Background
The goal of this study was to determine the relative contribution of system-related and cognitive components to diagnostic error and to develop a comprehensive working taxonomy.
Methods
One hundred cases of diagnostic error involving internists were identified through autopsy discrepancies, quality assurance activities, and voluntary reports. Each case was evaluated to identify system-related and cognitive factors underlying error using record reviews and, if possible, provider interviews.
Results
Ninety cases involved injury, including 33 deaths. The underlying contributions to error fell into 3 natural categories: “no fault,” system-related, and cognitive. Seven cases reflected no-fault errors alone. In the remaining 93 cases, we identified 548 different system-related or cognitive factors (5.9 per case). System-related factors contributed to the diagnostic error in 65% of the cases and cognitive factors in 74%. The most common system-related factors involved problems with policies and procedures, inefficient processes, teamwork, and communication. The most common cognitive problems involved faulty synthesis. Premature closure, ie, the failure to continue considering reasonable alternatives after an initial diagnosis was reached, was the single most common cause. Other common causes included faulty context generation, misjudging the salience of findings, faulty perception, and errors arising from the use of heuristics. Faulty or inadequate knowledge was uncommon.
Conclusions
Diagnostic error is commonly multifactorial in origin, typically involving both system-related and cognitive factors. The results identify the dominant problems that should be targeted for additional research and early reduction; they also further the development of a comprehensive taxonomy for classifying diagnostic errors.
Once we realize that imperfect understanding is the human condition, there is no shame in being wrong, only in failing to correct our mistakes.—George Soros
In his classic studies of clinical reasoning, Elstein1 estimated the rate of diagnostic error to be approximately 15%, in reasonable agreement with the 10% to 15% error rate determined in autopsy studies.2-4 Considering the frequency and impact of diagnostic errors, one is struck by how little is known about this type of medical error.5 Data on the types and causes of errors encountered in the practice of internal medicine are scant, and the field lacks both a standardized definition of diagnostic error and a comprehensive taxonomy, although preliminary versions have been proposed.6-12
According to the Institute of Medicine, the most powerful way to reduce error in medicine is to focus on system-level improvements,13,14 but these interventions are typically discussed in regard to patient treatment issues. The possibility that system-level dysfunction could also contribute to diagnostic errors has received little attention. Typically, diagnostic error is viewed as a cognitive failing.7,15-17 Diagnosis reflects the clinician’s knowledge, clinical acumen, and problem-solving skills.1,18 In everyday practice, clinicians use expert skills to arrive at a diagnosis, often taking advantage of various mental shortcuts known as heuristics.16,18-21 These strategies are highly efficient, relatively effortless, and generally accurate, but they are not infallible.
The goal of this study was to clarify the basic etiology of diagnostic errors in internal medicine and to develop a working taxonomy. To understand how these errors arise and how they might be prevented in the future, we systematically examined the etiology of error using root cause analysis to classify both system-related and cognitive components.
Based on a classification used by the Australian Patient Safety Foundation, we defined diagnostic error operationally as a diagnosis that was unintentionally delayed (sufficient information was available earlier), wrong (another diagnosis was made before the correct one), or missed (no diagnosis was ever made), as judged from the eventual appreciation of more definitive information.
Cases of suspected diagnostic error were collected from 5 large academic tertiary care medical centers over 5 years. To obtain a broad sampling of errors, we reviewed all eligible cases from 3 sources:
Performance improvement and risk management coordinators and peer review committees.
Voluntary reports from staff physicians and resident trainees.
Discrepancies between clinical impressions and autopsy findings.
Cases were included if internists (staff specialists or generalists or trainees) were primarily responsible for the diagnosis and if sufficient details about the case and the decision-making process could be obtained to allow analysis. In all cases, details were gathered from a review of the medical record, from fact-finding information obtained in the course of quality assurance activities when available, and in 42 cases, involved practitioners were interviewed, typically within 1 month of error identification. To minimize hindsight bias, reviews of medical records and interviews used a combination of open-ended queries and a root cause checklist developed by the Veterans Health Administration (VHA).22,23 The VHA instrument24,25 identifies specific flaws in the standard dimensions of organizational performance.26,27 and is well suited to exploring system-related factors. We developed the cognitive factors portion of the taxonomy by incorporating and expanding on the categories suggested by Chimowitz et al,11 Kassirer and Kopelman,12 and Bordage.28 These categories differentiate flaws in the clinician’s knowledge and skills, ability to gather data, and ability to synthesize all available information into verifiable hypotheses. Criteria and definitions for each category were refined, and new categories added, as the study progressed.
