of medicalerrors. The remaining one-third of medical error taxonomies failto take into account systemic factors, such as issues of workloador staffing. Interventions based on such classifications may focusprimarily on the person who made the error, highlighting that itis crucial for medical error taxonomies to include systemic factorsin their error classifications.
5.2. Classifying the causal mechanisms of medical errors based ontheoretical error concepts
Only a third of the medical error taxonomies reviewed classifiedmedical errors based on theoretical concepts on errors. As men-tioned above, using such theoretical concepts to classify the causalmechanism of medical errors is important because a contextual
, impact, setting, and prevention/mitigation aspect of medical errors.
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and behavioural level error classification may not uncover the cau-sal mechanisms of medical errors. Medical error taxonomies thatfail to use a cognitive or theoretical framework will fail to guideinterventions towards the underlying cause of a medical error.
5.3. Effect of using theoretical error concepts in medical errortaxonomies
The results indicated that medical error taxonomies that usedtheoretical error concepts were more likely to be generic thandomain-specific. A possible explanation for this observation is thaterror taxonomies that used theoretical concepts have categoriesbased on theories already applicable to various domains; forexample, Rasmussen’s Skill–Rule–Knowledge framework has apsychological basis that is applicable to various domains, suchas in paediatrics or intensive care units. Such theories providegeneric categories that can be adopted for use in medical errorclassification.
Medical error taxonomies that used theoretical error conceptswere also more likely to classify PSF and PEM than medical errortaxonomies that did not use theoretical concepts. The differencecould be attributed to two related factors. First, medical error taxo-nomies that used theoretical error concepts appeared to embracethe ‘system approach’ while medical error taxonomies that didnot use theoretical concepts appeared to embrace the ‘person ap-proach’ (Reason, 2000). The system approach sees errors as a con-sequence of work condition or environment while the personapproach sees errors as the product of workers themselves (Rea-son, 2000).
Second, medical error taxonomies that used theoretical errorconcepts developed their categories based on a ‘top-down’ ap-proach (van Vuuren, 1999). Their categories were heavily influ-enced by the theoretical error concepts that the taxonomiesadopted. For example, by adopting the Eindhoven ClassificationSystem or the Human Factors Accident Classification System asthe basis of a medical error taxonomy’s classification, the taxon-omy’s categories would cater for both the ‘sharp’ and ‘blunt’ endsof a system (El Bardissi et al., 2007). This meant that such taxono-mies were highly likely to classify PSF. If the adopted theoreticalconcepts were related to the psychological mechanisms of errors,for example in Zhang et al. (2004) and Kostopoulou (2006), thetaxonomies emphasized human cognition and were highly likelyto classify PEM.
Medical error taxonomies that did not use theoretical conceptswere developed using a ‘bottom-up’ approach (van Vuuren, 1999).The bottom-up approach relies on a collection of incident data todevelop taxonomy categories. Error taxonomies developed usinga bottom-up approach that relied solely on medical error reports,such as by Makeham et al. (2002), focused on events at the sharpend, such as the operator’s weaknesses. This may be due to the lim-ited scope of incident data; there is a tendency to focus on imme-diate and proximal causes in medical error reports (Sheridan,2003). Such taxonomies were therefore not likely to classify PSFand PEM.
5.4. Caveats of using medical error taxonomies that classify systemicfactors or with underlying theoretical error concepts
There are obstacles to using medical error taxonomies withunderlying theoretical error concepts. Such error taxonomiesmay require pre-requisite knowledge to perform classifications.For example, taxonomies based on human cognition (Kostopoulou,2006; Zhang et al., 2004), require knowledge of cognitive processesto interpret the differences between categories. Using such taxono-mies may be difficult for healthcare workers with non-specialized
training in psychology. To produce familiar categories, attemptshave been made to produce more ‘natural’ categories (Runcimanand Helps, 1998) derived from terminology commonly used inthe healthcare setting. Another obstacle to using such error taxo-nomies is the amount of information included in medical error re-ports used to perform classifications. For taxonomies based oncognitive processes, information pertaining to the worker’s cogni-tive processes must be known. Information on a worker’s cognitiveprocesses is not externally manifested and can only be obtainedfrom the operator involved. This may be problematic because er-rors are often detected and reported by people other than thosewho committed the error (Kostopoulou, 2006).
