Diagnosis最新文献

Objectives: Diagnostic imaging decision support (DI-DS) system has emerged as an innovative evidence-based solution to decrease inappropriate diagnostic imaging. The aim of the present study was to design and evaluate a DI-DS system for cerebrovascular diseases.

Methods: The present study was an applied piece of research. First, the conceptual model of the DI-DS system was designed based on its functional and non-functional requirements. Afterwards, to create the system's knowledge base, cerebrovascular diseases diagnostic imaging algorithms were extracted from the American College of Radiology Appropriateness Criteria (ACR-AC). Subsequently, the system was developed based on the obtained conceptual model and the extracted algorithms. The software was programmed by means of the C#. After debugging the system, it was evaluated regarding its performance and also the users' satisfaction with it.

Results: Assessing the users' satisfaction with the system demonstrated that all the evaluation criteria met the acceptable threshold (85 %). The retrospective evaluation of the system's performance indicated that from among 76 imaging examinations, which had previously been performed for 30 patients, 12 (15.78 %) were deemed inappropriate. And, the system accurately identified all the inappropriate physicians' decisions. The concurrent evaluation of the system's performance indicated that the system's recommendations helped the physicians remove 100 % (4 out of 4) of the inappropriate and 40 % (2 out of 5) of the inconclusive imaging examinations from their initial choices.

Conclusions: A DI-DS system could increase the compliance of the physicians' decisions with diagnostic imaging guidelines, and also improve treatment outcomes through correct diagnosis and providing timely care.

Objectives: Clinicians can rapidly and accurately diagnose disease, learn from experience, and explain their reasoning. Computational Bayesian medical decision-making might replicate this expertise. This paper assesses a computer system for diagnosing cardiac chest pain in the emergency department (ED) that decides whether to admit or discharge a patient.

Methods: The system can learn likelihood functions by counting data frequency. The computer compares patient and disease data profiles using likelihood. It calculates a Bayesian probabilistic diagnosis and explains its reasoning. A utility function applies the probabilistic diagnosis to produce a numerical BAYES score for making a medical decision.

Results: We conducted a pilot study to assess BAYES efficacy in ED chest pain patient disposition. Binary BAYES decisions eliminated patient observation. We compared BAYES to the HEART score. On 100 patients, BAYES reduced HEART's false positive rate 18-fold from 58.7 to 3.3 %, and improved ROC AUC accuracy from 0.928 to 1.0.

Conclusions: The pilot study results were encouraging. The data-driven BAYES score approach could learn from frequency counting, make fast and accurate decisions, and explain its reasoning. The computer replicated these aspects of diagnostic expertise. More research is needed to reproduce and extend these finding to larger diverse patient populations.

The ambulatory diagnostic process is potentially complex, resulting in faulty communication, lost information, and a lack of team coordination. Patients and families have a unique position in the ambulatory diagnostic team, holding privileged information about their clinical conditions and serving as the connecting thread across multiple healthcare encounters. While experts advocate for engaging patients as diagnostic team members, operationalizing patient engagement has been challenging. The team science literature links improved team performance with shared mental models, a concept reflecting the team's commonly held knowledge about the tasks to be done and the expertise of each team member. Despite their proven potential to improve team performance and outcomes in other settings, shared mental models remain underexplored in healthcare. In this manuscript, we review the literature on shared mental models, applying that knowledge to the ambulatory diagnostic process. We consider the role of patients in the diagnostic team and adapt the five-factor model of shared mental models to develop a framework for patient-clinician diagnostic shared mental models. We conclude with research priorities. Development, maintenance, and use of shared mental models of the diagnostic process amongst patients, families, and clinicians may increase patient/family engagement, improve diagnostic team performance, and promote diagnostic safety.