Case histories were redacted of identifying information and analyzed as a team to the point of consensus by 1 internist and 2 cognitive psychologists to confirm the existence of a diagnostic error and to assign the error type (delayed, wrong, or missed) and both the system-related and the cognitive factors contributing to the error.29 To identify 100 usable cases, 129 cases of suspected error were reviewed, 29 of which were rejected. Definitive confirmation of an error was lacking in 19 cases. In 6 cases, the diagnosis was somewhat delayed but judged to have been made within an acceptable time frame. In 3 cases, the data were inadequate for analysis, and 1 case was rejected because the error reflected an intentional act that violated local policies (wrong diagnosis of hyponatremia from blood drawn above an intravenous line).
Impact was judged by an internist using a VHA scale that multiplies the likelihood of recurrence (1, remote; up to 4, frequent) by the severity of harm (1, minor injury; up to 4, catastrophic injury).24,25 A minor injury with a remote chance of recurrence received an impact score of 1, and a catastrophic event with frequent recurrence received an impact score of 16. Close-call errors were assigned an impact score of 0, and psychological impact was similarly discounted. The relative frequency of error types was compared using the Fisher exact test. The impact scores of different groups were compared by 1-way analysis of variance, and if a significant difference was found, group means were compared by a t test.
The study was approved by the institutional review board(s) at the participating institutions. Confidentiality protections were provided by the federal Privacy Act and the Tort Claims Act, by New York State statutes, and by a certificate of confidentiality from the US Department of Health and Human Services.
We analyzed 100 cases of diagnostic error: 57 from quality assurance activities, 33 from voluntary reports, and 10 from autopsy discrepancies. The error was revealed by tissue in 53 cases (19 autopsy specimens and 34 surgical or biopsy specimens), by definitive tests in 44 cases (24 x-ray studies and 20 laboratory investigations), and from pathognomonic clinical findings or procedure results in the remaining 3 cases. The diagnosis was wrong in 38 cases, missed in 34 cases, and delayed in 28 cases.
Ten cases were classified as close calls, and 90 cases involved some degree of harm, including 33 deaths. The clinical impact averaged 3.80 ± 0.28 (mean ± SEM) on the VHA impact scale, indicating substantial levels of harm, on average. The impact score tended to be lower in cases of delayed diagnosis than in cases that were missed or wrong (3.79 ± 0.52 vs 4.76 ± 0.40 and 4.47 ± 0.46; P = .34). Cases with solely cognitive factors or with mixed cognitive and system-related factors had significantly higher impact scores than cases with only system-related factors (4.11 ± 0.46 and 4.27 ± 0.47 vs 2.54 ± 0.55; P = .03 for both comparisons). These 2 effects may be related, as delays were the type of error most likely to result from system-related factors alone.
Etiology of diagnostic error
Our results suggested that diagnostic error in medicine could best be described using a taxonomy that includes no-fault, system-related (Table 1), and cognitive (Table 2) factors.
No-fault errors
System-related errors
Cognitive errors
Faulty knowledge
Faulty data gathering
Faulty synthesis
In 46% of the cases, both system-related and cognitive factors contributed to diagnostic error. Cases involving only cognitive factors (28%) or only system-related factors (19%) were less common, and 7 cases were found to reflect solely no-fault factors, without any other system-related or cognitive factors. Combining the pure and the mixed cases, system-related factors contributed to the diagnostic error in 65% of the 100 cases and cognitive factors contributed in 74% (Figure). Overall, we identified 228 system-related factors and 320 cognitive factors, averaging 5.9 per case.