Although error taxonomies attempt to classify latent conditions,such information is not always found in error reports. Assessingthe system’s role in medical errors may involve a time consuminganalysis such as Root Cause Analysis (Schutz et al., 2007). In verycomplex situations, the ‘causal relation’ between errors andchanges made by the management are not ‘preserved’ in reports(Rasmussen et al., 1981). Another reason such information is notalways reported is because error reports are known to be filled invery quickly (Kostopoulou, 2006) and so vital information on thesystem’s role in errors may be left out.
5.5. Variation in terms of domain-specificity and granularity ofcategories
The medical error taxonomies reviewed were designed forapplication either in various domains, i.e., generic, such as thePatient Safety Event Taxonomy, or for a specific domain, i.e.,domain-specific, such as for otolaryngology (Shah et al., 2004)or paediatrics (Woods et al., 2005). There is a relationship be-tween an error taxonomy’s domain-specificity and the granularityof its categories. Granularity refers to the ability to categorizeevents thoroughly and in detail. For example, a less granulartaxonomy is only able to categorize the cause of an event as‘communication failure’ but a more granular taxonomy is able tocategorize the cause as ‘failure of verbal communication betweenstaff and patient’. The categories in generic medical error taxono-mies are less granular than the categories in domain-specificmedical error taxonomies (Pace et al., 2005). A more generictaxonomy would need to utilize categories that address the variedcontributions from the different levels within a system’s hierar-chy. On the other hand, a domain-specific taxonomy would utilizea more specific and granular category for incidents that may beunique to that domain.
5.6. Limitations
The review is not without limitations. First, the compilation ofmedical error taxonomies, though done in an extensive manner,may have not found all potentially relevant medical error taxono-mies. Second, the content of many medical error taxonomies wasnot fully accessible. The structure and method of classificationwere sometimes not included. As we were only able to use theaccessible information, we only included fully accessible taxono-mies to ease comparison. Third, the comparison process was akinto classification and the whole review a ‘meta-taxonomy’ – weclassified medical error taxonomies based on predefined catego-ries. Classifications were based on the author’s interpretation ofcategories in the medical error taxonomies. As with all classifica-tions, inter-reviewer and intra-reviewer factors need to be consid-ered. There were also other human factors attributes that could beused in comparing medical error taxonomies but not included inthis study, such as usability.
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6. Conclusion
This review describes the inclusion of essential attributesbased on a human factors perspective in medical error taxono-mies. The attributes were classification of systemic factors ofmedical errors and classification of the causal mechanisms ofmedical errors using theoretical error concepts. Of the 26 medi-cal error taxonomies reviewed, about two-thirds classified sys-temic factors of medical errors and only a third classified thecausal mechanisms of medical errors using knowledge of cogni-tive mechanisms and theoretical error concepts. A failure to pos-sess the two attributes can influence how medical errors are
Appendix A
Taxonomy/study Domain Year T
Eindhoven classification model (medicalversion) (van Vuuren et al., 1997)
Generic 1997 Emm
National coordinating council formedication error reporting andprevention (NCC MERP) taxonomy ofmedication errors (NCC MERP, 1998)
Medicationerror
1998 N
International taxonomy of medical errorsin primary care – version 2 (Linnaeus-PC-Collaboration, 2002)
Primary care 2002 N
A preliminary taxonomy of medicalerrors in family practice (Dovey et al.,2002); An international taxonomy forerrors in general practice: a pilot study(Makeham et al., 2002)
Primary care 2002 N
TERCAP: creating a national database onnursing errors (Benner et al., 2006;Arizona State Board of Nursing, 2008)
Nursing 2002 N
Classification of medical errors andpreventable adverse events in primarycare: a synthesis of the literature(Elder and Dovey, 2002)
Primary care 2002 N
ASIPS – dimensions of medical outcome(Victoroff and Pace, 2003)
Primary care 2003 N
Errors in general practice: developmentof an error classification and pilotstudy of a method for detecting errors(Rubin et al., 2003)
Generalpractice
2003 N
Classifying laboratory incident reports toidentify problems that jeopardizepatient safety (Astion et al., 2003)
Clinicallaboratory
2003 N
Classification and consequence of errorsin otolaryngology (Shah et al., 2004)
Otolaryngology 2004 N
Derivation of a typology for theclassification of risks in emergencymedicine (Thomas et al., 2004)
Emergencymedicine
2004 N
Analysing medical incident reports byuse of a human error taxonomy (Itohand Andersen, 2004)
Generic 2004 T
understood and managed. The review indicated the benefits ofusing medical error taxonomies that use theoretical error con-cepts. Using such taxonomies increases the likelihood that sys-temic factors, cognitive mechanisms, and cause of medicalerrors are classified. The review also revealed that medical errortaxonomies had different developmental processes. The reviewindicates the need for more in-depth studies on how differencesbetween medical error taxonomies can affect medical error man-agement – not just for the purpose of integrating data betweendifferent organizations but also for understanding how such dif-ferences lead to differences in understanding and interventionsof medical errors.
