Use of patient complaints to identify diagnosis-related safety concerns: a mixed-method evaluation

BackgroundPatient complaints are associated with adverse events and malpractice claims but underused in patient safety improvement.ObjectiveTo systematically evaluate the use of patient complaint data to identify safety concerns related to diagnosis as an initial step to using this information to facilitate learning and improvement.MethodsWe reviewed patient complaints submitted to Geisinger, a large healthcare organisation in the USA, from August to December 2017 (cohort 1) and January to June 2018 (cohort 2). We selected complaints more likely to be associated with diagnostic concerns in Geisinger’s existing complaint taxonomy. Investigators reviewed all complaint summaries and identified cases as ‘concerning’ for diagnostic error using the National Academy of Medicine’s definition of diagnostic error. For all ‘concerning’ cases, a clinician-reviewer evaluated the associated investigation report and the patient’s medical record to identify any missed opportunities in making a correct or timely diagnosis. In cohort 2, we selected a 10% sample of ‘concerning’ cases to test this smaller pragmatic sample as a proof of concept for future organisational monitoring.ResultsIn cohort 1, we reviewed 1865 complaint summaries and identified 177 (9.5%) concerning reports. Review and analysis identified 39 diagnostic errors. Most were categorised as ‘Clinical Care issues’ (27, 69.2%), defined as concerns/questions related to the care that is provided by clinicians in any setting. In cohort 2, we reviewed 2423 patient complaint summaries and identified 310 (12.8%) concerning reports. The 10% sample (n=31 cases) contained five diagnostic errors. Qualitative analysis of cohort 1 cases identified concerns about return visits for persistent and/or worsening symptoms, interpersonal issues and diagnostic testing.ConclusionsAnalysis of patient complaint data and corresponding medical record review identifies patterns of failures in the diagnostic process reported by patients and families. Health systems could systematically analyse available data on patient complaints to monitor diagnostic safety concerns and identify opportunities for learning and improvement.

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Recommendations for using the Revised Safer Dx Instrument to help measure and improve diagnostic safety

Diagnosis ◽

10.1515/dx-2019-0012 ◽

2019 ◽

Vol 6 (4) ◽

pp. 315-323 ◽

Cited By ~ 8

Author(s):

Hardeep Singh ◽

Arushi Khanna ◽

Christiane Spitzmueller ◽

Ashley N.D. Meyer

Keyword(s):

Health Care ◽

Data Collection ◽

Medical Record ◽

Diagnostic Errors ◽

Diagnostic Error ◽

Diagnostic Process ◽

Sources Of Information ◽

Missed Opportunities ◽

Health Care Settings ◽

Data Collection Instrument

Abstract The medical record continues to be one of the most useful and accessible sources of information to examine the diagnostic process. However, medical record review studies of diagnostic errors have often used subjective judgments and found low inter-rater agreement among reviewers when determining the presence or absence of diagnostic error. In our previous work, we developed a structured data-collection instrument, called the Safer Dx Instrument, consisting of objective criteria to improve the accuracy of assessing diagnostic errors in primary care. This paper proposes recommendations on how clinicians and health care organizations could use the Revised Safer Dx Instrument in identifying and understanding missed opportunities to make correct and timely diagnoses. The instrument revisions addressed both methodological and implementation issues identified during initial use and included refinements to the instrument to allow broader application across all health care settings. In addition to leveraging knowledge from piloting the instrument in several health care settings, we gained insights from multiple researchers who had used the instrument in studies involving emergency care, inpatient care and intensive care unit settings. This allowed us to enhance and extend the scope of this previously validated data collection instrument. In this paper, we describe the refinement process and provide recommendations for application and use of the Revised Safer Dx Instrument across a broad range of health care settings. The instrument can help users identify potential diagnostic errors in a standardized way for further analysis and safety improvement efforts as well as provide data for clinician feedback and reflection. With wider adoption and use by clinicians and health systems, the Revised Safer Dx Instrument could help propel the science of measuring and reducing diagnostic errors forward.

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Lessons in clinical reasoning – pitfalls, myths, and pearls: the contribution of faulty data gathering and synthesis to diagnostic error

Diagnosis ◽

10.1515/dx-2019-0108 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Martin A. Schaller-Paule ◽

Helmuth Steinmetz ◽

Friederike S. Vollmer ◽

Melissa Plesac ◽

Felix Wicke ◽

...

