A review of the use of human factors classification ...

A review of the use of human factors classification frameworks that identify causal factors foradverse events in the hospital setting

R.J. Mitchella*, A.M. Williamsona,b, B. Molesworthb and A.Z.Q. Chungb

aTransport and Road Safety (TARS) Research, University of New South Wales, Sydney, Australia; bSchool of Aviation, University of NewSouth Wales, Sydney, Australia

(Received 8 August 2013; accepted 4 June 2014)

Various human factors classification frameworks have been used to identified causal factors for clinical adverse events.A systematic review was conducted to identify human factors classification frameworks that identified the causal factors(including human error) of adverse events in a hospital setting. Six electronic databases were searched, identifying 1997articles and 38 of these met inclusion criteria. Most studies included causal contributing factors as well as error and errortype, but the nature of coding varied considerably between studies. The ability of human factors classification frameworks toprovide information on specific causal factors for an adverse event enables the focus of preventive attention on areas whereimprovements are most needed. This review highlighted some areas needing considerable improvement in order to meet thisneed, including better definition of terms, more emphasis on assessing reliability of coding and greater sophistication inanalysis of results of the classification.

Practitioner Summary: Human factors classification frameworks can be used to identify causal factors of clinical adverseevents. However, this review suggests that existing frameworks are diverse, limited in their identification of the context ofhuman error and have poor reliability when used by different individuals.

Keywords: patient safety; hospital; medical errors; causal factors; reliability

1. Introduction

It is estimated that clinical adverse events contribute to between 44,000 and 98,000 deaths each year in the USA (Kohn,

Corrigan, and Donaldson 2000); in the UK this figure has been estimated at 2181 based on data from 2004 to 2005 (Shah

et al. 2004), whereas in Australia during the same time frame there were 1809 known surgical and medical care-related

deaths (Henley and Harrison 2009). The estimated proportion of hospitalised adverse events has ranged from 3.8% to 16.6%

across countries (Brennan et al. 1991; Wilson et al. 1995; Vincent, Neale, and Woloshynowych 2001; Davis et al. 2002;

Baker et al. 2004). Identifying and describing adverse event characteristics are the essential initial first step to improving

patient safety.

Patient safety has been recognised by the World Health Organization (WHO) as a global area of importance, with the

WHO calling for the ‘strengthen[ing of] science-based systems necessary for improving patients’ safety and the quality of

health care’ (WHO 2006). The core feature of any improvement to health system performance relating to patient safety is

having the capacity to report, analyse and feedback information from adverse incident reporting systems, so preventive

action can be undertaken.

Over the past decade, there has been a growth in the development and number of human factors classification systems in

health care, including both in hospital and in general practice settings (Elder and Dovey 2002; Woloshynowych et al. 2005).

The focus of these classification frameworks has largely been on adverse outcomes as a result of medication errors (Santell

et al. 2003), although some setting-specific areas have also been examined, such as for the Emergency Department (Cosby

2003), and other types of adverse events, including during air medical transport (MacDonald, Banks, and Morrison 2007).

Within health-care settings, there have been various attempts to analyse adverse events from a human factors

perspective. The most widely known approach is the London Protocol developed by Vincent et al. (2000). The Protocol

prescribes a systematic process to analyse adverse events and is largely based on Reason’s Organisational Accident

Causation Model (Reason 1997). While mostly viewed as a success, the Protocol has inherent limitations, the most notable

being the focus on describing what went wrong and linking this to surface, job or task-related features associated with the

event, as opposed to identifying the underlying cause of human behaviour (such as the type of error) and understanding the

context in which it occurs. A human factors approach involves investigating all factors contributing to the occurrence of an

adverse event. In this approach, it is essential to establish how human behaviour fits among the range of factors that cause

adverse events in order to understand its causal role and how human behavioural failures might be prevented.

q 2014 Taylor & Francis

*Corresponding author. Email: [email protected]

Ergonomics, 2014

Vol. 57, No. 10, 1443–1472, http://dx.doi.org/10.1080/00140139.2014.933886

mailto:[email protected]

mailto:[email protected]

http://dx.doi.org/10.1080/00140139.2014.933886

Errors, defined as ‘the failure of a planned action to proceed as planned’ (US Institute of Medicine 2000), have been

analysed retrospectively using human factors classification frameworks in health care, but these studies have differed in the

way that medical errors are classified. Many studies used job-related descriptions of the nature of errors. For example, in a

study of errors in radiology, the error classification included ‘illegible request’ or ‘duplicate request’ (Martin 2005). This

type of approach is informative in providing direction in which task or job areas errors are most likely to occur, but it is not

descriptive in terms of the type of cognitive failure that explains why the particular error type occurred. The main advantage

of cognitive classifications of error is that they provide insight into the nature of error itself which is helpful in

understanding both why it occurred and developing methods to prevent a recurrence.

However, there are some disadvantages of a cognitive approach. The most prominent being when it is used in isolation,

just knowing the cognitive failure type says nothing about the context in which the error occurs. Several error classification

techniques used human factors classification methods in other settings and have had this problem, for example the Human

Factors Accident Classification System and the Systematic Human Error Reduction and Prediction Approach both of which

provide significant detail on the cognitive typology of error, but both have difficulty providing interpretation of the meaning

of the error type for preventive action in the setting in which it occurred.

A significant number of studies in health care have used human factors classification frameworks, but they often vary in

the type of causal factors they identify and the depth and/or detail of the factors examined. The classification frameworks

vary significantly in the sophistication of their development including their capacity to be reliable tools for describing the

causes of adverse events. To be successful in providing insight into the involvement of human factors in adverse events,

analytic methods need to: reflect how human factors sits in the causal sequence leading to an incident; understand the nature

of human factors involvement, especially the role and nature of human failure; and the classification and coding must

include reliable and valid measures.

The aim of this paper is to review the range of human factors classification systems that have been applied in hospital

settings and to evaluate their ability to provide information about the human factors involvement in adverse events.

Specifically, it aims to: (1) identify from the published literature human factors classification frameworks that have been

used to identify the causal factors to medical adverse events in a hospital setting and (2) describe the type of adverse events

examined, the type of causal factors able to be identified (including error) and whether any inter-rater reliability assessment

was conducted.

2. Method

2.1 Search strategy

The review was undertaken using Medline, PsycINFO, Embase, CINAHL, Web of Science and PubMed from 1980 to

January 2012. Each database was searched using the search string: (‘error’ OR ‘slip’ OR ‘human factor’ OR ‘mistake’ OR

‘unsafe act’ OR ‘accident’) AND (‘patient safety’ OR ‘adverse event’ OR ‘complications of care’ OR ‘patient harm’ OR

‘iatrogenic’) AND (‘tool’ OR ‘framework’ OR ‘classification’ OR ‘model’ OR ‘taxonom*’). The PubMed search was

conducted by searching the ‘title’ and ‘abstract’ only, and the PsycINFO database search was conducted using ‘abstract’

only. The limitations on the search tactics for these two databases were necessary due to the nature of the keyword search

terms. For PubMed, if ‘all fields’ were searched this resulted in .3000 citations and for PsycINFO it resulted in .11,000

citations. All non-English research was excluded. In addition to the systematic keyword search, snowballing using journal

reference lists was conducted to identify additional articles. Grey literature searches were conducted to identify relevant

documents and reports.

2.2 Stage one – abstract review

The abstracts of citations identified were independently assessed by three reviewers (RM, BM, AW) for relevance. For stage

one, a broad search strategy was adopted to limit the exclusion of possible relevant studies. Citations moved to stage two if

they described the creation or use of a human factors classification framework for medical adverse events. For this review,

human factors were considered in a broad sense to include ‘unsafe acts (including human error and violations), and also

other factors, such as individual, organisational, technological, and environmental factors, that might be considered to have

an effect on human or system performance’.

A classification framework was considered to be a conceptual, hierarchical structure that enabled the classification of

factors involved in an incident. For stage one, citations were also included for further review if there was evidence in the

abstract that classification of human error had occurred, even though no specific classification framework was mentioned.

The index of concordance between the three reviewers was 92.5%. The reviewers met and resolved any disagreements on

citation abstracts by consensus. All citations that were assessed as relevant moved to stage two.

