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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.

BackgroundDiagnostic error incurs enormous human and economic costs. The dual-process model reasoning provides a framework for understanding the diagnostic process and attributes certain errors to faulty cognitive shortcuts (heuristics). The literature contains many suggestions to counteract these and to enhance analytical and non-analytical modes of reasoning.AimsTo identify, describe and appraise studies that have empirically investigated interventions to enhance analytical and non-analytical reasoning among medical trainees and doctors, and to assess their effectiveness.MethodsSystematic searches of five databases were carried out (Medline, PsycInfo, Embase, Education Resource Information Centre (ERIC) and Cochrane Database of Controlled Trials), supplemented with searches of bibliographies and relevant journals. Included studies evaluated an intervention to enhance analytical and/or non-analytical reasoning among medical trainees or doctors.FindingsTwenty-eight studies were included under five categories: educational interventions, checklists, cognitive forcing strategies, guided reflection, instructions at test and other interventions. While many of the studies found some effect of interventions, guided reflection interventions emerged as the most consistently successful across five studies, and cognitive forcing strategies improved accuracy and confidence judgements. Significant heterogeneity of measurement approaches was observed, and existing studies are largely limited to early-career doctors.ConclusionsResults to date are promising and this relatively young field is now close to a point where these kinds of cognitive interventions can be recommended to educators. Further research with refined methodology and more diverse samples is required before firm recommendations may be made for medical education and policy; however, these results suggest that such interventions hold promise, with much current enthusiasm for new research.

Numerous studies have shown that diagnostic failure depends upon a variety of factors. Psychological factors are fundamental in influencing the cognitive performance of the decision maker. In this first of two papers, we discuss the basics of reasoning and the Dual Process Theory (DPT) of decision making. The general properties of the DPT model, as it applies to diagnostic reasoning, are reviewed. A variety of cognitive and affective biases are known to compromise the decision-making process. They mostly appear to originate in the fast intuitive processes of Type 1 that dominate (or drive) decision making. Type 1 processes work well most of the time but they may open the door for biases. Removing or at least mitigating these biases would appear to be an important goal. We will also review the origins of biases. The consensus is that there are two major sources: innate, hard-wired biases that developed in our evolutionary past, and acquired biases established in the course of development and within our working environments. Both are associated with abbreviated decision making in the form of heuristics. Other work suggests that ambient and contextual factors may create high risk situations that dispose decision makers to particular biases. Fatigue, sleep deprivation and cognitive overload appear to be important determinants. The theoretical basis of several approaches towards debiasing is then discussed. All share a common feature that involves a deliberate decoupling from Type 1 intuitive processing and moving to Type 2 analytical processing so that eventually unexamined intuitive judgments can be submitted to verification. This decoupling step appears to be the critical feature of cognitive and affective debiasing.

In a companion paper, we proposed that cognitive debiasing is a skill essential in developing sound clinical reasoning to mitigate the incidence of diagnostic failure. We reviewed the origins of cognitive biases and some proposed mechanisms for how debiasing processes might work. In this paper, we first outline a general schema of how cognitive change occurs and the constraints that may apply. We review a variety of individual factors, many of them biases themselves, which may be impediments to change. We then examine the major strategies that have been developed in the social sciences and in medicine to achieve cognitive and affective debiasing, including the important concept of forcing functions. The abundance and rich variety of approaches that exist in the literature and in individual clinical domains illustrate the difficulties inherent in achieving cognitive change, and also the need for such interventions. Ongoing cognitive debiasing is arguably the most important feature of the critical thinker and the well-calibrated mind. We outline three groups of suggested interventions going forward: educational strategies, workplace strategies and forcing functions. We stress the importance of ambient and contextual influences on the quality of individual decision making and the need to address factors known to impair calibration of the decision maker. We also emphasise the importance of introducing these concepts and corollary development of training in critical thinking in the undergraduate level in medical education.

Diagnostic errors are associated with patient harm and suboptimal outcomes. Despite national scientific efforts to advance definition, measurement and interventions for diagnostic error, diagnosis in mental health is not well represented in this ongoing work. We aimed to summarise the current state of research on diagnostic errors in mental health and identify opportunities to align future research with the emerging science of diagnostic safety. We review conceptual considerations for defining and measuring diagnostic error, the application of these concepts to mental health settings, and the methods and subject matter focus of recent studies of diagnostic error in mental health. We found that diagnostic error is well understood to be a problem in mental healthcare. Although few studies used clear definitions or frameworks for understanding diagnostic error in mental health, several studies of missed, wrong, delayed and disparate diagnosis of common mental disorders have identified various avenues for future research and development. Nevertheless, a lack of clear consensus on how to conceptualise, define and measure errors in diagnosis will pose a barrier to advancement. Further research should focus on identifying preventable missed opportunities in the diagnosis of mental disorders, which may uncover generalisable opportunities for improvement.