Keyword(s):

Patient Safety ◽

Clinical Reasoning ◽

Data Gathering ◽

Diagnostic Errors ◽

Diagnostic Error ◽

Care Physician ◽

Diagnostic Process ◽

Myasthenic Crisis ◽

Hospital Organization ◽

Substantial Impact

Abstract Objectives Errors in clinical reasoning are a major factor for delayed or flawed diagnoses and put patient safety at risk. The diagnostic process is highly dependent on dynamic team factors, local hospital organization structure and culture, and cognitive factors. In everyday decision-making, physicians engage that challenge partly by relying on heuristics – subconscious mental short-cuts that are based on intuition and experience. Without structural corrective mechanisms, clinical judgement under time pressure creates space for harms resulting from systems and cognitive errors. Based on a case-example, we outline different pitfalls and provide strategies aimed at reducing diagnostic errors in health care. Case presentation A 67-year-old male patient was referred to the neurology department by his primary-care physician with the diagnosis of exacerbation of known myasthenia gravis. He reported shortness of breath and generalized weakness, but no other symptoms. Diagnosis of respiratory distress due to a myasthenic crisis was made and immunosuppressive therapy and pyridostigmine were given and plasmapheresis was performed without clinical improvement. Two weeks into the hospital stay, the patient’s dyspnea worsened. A CT scan revealed extensive segmental and subsegmental pulmonary emboli. Conclusions Faulty data gathering and flawed data synthesis are major drivers of diagnostic errors. While there is limited evidence for individual debiasing strategies, improving team factors and structural conditions can have substantial impact on the extent of diagnostic errors. Healthcare organizations should provide the structural supports to address errors and promote a constructive culture of patient safety.

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Checklists to prevent diagnostic errors: a pilot randomized controlled trial

Diagnosis ◽

10.1515/dx-2015-0008 ◽

2015 ◽

Vol 2 (3) ◽

pp. 163-169 ◽

Cited By ~ 8

Author(s):

John W. Ely ◽

Mark A. Graber

Keyword(s):

Primary Care ◽

Usual Care ◽

Error Rate ◽

Primary Care Physicians ◽

Diagnostic Errors ◽

Diagnostic Error ◽

Diagnostic Process ◽

Emergency Physicians ◽

Mean Error ◽

Premature Closure

AbstractMany diagnostic errors are caused by premature closure of the diagnostic process. To help prevent premature closure, we developed checklists that prompt physicians to consider all reasonable diagnoses for symptoms that commonly present in primary care.We enrolled 14 primary care physicians and 100 patients in a randomized clinical trial. The study took place in an emergency department (5 physicians) and a same-day access clinic (9 physicians). The physicians were randomized to usual care vs. diagnostic checklist. After completing the history and physical exam, checklist physicians read aloud a differential diagnosis checklist for the chief complaint. The primary outcome was diagnostic error, which was defined as a discrepancy between the diagnosis documented at the acute visit and the diagnosis based on a 1-month follow-up phone call and record review.There were 17 diagnostic errors. The mean error rate among the seven checklist physicians was not significantly different from the rate among the seven usual-care physicians (11.2% vs. 17.8%; p=0.46). In a post-hoc subgroup analysis, emergency physicians in the checklist group had a lower mean error rate than emergency physicians in the usual-care group (19.1% vs. 45.0%; p=0.04). Checklist physicians considered more diagnoses than usual-care physicians during the patient encounters (6.5 diagnoses [SD 4.2] vs. 3.4 diagnoses [SD 2.0], p<0.001).Checklists did not improve the diagnostic error rate in this study. However further development and testing of checklists in larger studies may be warranted.