The relative frequency of both system-related and cognitive factors varied with the source of the case and the type of error involved. Cases identified from quality assurance reports and from voluntary reports had a similar prevalence of system-related factors (72% and 76%, respectively) and cognitive factors (65% and 85%, respectively). In contrast, cases identified from autopsy discrepancies involved cognitive factors 90% of the time (P>.50) and system-related factors only 10% of the time (P<.001). Cases of delayed diagnosis had relatively more system-related errors (89%) and fewer cognitive errors (36%) on average, and cases of wrong diagnosis involved more cognitive errors (92%) and fewer system-related errors (50%, P<.01 for both pairs).
No-fault factors were identified in 44 of the 100 cases and constituted the sole explanation in 7 cases. Eleven of these cases involved patient-related factors, including 2 instances of deception (surreptitious self-injection of saliva, mimicking sepsis and denial of high-risk sexual activity, which delayed diagnosis of Pneumocystis carinii pneumonia) and 9 cases involving delayed diagnoses related to missed appointments or instances in which patient statements were unintentionally misleading or incomplete. By far, the most common no-fault factor was an atypical or masked disease presentation, encountered in 33 cases.
System-related contributions to error
In 65 cases, system-related factors contributed to diagnostic error (Table 1). The vast majority of these (215 instances) were related to organizational problems, and a small fraction (13 instances) involved technical and equipment problems. The factors encountered most often related to policies and procedures, inefficient processes, and difficulty with teamwork and communication, especially communication of test results. Many error types were encountered more than twice in the same institution, an event we referred to as clustering.
Cognitive contributions to error
We identified 320 cognitive factors in 74 cases (Table 2). The most common category of factors was faulty synthesis (264 instances), or flawed processing of the available information. Faulty data gathering was identified in 45 instances. Inadequate or faulty knowledge or skills were identified in only 11 instances.
Faulty knowledge or skills
Inadequate knowledge was identified in only 4 cases, each concerning a rare condition: (1) a case of missed Fournier gangrene; (2) a missed diagnosis of calciphylaxis in a patient undergoing dialysis with normal levels of serum calcium and phosphorus; (3) a case of chronic thrombotic thrombocytopenic purpura; and (4) a wrong diagnosis of disseminated intravascular coagulation in a patient ultimately thought to have clopidogrel-associated thrombotic thrombocytopenic purpura. The 7 cases involving inadequate skills involved misinterpretations of x-ray studies and electrocardiograms by nonexperts.
The dominant cause of error in the faulty data-gathering category lay in the subcategory of ineffective, incomplete, or faulty workup (24 instances). For example, the diagnosis of subdural hematoma was missed in a patient who was seen after a motor vehicle crash because the physical examination was incomplete. Problems with ordering the appropriate tests and interpreting test results were also common in this group.
Faulty information synthesis
Faulty information synthesis, which includes a wide range of factors, was the most common cause of cognitive-based errors. The single most common phenomenon was premature closure: the tendency to stop considering other possibilities after reaching a diagnosis. Other common synthesis factors included faulty context generation, misjudging the salience of a finding, faulty perception, and failed use of heuristics. Faulty context generation and misjudging the salience of a finding often occurred in the same case (15 of 25 instances). Perceptual failures most commonly involved incorrect readings of x-ray studies by internists and emergency department staff before official reading by a radiologist. Of the 23 instances related to heuristics, 14 reflected the bias to assume that all findings were related to a single cause when a patient actually had more than 1 condition. In 7 cases, the most common condition was chosen as the likely diagnosis, although a less common condition was responsible.
Covariation among factors
Cognitive and system-related factors were found to often co-occur, and these factors may have led, directly or indirectly, to each other. For example, a mistake relatively early on (eg, an inadequate history or physical examination) is likely to lead to subsequent mistakes (eg, in interpreting test results, considering appropriate candidate diagnoses, or calling in appropriate specialists). We examined the patterns of factors identified in these 100 cases to identify clusters of cognitive factors that tended to co-occur. Using Pearson r tests and correcting for the use of multiple pairwise analyses, we found several such clusters of cognitive factors. The more common clusters of 3 factors, all of which have significant pairwise correlations within a cluster, were as follows:
Incomplete/faulty history and physical examination; failure to consider the correct candidate diagnosis; and premature closure
Incomplete/excessive data gathering; bias toward a single explanation; and premature closure
Underestimating the usefulness of a finding; premature closure; and failure to consult
In classifying the underlying factors contributing to error, 3 natural categories emerged: no fault, system-related, and cognitive. This classification validates the cognitive and no-fault distinctions described by Chimowitz et al,11 Kassirer and Kopelman,12 and Bordage28 and adds a third major category of system-level factors.