R.J. Mitchell et al.1444

2.3 Stage two – article review and data extraction

For each citation that was identified as relevant for inclusion, the full-text article was assessed by three reviewers (RM, BM,

AW) for relevance. A full-text article moved to the next stage if it described the use of a human factors classification

framework that identified the causal factors to medical adverse events that had occurred in a hospital setting. This included

classification systems that had been developed for another industry, but had been adapted for use in a hospital setting.

However, it excluded studies that identified potential failure modes. The index of concordance between the three reviewers

was 72.4%.

During this stage, two reviewers (RM, BM) extracted information from the final selection of full-text articles. The

following information, where available, was extracted from the final selection of full-text articles: author(s) and publication

year, study aim, type of adverse event, setting and sample size, reliability assessment method and result(s), and the types of

causal factors identified. A third reviewer (AC) compared the extract information for consistency, and disagreements were

resolved by consensus.

3. Results

Citations that were identified from each database using the search string were imported into the reference management

software, Endnote X3 (Thomson Reuters 2009). After deletion of duplicate references, erratum and corrections, 1997

references were identified using the search strategy.

3.1 Stage one

The initial review of citation abstracts identified 242 citations for further full article review. The abstracts identified either

mentioned a classification framework (or tool) that had been developed and/or used to identify the human factors

contribution to adverse events or there was evidence that classification of human error had occurred, even though no specific

classification framework was mentioned in the abstract.

3.2 Stage two

The review of 242 full-text articles identified 38 articles that used classification frameworks that identified causal factors to

adverse events in a hospital setting. There were also 22 potential articles identified from reference lists of full-text articles

during the stage two review. Of these, none were retained as they did not meet the review criteria.

Appendix 1 summarises the details of the 38 studies identified in the review and Table 1 summaries the key strengths

and limitations of the classification frameworks. The majority of published research that used classification frameworks to

Table 1. Strengths and limitations of existing human factors classification frameworks that identify the causal factors of adverse eventsin a hospital setting.

Strengths Limitations

Most human factors classification frameworks recordedinformation on a fairly comprehensive list of causalfactors, leading to a wide range of event causalfactors being able to be identified

Almost all human factors classification frameworks describedwhat went wrong in the context of either job/task errors orcognitive errors, rather than using both approaches. Only usingcognitive error classifications provides no information on thecontext in which the error occurs, while only using job/taskerrors fails to identify the cause of human behaviour

Around half the human factors classification frameworksrecorded information on job/task errors (e.g. duplicaterequest), which are useful to identify during whichactivity errors are likely to occur

There was considerable variety in the quantity, depth and definitionsof the causal factors examined in each human factors classificationframework. The frameworks did not often included factors relatedto the patient, e.g. complexity of the patient’s medical condition

Half the human factors classification frameworks recordedinformation on cognitive errors (e.g. skill-, rule-,knowledge-based errors), which can provide insight intothe nature of human error

None of the human factors classification frameworks examined thecausal sequence of events leading to the occurrence of theadverse incident

One-third of the human factors classification frameworks did not havea theoretical model underpinning the human factors framework

There was limited assessment of inter-rater reliability and noassessment of intra-rater reliability for the human factorsclassification frameworks

Ergonomics 1445

identify the causal factors in medical adverse events in the hospital setting took place in the USA (57.9%) and Europe

(26.3%). The size of studies varied considerably, with 21.1% involving fewer than 30 cases (including the four case studies)

and 21.1% involving more than 1000 cases with the remainder of studies between these two extremes. Around half of the

studies (55.3%) looked at error-related outcome events, mainly involving medication-related errors (26.3% of studies), with

studies of diagnostic, surgical and medical errors (5.3% of studies each) and single studies of errors including anaesthetic,

hypertension maintenance, medical imaging, nursing and simulation training errors (2.6% of cases each).

Approaching one-third (31.6%) of studies focussed on different types of incidents, some of which were specific types,

such as wrong-site surgery, neurosurgery and spinal surgery incidents (each with one study), but most were general and

unspecified such as avoidable failures, unintended incidents, adverse events and health-care incidents. In most studies, the

source data were from existing databases (68.4%) but six studies (15.8%) involved observational methodologies and three

involved questioning of health-care professionals by survey (7.9%) or interview (2.6%). Importantly, half the studies failed

to provide a definition of the outcome or adverse event that was the objective of the study with roughly equal proportions

involving general patient safety or adverse events and different types of error.

In over two-thirds of studies (71.1%), the framework was based on a specific model or an existing categorisation, in

particular Reason’s organisational accident causation model (31.6%) (Reason 1997), the Joint Commission on

Accreditation of Healthcare Organizations (JCAHO) classification including the Eindhoven classification of error (13.2%)

(Chang et al. 2005; Van der Schaaf and Habraken 2005) or the National Coordinating Council for Medication Error

Reporting and Prevention (NCCMERP) classification using the MEDMARX database (13.2%) (NCCMERP 2013). Just

over one-third (36.8%) developed the classification system for the context from the data set itself.

Most studies used a hierarchical classification system involving at least two levels of classification (63.2%) and up to

four levels in two studies. Most of the studies involved classification of both error type and contributing causal factors

(68.4%), 21.1% only classified contributing causal factors and 13.2% only classified error type. Of the studies that classified

causal factors, the most common were organisational factors, like supervision and training (76.3% of studies),

communication and teams (63.2%) and equipment-related factors (57.9%). In addition, some studies included factors

relating to procedures (42.1%), patients (42.1%), environment (26.3%) and workload (23.7%).

The studies that included classification of error type were split fairly equally between task-related classifications

(42.1%) and cognitive-related classifications (55.3%) and two studies used both (5.3%). While the task-related

classifications often reflected the nature of the outcome being studied, one-third used theMEDMARX classification (34.2%)

as they were applied to medication errors (Phillips et al. 2001; Hicks et al. 2004; Hicks, Cousins, and Williams 2004; Hicks,

Becker, and Jackson 2008; Santell et al. 2009). Half of the cognitive classifications of error type involved the skill, rule,

knowledge classification based on Rasmussen’s work (1982) or Reason’s modification of it (1990). Seven studies (18.4%)

also included classification of violations also based on the Reason classification. Three studies used the Eindhoven error

classification (Nast et al. 2005; Henneman et al. 2010; Rodrigues et al. 2011). The remainder involved variations mainly of

the Reason classification, including simple classification like active failures or not, faulty information processing or

cognitive or non-cognitive errors to more complex classifications including inattention, memory failure and confusion (Itoh,

Omata, and Andersen 2007), or carelessness and distraction, poor assessment of the situation (Kantelhardt et al. 2011).

Most studies simply described the occurrence of causal factors and error types by reporting of univariate results for the

presence or not of individual factors and types. Despite many studies putting forward a systems approach to the analysis of

causes, very few studies looked at the patterns of causation for patient safety incidents through looking at relationships

between causal features. Two studies looked at the relationships between causal factors and outcomes by statistical

modelling (Wiegmann et al. 2007; Chang and Mark 2009), both including organisational causal factors in the modelling but

not error types. Two studies described the framework for studying relationships between causal features through case

studies alone (Eagle, Davies, and Reason 1992; Taylor-Adams, Vincent, and Stanhope 1999).

Just over one-third of the studies (34.2%) conducted some sort of reliability assessment as part of the research, including

one (2.6%) study where the results of the reliability assessment were published in a second article relating to the same

research (Brennan et al. 1991, cited in Leape et al. 1991) and four (10.5%) studies used the same classification framework

where a reliability assessment was conducted as part of other research (Forrey, Pedersen, and Schneider 2007, cited in Hicks

et al. 2004; Hicks, Cousins, and Williams 2004; Hicks, Becker, and Jackson 2008; Santell et al. 2009).

The method of reliability assessment varied and included Cohen’s Kappa statistic (31.6%), Yule’s Q (2.6%),

Cronbach’s a (2.6%) and percent agreement (2.6%). Two (5.3%) studies reported using informal consensus to achieve

agreement on classifications. Of the 12 studies that involved a formal reliability estimate, most (75.0%) looked at the

reliability of coding of causal factors or error types, while the remainder looked only at the classification of outcome events

such as actual/potential adverse events, or whether a failure was involved or not. The studies that looked at reliability of the

classification of outcome typically involved a binary decision and found high reliabilities (e.g. Kappa scores, Cronbach’s aand Q scores exceeding 0.8) (Battles and Shea 2001; Tran and Johnson 2010; Albayati et al. 2011). The reliabilities of


causal factors including error were considerably more variable. Some studies reported very good reliabilities such as Kappa

scores around 0.8 (Friedman et al. 2007; Tourgeman-Bashkin, Shinar, and Zmora 2008; Parker et al. 2010), whereas for

others Kappa scores were more moderate (e.g. 0.61 (Forrey, Pedersen, and Schneider 2007)) or low (e.g. 0.34, 0.42 (Itoh,

Omata, and Andersen 2007); 0.38 (Astion et al. 2003)). One study reported the results for four observers in a study of spinal

surgery, and found that Kappa scores ranged from 0 to 0.85 between observers and that reliability varied greatly between

factors being coded (Mirza et al. 2006).

