International Journal for Quality in Health Care 2005; pp. 1 of 11 10.1093/intqhc/mzi021
International Journal for Quality in Health Care
© International Society for Quality in Health Care and Oxford University Press 2005; all rights reserved1 of 11
The JCAHO patient safety event
taxonomy: a standardized terminology
and classification schema for near
misses and adverse events
ANDREW CHANG, PAUL M. SCHYVE, RICHARD J. CROTEAU, DENNIS S. O’LEARY AND JEROD M. LOEB
JCAHO, Division of Research, Oakbrook Terrace, Illinois, USA
Background. The current US national discussions on patient safety are not based on a common language. This hinders system-
atic application of data obtained from incident reports, and learning from near misses and adverse events.
Objective. To develop a common terminology and classification schema (taxonomy) for collecting and organizing patient
Methods. The project comprised a systematic literature review; evaluation of existing patient safety terminologies and classifi-
cations, 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 pre-
liminary study of the taxonomy’s comparative reliability.
Results. 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
Conclusions. 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.
Keywords: patient safety, standardized terminology and classification, taxonomy
Concerns about safety in patient care have called attention to
the need for governmental agencies and private sector accred-
iting bodies to work together with health care organizations
to coordinate the monitoring, reporting, and analysis of medi-
cal errors. The 2003 Institute of Medicine report, Patient
Safety: Achieving a New Standard of Care , recommends that
standardization and better management of information on
patient safety—including near misses and adverse events—
are needed to inform the development of strategies that
reduce the risk of preventable medical incidents. However,
patient safety incident reporting systems differ in design and
therefore in their ability to define, count, and track adverse
events . Among reporting systems, there are often dispa-
rate data fields, conflicting patient safety terminologies,
classifications, characteristics, and uses that make standard-
ization difficult. In addition, each source of data on near
misses and adverse events usually requires different methods
for interpreting and deconstructing these events . Finally,
misused terminology in the research literature, conference
papers and presentations, and media contributes to wide-
spread misunderstandings about the language of patient
The proliferation of reporting systems has created a press-
ing need for organization of patient safety information
systems and terminology. Unfortunately, much of the work
to date has fallen short in meeting identified needs for epide-
miological data . Given the current state of the art, it is
Address reprint requests to A. Chang, JCAHO, Division of Research, Oakbrook Terrace, IL, USA. E-mail: email@example.com
International Journal for Quality in Health Care Advance Access published February 21, 2005
A. Chang et al.
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extremely difficult to achieve broad-based and timely
improvements in patient safety, since there is no standard
determination as to which events to capture and report [5,6].
Additionally, the lack of a common patient safety terminol-
ogy is a critical obstacle to sharing and aggregating data to
support patient safety.
The concept of a taxonomy combines terminology and the
science of classification—in the case of patient safety, the
identification and classification of things that go wrong in
health care, the reasons why they occur, and the preventive
strategies that can minimize their future occurrence. There is
consensus that standardization of patient safety data would
facilitate improvements in incident reporting, tracking, and
analysis [7,8]. The core set of terms in patient safety, like
other health disciplines, should incorporate both theoretical
concepts and generally accepted vocabulary.
Several methods have been developed to define and
classify medical errors, adverse events, near misses, and other
patient safety concepts and terms [9,10]. However, these
methods have tended to be, with notable exceptions,
narrowly and predominantly focused on specific areas of
health care—medication errors [11–13], transfusion reactions
, primary care [15,16], and nursing care [17,18].
In this project we developed and applied a method of
classification that is based on evaluations of extant taxono-
mies and reporting systems with feedback from individuals
who would use the taxonomy. This approach sought to
identify similarities and gaps in the terminology and classifi-
cation to create a multidimensional taxonomy that encom-
passes diverse health care settings and incident reporting
Terms and definitions used in patient safety were gathered
from a wide range of print and web resources (e.g. book glos-
saries, published journals). Current, practical, and colloquial
terms that underlie the communication among users were
listed in a comparative glossary. Because the terms and their
definitions are extensive, they are not reproduced herein.
