Diagnostic error: untapped potential for improving patient safety?

Achieving diagnostic excellence through prevention and teamwork (ADEPT) study protocol: A multicenter, prospective quality and safety program to improve diagnostic processes in medical inpatients

BACKGROUND

Few hospitals have built surveillance for diagnostic errors into usual care or used comparative quantitative and qualitative data to understand their diagnostic processes and implement interventions designed to reduce these errors.

OBJECTIVES

To build surveillance for diagnostic errors into usual care, benchmark diagnostic performance across sites, pilot test interventions, and evaluate the program's impact on diagnostic error rates.

METHODS AND ANALYSIS

Achieving diagnostic excellence through prevention and teamwork (ADEPT) is a multicenter, real-world quality and safety program utilizing interrupted time-series techniques to evaluate outcomes. Study subjects will be a randomly sampled population of medical patients hospitalized at 16 US hospitals who died, were transferred to intensive care, or had a rapid response during the hospitalization. Surveillance for diagnostic errors will occur on 10 events per month per site using a previously established two-person adjudication process. Concurrent reviews of patients who had a qualifying event in the previous week will allow for surveys of clinicians to better understand contributors to diagnostic error, or conversely, examples of diagnostic excellence, which cannot be gleaned from medical record review alone. With guidance from national experts in quality and safety, sites will report and benchmark diagnostic error rates, share lessons regarding underlying causes, and design, implement, and pilot test interventions using both Safety I and Safety II approaches aimed at patients, providers, and health systems. Safety II approaches will focus on cases where diagnostic error did not occur, applying theories of how people and systems are able to succeed under varying conditions. The primary outcome will be the number of diagnostic errors per patient, using segmented multivariable regression to evaluate change in y-intercept and change in slope after initiation of the program.

ETHICS AND DISSEMINATION

The study has been approved by the University of California, San Francisco Institutional Review Board (IRB), which is serving as the single IRB. Intervention toolkits and study findings will be disseminated through partners including Vizient, The Joint Commission, and Press-Ganey, and through national meetings, scientific journals, and publications aimed at the general public.

Prevalence and Causes of Diagnostic Errors in Hospitalized Patients Under Investigation for COVID-19

BACKGROUND

The COVID-19 pandemic required clinicians to care for a disease with evolving characteristics while also adhering to care changes (e.g., physical distancing practices) that might lead to diagnostic errors (DEs).

OBJECTIVE

To determine the frequency of DEs and their causes among patients hospitalized under investigation (PUI) for COVID-19.

DESIGN

Retrospective cohort.

SETTING

Eight medical centers affiliated with the Hospital Medicine ReEngineering Network (HOMERuN).

TARGET POPULATION

Adults hospitalized under investigation (PUI) for COVID-19 infection between February and July 2020.

MEASUREMENTS

We randomly selected up to 8 cases per site per month for review, with each case reviewed by two clinicians to determine whether a DE (defined as a missed or delayed diagnosis) occurred, and whether any diagnostic process faults took place. We used bivariable statistics to compare patients with and without DE and multivariable models to determine which process faults or patient factors were associated with DEs.

RESULTS

Two hundred and fifty-seven patient charts underwent review, of which 36 (14%) had a diagnostic error. Patients with and without DE were statistically similar in terms of socioeconomic factors, comorbidities, risk factors for COVID-19, and COVID-19 test turnaround time and eventual positivity. Most common diagnostic process faults contributing to DE were problems with clinical assessment, testing choices, history taking, and physical examination (all p < 0.01). Diagnostic process faults associated with policies and procedures related to COVID-19 were not associated with DE risk. Fourteen patients (35.9% of patients with errors and 5.4% overall) suffered harm or death due to diagnostic error.

LIMITATIONS

Results are limited by available documentation and do not capture communication between providers and patients.

CONCLUSION

Among PUI patients, DEs were common and not associated with pandemic-related care changes, suggesting the importance of more general diagnostic process gaps in error propagation.

Do my feelings fit the diagnosis? Avoiding misdiagnoses in psychosomatic consultation services

OBJECTIVE

Misdiagnoses are a major concern with far-reaching consequences, which have rarely been studied systematically. Therefore, the present study evaluated factors causing misdiagnoses identified by psychosomatic consultation services.

METHODS

Over a period of 5 years, all patients referred to the psychosomatic consultation services of a large university hospital were analyzed consecutively for misdiagnoses. We analyzed the reasons for suspecting a misdiagnosis through systematic introspection during peer supervision and evaluated the causes during semistructured interviews with the referring physician.

RESULTS

In 165 psychosomatic consultations, 24 disorders were misdiagnosed (15%). The reasons for questioning the initial diagnoses were the consulting physician's feelings and thoughts resulting from the patients' inappropriate behavior during the consultation and unusual clinical features. In eight cases, the misdiagnosis resulted from availability bias, and in three cases each it resulted from confirmation bias, search satisfaction bias, and framing effect and attribution bias. However, lack of medical knowledge played only a minor role.