4. Discussion

The current study identified a comparatively small number of peer-reviewed published research papers that have attempted

to identify causal factors of adverse events in a hospital setting, with only 38 studies identified in the last 32 years. While a

number of standard taxonomic systems have emerged, such as the JCAHO Patient Safety Event Taxonomy (Chang et al.

2005), most of the studies reviewed developed new approaches for understanding the causes of patient safety events using

existing databases and sometimes data collected for the purpose by observation, surveys or interview.

This review demonstrated that most of the classification frameworks were fairly comprehensive and acknowledged the

breadth of causes that might potentially be involved in patient safety including human factors-related causes, although there is

considerable diversity in the type of human factors classification frameworks used. Someof this diversity naturally occurred due

to the nature of the event studied,with causal factors identified for spinal surgery differing for those related tomedication errors,

yetmost classification systems had common categories, such as organisational factors. Classification systems ranged frombasic

frameworks with only single broad-level categories, to more sophisticated frameworks breaking down these single broad

categories into between two to four sub-levels to describe in more detail the causal factors of adverse events.

Most taxonomies included a number of different causal factors, reflecting thewide range of possible features that could play

a role in patient safety events, although there was considerable variation in specific factors between taxonomies. Almost all

studies included causal factors falling into broad organisational, equipment, communications and teamwork categories. The

majority of taxonomies included some classification of error type and these were split fairly evenly between task-related

classifications, such as the NCCMERP taxonomy formedication errors, and cognitive-related classifications based onReason’s

work. In contrast, only a few of the classification frameworks considered the role of patients in contributing to the adverse event,

with just over one-third (36.8%) identifying causal factors related to the patient. The role of the complexity of the patient’s

medical condition was rarely included. Leape et al. (1991), while not identifying complexity issues in their classification

framework, stated that complexity of the health condition or treatmentwas amajor determinant of adverse event risk, alongwith

patient co-morbid health conditions, and recommended that information on these items as potential contributing factors be

obtained.Benavidez et al. (2008) used the only framework that identified any complexity issues thatmay have contributed to the

event in terms of ‘rare or complex anatomy’ of the patient. Clearly, new classification systems need to be comprehensive and

reflect the potential factors and agents that might contribute to adverse events.

While comprehensiveness is an essential attribute of a patient safety classification system, to be useful, the classification and

codingmust alsobe reliable. The codingof factorsmust be consistent and reproducible between coders and betweenoccasions of

coding. Reliability assessments provide information on inter-rater (or intra-rater) agreement and can provide an indication of the

agreement between raters on the identification of events of interest (Stemler 2004). High inter-rater reliability, especially for

adverse event and causal factor identification, is important for validity of the framework and ultimately for the development of

effective preventive strategies.Unfortunately,most of the studies identified in this review failed to examine this important aspect

of their classification system. Although around one-third made at least some mention of reliability, it was often at the highest

level of coding and no studies looked at intra-coder reliability. Nevertheless, where assessed, the reliability scores reportedwere

generally moderate to good (Altman 1991). The exceptions were two studies that attempted coding of deeper classifications.

Itoh, Omata, and Andersen (2007) found that the reliability of the coded content was only fair, andMirza et al. (2006) obtained

quite low reliabilities between pairs of observers identifying causal factors to adverse surgical events. Reliability could be

improved by ensuring that causal factor categories are mutually exclusive, by developing a data dictionary that contains clear

definitions of each causal factor, along with examples of causal factor classifications. Improvements can also be made by

conducting exhaustive pilot testing of the framework and by providing training opportunities for staff in using the framework,

and regular discussion of difficult event classifications (Gliklich and Dreyer 2010).

There are a number of reasons why deeper coding might be expected to be less reliable. First, it can be difficult to

categorise the types of causal factors identified in the frameworks due to their divergent classification structures. For

example, when examining communication problems, Chang and colleagues (Chang et al. 2005; Chang and Mark 2009)

include ‘communication problems’ as a second sub-level classification within ‘team factors’, while Graber, Franklin, and

Gordon (2005) includes ‘teamwork or communications’ within ‘organisational factors’, as does Meurier (2000). However,

Mirza et al. (2006) and Leape et al. (1991) include ‘communication failures’ under ‘system factors’. Itoh, Omata, and

Ergonomics 1447

Andersen (2007), Phillips et al. (2001), Santell et al. (2009), Taxis and Barber (2003) and Webb et al. (1993) all include

‘communication’ problems as a broad first-order category. Some of these differences between frameworks will stem from

their method of development. Some classification frameworks were derived through naturalistic means, such as by grouping

natural categories within existing data (Runciman et al. 1998), while others were derived from a theoretical basis and/or

literature reviews (Vincent, Tayor-Adams, and Stanhope 1998; ElBardissi et al. 2007) so terms used for causal factors may

not be mutually exclusive, standardised nor formally defined. Second, some of the features included in the taxonomies may

be difficult to verify. This particularly applies to error-type coding especially of errors in decision-making or violations,

both of which require knowledge of the person’s intention. This is usually not known from the sources used to code adverse

events and is only available from the individual involved and only post hoc, which may in turn influence the recall of the

event. Both cases will make coding more difficult and unreliable. It is important to have high inter-rater reliability scores in

the identification of the circumstances leading to these events as this is an indicator that the classification framework can be

used consistently by a range of individuals to reflect the causes of adverse events.

Many of the studies reviewed recognised the importance of a systems-based approach to understanding the causes of

adverse events, in particular by reference to the Reason model of organisational accidents. The major conceptual basis to

this approach is that accidents occur due to interactions between features in the sequence of causes leading to the accident

event. In this approach, errors are created by systems that make them either more likely to occur in the first place or fail to

prevent them from occurring through use of safety barriers. Despite this acknowledgement, almost all of the studies simply

catalogued the existence of causal factors, errors and error types. They failed to look at the relationships between errors and

error types and the types of factors or events that typically occur with them. It is known from studies of occupational

fatalities, for example, that particular factors influence the occurrence of different types of errors. For instance, in an

analysis of the causes of all workplace fatalities in Australia over a three year period, errors in skilled behaviour were most

likely to be preceded by unsafe standard operating procedures, whereas errors in applying known rules were more likely to

occur with a range of other precursors (Williamson, Feyer, and Cairns 1996). Similarly, an analysis of the causes of safety-

related incidents in aircraft maintenance showed that errors due to memory lapses occurred most often under conditions of

time pressure (Hobbs and Williamson 2003). In the current study, the importance of other factors in the system in which the

error occurred is recognised, but no studies examined the relationships between these factors and particular error types.

Further work in this area might consider the capability of classification frameworks in identifying a causal sequence of

events to the adverse event. This would provide additional assistance to identify appropriate preventive measures. Eagle,

Davies, and Reason (1992) recognise the benefit of establishing a temporal sequence of key events leading to medication

errors. Likewise, Itoh, Omata, and Andersen (2007) also considered that obtaining information on the ‘relationship between

errors, situations, performance shaping factors and consequences’ was necessary to effectively establish the root causes of

an adverse event. Clearly, just knowing that an error occurred is not very useful for developing strategies to prevent them.

We need to understand when and why errors occur and what factors make them more likely. This requires more in-depth

analysis than is currently being conducted in studies of the causes of adverse events.

A notable feature of nearly half of the studies was that the outcome or focus of the study was unclear and not defined.