However, this patient safety dictionary is available electroni-
cally from the authors.
A comprehensive literature search was performed in
Medline (PubMed) and Excerpta Medica (Embase). Litera-
ture that describes approaches to the definition of medical
errors, adverse events, near misses, and other patient safety
concepts and terms, including existing classification schemes
on patient safety, was retrieved. The searches were not limited
to articles published in the English language or within a par-
ticular geographical area. The databases were searched for
articles with publication dates between January 1993 and June
2003. In addition to database searches, the Internet sites of
Departments of Public Health, Ministries of Health, and
Patient Safety Organizations and Groups in Africa, Asia,
Australia, Europe, and North America were searched. The
reference lists of major reports were also scanned for relevant
publications that date from the 1980s.
A total of 512 distinct references were identified from the
Medline search. The Embase search resulted in 15 additional
unique references. The titles and/or abstracts of these arti-
cles were initially scanned, and inclusion/exclusion decisions
made. Based on the review of the abstracts, we eliminated
429 articles on the following criteria: (i) not relevant to the
field of patient safety/medical error/adverse event classifi-
cation; (ii) relevant to the field of patient safety/medical
error/adverse event classification but did not provide ade-
quate description of the components needed to define a
coherent classification scheme; (iii) classifications that are in
the early stages of development; (iv) unpublished classifica-
tions. The very few exceptions to this are classifications that
hold particular conceptual or methodological interest in the
development of the field.
Of the 96 full articles that were reviewed, 73 were eliminated
according to the above criteria. Eleven formal classification
schemes identified in the remaining 23 articles that address
the frequencies, types, causes and contributing factors, conse-
quences, and prevention of medical/medication errors are
summarized in a report prepared for the World Health
The 11 classifications of medical and medication errors,
patient safety events, and incident reporting systems were
reviewed and compared for homogeneity. The semantic
relationships, equivalent categories, and linkages among
these classifications schemes were identified and used to
construct the overarching framework of a preliminary tax-
onomy. This version also referenced human factors and
We reviewed data collected by the Joint Commission’s
Sentinel Event Program from January 1995 to December
2002 to validate the construct of the preliminary taxonomy.
This was supplemented by recommendations from a nominal
expert advisory taxonomy workgroup (see Acknowledge-
ments for composition of workgroup). We asked the work-
group to assess the content and face validity of an initial
iteration of the taxonomy. They offered a checklist of five
attributes to be used in judging appropriateness of the elements
of the taxonomy; these judgments involved subjective assess-
ments rather than statistical analyses. Input was also solicited
from medical specialty societies, business groups, government
health care agencies, and health care organizations.
Since it is difficult, if not impossible, to prove formally that
the items chosen were representative of all relevant terms and
classifications, subjective tests of linguistic clarity were used
to indicate whether the terminology of the classifications was
clear. In the absence of a ‘gold standard’ to test criterion
validity, we conducted a simplified item analysis of each varia-
ble of the taxonomy against those found in an established
classification in one US hospital. Responses were coded as
follows: ‘unmatched’ = 0, ‘extrapolated’ = 1, ‘related’ = 2,
‘synonymous’ = 3, and ‘identical’ = 4. Results of this work
were used to inform the development of a beta version of the
patient safety event taxonomy.
The JCAHO patient safety event taxonomy
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Our review of the literature reinforces the fact that various
approaches used in the health care sector to define and clas-
sify near misses, adverse events, and other patient safety con-
cepts have generally been fragmented . Early efforts to
define and classify ‘error’ or ‘mistakes’ were burdened by the-
oretical and methodological flaws. The model of medical
error was largely unspecified. Where classification instru-
ments were described, their validity was found to be modest
and their reliability not reported. A systematic review of clas-
sification schemes used in primary care by Elder and Dovey
, found a limited number of studies that attempted to cat-
egorize medical errors, including near misses and adverse
events [21–25]. Most of these studies were not designed with
the development of a functional classification scheme in
mind; thus, they did not offer a conceptual explanation of
what they had classified.