heoretical error concept EEM PSF PEM Scope
indhoven classificationodel, Rasmussen’s SRKodel
� U U � Cause
/A U U � � Type� Setting� Cause� Impact
/A U U � � Type� Cause� Impact� Prevention/
mitigation/A U � � � Type
/A U U � � Type� Setting� Cause� Impact
/A U U � � Type� Cause
/A U U � � Type� Setting� Cause� Impact
/A U � � � Type
/A U � � � Type� Setting� Impact
/A U � � � Type
/A U � � � Type� Impact
RACEr U U � � Type� Setting� Cause
(continued on next page)
Appendix A (continued)
Taxonomy/study Domain Year Theoretical error concept EEM PSF PEM Scope
� Impact� Prevention/
mitigationA cognitive taxonomy of medical errors
(Zhang et al., 2004)Generic 2004 Reason’s generic error
modelling system andNorman’s seven stage actiontheory
� � U � Cause
Reporting and classification of patientsafety in a cardiothoracic intensivecare unit and cardiothoracicpostoperative care unit (Nast et al.,2005)
Generic 2005 Eindhoven classificationmodel
U U U � Type� Setting� Cause
Human errors in medical practice:systematic classification and reductionwith automated information systems(Kopec et al., 2003; Kopec et al., 2005)
Generic 2005 N/A U � � � Type
Patient safety event taxonomy (Changet al., 2005)
Generic 2005 Multiple U U U � Type� Setting� Cause� Impact� Prevention/
mitigationAnatomy of a patient safety event: a
paediatric patient safety taxonomy(Woods et al., 2005)
Paediatric 2005 N/A U U � � Type� Setting� Cause� Impact� Prevention/
mitigationJapanese primary care physicians’ errors
and perceived causes: a comparisonwith the United States (Miyasaka et al.,2006)
Primary care 2006 N/A U U � � Cause
Human factors in paediatric anaesthesiaincidents (Ritchie, 2006)
Paediatricanaesthesia
2006 Reason’s generic errormodelling system
� U U � Cause
From cognition to the system:developing a multilevel taxonomy ofpatient safety in general practice(Kostopoulou, 2006)
Generalpractice
2006 Human cognition,information-processingmodel
� U U � Cause
Application of the human factors analysisand classification system methodologyto the cardiovascular surgeryoperating room (El Bardissi et al.,2007)
Cardiacsurgeryoperatingroom
2007 Reason’s model of accidentcausation, human factorsanalysis and classificationsystem (HFACS)
� U U � Cause
Errors and adverse events in familymedicine: developing and validating aCanadian taxonomy of errors (Jacobset al., 2007)
Primary care 2007 N/A U U � � Type� Cause
Medication errors reported by US familyphysicians and their office staff (Kuoet al., 2008)
Medicationerror – primarycare
2008 N/A U U � � Type� Setting� Cause� Impact
Patient safety events reported in generalpractice: a taxonomy (Makeham et al.,2008)
Generalpractice
2008 N/A U � � � Type
Contributing factors identified byhospital incident report narratives(Nuckols et al., 2008)
Generic 2008 Reason’s generic errormodelling system
� U U � Cause
Human error, not communication andsystems, underlies surgicalcomplications (Fabri and Zayas-Castro,2008)
Surgery 2008 N/A U � � � Type
Note: N/A – Not Available; EEM – External Error Modes; PSF – Performance Shaping Factors; PEM – Psychological Error Mechanisms.
614 I.A. Taib et al. / Safety Science 49 (2011) 607–615
I.A. Taib et al. / Safety Science 49 (2011) 607–615 615
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