Occasionally, the outcome of interest was inherently defined such as wrong-site surgery events (Chang et al. 2005);

however, lack of definition is a particular issue for outcomes such as avoidable failures (Albayati et al. 2011) that could

include a range of possible outcomes, both severe and minor. The problem was not limited to general adverse events. Error-

related outcomes were also often not clearly defined, including surgical errors (ElBardissi et al. 2007), hypertension-related

errors (Lee, Cho, and Bakken 2010) and anaesthetic errors (Nast et al. 2005; Nyssen and Blavier 2006). Even more

troubling, a number of studies used the terms medical adverse events and medical errors interchangeably (e.g. Friedman

et al. 2007) which simply add to the confusion. Clear definitions of adverse events exist: for example, in the seminal paper

by Leape et al. (1991), an adverse event was defined as an unintended injury that was caused by medical management and

that resulted in measurable disability. Similarly, there are many definitions of error, usually involving planned or intended

human action that does not achieve its planned or intended outcome (Reason 1990). It seems that many of these attempts to

develop taxonomies for patient safety need firmer foundations in which the objective or outcome is much better defined.

It should be acknowledged that some of the classification frameworks were developed for a specific purpose, such as

MEDMARX, which is used to report on and classify medication-related adverse events. Therefore, it is possible that all

categories of causal factors in all classification frameworks may not necessarily be relevant for all types of adverse incidents

due to their being designed to principally identify and describe a particular type of event, such as medication errors.

However, there is still likely to be value in adopting a classification framework for a more generic application (i.e. for all

types of adverse events) to enable comparisons between facilities, if the information gained was still specific enough to

target effective preventive measures for all types of adverse events. The WHO has recognised the need for such standard

terminology in the patient safety areas, developing a conceptual framework for patient safety (WHO 2009). However, in its

current form, the WHO’s conceptual framework does not provide unique identifiers nor definitions for each item, key


‘concepts’, ‘classes’ of items and hierarchical links and relationships between items are not clearly identified, not all of the

classification categories are mutually exclusive or exhaustive, and the effectiveness of the framework to classify causal

factors to adverse events is yet to be tested extensively in the field (Schulz et al. 2009; Feijter et al. 2012). It is essential that

any recommended classification framework designed for use in health care uses recognised and well-defined standard terms

relating to adverse events so it provides the capability to benchmark safety performance internationally, nationally and

between local facilities.

There are limitations of the current review. It is possible that some unpublished research and grey literature were not

examined and thus some classification frameworks that have been used in a hospital setting were not considered. It is also

possible that some of the frameworks examined had some form of reliability testing conducted as part of their development

process that was not reported, although clearly it should have been.

5. Conclusion

Few peer-review studies have been published regarding the identification of causal factors of adverse events in a hospital

setting. Having information on specific causal factors for an adverse event enables attention to be focused on areas where

improvements are most needed. This enables, as much as possible, information to assist with the elimination or limitation of

the effect of antecedent events in the contribution to adverse events. This review has highlighted some opportunities for

improvement in the development and use of patient safety classification systems. These include that, first, more emphasis

needs to be placed on developing comprehensive, but reliable coding systems that allow understanding of what led to the

event. This means clearly defined outcomes and well-defined, mutually exclusive classifications that include the range of

possible factors that might contribute to the patient safety outcomes of interest. Second, and most importantly, more effort

needs to be placed on obtaining an understanding of and why these adverse events occur through more sophisticated

analysis of the relationships between causal factors, including the role of human factors, especially that of error.

Acknowledgements

R. Mitchell was supported by an ARC-linkage post-doctoral fellowship (LP0990057). A. Williamson is supported by an NHMRC SeniorResearch Fellowship. The views expressed in this paper are the views of the authors that do not necessarily reflect the views or policies ofthe funding agencies.

Funding

This research was funded by an Australian Research Council linkage grant (LP0990057) and the NSW Clinical Excellence Commissionand the NSW Ministry of Health.

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Ergonomics 1451

http://www.nccmerp.org/medErrorCatIndex.html

http://PAREonline.net/getvn.asp?v=9&n=4

Appendix

1.

Summaryofresearch

usingclassificationfram

eworksthat

identified

thecausalfactors

ofadverse

eventsin

ahospital

setting.

Author(s)

andyear

Aim

Typeofevent

Settingandsamplesize

Reliabilityassessment

Causalfactor/errortype

Astionet

al.

(2003)

Developanduse

aclassificationsystem

tocharacterise

incident

reportsrelatedto

lab-

oratory

services

Adverse

events

(actual

orpotential)

Academ

icmedical

centrein

theUSA,

June2000–September

2001.A

totalof129

laboratory

incident

reportswereexam

ined.

Ofthese,122were

potential

adverse

events,6wereactual

adverse

eventsand1

involved

actual

and

potential

adverse

events

Inter-raterreliability

measuredusing

Cohen’sK-statisticand

was

0.79forthe

identificationofthe

typeofevent(i.e.

actual

versuspoten-

tial),0.10forprevent-

ability,0.38for

cognitive/non-cogni-

tiveerrortype

1Non-cognitiveonly

(i.e.slips)

1Cognitiveonly

1Cognitiveandnon-cognitive

1Unable

todetermine

Albayatiet

al.

(2011)

Investigateavoidable

failuresin

patient

safety

forpatients

undergoingvascular

andendovascular

procedures

Avoidable

failures

Therewere1145fail-

uresidentified

from

1847eventsfrom

Sep-

tember

2009to

May

2010at

StMary’s

Hospital,London


was

assessed

forfailure

detection.Cronbach’s

awas

0.84

1Absence

1Communicationfailure

1Decision-related

surgical

error

1Directequipmentfailure

1Equipmentunavailability

1Equipment/workspacemanagem

entfailure

1Equipmentconfigurationfailure

1External

pressures

1External

resourcefailure

1Fatigue

1Faultresolutionfailure

1Patient-relatedproceduraldifficulties

1Planningfailure

1Procedure-related

error

1Psychomotorerror(general)

1Psychomotorerror(surgical-related)

1Psychomotorerror(radiological-related)

1Resourcemanagem

ent

1Safetyconsciousness

1Team

conflict

1Technical

failure

1Vigilance/awarenessfailure

Battles

andShea

(2001)

Comparisonstudyto

testthereliabilityof

therootcause

classifi-

cationprocess

ofthe

Eindhoven

Classifi-

cationModel

Medical

errors

Atotalof25cases

from

emergency

medicineandintensive

care

from

ateaching

hospital

intheUSA

Classificationofroot

causeswas

assess

usingYule’s

Q,witha

correlationof0.86

1Technical

1external

1design

1construction

1materials

1Organisational

1external

1protocols/procedures

(Continued)


Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




1transfer

ofknowledge

1managem

entpriorities

1culture

1Knowledge-based

behaviour

1Rule-based

behaviour

1qualifications

1coordination

1verification

1intervention

1monitoring

1Skill-based

behaviours

1slip

1tripping

1Other

factors

1patient-related

Benavidez

etal.

(2008)

Describeataxonomy

forandexam

ine

diagnostic

errors

inpaediatric

echocardiography

Diagnostic

errors

Children’s

Hospital

Boston,USA,Decem

-ber

2004–August

2007.Around50,660

echocardiogramsper-

form

edand87diag-

nostic

errors

identified

Inform

alconsensus

usedto

finalisecateg-

orisationoferror,

severity,preventability

androotcause

1Administrativeordataentryerrors

1incorrectnam

eassigned

toim

agingdata

1schedulingerror

1incorrectdataentry

1Proceduralorconditional

factors

1failure

toconfirm

patientidentity

ordiagnosis

1incomplete

physicalexam

ination

1poorim

agingenvironment

1failure

toim

proveim

agingconditions

1Communicationorinform

ationerrors

1lackingormisleadingpatienthistory

1noaccess

topriorstudies

1failure

toreportcritical

findingsin

atimely

fashionto

referringphysician

1incorrectrequisition

1Cognitiveerrors

1insufficientknowledgebase

1insufficienttechnical

skills

1faultydatasynthesis

1lackofconsiderationofpatient’ssituation/

conditionrelevantto

diagnosis

1misidentification/interpretationofafinding

1premature

case

closure

1distractionbyother

diagnoses/findings/

focusedquestion

1under

appreciation/considerationof

afinding

(Continued)

Ergonomics 1453

Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




1over

appreciationofafinding

1confirm

ationbias

1incorrect/im

proper

calculation

1Technical

factors

1artefact

1modalitylimitation

1pooracoustic

windows

1equipmentmalfunction

1Patient-ordisease-related

factors

1rareorcomplexanatomy

1misleadinganatomyorphysiology

Cagliano,

Grimaldi,and

Rafele(2011)

Operationalise

Reason’s

theory

of

failuresbydeveloping

amethodologyto

investigatehealth-care

processes

andrelated

risksim

pactingeither

directlyorindirectly

onpatients

Drugmanagem

ent

process

inaphar-

macy

A1372bed

teaching

hospital

locatedin

Torino,Italy.Thiswas

anexam

pleapplication

Nil

1Internal

risk

sources

1organisational

1organisational

structure

1human

resources

1operations

1technology

1inform

ationsystem

1equipment

1communication

1inform

ationexchanges

1communicatingvariationsanddecisions

1structure

1layout

1networks

1External

risk

sources

1product

supplying

1deliverylead

times

1qualityofdelivered

products

1documentationmanagem

ent

1delivered

item

s1fi

nance

1supplier

assets

1contractspecifications

1environment

1guidelines

byregional

council

1social

issues

1epidem

iological

events

1naturalevents

(Continued)


Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




Catchpole

etal.