Busse and Wright  proposed a more promising classifica-
tion methodology and an enhanced evaluation approach for
the Edinburgh Incident Classification. Focusing on in-depth
analysis and a search for multiple levels of causation and con-
tributing factors, including the identification of active and
latent failures, this classification model exemplifies a theory-
driven analytical framework that integrates, functionally and
technically, with an incident reporting system. This systematic
approach to classification in patient safety did not become the
de facto standard for quite some time, and is still often neglected.
The classification of error types framework and theoretical
and technical foundation for in-depth analysis of root causes
of adverse events did not materialize until after the publica-
tion of the seminal works by Reason , Rasmussen ,
and Hale . Contributions from aviation  and high-
technology/high-risk industries have also been instrumental
in advancing the reporting, analysis, and classification of
adverse events in health care. A few more theoretically based
studies—such as those reported by Makeham , Battles
, and Victoroff —have focused on more rigorous
classification schemes and give greater consideration to valid-
ity and reliability issues. Like the earlier classifications, how-
ever, the process and outcome ‘root causes’ of adverse events
in these schemes were only described where a significant
impact was recorded .
Finally, Runciman and colleagues  have developed a
structured approach based on Reason’s model and framework
of contributory and causative factors to draw out all of the rele-
vant information about an incident and to describe patient
safety phenomena in terms that can be analyzed statistically.
Homogeneous elements of these models—which comprise
terms and the relationships between terms that make up the
building blocks of a classification scheme—were categorized
into five complementary root nodes, or primary classifications.
1. Impact—the outcome or effects of medical error and
systems failure, commonly referred to as harm to the
2. Type—the implied or visible processes that were
faulty or failed.
3. Domain—the characteristics of the setting in which
an incident occurred and the type of individuals
4. Cause—the factors and agents that led to an incident.
5. Prevention and mitigation—the measures taken or
proposed to reduce incidence and effects of adverse
The root nodes were then divided into 21 subclassifications,
which were in turn subdivided into more than 200 coded cate-
gories and an indefinite number of non-coded text fields to
capture narrative information about specific incidents.
The ‘Impact’ classification (shown in Figure 1) comprised
three subclassifications that could discriminate between 18
types of outcomes or effects (harm). The harm index was
based on the NCC-MERP Medication Error Taxonomy ,
and is characterized by the degree of harm—ranging from no
harm to temporary or permanent impairment of physical or
psychological function. Broad distinctions were also made
between medical (psychological or physical) and non-medical
(legal, social, or economic) impacts.
The ‘Type’ classification included three levels that address
communication, patient management, and clinical perform-
ance (see Figure 2). The ‘communication’ subclassification
identified communication problems that exist between
provider and patient, provider and patient’s proxy, provider
and non-medical staff, and among providers. The ‘patient
management’ node classified substandard patient manage-
ment that involved improper delegation, failure in tracking or
follow-up, wrong referral or consultation, or questionable use
of resources. The ‘clinical performance’ subclassification
included the full range of failures that could lead to iatrogenic
events during the pre-intervention, intervention, and post-
intervention phases of care. Analysis of Joint Commission
sentinel event data (reported from 1995 to 2002) related to
wrong-site surgeries (n = 209) showed that these adverse
events could be classified in the following principal groups:
(i) Communication—including communication with the patient
and among members of the surgical team; availability of
information; and operating room hierarchy; (ii) Patient man-
agement—such as preoperative assessment of the patient; and
(iii) Clinical performance—including orientation and training,
the procedures used to verify the operative site, and distraction.
Alternatively, these areas could represent the clinical or man-
agement processes that are associated with events without any
judgments about root causes within those processes.