Dynamic teaming is required whenever people must coordinate with one another in a fluid context, particularly when the fundamental structures of a team, such as membership, priorities, tasks, modes of communication, and location are in near-constant flux. This is certainly the case in the contemporary ambulatory care diagnostic process, where circumstances and conditions require a shifting cast of individuals to coordinate dynamically to ensure patient safety. This article offers an updated perspective on dynamic teaming commonly required during the ambulatory diagnostic process. Drawing upon team science, it clarifies the characteristics of dynamic diagnostic teams, identifies common risk points in the teaming process and the practical implications of these risks, considers the role of providers and patients in averting adverse outcomes, and provides a case example of the challenges of dynamic teaming during the diagnostic process. Based on this, future research needs are offered as well as clinical practice recommendations related to team characteristics and breakdowns, team member knowledge/cognitions, teaming dynamics, and the patient as a team member.

Cancer will affect more than one in three U.S. residents in their lifetime, and although the diagnosis will be made efficiently in most of these cases, roughly one in five patients will experience a delayed or missed diagnosis. In this integrative review, we focus on missed opportunities in the diagnosis of breast, lung, and colorectal cancer in the ambulatory care environment. From a review of 493 publications, we summarize the current evidence regarding the contributing factors to missed or delayed cancer diagnosis in ambulatory care, as well as evidence to support possible strategies for intervention. Cancer diagnoses are made after follow-up of a positive screening test or an incidental finding, or most commonly, by following up and clarifying non-specific initial presentations to primary care. Breakdowns and delays are unacceptably common in each of these pathways, representing failures to follow-up on abnormal test results, incidental findings, non-specific symptoms, or consults. Interventions aimed at 'closing the loop' represent an opportunity to improve the timeliness of cancer diagnosis and reduce the harm from diagnostic errors. Improving patient engagement, using 'safety netting,' and taking advantage of the functionality offered through health information technology are all viable options to address these problems.

Objectives: The inpatient setting is a challenging clinical environment where systems and situational factors predispose clinicians to making diagnostic errors. Environmental complexities limit trialing of interventions to improve diagnostic error in active inpatient clinical settings. Informed by prior work, we piloted a multi-component intervention designed to reduce diagnostic error to understand its feasibility and uptake.

Methods: From September 2018 to June 2019, we conducted a prospective, pre-test/post-test pilot study of hospital medicine physicians during admitting shifts at a tertiary-care, academic medical center. Optional intervention components included use of dedicated workspaces, privacy barriers, noise cancelling headphones, application-based breathing exercises, a differential diagnosis expander application, and a checklist to enable a diagnostic pause. Participants rated their confidence in patient diagnoses and completed a survey on intervention component use. Data on provider resource utilization and patient diagnoses were collected, and qualitative interviews were held with a subset of participants in order to better understand experience with the intervention.

Results: Data from 37 physicians and 160 patients were included. No intervention component was utilized by more than 50 % of providers, and no differences were noted in diagnostic confidence or number of diagnoses documented pre-vs. post-intervention. Lab utilization increased, but there were no other differences in resource utilization during the intervention. Qualitative feedback highlighted workflow integration challenges, among others, for poor intervention uptake.

Conclusions: Our pilot study demonstrated poor feasibility and uptake of an intervention designed to reduce diagnostic error. This study highlights the unique challenges of implementing solutions within busy clinical environments.

Objectives: Educators need tools for the assessment of clinical reasoning that reflect the ambiguity of real-world practice and measure learners' ability to determine diagnostic likelihood. In this study, the authors describe the use of the Brier score to assess and provide feedback on the quality of probabilistic diagnostic reasoning.

Methods: The authors describe a novel format called Diagnostic Forecasting (DxF), in which participants read a brief clinical case and assign a probability to each item on a differential diagnosis, order tests and select a final diagnosis. DxF was piloted in a cohort of senior medical students. DxF evaluated students' answers with Brier scores, which compare probabilistic forecasts with case outcomes. The validity of Brier scores in DxF was assessed by comparison to subsequent decision-making in the game environment of DxF, as well as external criteria including medical knowledge tests and performance on clinical rotations.

Results: Brier scores were statistically significantly correlated with diagnostic accuracy (95 % CI -4.4 to -0.44) and with mean scores on the National Board of Medical Examiners (NBME) shelf exams (95 % CI -474.6 to -225.1). Brier scores did not correlate with clerkship grades or performance on a structured clinical skills exam. Reliability as measured by within-student correlation was low.