(2006)

Developaprospective

directobservation

methodologyforiden-

tifyingweaknessesin

thesystem

ofsurgery

Majorandminor

failuresin

surgery

Atotalof24paediatric

cardiacsurgeriesin

the

UK,October

2003–

July

2004.Therewere

366minorfailures

identified

Nil

1Threats

1culturalandorganisational

threats,e.g.

external

pressures,fatigue,safety

consciousness

1patientthreats,e.g.patient-sourced

procedural

difficulties,temperature

controldifficulties

1taskthreats,e.g.cannulationdifficulties,fault

resolutionfailure

1environmentalthreats,e.g.equipment/work-

spacemanagem

entfailure,external

resource

failure

1Errors

1technical

errors,e.g.decision-related

surgical

error,expertise/skillfailures

1non-technical

errors,e.g.planningfailure,

vigilance/awarenessfailure

Changet

al.

(2005)

Developandapply

amethodofclassifi-

cationfornear-misses

andadverse

events.

Wrongsite

sur-

geries

JointCommissionsen-

tinel

eventdatain

the

USA,January1995–

Decem

ber

2002.

Identified

209wrong

site

surgeries

Nil

1Organisational

1external

1managem

ent

1maintenance

oforganisational

resources

(selection,training,staffing)

1monetarybudgets

1organisational

culture

1chainofcommand

1delegationofauthority

andresponsibility

1communicationchannels

1form

alaccountability

1safetyculture

1protocols/processes

1processes

(tim

epressure,incentivesystem

s,schedules)

1organisational

procedures(perform

ance

standards,objectives,documentation,

instructionsaboutprocedures)

1oversight(riskmanagem

ent,establishment

anduse

ofsafety

programmes)

1transfer

ofknowledge

1supervision

1training

1Technical

1facilities–equipment/materials

1design,construction

(Continued)

Ergonomics 1455

Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




1malfunction,obsolescence

1availability

1external

1Error(actual

andnearmisses)

1patientfactors

1practitioner

1skill-based

1rule-based

1knowledge-based

1unclassifiable

1external

1Violations

1negligence

1recklessness

1intentional

rule

violations

Chang(2007)

Investigatethecharac-

teristicsofnursing

unitsthat

contribute

tomedicationerrors

Severemedication

errors

Atotalof286nursing

unitsin

146acute

care

hospitalsin

theUSA,

January–June2003for

firstgrouphospitals

andJanuary–June

2004forsecondgroup

hospitals.Therewas

anaverageof3.71errors

over

sixmonths

Nil

1Work

environmentfactors

1carecomplexity

1work

dynam

ics

1RN

proportion

1RN

hours

1Team

factors

1communicationwithphysicians

1communicationwithpharmacists

1expertise

1commitmentto

care

1Personfactors

1education

1unitexperience

1Supportservices

availability

1unit-dose

system

1CPOE

1automated

medicationadministrationsystem

1IV

team

services

1transcribingorders

1pharmacistconsultation

1Patientfactors

1age

1health

1previoushospitalisation

(Continued)


Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




ChangandMark

(2009)

Comparetheantece-

dentsofsevereand

non-severemedication

errors

Severeandnon-

severemedication

errors

Atotalof146acute

care

hospitalsin

the

USA.Duringasix-

month

period,1671

observationswere

conducted,withan

averageof0.61severe

and3.86non-severe

medicationerrors

per

month

Nil

1Work

environmentfactors

1work

dynam

ics

1RN

hours

1Team

factors

1communicationwithphysicians

1nursingexpertise

1Personfactors

1education

1experience

1medication-related

supportservices

1Patientfactors

1age

1healthstatus

1previoushospitalisation

Chippset

al.

(2011)

Exam

inenurses’jud-

gem

entsoferror

classificationandlevel

ofrisk

severityassoci-

ated

witherrors;deter-

minewhether

thereare

differencesin

error

classificationandrisk

severityjudgem

entsby

years

ofexperience,

clinical

specialty,level

ofeducationandjob

title,

anddescribe

nurses’judgem

ents

aboutspecificfactors

contributingto

anerror

andtheassociationof

contributingfactors

withrisk

severity

judgem

ents

Fourclinical

vign-

ettes

Survey

of435nurses

whoreceiveem

ails

from

theOhio

Nurses

Association(9%

response

rate)

Nil

1Knowledgeandexperience

1inadequateknowledge

1inadequateclinical

experience

1poorclinical

decision-m

aking

1Clinical

practice

1compromised

physicalstateofnurse

1automatic/habitual

response

1poorhandoff

1Failure

tofollow

standardsofpractice

1Work

environment

1excessiveworkload

1poorteam

work

1stressfulworkingenvironment

1unexpectedchangein

workload

1disruption/interruptionin

workflow

Eagle,Davies,

andReason

(1992)

Toillustrate

theprin-

cipal

featuresofthe

active/latentfailure

model

usingacase

reportofpulm

onary

aspirationofgastric

contents

Casestudy

Operatingroom,

unspecified

hospital.

Onecase

studyin

Canada

Nil

1Human

error

1Personnel

1Equipment

1Procedures

1Policies

(Continued)

Ergonomics 1457

Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




ElBardissiet

al.

(2007)

Determinethetrans-

ferabilityofahuman

factors

model

devel-

oped

intheaviation

industry

topatient

safety

Surgical

errors

MayoClinic

inthe

USA.Structuredinter-

viewswith68individ-

uals

Nil

1Organisational

influences

1climate

1process

1resourcemanagem

ent

1Unsafe

supervision

1inadequatesupervision

1problem

correction

1inappropriateoperations

1Preconditionsto

unsafe

acts

1environmentalfactors:technological,physical

1adverse

mentalstates

1adverse

physiological

states

1physical/mentallimitations

1teamwork

1personal

readiness

1Unsafe

acts

1decisionerrors

1skill-based

errors

1perceptual

errors

1routineviolations

1exceptional

violations

Friedman

etal.

(2007)

Understandandclas-

sify

causalfactors

linked

tomedication

errors

Medicationerrors

YaleNew

Haven

Organ

Transplantation

Center,April2004–

March

2005.Identified

149errors

in93

patientswhowerepre-

scribed

ameanof10.9

medicationseach

Inter-rateragreem

ent

forerrortypes

assessed

bycalculatingkcoef-

ficients.Pairw

isecom-

parisonsofthethree

raters

had

coefficients

of0.87,0.93and0.88.

Therewas

anoverallk

of0.89(95%

CI:0.84–

0.94)

1Finance

1Pharmacy

1Other

physician

1Outpatienttransplantteam

1Inpatienttransplantteam

1Nursinghome

1Patient

(Continued)


Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




Graber,Frank-

lin,andGordon

(2005)

Clarify

theaetiologyof

diagnostic

errors

ininternal

medicineand

todevelopaworking

taxonomy

Diagnostic

errors

Fivelargeacadem

ictertiary

care

medical

centres

intheUSA.

