Background: All humans are fallible. Because physicians are human, unintentional errors unfortunately occur. While unintentional medical errors have an impact on patients and their families, they may also contribute to adverse mental and emotional effects on the involved provider(s). These may include burnout, lack of concentration, poor work performance, posttraumatic stress disorder, depression, and even suicidality.
Objectives: The objectives of this article are to 1) discuss the impact medical error has on involved provider(s), 2) provide potential reasons why medical error can have a negative impact on provider mental health, and 3) suggest solutions for providers and health care organizations to recognize and mitigate the adverse effects medical error has on providers.
Discussion: Physicians and other providers may feel a variety of adverse emotions after medical error, including guilt, shame, anxiety, fear, and depression. It is thought that the pervasive culture of perfectionism and individual blame in medicine plays a considerable role toward these negative effects. In addition, studies have found that despite physicians' desire for support after medical error, many physicians feel a lack of personal and administrative support. This may further contribute to poor emotional well-being. Potential solutions in the literature are proposed, including provider counseling, learning from mistakes without fear of punishment, discussing mistakes with others, focusing on the system versus the individual, and emphasizing provider wellness. Much of the reviewed literature is limited in terms of an emergency medicine focus or even regarding physicians in general. In addition, most studies are survey- or interview-based, which limits objectivity. While additional, more objective research is needed in terms of mitigating the effects of error on physicians, this review may help provide insight and support for those who feel alone in their attempt to heal after being involved in an adverse medical event.
Conclusions: Unintentional medical error will likely always be a part of the medical system. However, by focusing on provider as well as patient health, we may be able to foster resilience in providers and improve care for patients in healthy, safe, and constructive environments.
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http://dx.doi.org/10.1016/j.jemermed.2017.12.001 | DOI Listing |
Br J Hosp Med (Lond)
January 2025
Speech and Language Rehabilitation Department, Beijing Rehabilitation Hospital Affiliated with Capital Medical University, Beijing, China.
The background for establishing and verifying a dehydration prediction model for elderly patients with post-stroke dysphagia (PSD) based on General Utility for Latent Process (GULP) is as follows: For elderly patients with PSD, GULP technology is utilized to build a dehydration prediction model. This aims to improve the accuracy of dehydration risk assessment and provide clinical intervention, thereby offering a scientific basis and enhancing patient prognosis. This research highlights the innovative application of GULP technology in constructing complex medical prediction models and addresses the special health needs of elderly stroke patients.
View Article and Find Full Text PDFPaediatr Anaesth
January 2025
University of Washington School of Medicine, Seattle, Washington, USA.
Introduction: The Society for Pediatric Anesthesia Quality and Safety Committee developed the Pediatric Regional Anesthesia Time-Out Checklist, consisting of 14 safety items intended to be reviewed by an anesthesia team prior to a regional anesthetic. Primarily, we hypothesized that use of this Checklist would increase the number of safety items performed compared with no checklist, evaluating the usefulness of this tool. Secondarily, we hypothesized that, after checklist training, subjects would show better clinical judgment by electing to perform a regional anesthetic in scenarios in which no programmed error existed and electing to not perform a regional anesthetic in scenarios in which a programmed error did exist.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Research Centre for Biomedical Engineering, City St George's, University of London, London, EC1V 0HB, UK.
Over the past ten years, there has been an increasing demand for reliable consumer wearables as users are inclined to monitor their health and fitness metrics in real-time, especially since the COVID-19 pandemic. Reflectance pulse oximeters in fitness trackers and smartwatches provide convenient, non-invasive SpO measurements but face challenges in achieving medical-grade accuracy, particularly due to difficulties in capturing physiological signals, which may be affected by skin pigmentation. Hence, this study sets out to investigate the influence of skin pigmentation, particularly in individuals with darker skin, on the accuracy and reliability of SpO measurement in consumer wearables that utilise reflectance pulse oximeters.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.
Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering more consistent, precise, and faster scans, potentially reducing human error and healthcare costs. Effective force control is crucial in robotic ultrasound scanning to ensure consistent image quality and patient safety.
View Article and Find Full Text PDFJ Clin Med
January 2025
Guthrie Cortland Medical Center, Cortland, NY 13045, USA.
Artificial intelligence (AI) in echocardiography represents a transformative advancement in cardiology, addressing longstanding challenges in cardiac diagnostics. Echocardiography has traditionally been limited by operator-dependent variability and subjective interpretation, which impact diagnostic reliability. This study evaluates the role of AI, particularly machine learning (ML), in enhancing the accuracy and consistency of echocardiographic image analysis and its potential to complement clinical expertise.
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