Diagnosis最新文献

Objectives: To investigate the relationship between language and diagnostic errors (DxE) in the pediatric emergency department (ED).

Methods: Electronic trigger identified ED encounters resulting in unplanned hospital admission that occurred within 10 days of an index visit from January 2018 through February 2022. Manual screening of each triggered encounter identified cases where the index visit diagnosis and hospitalization discharge diagnosis differed, and these were screened in for review using the Revised Safer Dx instrument to determine if a diagnostic error (DxE) occurred. Non-English primary language (NEPL) and English-proficient (EP) groups were established based on caregiver language. The primary outcome was the proportion of DxE each group. Data were analyzed using univariate analysis and multivariable logistic regression to identify independent predictors of DxE.

Results: Electronic trigger identified 3,551 patients, of which 806 (22.7 %) screened in for Safer Dx review. 172 (21.3 %) experienced DxE. The proportion of DxE was similar between EP and NEPL groups (21.5 vs. 21.7 %; p=0.97). Age≥12 years and fewer prior admissions in the preceding 6 months predicted higher odds of DxE. NEPL did not predict higher odds of DxE.

Conclusions: NEPL was not associated with increased odds DxE resulting in unplanned admission.

Objectives: No framework currently exists to guide how payers and providers can collaboratively develop and implement incentives to improve diagnostic safety. We conducted a literature review and interviews with subject matter experts to develop a multi-component 'Payer Relationships for Improving Diagnoses (PRIDx)' framework, that could be used to engage payers in diagnostic safety efforts.

Content: The PRIDx framework, 1) conceptualizes diagnostic safety links to care provision, 2) illustrates ways to promote payer and provider engagement in the design and adoption of accountability mechanisms, and 3) explicates the use of data analytics. Certain approaches suggested by PRIDx were refined by subject matter expert interviewee perspectives.

Summary: The PRIDx framework can catalyze public and private payers to take specific actions to improve diagnostic safety.

Outlook: Implementation of the PRIDx framework requires new types of partnerships, including external support from public and private payer organizations, and requires creation of strong provider incentives without undermining providers' sense of professionalism and autonomy. PRIDx could help facilitate collaborative payer-provider approaches to improve diagnostic safety and generate research concepts, policy ideas, and potential innovations for engaging payers in diagnostic safety improvement activities.

Background: Diagnostic imaging decision support (DI-DS) systems could be effective tools for reducing inappropriate diagnostic imaging examinations. Since effective design and evaluation of these systems requires in-depth understanding of their features and functions, the present study aims to map the existing literature on DI-DS systems to identify features and functions of these systems.

Methods: The search was performed using Scopus, Embase, PubMed, Web of Science, and Cochrane Central Registry of Controlled Trials (CENTRAL) and was limited to 2000 to 2021. Analytical studies, descriptive studies, reviews and book chapters that explicitly addressed the functions or features of DI-DS systems were included.

Results: A total of 6,046 studies were identified. Out of these, 55 studies met the inclusion criteria. From these, 22 functions and 22 features were identified. Some of the identified features were: visibility, content chunking/grouping, deployed as a multidisciplinary program, clinically valid and relevant feedback, embedding current evidence, and targeted recommendations. And, some of the identified functions were: displaying an appropriateness score, recommending alternative or more appropriate imaging examination(s), providing recommendations for next diagnostic steps, and providing safety alerts.

Conclusions: The set of features and functions obtained in the present study can provide a basis for developing well-designed DI-DS systems, which could help to improve adherence to diagnostic imaging guidelines, minimize unnecessary costs, and improve the outcome of care through appropriate diagnosis and on-time care delivery.

Objectives: Current autopsy practice guidelines do not provide a mechanism to identify potential causes of diagnostic error (DE). We used our autopsy data registry to ask if gender or race were related to the frequency of diagnostic error found at autopsy.