The ‘Domain’ classification included the types of health
care professionals commonly involved in patient care and the
demographics of patients in a variety of health care settings
where events might have occurred (see Figure 3). Analysis of
voluntarily reported sentinel events showed that they occur
most frequently in the following settings: general hospital
(64%); psychiatric hospital (13%); psychiatric unit (6%); out-
patient behavioral health (5%); emergency department (4%);
long-term care facility (4%); home care service (3%); and
ambulatory care setting (1.5%). From this, we postulated a
link between where the event took place (>10 coded cate-
gories) and which medical specialty was involved (>21 coded
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Figure 1 Classification of impact.
The JCAHO patient safety event taxonomy
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categories). In addition, we specified the intended patient care
intervention (eight coded categories—therapeutic, diagnostic,
rehabilitative, preventive, palliative, research, cosmetic,
and other), which pre-existing conditions the patient had
(ICD-9-CM coded categories), and the associated causes and
outcomes delineated in the other four primary classifications.
The classification of ‘Causes’ is shown in Figure 4. Root
cause analyses of sentinel events in all categories showed that
the underlying causes of these events could be classified into
two principal groupings: system failures and human failures.
The principal nodes of the ‘Cause’ classification comprised
two subclassifications: system (process/structure) failures and
human failures. System failures are remote from the direct
control of the clinician and are usually the distal cause of
structure and process failures among reported sentinel events
(e.g. orientation/training, availability of information, staffing
levels; physical environment, alarm systems, organizational
culture). System failures are errors in the design, organization,
Figure 2 Classification of type.
Figure 3 Classification of domain.
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Figure 4 Classification of cause.
The JCAHO patient safety event taxonomy
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training, or maintenance that lead to operator errors. Those
failures involving direct contact with the patient—human
failures—are often part of the proximate cause of an event
. The root cause analysis data yielded groupings that
included communication, patient assessment, and continuum
of care, among others. The subclassification, ‘latent organiza-
tional failure’, included five coded categories: (i) management,
(ii) organizational culture, (iii) protocols and processes,
(iv)transfer of knowledge, and (v) external factors. Two catego-
ries for latent technical failure—facilities and external factors—
were derived from the Eindhoven Classification System .
Terminology for the ‘Prevention and Mitigation’ classifica-
tion was adopted from the definitions proposed by Gordon
 for physical disease prevention. In this classification,
three types of prevention and mitigation were identified:
universal, selective, and indicated. The ‘universal’ subclassifi-
cation covered preventive and corrective measures that are
designed for everyone in the eligible population. Prevention
and mitigation measures that are directed toward a subgroup
of the population whose risk of adverse events is above aver-
age were grouped in the ‘selective’ subclassification. Lastly,
the ‘indicated’ subclassification combined interventions that
are targeted to specific high-risk individuals identified as
having a minimal but detectable risk for sustaining an adverse
event. Figure 5 illustrates how the preventive strategies of the
Joint Commission’s 2004 National Patient Safety Goals 
could be classified according to this scheme.
The proposed interrelationships depicted in Figure 6 show
the assumptions underlying the Taxonomy framework. The link-
ages in this visual analytical framework provide an organized
approach to guide the retrospective process of identifying the
factors (causes) that contribute to systems failures (type) and
adverse events, or to prospectively identify potential risk fac-
tors and devise preventive strategies (prevention) and correc-
tive actions (mitigation) to protect the patient (in a domain)
from harm (impact). The linkages are not meant to lead to pre-
mature conclusions about an event, nor are they intended as
the only analytical framework. Although the linkages define the
specific types of queries, they do not identify precise data
sources nor which units of data should populate the taxonomy.
A preliminary test of the alpha version taxonomy con-
ducted at one hospital with an active incident reporting system
(Stanford’s ICUsrs) demonstrated acceptable correlation
between its coded categories (n = 111) and the categorized
data requirements of the system. Thirteen (12%) categories
were identical, 42 (38%) were synonymous, 45 (41%) were
related, and six (5%) had to be extrapolated. Five (4%) cate-
gories were unmatched—date and time of incident, patient or
family dissatisfaction, and two patient identifiers—and were
therefore omitted from the taxonomy.