Conclusions: Brier scoring showed evidence for validity as a measurement of medical knowledge and predictor of clinical decision-making. Further work must evaluated the ability of Brier scores to predict clinical and workplace-based outcomes, and develop reliable approaches to measuring probabilistic reasoning.

Introduction: Diagnostic errors in emergency departments (ED) are a significant concern and exacerbated by cognitive biases during patient handoffs. The timing and accuracy of disclosing working diagnoses during these handoffs potentially influence diagnostic decisions, yet empirical evidence remains limited.

Materials and methods: This parallel, quasi-experimental study involved 40 interns from Japanese teaching hospitals, randomly assigned to control or intervention groups. Each group reviewed eight audio-recorded patient handoff scenarios where working diagnoses were disclosed at the start (control) or end (intervention). Four cases presented correct diagnoses, while four featured incorrect ones. The main measure was diagnostic error rate, calculated as the proportion of incorrect post-handoff responses to total questions asked.

Results: No significant difference in diagnostic error rates emerged between the control (39.4 %, 63/160) and intervention (38.8 %, 62/160) groups (point estimate -0.6 %; 95 % CI: -11.3-10.1 %, p=0.91). However, a substantial difference was evident between diagnostic errors after correct (20.6 %, 33/160) and incorrect (57.5 %, 92/160) working diagnoses presented (point estimate: 36.9 %; 95 % CI: 27.0-46.8 %, p<0.001). Diagnostic momentum accounted for 52 % (48/92) of errors under incorrect diagnoses.

Discussion: While the timing of working diagnosis disclosure did not significantly alter diagnostic accuracy during ED handoffs, exposure to incorrect diagnoses markedly increased error rates. These findings underscore the imperative to refine diagnostic skills and reconsider ED handoff protocols to mitigate cognitive biases and optimize patient care outcomes.

Over the last decades, in addition to the improvement of pathophysiological knowledge regarding the role and mechanisms of action of vitamin D, there has been a progressive advancement in analytical technologies for its measurement, as well as in methodological standardization. A significant number of scientific works, meta-analyses, and guidelines have been published on the importance of vitamin D and the need for supplementation in deficient individuals. However, it appears necessary to clarify the fundamental elements related to the measurement of vitamin D (both at the strictly analytical and post-analytical levels) and the scientific evidence related to the efficacy/safety of supplementation. In particular, there is a need to discuss current recommended levels for deficiency, insufficiency and possible toxicity in the light of evidence from standardization projects. Additionally, given the important interrelations between vitamin D, parathyroid hormone (PTH), and fibroblast growth factor-23 (FGF23), the analytical issues and clinical utility of these biomarkers will be discussed.

Objectives: To describe rare genetic interactions of α-thalassemia alleles causing Hb H disease and Hb Bart's hydrops fetalis which could lead to diagnostic errors in a routine practice.

Methods: Hematological and molecular characterization were carried out in a Thai family with a risk of having fetus with Hb Bart's hydrops fetalis.

Results: Both parents were found to be the thalassemia intermedia patients associated with unusual forms of Hb H disease. DNA analysis of common α-thalassemia mutations in Thailand identified α⁺-thalassemia (-α^{3.7 kb del}) and unknown α⁰-thalassemia in the father and α⁰-thalassemia (--^SEA) with unknown α⁺-thalassemia in the mother. Fetal DNA analysis unlikely identified a homozygosity for α⁰-thalassemia (--^SEA/--^SEA). Further analysis identified that the father carried a rare South African α⁰-thalassemia in combination with α⁺-thalassemia (--^SA/-α), whereas the mother was a patient with Hb H-Queens Park disease (--^SEA/αα^QP). The fetus was, in fact, a compound heterozygote for (--^SA/--^SEA).

Conclusions: As shown in this study, routine screening for α-thalassemia at prenatal diagnosis in the region should include both common and rare α⁰-thalassemia alleles found in the population to effectively prevent a fatal condition of Hb Bart's hydrops fetalis syndrome.