Methods: Our autopsy reports include International Classification of Diseases (ICD) 9 or ICD 10 diagnostic codes for major diagnoses as well as codes that identify types of error. From 2012 to mid-2015 only 2 codes were used: UNDOC (major undocumented diagnoses) and UNCON (major unconfirmed diagnoses). Major diagnoses contributed to death or would have been treated if known. Since mid-2015, codes included specific diagnoses, i.e. undiagnosed or unconfirmed myocardial infarction, infection, pulmonary thromboembolism, malignancy, or other diagnosis as well as cause of death. Adult autopsy cases from 2012 to 2019 were assessed for DE associated with reported sex or race (nonwhite or white). 528 cases were evaluated between 2012 and 2015 and 699 between 2015 and 2019.

Results: Major DEs were identified at autopsy in 65.9 % of cases from 2012 to 2015 and in 72.1 % from 2015 to 2019. From 2012 to 2015, female autopsy cases showed a greater frequency in 4 parameters of DE, i.e., in the total number of cases with any error (p=0.0001), in the number of cases with UNDOC errors (p=0.0038) or UNCON errors (p=0.0006), and in the relative proportions of total numbers of errors (p=0.0001). From 2015 to 2019 undocumented malignancy was greater among males (p=0.0065); no other sex-related error was identified. In the same period some DE parameters were greater among nonwhite than among white subjects, including unconfirmed cause of death (p=0.035), and proportion of total error diagnoses (p=0.0003), UNCON diagnoses (p=0.0093), and UNDOC diagnoses (p=0.035).

Conclusions: Coding for DE at autopsy can identify potential effects of biases on diagnostic error.

Objectives: This study aimed to elucidate effective methodologies for utilizing the generative artificial intelligence (AI) system, namely the Chat Generative Pre-trained Transformer (ChatGPT), in improving clinical reasoning abilities among clinicians.

Methods: We conducted a comprehensive exploration of the capabilities of ChatGPT, emphasizing two main areas: (1) efficient utilization of ChatGPT, with a focus on application and language selection, input methodology, and output verification; and (2) specific strategies to bolster clinical reasoning using ChatGPT, including self-learning via simulated clinical case creation and engagement with published case reports.

Results: Effective AI-based clinical reasoning development requires a clear delineation of both system roles and user needs. All outputs from the system necessitate rigorous verification against credible medical resources. When used in self-learning scenarios, capabilities of ChatGPT in clinical case creation notably enhanced disease comprehension.

Conclusions: The efficient use of generative AIs, as exemplified by ChatGPT, can impressively enhance clinical reasoning among medical professionals. Adopting these cutting-edge tools promises a bright future for continuous advancements in clinicians' diagnostic skills, heralding a transformative era in digital healthcare.

Objectives: Little is known about how patients perceive diagnostic uncertainty. We sought to understand how patients and care partners perceive uncertainty in an emergency or urgent care setting, where making a final diagnosis is often not possible.

Methods: We administered a survey to a nationally representative panel on patient-reported diagnostic excellence in an emergency department or urgent care setting. The survey included items specific to perceived diagnostic excellence, visit characteristics, and demographics. We analyzed responses to two open-ended questions among those who reported uncertainty in the explanation they were given. Themes were identified using an inductive approach, and compared by whether respondents agreed or disagreed the explanation they were given was true.

Results: Of the 1,116 respondents, 106 (10 %) reported that the care team was not certain in the explanation of their health problem. Five themes were identified in the open-ended responses: poor communication (73 %), uncertainty made transparent (10 %), incorrect information provided (9 %), inadequate testing equipment (4 %), and unable to determine (4 %). Of the respondents who reported uncertainty, 21 % (n=22/106) reported the explanation of their problem given was not true.

Conclusions: The findings of this analysis suggest that the majority of patients and their care partners do not equate uncertainty with a wrong explanation of their health problem, and that poor communication was the most commonly cited reason for perceived uncertainty.