Using the desirable attributes of patient safety taxonomy
identified by the expert advisory workgroup (see Box 1), the
face validity of the terminology and classifications inferred
from the comments of the experts who reviewed their clarity
and completeness was judged to be high. The workgroup
recommended inclusion of external factors that are perceived
to influence patient safety. The workgroup concluded that the
Taxonomy was well suited to meet the need for integration of
patient safety data from disparate sources. A variety of patient
safety stakeholders concurred in the taxonomy’s suitability
and feasibility for application in incident investigation, report-
ing, tracking, and analysis at US hospitals and elsewhere.
The Patient Safety Event Taxonomy developed and tested in this
study represents a synthesis of traditional, hierarchical classifica-
tions represented by single topic areas and settings and the heu-
ristic, multidimensional/multisetting classifications that rely on
a systems approach to understanding patient safety . It
includes all events that are not due to an underlying physiologi-
cal or pathological process and is sensitive to minor variations
among similar events. This approach compels the user to make
explicit, a priori decisions about the key variations in structure
and process that relate to any given patient safety event. It also
allows others to judge whether important variables were over-
looked. Finally, it makes explicit the relationships between these
variables and their relevance as valid markers of patient safety.
The number of relevant categories constituting the
optimum classification scheme or how best to deconstruct
an adverse event will always be subject to debate .
Hobgood , using a modified Delphi process to differen-
tiate between specific classes of medical error common to
emergency medicine practice, found that cognitive errors in
medical decision-making can be difficult to identify, and
suggested that consensus on terminology and classification
may be challenging. One source of difficulty we encoun-
tered in choosing logical data variables to link disparate ter-
minologies and classifications is that they are all loosely
attached in an intricate network of information character-
ized by events, settings, individuals, and teams of people,
protocols, procedures, policies, and communications that
function in an uncertain environment. Understanding these
relationships could provide a useful basis to guide the devel-
opment and improvement of information about near misses
and adverse events, and use of the information to make
health care safer for patients.
We critiqued existing taxonomies on several grounds. Most
were developed in relative isolation from other classification
approaches for a specific medical specialty, and few were
improvements of earlier work. In this regard, we believe that
research that compares different classification schemas con-
stitutes a crucial stage in consolidating the discipline of
patient safety event reporting.
Aggregating data gathered through different measurement
methods into the framework of a standardized taxonomy has
been used successfully by epidemiologists to detect nosoco-
mial infections , and is likely to be useful in detecting
trends and patterns in patient safety. In a number of studies,
there appears to be an evolving effort to build a science of
patient safety measurement that is equivalent to health
measurement or psychometrics. This is important because
decisions affecting the welfare of patients and the expenditure
of public funds are based on the results of patient safety
A. Chang et al.
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The potential applications for patient safety event infor-
mation vary widely depending on the identity of the user—
e.g. internal evaluations, oversight bodies, patient safety
managers, patients, ethicists, and lawyers, among others.
In order to meet the needs of these diverse audiences it is
essential to identify a common language that is widely
applicable and straightforward. The vocabulary adopted
for the Taxonomy closely resembles the lexicon commonly
used among various users today, and avoids pejorative
In its simplest form, the Taxonomy’s classifications can
represent individual fields for the front end of paper-based or
electronic reporting systems with individual incidents com-
prising the records. At its broadest application, the Taxonomy
describes processes that determine the quality of incident
reports, the effectiveness of reporting systems, and the
success of intervention strategies. The significance is that the
Taxonomy could potentially be used as a common backbone
when mapped to disparate reporting systems unifying termi-
nologies and classifications. This allows aggregated data to be
Figure 5 Classification of prevention and mitigation.