A second objective was to assess the relative contributions of system-related and cognitive root cause factors. The results allow 3 major conclusions regarding diagnostic error in internal medicine settings.
Diagnostic error is typically multifactorial in origin
Excluding the 7 cases of pure no-fault error, we identified an average of 5.9 factors contributing to error in each case. Reason’s6 “Swiss cheese” model of error suggests that harm results from multiple breakdowns in the series of barriers that normally prevent injury. This phenomenon was identified in many of our cases, in which the ultimate diagnostic failure involved separate factors at multiple levels of both the system-related and the cognitive pathways.
A second reason for encountering multiple factors in a single case is the tendency for one type of error to lead to another. For example, a patient with retrosternal and upper epigastric pain was given a diagnosis of myocardial infarction on the basis of new Q waves in his electrocardiogram and elevated levels of troponin. The clinicians missed a coexisting perforated ulcer, illustrating that if a case is viewed in the wrong context, clinicians may miss relevant clues and may not consider the correct diagnosis.
System flaws contribute commonly to diagnostic error
System-related factors were identified in 65% of cases. This finding supports a previous study linking diagnostic errors to system issues30 but contrasts with the prevailing belief that diagnostic errors overwhelmingly reflect defective cognition. The system flaws identified in our study reflected far more organizational issues than technical problems. Errors related to suboptimal supervision of trainees occurred, but uncommonly.
Cognitive errors are a common cause of diagnostic error and predominantly reflect problems with synthesis of the available information
Faulty data gathering was much less commonly encountered, and defective knowledge was rare. These results are consistent with conclusions from earlier studies28,30 and from autopsy data almost 50 years ago: “ . . . mistakes were due not so much to lack of knowledge of factual data as to certain deficiencies of approach and judgment.”31 This finding may distinguish medical diagnosis from other types of expert decision making, in which knowledge deficits are more commonly encountered as the cause of error.32
As predicted by other authors,1,9,15,33 premature closure was encountered more commonly than any other type of cognitive error. Simon34 described the initial stages of problem solving as a search for an explanation that best fits the known facts, at which point one stops searching for additional explanations, a process he termed satisficing. Experienced clinicians are as likely as more junior colleagues to exhibit premature closure,15 and elderly physicians may be particularly predisposed.35
This study has a variety of limitations that restrict the generality of the conclusions. First, because the types of error are dependent on the source of the cases,36-38 a different spectrum of case types would be expected outside internal medicine. Also, selection bias might be expected in cases that are reported voluntarily. Distortions could also result from our nonrandomized method of case selection if they are not representative of the errors that actually occurred.
A second limitation is the difficulty in discerning exactly how a given diagnosis was reached. Clinical reasoning is hidden from direct examination, and may be just as mysterious to the clinician involved. A related problem is our limited ability to identify other factors that likely affect many clinical decision-making situations, such as stress, fatigue, and distractions. Clinicians had difficulty recalling such factors, which undoubtedly existed. Their recollections might also be distorted because of the unavoidable lag time between the experience and the interview, and by their knowledge of the clinical outcomes.
A third weakness is the subjective assignment of root causes. The field as it evolves will benefit from further clarification and standardization for each of these causes. A final concern is the inevitable bias that is introduced in a retrospective analysis in which the outcomes are known.23,39,40 With this in mind, we did not attempt to evaluate the appropriateness of care or the preventability of adverse events, judgments that are highly sensitive to hindsight bias.