Identified

100diag-

nostic

errors

over

five

years

Inform

alconsensus

1System

-related

contributionsto

diagnostic

error

1technical

1technical

failure

andequipmentproblems

1organisational

1clustering

1policy

andprocedures

1inefficientprocesses

1teamwork

orcommunications

1patientneglect

1managem

ent

1coordinationofcare

1supervision

1expertise

unavailable

1trainingandorientation

1personnel

1external

interference

1Cognitivecontributionsto

diagnostic

error

1faultyknowledge

1knowledgebaseinadequateordefective

1skillsinadequateordefective

1faultydatagathering

1ineffective,

incomplete

orfaultyworkup

1ineffective,

incomplete,orfaultyhistory

andphysicalexam

ination

1faultytestorprocedure

techniques

1failure

toscreen

(pre-hypothesis)

1pooretiquette

leadingto

poordataquality

1Faultysynthesis:faultyinform

ationproces-

sing

1faultycontextgeneration

1overestimatingorunderestimatinguseful-

nessorsalience

ofafinding

1faultydetectionorperception

1failedheuristics

1failure

toactsooner

1faultytriggering

1misidentificationofasymptom

orsign

1distractionbyother

goalsorissues

1faultyinterpretationofatestresult

1reportingorremem

beringfindingsnot

gathered

1Faultysynthesis:faultyverification (Continued)

Ergonomics 1459

Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




1premature

closure

1failure

toorder

orfollow

uponappropriate

test

Hennem

anet

al.

(2010)

Describethetypes

of

errors

committedor

recovered

inasimu-

latedenvironmentby

studentnurses

Sim

ulatederror

Atotalof50senior

nursingstudentspar-

ticipated

inoneoftwo

simulationexercises

lastingbetween15and

30minutesin

theUSA

Inter-raterreliabilityof

95%

betweentwo

researcherswhoinde-

pendentlyreviewed

fivevideotapes

(not

included

infinal

anal-

ysis)

1Coordination

1errorrelatedto

communicationwithMD

1errorrelatedto

communicationwithpatient/

family

1Verification

1errorrelatedto

identificationofpatient

1errorrelatedto

patientallergyinform

ation

1Monitoring

1errorrelatedto

failure

tomonitororcorrectly

monitorcritical

patientassessmentinfor-

mation

1errorrelatedto

failure

torecogniseabnorm

alfindings

1Intervention

1errorsrelatedto

failure

toinstituteappropriate

intervention/treatment(omission)

1errors

relatedto

incorrectordelayed

intervention/treatment(commission)

Hicks,Becker,

andJackson

(2008)

Describetheerror

term

susedin

MED-

MARX

Medicationerrors

Medical

intensivecare

unitin

theUSA.One

case

study

Forrey,Pedersen,and

Schneider

(2007)

obtained

inter-rater

reliabilityofk¼

0.61

incategorisingmedical

errors

usingMED-

MARX

in27medical

errorscenarios

1Situational

1Environmental

1Organisational

Hickset

al.

(2004)

Describemedication

errorrecordsfrom

MEDMARX

inpost-

anesthesia

care

units

(PACU)

Medicationerrors

Atotalof189facilities

intheUSA

that

sub-

mittedamedication

errorrecord

specificto

PACUs,August1998–

March

2002.There

were645medication

errors

specificto

aPACU

Forrey,Pedersen,and

Schneider

(2007)

obtained

inter-rater

reliabilityofk¼

0.61


errors

usingMED-

MARX

in27medical

errorscenarios

1Distractions

1Workload

increase

1Staff,inexperienced

1Noaccess

topatientinform

ation

1Shiftchange

1Cross

coverage

1Emergency

situation

1Staffing,insufficient

1Stafffloating

1No24-hourpharmacy

1Staff,agency/tem

porary

1Poorlighting

(Continued)


Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




Hicks,Cousins,

andWilliam

s(2004)

Describemedication

errors

from

MED-

MARX

Medicationerrors

Atotalof482MED-

MARX

participating

healthcentres

inthe

USA,2002.There

were192,477medi-

cationerrors

inthe

database

Forrey,Pedersen,and

Schneider

(2007)

obtained

inter-rater

reliabilityofk¼

0.61


errors

usingMED-

MARX

in27medical

errorscenarios

1Distractions

1Workload

increase

1Staffinexperienced

1Staffinginsufficient

1Shiftchange

1Stafffrom

agency

ortemporary

1Emergency

situation

Itoh,O

mata,and

Andersen(2007,

2009)

Describeahuman

error

taxonomysystem

and

itsapplicationto

eval-

uatingsafety

perform

-ance

andreporting

culture

Patentsafety

event

Tworegional

hospitals

inJapan,hospital

AApril2002–September

2004andhospital

BApril2000–September

2002.Therewere3749

incidentreports


Kappaonsixdim

en-

sionsfor138randomly

selected

incidents:out-

comeseverity

k¼

0.87;eventcap-

ture

cuek¼

0.47;time

ofreportingk¼

0.27;

reported

content

k¼

0.34;timebandof

descriptionk¼

0.36;

anddescriptionlevel

k¼

0.42

1Communicationfactors

1communicationproblem

betweendifferent

professional

groups(e.g.doctorandnurse)

1betweenmem

bersin

thesameprofessional

group(e.g.betweennurses)

1betweenstaffandpatient

1betweenstaffandpatientfamilyorrelatives

1inter-departm

entcollaborationorcommuni-

cationproblem

1Staffhuman

factors

1knowledgeandskills(includingwork

experi-

ence)

1health

1fatigue

1mem

ory

failure

1inattention

1confusion

1wrongassumptionorpreconception

1tim

epressure

1personal

fear

1emotional

stress

1decreased

motivation

1other

psychological

orem

otional

factors

1Patienthuman

factors

1age(e.g.elderly

orbaby)

1dem

entia

1cognitivefailure

1motordisability

1perceptual

impairm

ent

1reducedconsciousness

1anaesthetised

orsleep

1mentalinstability

1other

specificfactors

1Taskfactors

(Continued)

Ergonomics 1461

Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




1overload

(hightask

dem

and)

1complicatedtask

1novel

task

1multiple

goals

1work

interruption

1busy

situation

1exceptional

work

condition

1midnight

1holiday

1other

task-related

factors

1Equipment/materialfactors

1equipment/materialsfailure

1obsolescentequipment/materials

1accessproblems

1bad

interfacedesign

1bad

materialsdesign

1inconsistentdesignofequipment/materials

1Organisational

factors

1bad/inappropriatemedical

document

1rulesorprocedures

1training

1manualsandchecklists

1taskallocationorstaffing

1lackofresources

1scheduling

1decision-m

akingprocedure

1leadership

1safetyculture-related

problem

1other

organisational

issues

1Environmentalfactors

1layoutin

sickroom/workplace

1lightingin

sickroom/workplace

1available

spacein

sickroom/workplace

1patientswithsameorsimilar

nam

es1m

edicines

withsimilar

nam

es/bottles/colour

1other

environmentalfactors

(Continued)


Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




Kantelhardt

etal.(2011)

Applicationofneuro-

surgical

critical

inci-

dentmonitoring

Neurosurgical

criti-

calincidents

Allstafffrom

aneuro-

surgical

departm

entin

Germanyadvised

toreportcritical

inci-

dents.Therewere216

incidentsduringone

year

Nil

1Communication

1Missingknowledge

1Organisation

1Overwork

1Technical

problems

1Material

1Carelessness

1Distraction

1Poorassessmentofthesituation

1Sound-alike

1Misinterpretationofclinical

data

1Oralprescription

1Social

factors

1Abbreviations

1Bad

handwriting

1Others

1Unknown

Kim

andKim

(2009)

Delineate

andevaluate

thefeasibilityofaweb-

based

errorreporting

system

usingtheInter-

national

Classification

ofPatientSafety

(ICPS)in

aKorean

university

hospital

Fourcommoninci-

denttypes:medi-

cationerror;aseptic

techniqueerror;

falls;others(e.g.

pressure

ulcer,use

ofrestraint,delayed

treatm

ent,burns)

Therewere75.4

errors

per

10,000patientdays

duringan

eight-week

periodfrom

Decem

ber

2008to

February2009

inaKorean

university

hospital

Nil

1Staff

1slip/lapse

error/absent-mindedness/forgetful-

ness

1violationofrule


withstafforpatient

1emotional

problem

1others

1Patient

1lackofperceptionorunderstandingof

treatm

ent


withstaff

1pathophysiological

problem

1slip/lapse

error/absentmindedness/forgetful-

ness

1emotional

problem

1violationofrule

1others

1Organisation/work

environmentfactors

1overworked

1defectofphysicalenvironmental/infrastruc-

ture

1others

(Continued)

Ergonomics 1463

Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




Leapeet

al.