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The psychology of diagnostic error

Experimental Psychology (Russia) ◽

10.17759/exppsy.2015080309 ◽

2015 ◽

Vol 8 (3) ◽

pp. 91-98

Author(s):

L. Zwaan

Keyword(s):

Diagnostic Errors ◽

Diagnostic Error ◽

Dual Process ◽

Dual Process Theory ◽

Process Theory ◽

Diagnostic Process ◽

Cognitive Errors ◽

System 2 ◽

System 1 ◽

Analytic Reasoning

Diagnostic errors in medicine occur frequently and the consequences for the patient can be severe. Cognitive errors as well as system related errors contribute to the occurrence of diagnostic error, but it is generally accepted that cognitive errors are the main contributor. The diagnostic reasoning process in medicine, is an understudied area of research. One reason is because of the complexity of the diagnostic process and therefore the difficulty to measure diagnostic errors and the causes of diagnostic error. In this paper, I discuss some of the complexities of the diagnostic process. I describe the dual-process theory, which defines two reasoning modes, 1. a fast, automatic and unconscious reasoning mode called system 1, and a slow and analytic reasoning mode called system 2. Furthermore, the main cognitive causes of diagnostic error are described.

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Mind the overlap: how system problems contribute to cognitive failure and diagnostic errors

Diagnosis ◽

10.1515/dx-2018-0014 ◽

2018 ◽

Vol 5 (3) ◽

pp. 151-156 ◽

Cited By ~ 8

Author(s):

Ashwin Gupta ◽

Molly Harrod ◽

Martha Quinn ◽

Milisa Manojlovich ◽

Karen E. Fowler ◽

...

Keyword(s):

Information Processing ◽

Data Gathering ◽

Diagnostic Errors ◽

Organizational Structures ◽

Diagnostic Error ◽

Diagnostic Process ◽

Cognitive Errors ◽

Data Verification ◽

Consulting Services ◽

Cognitive Failure

Abstract Background Traditionally, research has examined systems- and cognitive-based sources of diagnostic error as individual entities. However, half of all errors have origins in both domains. Methods We conducted a focused ethnography of inpatient physicians at two academic institutions to understand how systems-based problems contribute to cognitive errors in diagnosis. Medicine teams were observed on rounds and during post-round work after which interviews were conducted. Field notes related to the diagnostic process and the work system were recorded, and findings were organized into themes. Using deductive content analysis, themes were categorized based on a published taxonomy to link systems-based contributions and cognitive errors such as faulty data gathering, information processing, data verification and errors associated with multiple domains. Results Observations, focus groups and interviews of 10 teams were conducted between January 2016 and April 2017. The following themes were identified: (1) challenges with interdisciplinary communication and communication within the electronic medical record (EMR) contributed to faulty data gathering; (2) organizational structures such as the operation of consulting services in silos promoted faulty information processing; (3) care handoffs led to faulty data verification and (4) interruptions, time constraints and a cluttered physical environment negatively influenced multiple cognitive domains. Conclusions Systems-based factors often facilitate and promote cognitive problems in diagnosis. Linking systems-based contributions to downstream cognitive impacts and intervening on both in tandem may help prevent diagnostic errors.

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Serious misdiagnosis-related harms in malpractice claims: The “Big Three” – vascular events, infections, and cancers

Diagnosis ◽

10.1515/dx-2019-0019 ◽

2019 ◽

Vol 6 (3) ◽

pp. 227-240 ◽

Cited By ~ 20

Author(s):

David E. Newman-Toker ◽

Adam C. Schaffer ◽

C. Winnie Yu-Moe ◽

Najlla Nassery ◽

Ali S. Saber Tehrani ◽

...

Keyword(s):