Strategies to decrease diagnostic error
Although diagnostic error can never be eliminated,41 our results identify the common causes of diagnostic error in medicine, ideal targets for future efforts to reduce the incidence of these errors. The high prevalence of system-related factors offers the opportunity to reduce diagnostic errors if health care institutions accept the responsibility of addressing these factors. For example, errors could be avoided if radiologists were reliably available to interpret x-ray studies and if abnormal test results were reliably communicated. Institutions should be especially sensitive to clusters of errors of the same type. Although these institutions may simply excel at error detection, clustering could also indicate misdirected resources or a culture of tolerating suboptimal performance.
Devising strategies for reducing cognitive error is a more complex problem. Our study suggests that internists generally have sufficient medical knowledge and that errors of clinical reasoning overwhelmingly reflect inappropriate cognitive processing and/or poor skills in monitoring one’s own cognitive processes (metacognition).42 Croskerry43 and others44 have argued that clinicians who are oriented to the common pitfalls of clinical reasoning would be better able to avoid them. High-fidelity simulations may be one way to provide this training.45,46 Elstein1 has suggested the value of compiling a complete differential diagnosis to combat the tendency to premature closure, the most common cognitive factor we identified. A complementary strategy for considering alternatives involves the technique of prospective hindsight: the crystal ball experience: The clinician would be told to assume that his or her working diagnosis is incorrect, and asked, “What alternatives should be considered?”47 A final strategy is to augment a clinician’s inherent metacognitive skills by using expert systems, an approach currently under active research and development.48-50
Correspondence: Mark L. Graber, MD, Medical Service 111, Veterans Affairs Medical Center, Northport, NY 11768 (mark.graber@med.va.gov).
Accepted for Publication: February 21, 2005.
Financial Disclosure: None.
Funding/Support: This work was supported by a research support grant honoring James S. Todd, MD, from the National Patient Safety Foundation, North Adams, Mass.
Acknowledgment: We thank Grace Garey and Kathy Kessel for their assistance with the manuscript and references.
1.Elstein
AS Clinical reasoning in medicine. Higgs
JJones
MAeds.
Clinical Reasoning in the Health Professions Woburn, Mass Butterworth-Heinemann1995;49- 59
Google Scholar 3.Shojania
KGBurton
ECMcDonald
KMGoldman
L Changes in rates of autopsy-detected diagnostic errors over time.
JAMA 2003;2892849- 2856
PubMedGoogle ScholarCrossref 4.Goldman
LSayson
RRobbins
SCohn
LHBettmann
MWeisberg
M The value of the autopsy in three different eras.
N Engl J Med 1983;3081000- 1005
PubMedGoogle ScholarCrossref 5.Graber
M Diagnostic error in medicine: a case of neglect.
Jt Comm J Qual Patient Saf 2005;31106- 113
PubMedGoogle Scholar 6.Reason
J Human Error. New York, NY Cambridge University Press1990;
7.Zhang
JPatel
VLJohnson
TR Medical error: is the solution medical or cognitive?
J Am Med Inform Assoc 2002;9
((suppl))
S75- S77
PubMedGoogle ScholarCrossref 8.Leape
LLawthers
AGBrennan
TAJohnson
WG Preventing medical injury.
QRB Qual Rev Bull 1993;19144- 149
PubMedGoogle Scholar 9.Voytovich
AERippey
RMSuffredini
A Premature conclusions in diagnostic reasoning.
J Med Educ 1985;60302- 307
PubMedGoogle Scholar 10.Friedman
MHConnell
KJOlthoff
AJSinacore
JMBordage
G Medical student errors in making a diagnosis.
Acad Med 1998;73
((suppl))
S19- S21
PubMedGoogle ScholarCrossref 12.Kassirer
JPKopelman
RI Cognitive errors in diagnosis: instantiation, classification, and consequences.
Am J Med 1989;86433- 441
PubMedGoogle ScholarCrossref 13.Institute of Medicine, To Err is Human; Building a Safer Health System. Washington, DC National Academy Press1999;
14.Spath
PL Reducing errors through work system improvements.
