Chang A, Schyve PM, Croteau RJ, et al. The JCAHO patient safety event taxonomy: a standardized terminology and classification schema for near misses and adverse events

JCAHO, Division of Research, Oakbrook Terrace, IL, USA.
International Journal for Quality in Health Care (Impact Factor: 1.76). 05/2005; 17(2):95-105. DOI: 10.1093/intqhc/mzi021
Source: PubMed

ABSTRACT The current US national discussions on patient safety are not based on a common language. This hinders systematic application of data obtained from incident reports, and learning from near misses and adverse events.
To develop a common terminology and classification schema (taxonomy) for collecting and organizing patient safety data.
The project comprised a systematic literature review; evaluation of existing patient safety terminologies and classifications, and identification of those that should be included in the core set of a standardized taxonomy; assessment of the taxonomy's face and content validity; the gathering of input from patient safety stakeholders in multiple disciplines; and a preliminary study of the taxonomy's comparative reliability.
Elements (terms) and structures (data fields) from existing classification schemes and reporting systems could be grouped into five complementary root nodes or primary classifications: impact, type, domain, cause, and prevention and mitigation. The root nodes were then divided into 21 subclassifications which in turn are subdivided into more than 200 coded categories and an indefinite number of uncoded text fields to capture narrative information. An earlier version of the taxonomy (n = 111 coded categories) demonstrated acceptable comparability with the categorized data requirements of the ICU safety reporting system.
The results suggest that the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) Patient Safety Event Taxonomy could facilitate a common approach for patient safety information systems. Having access to standardized data would make it easier to file patient safety event reports and to conduct root cause analyses in a consistent fashion.