CONCLUSION

This study highlights the nonrational elements of the diagnostic process. In the context of psychosomatic consultation services, introspection and intuitive thought processes are helpful in identifying misdiagnoses. Self-satisfaction (availability bias) and overconfidence (confirmation bias) are most likely to result in misdiagnoses.

[The Role of Gut Feeling as an Alarm Signal for Identifying Misdiagnoses in the Psychosomatic Consultation]

BACKGROUND

A psychosomatic consultation service offers an opportunity to correct misdiagnoses. The doctor's non-analytical, experience-based gut feeling plays an essential role as diagnostic trailblazer.

METHODOLOGY

In a case series study, the procedures of 165 psychosomatic consultations and the emotional and cognitive processes occurring in the consultant were recorded. The diagnostic process was analysed with reference to the dual process theory. With gut feeling, a distinction was made between a sense of alarm and a sense of reassurance. As an example, the processes that led to the discovery of misdiagnoses were presented at four consultations.

RESULTS

A misdiagnosis was found in 24 consultations (16%). The reason for this was always a sense of alarm, which appeared in 29 of 165 consultations (18%). In 11 consultations (7%) there was a sense of reassurance, which in ten consultations was associated with a confirmation of the diagnosis and only in one case gave way to a sense of alarm and led to a later correction of the diagnosis.

CONCLUSIONS

Paying attention to gut feeling and its sense of alarm is an effective method of recognizing misdiagnosis. Training programs should not only promote rational-analytical thinking, but also the doctor's self-critical introspection skills.

Diagnostic heuristics in dermatology, part 2: metacognition and other fixes

Diagnostic errors are the most common, costly and dangerous of medical mistakes. In part 1 of this series, we described how general and dermatology-specific cognitive and perceptual biases underlie most of our correct diagnoses, as well as being a source of diagnostic medical errors. In this second part of the series, we describe some tactics to combat diagnostic error. Metacognition, or thinking about how we think, is the central approach advocated to avoid errors of 'uncritical' diagnostic thinking. Current individual and medical cultural attitudes need to be modified in order to incorporate improvements in diagnosis. Algorithms, artificial intelligence and system changes are being developed to address error and improve diagnostic accuracy.

From To Err Is Human to Improving Diagnosis in Health Care: The risk management perspective

The publication of To Err Is Human in 2000, followed by Crossing the Quality Chasm in 2001, marked a watershed in patient safety. The Institute of Medicine (IOM) reports intensified the focus on patient safety and demanded a redesign of the healthcare system to improve quality and safety. Since publication of these reports, the focus has been on improving processes--those methods of healthcare delivery prone to failure and errors. Recently, there has been a concerted and sustained drive to add cognitive (diagnostic) errors to the focus. The recent publication of the IOM's Improving Diagnosis in Health Care has expanded the focus on patient safety and quality improvement. A new focus on diagnostic errors augments rather than replaces the previous focus. In this article, the authors offer a brief review of To Err Is Human and Crossing the Quality Chasm to lay a historical foundation. They then discuss a transition into the focus on diagnostic errors and summarize the latest recommendations from Improving Diagnosis in Health Care. This collated synthesis of 3 powerful IOM reports should guide risk managers and other healthcare personnel as they strive to improve every aspect of healthcare delivery.

Advancing the science of measurement of diagnostic errors in healthcare: the Safer Dx framework

Diagnostic errors are major contributors to harmful patient outcomes, yet they remain a relatively understudied and unmeasured area of patient safety. Although they are estimated to affect about 12 million Americans each year in ambulatory care settings alone, both the conceptual and pragmatic scientific foundation for their measurement is under-developed. Health care organizations do not have the tools and strategies to measure diagnostic safety and most have not integrated diagnostic error into their existing patient safety programs. Further progress toward reducing diagnostic errors will hinge on our ability to overcome measurement-related challenges. In order to lay a robust groundwork for measurement and monitoring techniques to ensure diagnostic safety, we recently developed a multifaceted framework to advance the science of measuring diagnostic errors (The Safer Dx framework). In this paper, we describe how the framework serves as a conceptual foundation for system-wide safety measurement, monitoring and improvement of diagnostic error. The framework accounts for the complex adaptive sociotechnical system in which diagnosis takes place (the structure), the distributed process dimensions in which diagnoses evolve beyond the doctor's visit (the process) and the outcomes of a correct and timely "safe diagnosis" as well as patient and health care outcomes (the outcomes). We posit that the Safer Dx framework can be used by a variety of stakeholders including researchers, clinicians, health care organizations and policymakers, to stimulate both retrospective and more proactive measurement of diagnostic errors. The feedback and learning that would result will help develop subsequent interventions that lead to safer diagnosis, improved value of health care delivery and improved patient outcomes.