The JCAHO patient safety event taxonomy
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combined and tracked over time, provides for consistency
across reporting systems, and structures data documentation
and presentation using a standardized format. Applied to an
electronic health record system, the taxonomy offers a means
for interoperability, facilitating exchange of patient safety data
A decentralized approach to patient safety reporting, using a
standardized terminology and classification framework, would
simplify the development and maintenance of a coding struc-
ture for reporting. Reconciling the data collected by local or
focused reporting programs to a national standard would pro-
vide a means to integrate the already existing data collection
efforts relating to health care errors and systems failures. The
framework of the Taxonomy will also lessen the burden on
patient safety organizations that operate in multiple states and/
or must be responsive to multiple government agencies, private
oversight bodies, and group purchasers, without requiring
expensive re-engineering of existing reporting systems.
Health care error classification systems are not free of their
own problems. For example, they partition categories more
coarsely than do keywords, and users, who are accustomed to
the everyday colloquial language of patient safety used in the
workplace environment, may not be fluent in the terminology
of the classifications. The finite number of elements in the
Taxonomy nevertheless encompasses a broad range of areas
that could possibly be classified, but there are still likely many
areas that could escape detection and reporting. Furthermore,
because the anatomy of an event is multidimensional, its
deconstructed components may not be mutually exclusive to
each of the classifications, subclassifications, coded catego-
ries, and narrative fields in the taxonomy. In addition, the
multi-tiered features may be too complicated for some audi-
ences to use. For example, wrong-site surgery not only results
in physical harm, but may also affect the emotional (psycho-
logical) and functional status of the patient, and his or her
ability to return to work (economics). Near misses in the
taxonomy are assumed to have the same root causes as the
much smaller subset that actually develops into adverse
events. Arguably, the very advantage of using near-miss data
to provide information on how an incident ‘recovered’ from a
potential adverse event also has a downside. Adverse events
are by definition near misses that failed to be recovered in
time . By contrast, the events that a hospital successfully
prevents from occurring will be just those events that will
never be identified in a near-miss information system. Thus,
Figure 6 Analytical framework of the JCAHO patient safety event taxonomy.
Box 1 Desirable attributes of a patient safety event taxonomy
Based on unambiguous and generally agreed terminologies and classifications.
Useful for analyzing the processes and outcomes that underlie an event, including its root causes and contributing factors.
Facilitates consistent collection and analysis of near miss and adverse event data across the continuum of health care
Facilitates expedient data exchange and dissemination of patient safety information.
Useful for identifying priority areas for remedial attention and opportunities to improve patient safety.
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the Taxonomy must be clear on just what near misses have in
common, or not, with adverse events. Notwithstanding the
potential limitations of near-miss data, near misses are suffi-
ciently clear precursors of adverse events to point the way to
identification of specific individual and systems failures.
The Joint Commission Patient Safety Event Taxonomy focuses
on the most salient terminologies and classifications. Its
design will permit the progressive incorporation of new
patient safety data and information over time. However, addi-
tional field-testing will be required to bring the taxonomy to
full maturity and permit it to realize its overall objectives.
The authors gratefully acknowledge the Joint Commission
Corporate Members Planning Group on Patient Safety, which
included representatives of the American College of Physi-
cians (ACP), the American College of Surgeons (ACS), the
American Dental Association (ADA), the American Hospital
Association (AHA), and the American Medical Association
(AMA), for their continuing assistance in this initiative. The
authors also wish to thank Jeanne P. Altieri, D.D.S. (ADA),
Jim Battles, Ph.D. (AHRQ), William A. Bornstein, M.D.,
Ph.D. (ACS), Karen S. Ehrat, Ph.D., R.N. (AHA), Thomas
Houston, M.D. (AMA), and Don M. Nielsen, M.D. (AHA),
who served as members of the Patient Safety Taxonomy
Workgroup. Gerry Castro, M.P.H. (Joint Commission) pro-
vided beneficial editorial assistance. The U.S. Agency for
Healthcare Research and Quality (AHRQ) provided specific
expertise and encouragement which made this effort possible.
The opinions expressed in this paper are those of the authors
and do not reflect the policies of the Corporate Members.
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Accepted for publication 8 December 2004