Diagnostic Errors ◽

Diagnostic Error ◽

Healthcare Research ◽

Insurance Company ◽

Vascular Events ◽

Cross Sectional ◽

Permanent Disability ◽

High Severity ◽

Malpractice Claims ◽

Severity Of Injury

Abstract Background Diagnostic errors cause substantial preventable harm, but national estimates vary widely from 40,000 to 4 million annually. This cross-sectional analysis of a large medical malpractice claims database was the first phase of a three-phase project to estimate the US burden of serious misdiagnosis-related harms. Methods We sought to identify diseases accounting for the majority of serious misdiagnosis-related harms (morbidity/mortality). Diagnostic error cases were identified from Controlled Risk Insurance Company (CRICO)’s Comparative Benchmarking System (CBS) database (2006–2015), representing 28.7% of all US malpractice claims. Diseases were grouped according to the Agency for Healthcare Research and Quality (AHRQ) Clinical Classifications Software (CCS) that aggregates the International Classification of Diseases diagnostic codes into clinically sensible groupings. We analyzed vascular events, infections, and cancers (the “Big Three”), including frequency, severity, and settings. High-severity (serious) harms were defined by scores of 6–9 (serious, permanent disability, or death) on the National Association of Insurance Commissioners (NAIC) Severity of Injury Scale. Results From 55,377 closed claims, we analyzed 11,592 diagnostic error cases [median age 49, interquartile range (IQR) 36–60; 51.7% female]. These included 7379 with high-severity harms (53.0% death). The Big Three diseases accounted for 74.1% of high-severity cases (vascular events 22.8%, infections 13.5%, and cancers 37.8%). In aggregate, the top five from each category (n = 15 diseases) accounted for 47.1% of high-severity cases. The most frequent disease in each category, respectively, was stroke, sepsis, and lung cancer. Causes were disproportionately clinical judgment factors (85.7%) across categories (range 82.0–88.8%). Conclusions The Big Three diseases account for about three-fourths of serious misdiagnosis-related harms. Initial efforts to improve diagnosis should focus on vascular events, infections, and cancers.

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Malpractice claims related to diagnostic errors in the hospital

BMJ Quality & Safety ◽

10.1136/bmjqs-2017-006774 ◽

2017 ◽

Vol 27 (1) ◽

pp. 53-60 ◽

Cited By ~ 8

Author(s):

Ashwin Gupta ◽

Ashley Snyder ◽

Allen Kachalia ◽

Scott Flanders ◽

Sanjay Saint ◽

...

Keyword(s):

Diagnostic Errors ◽

Diagnostic Error ◽

Trend Test ◽

Inpatient Setting ◽

Risk Of Death ◽

Provider Characteristics ◽

Hospitalised Patients ◽

Malpractice Claims ◽

Male Patients ◽

Paid Claims

BackgroundLittle is known about the incidence or significance of diagnostic error in the inpatient setting. We used a malpractice claims database to examine incidence, predictors and consequences of diagnosis-related paid malpractice claims in hospitalised patients.MethodsThe US National Practitioner Database was used to identify paid malpractice claims occurring between 1 January 1999 and 31 December 2011. Patient and provider characteristics associated with paid claims were analysed using descriptive statistics. Differences between diagnosis-related paid claims and other paid claim types (eg, surgical, anaesthesia, medication) were assessed using Wilcoxon rank-sum and χ2 tests. Multivariable logistic regression was used to identify patient and provider factors associated with diagnosis-related paid claims. Trends for incidence of diagnosis-related paid claims and median annual payment were assessed using the Cochran-Armitage and non-parametric trend test.Results13 682 of 62 966 paid malpractice claims (22%) were diagnosis-related. Compared with other paid claim types, characteristics significantly associated with diagnosis-related paid claims were as follows: male patients, patient aged >50 years, provider aged <50 years and providers in the northeast region. Compared with other paid claim types, diagnosis-related paid claims were associated with 1.83 times more risk of disability (95% CI 1.75 to 1.91; p<0.001) and 2.33 times more risk of death (95% CI 2.23 to 2.43; p<0.001) than minor injury, after adjusting for patient and provider characteristics. Inpatient diagnostic error accounted for $5.7 billion in payments over the study period, and median diagnosis-related payments increased at a rate disproportionate to other types.ConclusionInpatient diagnosis-related malpractice payments are common and more often associated with disability and death than other claim types. Research focused on understanding and mitigating diagnostic errors in hospital settings is necessary.

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The critical step to reduce diagnostic errors in medicine: addressing the limitations of human information processing

Diagnosis ◽

10.1515/dx-2013-0018 ◽

2014 ◽

Vol 1 (1) ◽

pp. 139-141 ◽

Cited By ~ 3

Author(s):

Laura Zwaan

Keyword(s):

Information Processing ◽

Diagnostic Testing ◽

Diagnostic Errors ◽

Diagnostic Error ◽

Human Information Processing ◽

Process Information ◽

Critical Step ◽

Common Diseases ◽

Over Time ◽

The Way

AbstractOver the last 50 years diagnostic testing has improved dramatically and we are now able to diagnose patients faster and more precisely than ever before. However, the incidence of diagnostic errors, particularly of common diseases, has remained relatively stable over time. In this paper, I argue that the intrinsic limitations of human information processing are crucial. The way people process information has not changed over the years and is the main cause of diagnostic error. To take a decisive step forward and substantially reduce the number of diagnostic errors in medicine, we need to create an environment which takes the intrinsic limitations of in human information processing into account.