(1991)

Describetypes

of

accidentalinjuries

and

developaconceptual

fram

ework

fornegli-

gence,errorandpre-

ventability

Adverse

events

Atotalof51hospitals

inNew

York

state,

USA,1984.There

were1133adverse

eventsinoneyearfrom

asample

of30,195

patients

Brennan

etal.(1991)

identified

inter-rater

agreem

entofk¼

0.61

inidentificationof

adverse

eventand

k¼

0.24in

identifying

negligentcare

1Perform

ance

1inadequatepreparationofpatientbefore

procedure

1technical

error

1inadequatemonitoringofpatientafterpro-

cedure

1use

ofinappropriateoroutm

oded

form

of

therapy

1avoidable

delay

intreatm

ent

1physician

orother

professional

practicing

outsidearea

ofexpertise

1other

1Prevention

1failure

totakeprecautionsto

prevent

accidentalinjury

1failure

touse

indicated

tests

1failure

toactonresultsoftestsorfindings

1use


oded

diagnostic

tests

1avoidable

delay

intreatm

ent

1physician

orother

professional

practicing

outsidearea

ofexpertise

1other

1Diagnostic

1failure

touse

indicated

tests

1failure

toactonresultsoftestsorfindings

1use


oded

diagnostic

tests

1avoidable

delay

indiagnosis

1physician

orother

professional

practicing

outsidearea

ofexpertise

1other

1Drugtreatm

ent

1errorin

dose

ormethodofuse

1failure

torecognisepossible

antagonisticor

complementary

drug–druginteractions

1adequatefollow-upoftherapy

1use

ofinappropriatedrug

1avoidable

delay

indiagnosis

1physician

orother

professional

practicing

outsidearea

ofexpertise

1other

(Continued)


Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




1System

1defectiveequipmentorsupplies

1equipmentorsupplies

notavailable

1inadequatemonitoringsystem

1inadequatereportingorcommunications

1inadequatetrainingorsupervisionofphys-

icianorother

personnel

1delay

inprovisionorschedulingofservice

1inadequatestaffing

1inadequatefunctioningofhospital

service

1other

Lee,Cho,and

Bakken

(2010)

Developataxonomy

fordetectionoferrors

relatedto

hypertension

managem

entandto

apply

thetaxonomyto

retrospectivelyanalyse

thedocumentationof

nurses

inAdvanced

PracticeNurse(A

PN)

training

Hypertension-

relatederrors

Therewere15,862

patientencounters

(patientaged

$18)

documentedbyAPN

studentsusingaPDA-

based

clinical

logfrom

2006to

2008in

the

USA

Nil

1Hypertensiondiagnosis

1misdiagnosis

1incomplete

diagnosis

1Nohypertensiondiagnosis

1misseddiagnosis

1Omissionofessentials

1medication

1patientteaching

1follow-up

1Interventioncontraindicated

1medication

Meurier

(2000)

Use

anOrganisational

AccidentModel

toanalyse

critical

inci-

dentsoferrors

innur-

sing

Casestudy

Onenursingcase

study

from

theUK

Nil

1Organisational

factors

1staffingoftheward

1managem

entsupport

1communication

1protocol/policies

1training

Mirza

etal.

(2006)

Developstandardised

methodologyfor

describingthesafety

of

spinal

operationsand

apply

thesemethodsto

studylumbar

surgery

Spinesurgery

Twoinstitutionsin

the

USA,January–July

2003.Therewere172

adverse

occurrencesin

210patientswhocon-

sentedto

participate

Agreem

entbetween

fourobserversonthe

causalfactors

of141

adverse

eventsranged

from

k¼

0to

0.85,

withaverageof

k¼

0.35

1Diagnostic

1errorordelay

indiagnosis

1failure

toem

ployan

indicated

test

1use

ofoutm

oded

test/therapy

1failure

toactonmonitoring/testingresults

1Treatment

1technical

errorin

operation,procedure

ortest

1errorin

treatm

entadministration

1errorin

drugdose

ormethodofdruguse

1avoidable

delay

intreatm

entorrespondingto

anabnorm

altest

1inappropriatecare

1Preventive

1failure

toprovideindicated

treatm

ent

(Continued)

Ergonomics 1465

Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




1inadequatemonitoringorfollow-upof

treatm

ent

1System

1communicationfailure

1equipmentfailure

1other

system

sfailure

1Other

1Noerror

1patientdisease,expectedrisk

1patientnon-compliance

1patientdisease,unrelatedto

surgery

Nastet

al.

(2005)

Evaluateanew

mech-

anismforreportingand

classifyingpatient

safety

eventsto

increase

reportingand

identify

patientsafety

priorities

Medical

errors,

nearmissesand

riskysituations

CardiothoracicInten-

siveCareandPost

Anesthesia

CareUnits

attheBarnes-Jew

ish

Hospital

intheUSA,

January?D

ecem

ber

2003.Therewere163

reportsdescribing157

events

Nil

1Human

factors

1slip

1coordination

1intervention

1verification

1monitoring

1knowledge

1qualifications

1tripping

1Organisational

factors

1protocols/procedures

1transfer

ofknowledge

1culture

1managem

entpriorities

1external

1Technical

factors

1design

1materials

1external

1construction

1Other

1patient-relatedfactors

1unclassifiable

Nyssen

and

Blavier(2006)

Synthesisetheexisting

scientificknowledge

onerrordetectionand

tofurther

knowledge

throughtheanalysisof

casescollectedin

acomplexsystem

,anesthesia

Anesthesia

errors

Anesthesia

depart-

mentsattwouniversity

hospitalsin

Belgium.

Therewere212reports

duringoneyearand

177errors

Nil

1Equipment

1Individual

1Team

1Organisation

(Continued)


Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




Parker

etal.

(2010)

Developatoolto

clarifyandcategorise

precursoreventsto

designinterventions

that

reduce

thenumber

ofprecursorevents

Surgical

flow

dis-

ruptionsandthe

conditionsthat

pre-

dispose

asurgical

team

toadverse

events

Atotalof10cardio-

vascularoperations

perform

edbyeight

differentsurgeons,

with328surgical

flow

disruptionsobserved

atStMary’s

Hospital

attheMayoClinic


was

measuredusing

Kappa.Kappascores

werecalculatedfor

each

category,then

averaged

foreach

cat-

egory

across

the10

observations.Aver-

aged

kappascoresran-

ged

from

0.71to

0.89

1Technical

factor

1technical/skillissue

1Environmentalfactors

1case-irrelevantconversations

1pagers/phones

1noises/alarm

s/music

1Technologyandinstruments

1instruments/devices

notat

table

1equipmentmalfunction/notready

1Trainingandprocedures

1unexpectedpatientissues

1training/difficultyperform

ingprocedure

1Teamwork

1miscommunication/coordination

1Other

1notspecified

(e.g.visitors/shiftturnover)

Phillipset

al.

(2001)

Describemedication

errors

from

FDA’s

Adverse

EventReport-

ingSystem

Medicationerrors

resultingin

death

FDA’s

Adverse

Event

ReportingSystem

intheUSA,1993–1998.

Therewere5366

medicationerror

reportswith217deaths

resultingfrom

error

and219deathsposs-

ibly

relatedto

error

Nil

1Human

factors

1perform

ance

deficit

1knowledgedeficit

1miscalculationofdosage

1drugpreparation

1other

1Communication

1misinterpretationoforder

1oralmiscommunication

1written

miscommunication

1Nam

econfusion

1proprietarynam

econfusion

1established

nam

econfusion

1Labelling

1immediatecontainer

labelofthemanufacturer

1label

ofdispensedproduct

(practitioner)

1manufacturer’scarton

1printedreference

material

1Packageanddesign

1deviceproblems

1inappropriatepackagingordesign

1tabletorcapsule

confusion

(Continued)

Ergonomics 1467

Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




Portaluriet

al.

(2010)

Reportontheuse

ofan

internal

system

for

incidentreporting

Incidentsinvolving

radiationtreatm

ent

inaRadiotherapy

departm

ent

Therewere37inci-

dentsover

5635treat-

mentsduringOctober

2001–June2009in

aradiotherapydepart-

mentin

Italy

Nil

1Organisational

influences

1organisational

clim

ate

1organisational

process

1resourcemanagem

ent

1Unsafe

supervision


1planned

inappropriateoperations

1failedto

correctproblem

1supervisory

violations

1Preconditionsto

unsafe

acts

1substandardconditionsofoperators

1adverse

mentalstates

1adverse

physiological

states

1physical-mentallimitations

1substandardpractices

ofoperators

1equipmentresourcemanagem

ent

1personal

readiness

1Unsafe

acts

1errors

1decisionerrors

1skill-based

errors

1attentional

failures

1violations

1routine

1exceptional

Rodrigues

etal.