Much diagnostic error is caused by cognitive bias. More than 100 biases affecting clinical decision making have been described, and many medical disciplines acknowledge their pervasive influence on our thinking. Training in critical thinking may ameliorate the problem. The two major products of clinical decision making are diagnoses and treatment plans. If the first is correct, the second has a greater chance of being correct too. Surprisingly, we don't make correct diagnoses as often as we think: the diagnostic failure rate is estimated to be 10 to 15%. The rate is highest among specialties in which patients are diagnostically undifferentiated, such as emergency medicine, family medicine, and internal medicine. Error in the visual specialties, such as radiology and pathology, is considerably lower, probably around 2%. 1 Diagnostic error has multiple causes, but principal among them are cognitive errors. Usually, . . .

BackgroundThe public health burden associated with diagnostic errors is likely enormous, with some estimates suggesting millions of individuals are harmed each year in the USA, and presumably many more worldwide. According to the US National Academy of Medicine, improving diagnosis in healthcare is now considered ‘a moral, professional, and public health imperative.’ Unfortunately, well-established, valid and readily available operational measures of diagnostic performance and misdiagnosis-related harms are lacking, hampering progress. Existing methods often rely on judging errors through labour-intensive human reviews of medical records that are constrained by poor clinical documentation, low reliability and hindsight bias.MethodsKey gaps in operational measurement might be filled via thoughtful statistical analysis of existing large clinical, billing, administrative claims or similar data sets. In this manuscript, we describe a method to quantify and monitor diagnostic errors using an approach we call ‘Symptom-Disease Pair Analysis of Diagnostic Error’ (SPADE).ResultsWe first offer a conceptual framework for establishing valid symptom-disease pairs illustrated using the well-known diagnostic error dyad of dizziness-stroke. We then describe analytical methods for both look-back (case–control) and look-forward (cohort) measures of diagnostic error and misdiagnosis-related harms using ‘big data’. After discussing the strengths and limitations of the SPADE approach by comparing it to other strategies for detecting diagnostic errors, we identify the sources of validity and reliability that undergird our approach.ConclusionSPADE-derived metrics could eventually be used for operational diagnostic performance dashboards and national benchmarking. This approach has the potential to transform diagnostic quality and safety across a broad range of clinical problems and settings.

Diagnostic error research has largely focused on individual clinicians' decision making and system design, while overlooking information from patients. We analyzed a unique new data source of patient- and family-reported error narratives to explore factors that contribute to diagnostic errors. From reports of adverse medical events submitted in the period January 2010-February 2016, we identified 184 unique patient narratives of diagnostic error. Problems related to patient-physician interactions emerged as major contributors. Our analysis identified 224 instances of behavioral and interpersonal factors that reflected unprofessional clinician behavior, including ignoring patients' knowledge, disrespecting patients, failing to communicate, and manipulation or deception. Patients' perspectives can lead to a more comprehensive understanding of why diagnostic errors occur and help develop strategies for mitigation. Health systems should develop and implement formal programs to collect patients' experiences with the diagnostic process and use these data to promote an organizational culture that strives to reduce harm from diagnostic error.

ABSTRACT BACKGROUND Suicide prevention is a public health priority, but no data on the health care individuals receive prior to death are available from large representative United States population samples. OBJECTIVE To investigate variation in the types and timing of health services received in the year prior to suicide, and determine whether a mental health condition was diagnosed. DESIGN Longitudinal study from 2000 to 2010 within eight Mental Health Research Network health care systems serving eight states. PARTICIPANTS In all, 5,894 individuals who died by suicide, and were health plan members in the year before death. MAIN MEASURES Health system contacts in the year before death. Medical record, insurance claim, and mortality records were linked via the Virtual Data Warehouse, a federated data system at each site. KEY RESULTS Nearly all individuals received health care in the year prior to death (83 %), but half did not have a mental health diagnosis. Only 24 % had a mental health diagnosis in the 4-week period prior to death. Medical specialty and primary care visits without a mental health diagnosis were the most common visit types. The individuals more likely to make a visit in the year prior to death ( p  < 0.05) tended to be women, individuals of older age (65+ years), those where the neighborhood income was over $40,000 and 25 % were college graduates, and those who died by non-violent means. CONCLUSIONS This study indicates that opportunities for suicide prevention exist in primary care and medical settings, where most individuals receive services prior to death. Efforts may target improved identification of mental illness and suicidal ideation, as a large proportion may remain undiagnosed at death.