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The COVID trap: pediatric diagnostic errors in a pandemic world

Diagnosis ◽

10.1515/dx-2020-0150 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Yasaman Fatemi ◽

Susan Coffin

Keyword(s):

Care Delivery ◽

Diagnostic Errors ◽

Diagnostic Error ◽

Cognitive Factors ◽

Diagnostic Process ◽

Case Presentation ◽

Medical Environment ◽

Early Phases ◽

Specific Influence ◽

Missed Diagnoses

Abstract Objectives The COVID-19 pandemic has introduced strains in the diagnostic process through uncertainty in diagnosis, changes to usual clinical processes, and introduction of a unique social context of altered health care delivery and fear of the medical environment. These challenges created a context ripe for diagnostic error involving both systems and cognitive factors. Case presentation We present a series of three pediatric cases presenting to care during the early phases of the COVID-19 pandemic that highlight the heightened potential for diagnostic errors in the pandemic context with particular focus on the interplay of systems and cognitive factors leading to delayed and missed diagnoses. These cases illustrate the particular power of availability bias, diagnostic momentum, and premature closure in the diagnostic process. Conclusions Through integrated commentary and a fishbone analysis of the cognitive and systems factors at play, these three cases emphasize the specific influence of the COVID-19 pandemic on pediatric patients.

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Rate of diagnostic errors and serious misdiagnosis-related harms for major vascular events, infections, and cancers: toward a national incidence estimate using the “Big Three”

Diagnosis ◽

10.1515/dx-2019-0104 ◽

2020 ◽

Vol 0 (0) ◽

Cited By ~ 1

Author(s):

David E. Newman-Toker ◽

Zheyu Wang ◽

Yuxin Zhu ◽

Najlla Nassery ◽

Ali S. Saber Tehrani ◽

...

Keyword(s):

Myocardial Infarction ◽

False Negative ◽

Diagnostic Errors ◽

Diagnostic Error ◽

Vascular Events ◽

Spinal Abscess ◽

Malpractice Claims ◽

Disease Case ◽

Prior Literature ◽

Related Harm

AbstractBackgroundMissed vascular events, infections, and cancers account for ~75% of serious harms from diagnostic errors. Just 15 diseases from these “Big Three” categories account for nearly half of all serious misdiagnosis-related harms in malpractice claims. As part of a larger project estimating total US burden of serious misdiagnosis-related harms, we performed a focused literature review to measure diagnostic error and harm rates for these 15 conditions.MethodsWe searched PubMed, Google, and cited references. For errors, we selected high-quality, modern, US-based studies, if available, and best available evidence otherwise. For harms, we used literature-based estimates of the generic (disease-agnostic) rate of serious harms (morbidity/mortality) per diagnostic error and applied claims-based severity weights to construct disease-specific rates. Results were validated via expert review and comparison to prior literature that used different methods. We used Monte Carlo analysis to construct probabilistic plausible ranges (PPRs) around estimates.ResultsRates for the 15 diseases were drawn from 28 published studies representing 91,755 patients. Diagnostic error (false negative) rates ranged from 2.2% (myocardial infarction) to 62.1% (spinal abscess), with a median of 13.6% [interquartile range (IQR) 9.2–24.7] and an aggregate mean of 9.7% (PPR 8.2–12.3). Serious misdiagnosis-related harm rates per incident disease case ranged from 1.2% (myocardial infarction) to 35.6% (spinal abscess), with a median of 5.5% (IQR 4.6–13.6) and an aggregate mean of 5.2% (PPR 4.5–6.7). Rates were considered face valid by domain experts and consistent with prior literature reports.ConclusionsDiagnostic improvement initiatives should focus on dangerous conditions with higher diagnostic error and misdiagnosis-related harm rates.

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