(2011)

Generatereportsthat

identify

themostrel-

evantcausesoferror

Medical

imaging

TwoPortuguese

health-careinstitutions

Nil

1Technical

1external

1design

1Organisational

1external

1culture

1Human

behaviour

1external

1knowledge-based

behaviour

1knowledge-based

errors

1rule-based

behaviour

1coordination

1intervention

1Skill-based

behaviour

1slips

1tripping

1Other

(Continued)


Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




1patient-relatedfactor

1unclassifiable

Santellet

al.

(2009)

Describecomputer-

relatedmedication

errors

madebynon-

prescribers

Computer-related

medicationerrors

Datavoluntarily

sub-

mittedto

MEDMARX

andUniversity

of

PittsburghMedical

Centerhealthsystem

intheUSA,July

2001–

Decem

ber

2005.

Duringthistime

period,693unique

facilities

submitted

90,001medication

errors

Forrey,Pedersen,and

Schneider

(2007)

obtained

inter-rater

reliabilityofk¼

0.61


errors

usingMED-

MARX

in27medical

errorscenarios"]

1Perform

ance

deficit

1Transcriptioninaccurate

oromitted

1Procedure

orprotocolnotfollowed

1Documentation

1Communication

1Knowledgedeficit

1Written

order

1Workflow

disruption

1Computersoftware

1System

safeguard(s)

1Fax

orscanner

involved

1Monitoringinadequateorlacking

1Pre-printedmedicationorder

form

1Handwritingillegible

orunclear

1Dispensingdeviceinvolved

1Drugdistributionsystem

1Abbreviations

1Calculationerror

1Incorrectmedicationactivation

1Inform

ationmanagem

entsystem

1Dosageform

confusion

1Verbal

order

1Patientidentificationfailure

1Brandorgeneric

nam

eslook-alike

1Generic

nam

eslook-alike

TaxisandBar-

ber

(2003)

Toinvestigatecauses

oferrors

inIV

drug

preparationand

administrationusinga

fram

ework

ofhuman

errortheory

IVdrugpreparation

errors

Observationswere

conducted

during6–

10consecutivedays

(n¼

76days)

on10

wardsin

twohospitals

(oneuniversity

teach-

inghospital

andone

non-teachinghospital)

intheUK,June–

Decem

ber

1999.There

were483IV

drug

preparationsand447

drugadministrations

observed

and265

errors

identified

Nil

1Handlingtechnology

1lackofknowledge,routineandexperience

in1drugpreparation

1drugadministration

1inadequateuse

oftechnology,e.g.drugcharts

1Designoftechnology

1ambiguousmanufacturerleaflets

1unsuitable

workingenvironment

1designofdrugvialpresentations/equipment

1unsuitable

preparationsprocedures

1Communicationproblemsbetween

1nurses

(Continued)

Ergonomics 1469

Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




1nurses

andpharmacists

1doctors

andother

healthprofessionals,e.g.

ambiguousprescriptions

1Workload

1several

tasksat

thesametime

1endofshift

1lackofqualified

staff

1Patient-relatedfactors

1lim

ited

venousaccess

1non-cooperativepatient

1Supervision

1lackofsupervisionofstudentnurse/agency

nurse

1Other

factors

1tryingto

savedisposable

equipment

Taylor-Adam

s,Vincent,and

Stanhope(1999)

Utilise

asemi-struc-

turedinterview

toana-

lyse

aclinical

adverse

event

Casestudy

Labourwardofhospi-

talin

theUK.Onecase

study

Nil

1Organisational/corporate

culture

1Localwork,task

andenvironmentalconditions

1Individual

TranandJohn-

son(2010)

Developaclassifi-

cationsystem

fornur-

singerrors

relatingto

clinical

managem

ent

(NECM

taxonomy)

andto

describecon-

tributingfactors

and

patientconsequences

Nursingerrors

Therewere241self-

reported

incidents

relatingto

clinical

managem

entinnursing

inametropolitanhos-

pital

inAustralia.

Thesewereidentified

throughtheIncident

Inform

ationManage-

mentSystem

(IIM

S)

databasebetween1

July

2007and30June

2008

Nil

1Nursingcare

processes

1clinical

judgem

ent

1patientassessment

1failedassessment

1misinterpretationofassessment

1clinical

decision

1inappropriatedecision

1failedto

consultrelevantHPs

1confrontunsafe

orders

1failedrecognition

1failedresponse

1clinical

task

execution

1taskperform

ing

1poornursingcare

1failedpatientID

/consent

1failedto

follow

protocol/procedure

1missedordersfrom

other

HPs

1beyondnursingscope

1technologyapplied/required

1incorrecttechnique

1inappropriateequipment

1precautionaryaction

1failedassessingenvironment (C

ontinued)


Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




1failedcheckingnotes

1failedseekingclarification

1continuityofcare

1failedescortingpatient

1inadequateprovisionofinform

ation

1tonurses

1toother

HPs

1missedreferral/follow-up

1Communication

1verbal

communication

1nurse–nursecommunication

1nurse–patientcommunication

1nurse–other

HPscommunication

1written

communication

1incomplete

chart

1omissionin

documentation

1incomplete

paper

work

1Administrativeprocesses

1inappropriatebed

managem

ent

1staffsupervisionandplanning

1insufficientstaffroster

planning


1Knowledgeandskill

1lackofknowledge

1lackofskillsandexperience

Tourgem

an-

Bashkin,Shinar,

andZmora

(2008)

Exam

inethenature

andcausesofmedical

errorsknownas

almost

adverse

events(A

AEs)

andpotential

adverse

events(PAEs)

inintensivecare

units

(ICU)

Alm

ostadverse

events(A

AEs)

and

potential

adverse

events(PAEs)

TwoICUsin

Soroka

Medical

Center,Israel.

Therewere114AAEs

observed

inthecourse

of500hofobser-

vation:49in

theneo-

natal

ICU

(NICU)and

65in

thepaediatric

ICU

(PICU)

Inter-rateragreem

ent

forall114AAEsfor

thethreemaincauses

k¼

0.79andforall

causesk¼

0.73.

Agreem

entforthe49

NICU

AAEsforthe

threemaincauseswas

k¼

0.75andforall

causesk¼

0.58.

Agreem

entforthe65

PICU

AAEsforthe

threemaincauseswas

k¼

0.82andforall

causesk¼

0.76

1Work

environmentfactors

1System

factors

1Human

factors

(Continued)

Ergonomics 1471

Appendix

1–continued

Author(s)

andyear

Aim

Typeofevent




Webbet

al.

(1993)

Describethefirst2000

Australian

Incident

MonitoringStudy

(AIM

S)incident

reports

Unintended

inci-

dentsinvolving

anaesthetists

Atotalof90hospitals

orgrouppractices

inAustraliaandNew

Zealand.First2000

incidentsreported

toAIM

S

Nil

1Communicationproblem

1Druglabel

1Errorofjudgem

ent

1Failure

tocheckequipment

1Fatigue

1Faultoftechnique

1Haste

1Illness

1Inadequateassistance

1Inattention

1Inexperience

1Lackoffacility

1Monitorproblem

1Pre-oppatientassessmentinadequateorincor-

rect

1Pre-oppatientpreparationinadequateorincor-

rect

1Reliefanaesthetistorstaffchange

1Unfamiliarenvironmentorequipment

1Other

factor

1Other

equipmentproblem

1Other

stress

Wiegmannet

al.

(2007)

Studysurgical

errors

andtheirrelationship

tosurgical

flow

dis-

ruptionsin

cardiovas-

cularsurgery

prospectivelyto

understandbetterthe

effect

ofthesedisrup-

tionsonsurgical

errors

andpatientsafety

Surgical

errors

Cases

werechosen

randomly

from

the

non-emergency

oper-

ativeschedule

ofcar-

diovascularsurgeons

whoagreed

toallow

observationsto

take

place

intheiroperating

room;MayoClinic

Rochester,USA.There

were31cardiacsur-

geriesover

athree-

weekperiodand155

technical

operative

errors

wereidentified

(3.7

per

h,5.0

per

operation)

Nil

1Teamwork

1Extraneousinterruptions

1Equipmentandtechnology

1Resource-based

issues

1Supervisory/training-related

issues


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