Error Reduction in Health Care A Systems Approach to Improving Patient Safety San Francisco, Calif Jossey-Bass2000;199- 234
Google Scholar 16.Croskerry
P Achieving quality in clinical decision making: cognitive strategies and detection of bias.
Acad Emerg Med 2002;91184- 1204
PubMedGoogle ScholarCrossref 23.Henriksen
KKaplan
H Hindsight bias, outcome knowledge and adaptive learning.
Qual Saf Health Care 2003;12
((suppl))
ii46- ii50
PubMedGoogle ScholarCrossref 24.Bagian
JPGosbee
JLee
CZWilliams
LMcKnight
SDMannos
DM The Veterans Affairs root cause analysis system in action.
Jt Comm J Qual Improv 2002;28531- 545
PubMedGoogle Scholar 26.Battles
JBKaplan
HSVan der Schaaf
TWShea
CE The attributes of medical event-reporting systems.
Arch Pathol Lab Med 1998;122231- 238
PubMedGoogle Scholar 27.Reason
J Managing the Risks of Organizational Accidents. Brookfield, Vt Ashgate Publishing Co1997;
28.Bordage
G Why did I miss the diagnosis? some cognitive explanations and educational implications.
Acad Med 1999;74
((suppl))
S128- S143
PubMedGoogle ScholarCrossref 29.Graber
M Expanding the goals of peer review to detect both practitioner and system error.
Jt Comm J Qual Improv 1999;25396- 407
PubMedGoogle Scholar 32.Klein
G Sources of error in naturalistic decision making tasks. Proceedings of the 37th Annual Meeting of the Human Factors and Ergonomics Society: Designing for Diversity; October 11-15, 1993 Seattle, Wash
33.Dubeau
CEVoytovich
AERippey
RM Premature conclusions in the diagnosis of iron-deficiency anemia: cause and effect.
Med Decis Making 1986;6169- 173
PubMedGoogle ScholarCrossref 35.Eva
KW The aging physician: changes in cognitive processing and their impact on medical practice.
Acad Med 2002;77
((suppl))
S1- S6
PubMedGoogle ScholarCrossref 37.O'Neil
ACPetersen
LACook
FBates
DWLee
THBrennan
TA Physician reporting compared with medical-record review to identify adverse medical events.
Ann Intern Med 1993;119370- 376
PubMedGoogle ScholarCrossref 38.Heinrich
J Adverse Events: Surveillance Systems for Adverse Events and Medical Errors. Washington, DC US Government Accounting Office; February9 2000;
40.Fischoff
B Hindsight does not equal foresight: the effect of outcome knowledge on judgment under uncertainty.
J Exp Psychol Hum Percept Perform 1975;1288- 299
Google ScholarCrossref 44.Hall
KH Reviewing intuitive decision-making and uncertainty: the implications for medical education.
Med Educ 2002;36216- 224
PubMedGoogle ScholarCrossref 45.Satish
UStreufert
S Value of a cognitive simulation in medicine: towards optimizing decision making performance of healthcare professionals.
Qual Saf Health Care 2002;11163- 167
PubMedGoogle ScholarCrossref 46.Bond
WFDeitrick
LMArnold
DC
et al. Using simulation to instruct emergency medicine residents in cognitive forcing strategies.
Acad Med 2004;79438- 446
PubMedGoogle ScholarCrossref 47.Mitchell
DJRusso
JEPennington
N Back to the future: temporal perspective in the explanation of events.
J Behav Decis Making 1989;225- 38
Google ScholarCrossref 48.Trowbridge
RWeingarten
S Clinical decision support systems. Shojania
KGDuncan
BWMcDonald
KMWachter
RMeds.
Making Health Care Safer A Critical Analysis of Patient Safety Practices Rockville, Md Agency for Healthcare Research and Quality2001;
Google Scholar 49.Hunt
DLHaynes
RBHanna
SESmith
K Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review.
JAMA 1998;2801339- 1346
PubMedGoogle ScholarCrossref 50.Sim
IGorman
PGreenes
RA
et al. Clinical decision support systems for the practice of evidence-based medicine.
J Am Med Inform Assoc 2001;8527- 534
PubMedGoogle ScholarCrossref