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Available from: Paul M Schyve, Dec 26, 2014
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    • "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. "
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    ABSTRACT: 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 1997 articles and 38 of these met inclusion criteria. Most studies included causal contributing factors as well as error and error type, but the nature of coding varied considerably between studies. The ability of human factors classification frameworks to provide information on specific causal factors for an adverse event enables the focus of preventive attention on areas where improvements are most needed. This review highlighted some areas needing considerable improvement in order to meet this need, including better definition of terms, more emphasis on assessing reliability of coding and greater sophistication in analysis of results of the classification. Practitioner Summary: Human factors classification frameworks can be used to identify causal factors of clinical adverse events. However, this review suggests that existing frameworks are diverse, limited in their identification of the context of human error and have poor reliability when used by different individuals.
    Ergonomics 07/2014; 57(10):1-30. DOI:10.1080/00140139.2014.933886 · 1.56 Impact Factor
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    • "Contributory factors underlying adverse events show them having a high commonality internationally,97 and developing a similarly universal taxonomy and procedure for analysing patient complaints would allow for international learning and benchmarking (eg, at present consensus is often lacking between studies on the concepts used to analyse complaints). As indicated by the patient safety literature,3 this is likely to focus on different issues than those capture by adverse event analysis taxonomies; for example, subjective concepts such as compassion and sensitivity, which patients view as important, but cannot be easily managed by the organisation98 and are not typically investigated through adverse event taxonomies.99 "
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    ABSTRACT: Background Patient complaints have been identified as a valuable resource for monitoring and improving patient safety. This article critically reviews the literature on patient complaints, and synthesises the research findings to develop a coding taxonomy for analysing patient complaints. Methods The PubMed, Science Direct and Medline databases were systematically investigated to identify patient complaint research studies. Publications were included if they reported primary quantitative data on the content of patient-initiated complaints. Data were extracted and synthesised on (1) basic study characteristics; (2) methodological details; and (3) the issues patients complained about. Results 59 studies, reporting 88 069 patient complaints, were included. Patient complaint coding methodologies varied considerably (eg, in attributing single or multiple causes to complaints). In total, 113 551 issues were found to underlie the patient complaints. These were analysed using 205 different analytical codes which when combined represented 29 subcategories of complaint issue. The most common issues complained about were ‘treatment’ (15.6%) and ‘communication’ (13.7%). To develop a patient complaint coding taxonomy, the subcategories were thematically grouped into seven categories, and then three conceptually distinct domains. The first domain related to complaints on the safety and quality of clinical care (representing 33.7% of complaint issues), the second to the management of healthcare organisations (35.1%) and the third to problems in healthcare staff–patient relationships (29.1%). Conclusions Rigorous analyses of patient complaints will help to identify problems in patient safety. To achieve this, it is necessary to standardise how patient complaints are analysed and interpreted. Through synthesising data from 59 patient complaint studies, we propose a coding taxonomy for supporting future research and practice in the analysis of patient complaint data.
    BMJ quality & safety 05/2014; 23(8). DOI:10.1136/bmjqs-2013-002437 · 3.99 Impact Factor
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    • "With the funding of SPOTRIAS, the Principal Investigators (PIs) of the original SPOTRIAS sites decided to obtain common data elements that described essential demographics of all patients treated with IV tPA or enrolled in any clinical trials. UTHSR was consequently expanded to incorporate other elements including those variables that were needed for clinical trials that were conducted by the UT stroke team and variables that were needed for reporting to The Joint Commission (TJC) [38,39], as well as select variables to meet minimum requirements for reporting to Centers for Medicare and Medicaid Services (CMS) as they pertain to the vascular neurology aspects of required reporting [40]. All patients who have been admitted to the stroke unit at MHH-TMC are classified by stroke diagnosis subtypes, including infarct (non-hemorrhagic stroke), intracerebral hemorrhage (ICH), intraventricular hemorrhage (IVH), transient ischemic attack (TIA), subarachnoid hemorrhage (SAH), epidural hematomas (EDH), subdural hematomas (SDH), non-acute infarct, and others that could not be classified as any of the above (“Not stroke”), and are entered in UTHSR. "
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    ABSTRACT: Background Limited information has been published regarding standard quality assurance (QA) procedures for stroke registries. We share our experience regarding the establishment of enhanced QA procedures for the University of Texas Houston Stroke Registry (UTHSR) and evaluate whether these QA procedures have improved data quality in UTHSR. Methods All 5093 patient records that were abstracted and entered in UTHSR, between January 1, 2008 and December 31, 2011, were considered in this study. We conducted reliability and validity studies. For reliability and validity of data captured by abstractors, a random subset of 30 records was used for re-abstraction of select key variables by two abstractors. These 30 records were re-abstracted by a team of experts that included a vascular neurologist clinician as the “gold standard”. We assessed inter-rater reliability (IRR) between the two abstractors as well as validity of each abstractor with the “gold standard”. Depending on the scale of variables, IRR was assessed with Kappa or intra-class correlations (ICC) using a 2-way, random effects ANOVA. For assessment of validity of data in UTHSR we re-abstracted another set of 85 patient records for which all discrepant entries were adjudicated by a vascular neurology fellow clinician and added to the set of our “gold standard”. We assessed level of agreement between the registry data and the “gold standard” as well as sensitivity and specificity. We used logistic regression to compare error rates for different years to assess whether a significant improvement in data quality has been achieved during 2008–2011. Results The error rate dropped significantly, from 4.8% in 2008 to 2.2% in 2011 (P < 0.001). The two abstractors had an excellent IRR (Kappa or ICC ≥ 0.75) on almost all key variables checked. Agreement between data in UTHSR and the “gold standard” was excellent for almost all categorical and continuous variables. Conclusions Establishment of a rigorous data quality assurance for our UTHSR has helped to improve the validity of data. We observed an excellent IRR between the two abstractors. We recommend training of chart abstractors and systematic assessment of IRR between abstractors and validity of the abstracted data in stroke registries.
    BMC Neurology 06/2013; 13(1):61. DOI:10.1186/1471-2377-13-61 · 2.04 Impact Factor
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