Improving Diagnosis in Health Care
Getting the right diagnosis is a key aspect of health care - it provides an explanation of a patient's health problem and informs subsequent health care decisions. The diagnostic process is a complex, collaborative activity that involves clinical reasoning and information gathering to determine a patient's health problem. According to Improving Diagnosis in Health Care, diagnostic errors—inaccurate or delayed diagnoses—persist throughout all settings of care and continue to harm an unacceptable number of patients. It is likely that most people will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences. Diagnostic errors may cause harm to patients by preventing or delaying appropriate treatment, providing unnecessary or harmful treatment, or resulting in psychological or financial repercussions. The committee concluded that improving the diagnostic process is not only possible, but also represents a moral, professional, and public health imperative. Improving Diagnosis in Health Care—a continuation of the landmark Institute of Medicine reports To Err Is Human (2000) and Crossing the Quality Chasm (2001)—finds that diagnosis–and, in particular, the occurrence of diagnostic errors–has been largely unappreciated in efforts to improve the quality and safety of health care. Without a dedicated focus on improving diagnosis, diagnostic errors will likely worsen as the delivery of health care and the diagnostic process continue to increase in complexity. Just as the diagnostic process is a collaborative activity, improving diagnosis will require collaboration and a widespread commitment to change among health care professionals, health care organizations, patients and their families, researchers, and policy makers. The recommendations of Improving Diagnosis in Health Care contribute to the growing momentum for change in this crucial area of health care quality and safety.
Visit PaperThe frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations
Background: The frequency of outpatient diagnostic errors is challenging to determine due to varying error definitions and the need to review data across multiple providers and care settings over time. We estimated the frequency of diagnostic errors in the US adult population by synthesizing data from three previous studies of clinic-based populations that used conceptually similar definitions of diagnostic error.
Methods: Data sources included two previous studies that used electronic triggers, or algorithms, to detect unusual patterns of return visits after an initial primary care visit or lack of follow-up of abnormal clinical findings related to colorectal cancer, both suggestive of diagnostic errors. A third study examined consecutive cases of lung cancer. In all three studies, diagnostic errors were confirmed through chart review and defined as missed opportunities to make a timely or correct diagnosis based on available evidence. We extrapolated the frequency of diagnostic error obtained from our studies to the US adult population, using the primary care study to estimate rates of diagnostic error for acute conditions (and exacerbations of existing conditions) and the two cancer studies to conservatively estimate rates of missed diagnosis of colorectal and lung cancer (as proxies for other serious chronic conditions).
Results: Combining estimates from the three studies yielded a rate of outpatient diagnostic errors of 5.08%, or approximately 12 million US adults every year. Based upon previous work, we estimate that about half of these errors could potentially be harmful.
Conclusions: Our population-based estimate suggests that diagnostic errors affect at least 1 in 20 US adults. This foundational evidence should encourage policymakers, healthcare organisations and researchers to start measuring and reducing diagnostic errors.
Visit PaperBurden of serious harms from diagnostic error in the USA
Background: Diagnostic errors cause substantial preventable harms worldwide, but rigorous estimates for total burden are lacking. We previously estimated diagnostic error and serious harm rates for key dangerous diseases in major disease categories and validated plausible ranges using clinical experts.
Objective: We sought to estimate the annual US burden of serious misdiagnosis-related harms (permanent morbidity, mortality) by combining prior results with rigorous estimates of disease incidence.
Methods: Cross-sectional analysis of US-based nationally representative observational data. We estimated annual incident vascular events and infections from 21.5 million sampled US hospital discharges (2012-2014). Annual new cancers were taken from US-based registries (2014). Years were selected for coding consistency with prior literature. Disease-specific incidences for 15 major vascular events, infections and cancers ("Big Three" categories) were multiplied by literature-based rates to derive diagnostic errors and serious harms. We calculated uncertainty estimates using Monte Carlo simulations. Validity checks included sensitivity analyses and comparison with prior published estimates.
Results: Annual US incidence was 6.0 million vascular events, 6.2 million infections and 1.5 million cancers. Per "Big Three" dangerous disease case, weighted mean error and serious harm rates were 11.1% and 4.4%, respectively. Extrapolating to all diseases (including non-"Big Three" dangerous disease categories), we estimated total serious harms annually in the USA to be 795,000 (plausible range 598,000-1,023,000). Sensitivity analyses using more conservative assumptions estimated 549,000 serious harms. Results were compatible with setting-specific serious harm estimates from inpatient, emergency department and ambulatory care. The 15 dangerous diseases accounted for 50.7% of total serious harms and the top 5 (stroke, sepsis, pneumonia, venous thromboembolism and lung cancer) accounted for 38.7%.
Conclusion: An estimated 795,000 Americans become permanently disabled or die annually across care settings because dangerous diseases are misdiagnosed. Just 15 diseases account for about half of all serious harms, so the problem may be more tractable than previously imagined.
Visit PaperMedical error—the third leading cause of death in the US
Medical error is not included on death certificates or in rankings of cause of death. Martin Makary and Michael Daniel assess its contribution to mortality and call for better reporting. The annual list of the most common causes of death in the United States, compiled by the Centers for Disease Control and Prevention (CDC), informs public awareness and national research priorities each year. The list is created using death certificates filled out by physicians, funeral directors, medical examiners, and coroners. However, a major limitation of the death certificate is that it relies on assigning an International Classification of Disease (ICD) code to the cause of death. As a result, causes of death not associated with an ICD code, such as human and system factors, are not captured. The science of safety has matured to describe how communication breakdowns, diagnostic errors, poor judgment, and inadequate skill can directly result in patient harm and death. We analyzed the scientific literature on medical error to identify its contribution to US deaths in relation to causes listed by the CDC. Medical error has been defined as an unintended act (either of omission or commission) or one that does not achieve its intended outcome, the failure of a planned action to be completed as intended (an error of execution), the use of a wrong plan to achieve an aim (an error of planning), or a deviation from the process of care that may or may not cause harm to the patient. Patient harm from medical error can occur at the individual or system level. The taxonomy of errors is expanding to better categorize preventable factors and events. We focus on preventable lethal events to highlight the scale of potential for improvement.
