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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.

An IOM report highlights diagnostic errors as common patient-safety problems. Its recommendations address myriad system features and activities affecting diagnosis, acknowledging that many contributing factors are intricately related to health care delivery problems. The 1999 Institute of Medicine (IOM) report To Err Is Human transformed thinking about patient safety in U.S. health care. On its 15th anniversary, a topic largely missing from that report is finally getting its due. With its new report, Improving Diagnosis in Health Care, the IOM has acknowledged the need to address diagnostic error as a “moral, professional, and public health imperative.” 1 The new report emphasizes that diagnostic errors may be one of the most common and harmful of patient-safety problems. Why has it taken so long for the patient-safety movement to recognize the importance of diagnostic errors? Perhaps . . .

Dred Scott and the Problem of Constitutional Evil , first published in 2006, concerns what is entailed by pledging allegiance to a constitutional text and tradition saturated with concessions to evil. The Constitution of the United States was originally understood as an effort to mediate controversies between persons who disputed fundamental values, and did not offer a vision of the good society. In order to form a 'more perfect union' with slaveholders, late-eighteenth-century citizens fashioned a constitution that plainly compelled some injustices and was silent or ambiguous on other questions of fundamental right. This constitutional relationship could survive only as long as a bisectional consensus was required to resolve all constitutional questions not settled in 1787. Dred Scott challenges persons committed to human freedom to determine whether antislavery northerners should have provided more accommodations for slavery than were constitutionally strictly necessary or risked the enormous destruction of life and property that preceded Lincoln's new birth of freedom.

Diagnosis is one of the most important tasks performed by primary care physicians. The World Health Organization (WHO) recently prioritized patient safety areas in primary care, and included diagnostic errors as a high-priority problem. In addition, a recent report from the Institute of Medicine in the USA, ‘Improving Diagnosis in Health Care’, concluded that most people will likely experience a diagnostic error in their lifetime. In this narrative review, we discuss the global significance, burden and contributory factors related to diagnostic errors in primary care. We synthesize available literature to discuss the types of presenting symptoms and conditions most commonly affected. We then summarize interventions based on available data and suggest next steps to reduce the global burden of diagnostic errors. Research suggests that we are unlikely to find a ‘magic bullet’ and confirms the need for a multifaceted approach to understand and address the many systems and cognitive issues involved in diagnostic error. Because errors involve many common conditions and are prevalent across all countries, the WHO’s leadership at a global level will be instrumental to address the problem. Based on our review, we recommend that the WHO consider bringing together primary care leaders, practicing frontline clinicians, safety experts, policymakers, the health IT community, medical education and accreditation organizations, researchers from multiple disciplines, patient advocates, and funding bodies among others, to address the many common challenges and opportunities to reduce diagnostic error. This could lead to prioritization of practice changes needed to improve primary care as well as setting research priorities for intervention development to reduce diagnostic error.

Aim The emergence and evolution of artificial intelligence (AI) has generated increasing interest in machine learning applications for health care. Specifically, researchers are grasping the potential of machine learning solutions to enhance the quality of care in emergency medicine. Methods We undertook a narrative review of published works on machine learning applications in emergency medicine and provide a synopsis of recent developments. Results This review describes fundamental concepts of machine learning and presents clinical applications for triage, risk stratification specific to disease, medical imaging, and emergency department operations. Additionally, we consider how machine learning models could contribute to the improvement of causal inference in medicine, and to conclude, we discuss barriers to safe implementation of AI. Conclusion We intend that this review serves as an introduction to AI and machine learning in emergency medicine. This article covers a basic concept of machine learning in emergency medicine.

BACKGROUNDThere is widespread agreement that the full potential of health information technology (health IT) has not yet been realized and of particular concern are the examples of unintended consequences of health IT that detract from the safety of health care or from the use of health IT itself. The goal of this project was to obtain additional information on these health IT–related problems, using a mixed methods (qualitative and quantitative) analysis of electronic health record–related harm in cases submitted to a large database of malpractice suits and claims. METHODSCases submitted to the CRICO claims database and coded during 2012 and 2013 were analyzed. A total of 248 cases (<1%) involving health IT were identified and coded using a proprietary taxonomy that identifies user- and system-related sociotechnical factors. Ambulatory care accounted for most of the cases (146 cases). Cases were most typically filed as a result of an error involving medications (31%), diagnosis (28%), or a complication of treatment (31%). More than 80% of cases involved moderate or severe harm, although lethal cases were less likely in cases from ambulatory settings. Etiologic factors spanned all of the sociotechnical dimensions, and many recurring patterns of error were identified. CONCLUSIONSAdverse events associated with health IT vulnerabilities can cause extensive harm and are encountered across the continuum of health care settings and sociotechnical factors. The recurring patterns provide valuable lessons that both practicing clinicians and health IT developers could use to reduce the risk of harm in the future. The likelihood of harm seems to relate more to a patientʼs particular situation than to any one class of error.

Background As many as 90% of patients develop anemia by their third day in an intensive care unit (ICU). We evaluated the efficacy of interventions to reduce phlebotomy-related blood loss on the volume of blood lost, hemoglobin levels, transfusions, and incidence of anemia. Methods We conducted a systematic review and meta-analysis using the Laboratory Medicine Best Practices (LMBP) systematic review methods for rating study quality and assessing the body of evidence. Searches of PubMed, Embase, Cochrane, Web of Science, PsychINFO, and CINAHL identified 2564 published references. We included studies of the impact of interventions to reduce phlebotomy-related blood loss on blood loss, hemoglobin levels, transfusions, or anemia among hospital inpatients. We excluded studies not published in English and studies that did not have a comparison group, did not report an outcome of interest, or were rated as poor quality. Twenty-one studies met these criteria. We conducted a meta-analysis if > 2 homogenous studies reported sufficient information for analysis. Results We found moderate, consistent evidence that devices that return blood from flushing venous or arterial lines to the patient reduced blood loss by approximately 25% in both neonatal ICU (NICU) and adult ICU patients [pooled estimate in adults, 24.7 (95% CI = 12.1–37.3)]. Bundled interventions that included blood conservation devices appeared to reduce blood loss by at least 25% (suggestive evidence). The evidence was insufficient to determine if these devices reduced hemoglobin decline or risk of anemia. The evidence suggested that small volume tubes reduced the risk of anemia, but was insufficient to determine if they affected the volume of blood loss or the rate of hemoglobin decline. Conclusions Moderate, consistent evidence indicated that devices that return blood from testing or flushing lines to the patient reduce the volume of blood loss by approximately 25% among ICU patients. The results of this systematic review support the use of blood conservation systems with arterial or venous catheters to eliminate blood waste when drawing blood for testing. The evidence was insufficient to conclude the devices impacted hemoglobin levels or transfusion rates. The use of small volume tubes may reduce the risk of anemia.