Diagnosis最新文献

Background: Diagnostic scope is the range of diagnoses found in a clinical setting. Although the diagnostic scope is an essential feature of training and evaluating artificial intelligence (AI) systems to promote diagnostic excellence, its impact on AI systems and the diagnostic process remains under-explored.

Content: We define the concept of diagnostic scope, discuss its nuanced role in building safe and effective AI-based diagnostic decision support systems, review current challenges to measurement and use, and highlight knowledge gaps for future research.

Summary: The diagnostic scope parallels the differential diagnosis although the latter is at the level of an encounter and the former is at the level of a clinical setting. Therefore, diagnostic scope will vary by local characteristics including geography, population, and resources. The true, observed, and considered scope in each setting may also diverge, both posing challenges for clinicians, patients, and AI developers, while also highlighting opportunities to improve safety. Further work is needed to systematically define and measure diagnostic scope in terms that are accurate, equitable, and meaningful at the bedside. AI tools tailored to a particular setting, such as a primary care clinic or intensive care unit, will each require specifying and measuring the appropriate diagnostic scope.

Outlook: AI tools will promote diagnostic excellence if they are aligned with patient and clinician needs and trained on an accurately measured diagnostic scope. A careful understanding and rigorous evaluation of the diagnostic scope in each clinical setting will promote optimal care through human-AI collaborations in the diagnostic process.

Objectives: Time pressure and time constraints have been shown to affect diagnostic accuracy, but how they interact is not clear. The current study aims to investigate the effects of both perceived time pressure (sufficient vs. insufficient time) and actual time constraints (lenient vs. restricted time limit) with regard to diagnostic accuracy.

Methods: Residents from two university-affiliated training programs in the USA participated in this online within-subjects experiment. They diagnosed cases under two perceived time pressure conditions: one where they were told they had sufficient time to diagnose the cases and one where they were told they had insufficient time. The actual time limit was either restricted or lenient (± one standard deviation from the mean time to diagnose). Participants provided their most likely diagnosis and a differential diagnosis for each case, and rated their confidence in their most likely diagnosis.

Results: A restricted time limit was associated with lower accuracy scores (p=0.044) but no effects of perceived time pressure on diagnostic accuracy were found. However, participants self-reported feeling more time pressure when they thought they had insufficient time (p<0.001). In addition, there was an effect of the actual time limit (p=0.012) and perceived time pressure (p=0.048) on confidence.

Conclusions: This study showed that a restricted time limit can negatively affect diagnostic accuracy. Although participants felt more time pressure and were less confident when they thought they had insufficient time, perceived time pressure did not affect diagnostic accuracy. More research is needed to further investigate the effects of time pressure and time limits on diagnostic accuracy.

Nearly a decade after the National Academy of Medicine released the "Improving Diagnosis in Health Care" report, diagnostic errors remain common, often leading to physical, psychological, emotional, and financial harm. Despite a robust body of research on potential solutions and next steps, the translation of these efforts to patient care has been limited. Improvement initiatives are still narrowly focused on selective themes such as diagnostic stewardship, preventing overdiagnosis, and enhancing clinical reasoning without comprehensively addressing vulnerable systems and processes surrounding diagnosis. To close this implementation gap, the US Centers for Disease Control and Prevention (CDC) released the Core Elements of Hospital Diagnostic Excellence programs on September 17, 2024. This initiative aligns with the World Health Organization's (WHO) 2024 World Patient Safety Day focus on improving diagnosis. These Core Elements provide guidance for the formation of hospital programs to improve diagnosis and aim to integrate various disparate efforts in hospitals. By creating a shared mental model of diagnostic excellence, the Core Elements of Diagnostic Excellence supports actions to break down silos, guide hospitals toward multidisciplinary diagnostic excellence teams, and provide a foundation for building diagnostic excellence programs in hospitals.

Objectives: To investigate longitudinal trends in the incidence, preventability, and causes of DAEs (diagnostic adverse events) between 2008 and 2019 and compare DAEs to other AE (adverse event) types.

Methods: This study investigated longitudinal trends of DAEs using combined data from four large Dutch AE record review studies. The original four AE studies included 100-150 randomly selected records of deceased patients from around 20 hospitals in each study, resulting in a total of 10,943 patient records. Nurse reviewers indicated cases with potential AEs using a list of triggers. Subsequently, experienced physician reviewers systematically judged the occurrence of AEs, the clinical process in which these AEs occurred, and the preventability and causes.

Results: The incidences of DAEs, potentially preventable DAEs and potentially preventable DAE-related deaths initially declined between 2008 and 2012 (2.3 vs. 1.2; OR=0.52, 95 % CI: 0.32 to 0.83), after which they stabilized up to 2019. These trends were largely the same for other AE types, although compared to DAEs, the incidence of other AE types increased between 2016 (DAE: 1.0, other AE types: 8.5) and 2019 (DAE: 0.8, other AE types: 13.0; rate ratio=1.88, 95 % CI: 1.12 to 2.13). Furthermore, DAEs were more preventable (p<0.001) and were associated with more potentially preventable deaths (p=0.016) than other AE types. In addition, DAEs had more and different underlying causes than other AE types (p<0.001). The DAE causes remained stable over time, except for patient-related factors, which increased between 2016 and 2019 (29.5 and 58.6 % respectively, OR=3.40, 95 % CI: 1.20 to 9.66).

Conclusions: After initial improvements of DAE incidences in 2012, no further improvement was observed in Dutch hospitals in the last decade. Similar trends were observed for other AEs. The high rate of preventability of DAEs suggest a high potential for improvement, that should be further investigated.

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.