Visit PaperA unified conceptual model for diagnostic errors: underdiagnosis, overdiagnosis, and misdiagnosis
Progress in diagnostic error research has been hampered by a lack of unified terminology and definitions. This article proposes a novel framework for considering diagnostic errors, offering a unified conceptual model for underdiagnosis, overdiagnosis, and misdiagnosis. The model clarifies the critical separation between 'diagnostic process failures' (incorrect workups) and 'diagnosis label failures' (incorrect diagnoses). By dividing processes into those that are substandard, suboptimal, or optimal, important distinctions are drawn between 'preventable', 'reducible,' and 'unavoidable' diagnostic errors. The new model emphasizes the importance of mitigating diagnosis-related harms, regardless of whether the solutions require traditional safety strategies (preventable errors), more effective evidence dissemination (reducible errors; harms from overtesting and overdiagnosis), or new scientific discovery (currently unavoidable errors). Doing so maximizes our ability to prioritize solving various diagnosis-related problems from a societal value perspective. This model should serve as a foundation for developing consensus terminology and operationalized definitions for relevant diagnostic-error categories.
Visit PaperAchieving quality in clinical decision making: cognitive strategies and detection of bias
Clinical decision making is a cornerstone of high-quality care in emergency medicine. The density of decision making is unusually high in this unique milieu, and a combination of strategies has necessarily evolved to manage the load. In addition to the traditional hypothetico-deductive method, emergency physicians use several other approaches, principal among which are heuristics. These cognitive short-cutting strategies are especially adaptive under the time and resource limitations that prevail in many emergency departments (EDs), but occasionally they fail. When they do, we refer to them as cognitive errors. They are costly but highly preventable. It is important that emergency physicians be aware of the nature and extent of these heuristics and biases, or cognitive dispositions to respond (CDRs). Thirty are catalogued in this article, together with descriptions of their properties as well as the impact they have on clinical decision making in the ED. Strategies are delineated in each case, to minimize their occurrence. Detection and recognition of these cognitive phenomena are a first step in achieving cognitive de-biasing to improve clinical decision making in the ED.
Visit PaperResearch in clinical reasoning: past history and current trends
Background: Research in clinical reasoning has been conducted for over 30 years. Throughout this time there have been a number of identifiable trends in methodology and theory.
Purpose: This paper identifies three broad research traditions, ordered chronologically, as: (a) attempts to understand reasoning as a general skill—the 'clinical reasoning' process; (b) research based on probes of memory—reasoning related to the amount of knowledge and memory; and (c) research related to different kinds of mental representations—semantic qualifiers, scripts, schemas and exemplars.
Results and conclusions: Several broad themes emerge from this review. First, there is little evidence that reasoning can be characterised in terms of general process variables. Secondly, it is evident that expertise is associated, not with a single basic representation but with multiple coordinated representations in memory, from causal mechanisms to prior examples. Different representations may be utilised in different circumstances, but little is known about the characteristics of a particular situation that led to a change in strategy.
Implications: It becomes evident that expertise lies in the availability of multiple representations of knowledge. Perhaps the most critical aspect of learning is not the acquisition of a particular strategy or skill, nor is it the availability of a particular kind of knowledge. Rather, the critical element may be deliberate practice with multiple examples which, on the one hand, facilitates the availability of concepts and conceptual knowledge (i.e., transfer) and, on the other hand, adds to a storehouse of already solved problems.
Visit PaperThe incidence of diagnostic error in medicine
A wide variety of research studies suggest that breakdowns in the diagnostic process result in a staggering toll of harm and patient deaths. These include autopsy studies, case reviews, surveys of patient and physicians, voluntary reporting systems, using standardised patients, second reviews, diagnostic testing audits and closed claims reviews. Although these different approaches provide important information and unique insights regarding diagnostic errors, each has limitations and none is well suited to establishing the incidence of diagnostic error in actual practice, or the aggregate rate of error and harm. We argue that being able to measure the incidence of diagnostic error is essential to enable research studies on diagnostic error, and to initiate quality improvement projects aimed at reducing the risk of error and harm. Three approaches appear most promising in this regard: (1) using 'trigger tools' to identify from electronic health records cases at high risk for diagnostic error; (2) using standardised patients (secret shoppers) to study the rate of error in practice; (3) encouraging both patients and physicians to voluntarily report errors they encounter, and facilitating this process.
Visit PaperDiagnostic error in internal medicine
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 three 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, i.e., 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.
Visit PaperClinical cognition and diagnostic error: applications of a dual process model of reasoning
Both systemic and individual factors contribute to missed or delayed diagnoses. Among the multiple factors that impact clinical performance of the individual, the caliber of cognition is perhaps the most relevant and deserves our attention and understanding. In the last few decades, cognitive psychologists have gained substantial insights into the processes that underlie cognition, and a new, universal model of reasoning and decision making has emerged: Dual Process Theory. The theory has immediate application to medical decision making and provides an overall schema for understanding the variety of theoretical approaches that have been taken in the past. The model has important practical applications for decision making across the multiple domains of healthcare, and may be used as a template for teaching decision theory, as well as a platform for future research. Importantly, specific operating characteristics of the model explain how diagnostic failure occurs.
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