Background: Early stages of hepatitis B virus (HBV) infection usually involve inflammation of the liver. Patients with chronic infection have an increased risk of progressive liver fibrosis, cirrhosis, and life-threatening clinical complications of end-stage hepatocellular carcinoma (HCC).

Content: Early diagnosis of hepatic fibrosis and timely clinical management are critical to controlling disease progression and decreasing the burden of end-stage liver cancer. Fibrosis staging, through its current gold standard, liver biopsy, improves patient outcomes, but the clinical procedure is invasive with unpleasant post-procedural complications. Routine blood test markers offer promising diagnostic potential for early detection of liver disease without biopsy. There is a plethora of candidate routine blood test markers that have gone through phases of biomarker validation and have shown great promise, but their current limitations include a predictive ability that is limited to only a few stages of fibrosis. However, the advent of machine learning, notably pattern recognition, presents an opportunity to refine blood-based non-invasive models of hepatic fibrosis in the future.

Summary: In this review, we highlight the current landscape of routine blood-based non-invasive models of hepatic fibrosis, and appraise the potential application of machine learning (pattern recognition) algorithms to refining these models and optimising clinical predictions of HBV-associated liver disease.

Outlook: Machine learning via pattern recognition algorithms takes data analytics to a new realm, and offers the opportunity for enhanced multi-marker fibrosis stage prediction using pathology profile that leverages information across patient routine blood tests.

Objectives: Fevers have been used as a marker of disease for hundreds of years and are frequently used for disease screening. However, body temperature varies over the course of a day and across individual characteristics; such variation may limit the detection of febrile episodes complicating the diagnostic process. Our objective was to describe individual variation in diurnal temperature patterns during episodes of febrile activity using millions of recorded temperatures and evaluate the probability of recording a fever by sex and for different age groups.

Methods: We use timestamped deidentified temperature readings from thermometers across the US to construct illness episodes where continuous periods of activity in a single user included a febrile reading. We model the mean temperature recorded and probability of registering a fever across the course of a day using sinusoidal regression models while accounting for user age and sex. We then estimate the probability of recording a fever by time of day for children, working-age adults, and older adults.

Results: We find wide variation in body temperatures over the course of a day and across individual characteristics. The diurnal temperature pattern differed between men and women, and average temperatures declined for older age groups. The likelihood of detecting a fever varied widely by the time of day and by an individual's age or sex.

Conclusions: Time of day and demographics should be considered when using body temperatures for diagnostic or screening purposes. Our results demonstrate the importance of follow-up thermometry readings if infectious diseases are suspected.

Objectives: There is limited information on the influence of collecting small amounts of blood on the quality of blood gas analysis. Therefore, the purpose of this study was to investigate the effects of different degrees of underfilling of syringes on test results of venous blood gas analysis.

Methods: Venous blood was collected by venipuncture from 19 healthcare workers in three 1.0 mL syringes for blood gas analysis, by manually aspirating different volumes of blood (i.e., 1.0, 0.5 and 0.25 mL). Routine blood gas analysis was then immediately performed with GEM Premier 5,000. The results of the two underfilled syringes were compared with those of the reference syringe filled with appropriate blood volume.

Results: The values of most assayed parameters did not differ significantly in the two underfilled syringes. Statistically significant variations were found for lactate, hematocrit and total hemoglobin, the values of which gradually increased as the fill volume diminished, as well as for sodium concentration, which decreased in both insufficiently filled blood gas syringes. The bias was clinically meaningful for lactate in syringe filled with 0.25 mL of blood, and for hematocrit, total hemoglobin and sodium in both syringes containing 0.5 and 0.25 mL of blood.

Conclusions: Collection of smaller volumes of venous blood than the specified filling volume in blood gas syringes may have an effect on the quality of some test results, namely lactate, hematocrit, total hemoglobin and sodium. Specific indications must be given for standardizing the volume of blood to be collected within these syringes.