Diagnostic errors and characteristics of patients seen at a general internal medicine outpatient clinic with a referral for diagnosis.

OBJECTIVES: Patients referred to general internal medicine (GIM) outpatient clinics may face a higher risk of diagnostic errors than non-referred patients. This difference in risk is assumed to be due to the differences in diseases and clinical presentations between referred and non-referred patients; however, clinical data regarding this issue are scarce. This study aimed to determine the frequency of diagnostic errors and compare the characteristics of referred and non-referred patients visit GIM outpatient clinics. METHODS: This study included consecutive outpatients who visited the GIM outpatient clinic at a university hospital, with or without referral. Data on age, sex, chief complaints, referral origin, and final diagnosis were collected from medical records. The Revised Safer Dx Instrument was used to detect diagnostic errors. RESULTS: Data from 534 referred and 599 non-referred patients were analyzed. The diagnostic error rate was higher in the referral group than that in the non-referral group (2.2 % vs. 0.5 %, p=0.01). The prevalence of abnormal test results and sensory disturbances was higher in the chief complaints, and the prevalence of musculoskeletal system disorders, connective tissue diseases, and neoplasms was higher in the final diagnoses of referred patients compared with non-referred patients. Among referred patients with diagnostic errors, abnormal test results and sensory disturbances were the two most common chief complaints, whereas neoplasia was the most common final diagnosis. Problems with data integration and interpretation were found to be the most common factors contributing to diagnostic errors. CONCLUSIONS: Paying more attention to patients with abnormal test results and sensory disturbances and considering a higher pre-test probability for neoplasms may prevent diagnostic errors in patients referred to GIM outpatient clinics.

Prevalence of atypical presentations among outpatients and associations with diagnostic error.

OBJECTIVES: This study aimed to assess the prevalence of atypical presentations and their association with diagnostic errors in various diseases. METHODS: This retrospective observational study was conducted using cohort data between January 1 and December 31, 2019. Consecutive outpatients consulted by physicians from the Department of Diagnostic and Generalist Medicine at a university hospital in Japan were included. Patients for whom the final diagnosis was not confirmed were excluded. Primary outcomes were the prevalence of atypical presentations, and the prevalence of diagnostic errors in groups with typical and atypical presentations. Diagnostic errors and atypical presentations were assessed using the Revised Safer Dx Instrument. We performed primary analyses using a criterion; the average score of less than five to item 12 of two independent reviewers was an atypical presentation (liberal criterion). We also performed additional analyses using another criterion; the average score of three or less to item 12 was an atypical presentation (conservative criterion). RESULTS: A total of 930 patients were included out of a total of 2022 eligible. The prevalence of atypical presentation was 21.7 and 6.7 % when using liberal and conservative criteria for atypical presentation, respectively. Diagnostic errors (2.8 %) were most commonly observed in the cases with slight to moderate atypical presentation. Atypical presentation was associated with diagnostic errors with the liberal criterion for atypical presentation; however, this diminished with the conservative criterion. CONCLUSIONS: An atypical presentation was observed in up to 20 % of outpatients with a confirmed diagnosis, and slight to moderate atypical presentation may be the highest risk population for diagnostic errors.

Difficulty in diagnosing peritonitis caused by multidrug-resistant tuberculosis.

Key Clinical Message: The low sensitivity of ascites culture for acid-fast bacilli necessitates a peritoneal biopsy when tuberculous peritonitis is suspected. Findings in the peritoneum on computed tomography may prompt suspicion of tuberculous peritonitis. Abstract: A 47-year-old Nigerian man presented with fever, abdominal distention, and weight loss. Abdominal computed tomography revealed massive ascites and peritoneal thickening. Despite failing to culture acid-fast bacilli from ascites, histological examination and culture of peritoneum revealed multidrug-resistant tuberculosis peritonitis. Peritoneal biopsy is mandatory when tuberculosis peritonitis is suspected.

Effect of contextual factors on the prevalence of diagnostic errors among patients managed by physicians of the same specialty: a single-centre retrospective observational study.

BACKGROUND: There has been growing recognition that contextual factors influence the physician's cognitive processes. However, given that cognitive processes may depend on the physicians' specialties, the effects of contextual factors on diagnostic errors reported in previous studies could be confounded by difference in physicians. OBJECTIVE: This study aimed to clarify whether contextual factors such as location and consultation type affect diagnostic accuracy. METHODS: We reviewed the medical records of 1992 consecutive outpatients consulted by physicians from the Department of Diagnostic and Generalist Medicine in a university hospital between 1 January and 31 December 2019. Diagnostic processes were assessed using the Revised Safer Dx Instrument. Patients were categorised into three groups according to contextual factors (location and consultation type): (1) referred patients with scheduled visit to the outpatient department; (2) patients with urgent visit to the outpatient department; and (3) patients with emergency visit to the emergency room. The effect of the contextual factors on the prevalence of diagnostic errors was investigated using logistic regression analysis. RESULTS: Diagnostic errors were observed in 12 of 534 referred patients with scheduled visit to the outpatient department (2.2%), 3 of 599 patients with urgent visit to the outpatient department (0.5%) and 13 of 859 patients with emergency visit to the emergency room (1.5%). Multivariable logistic regression analysis showed a significantly higher prevalence of diagnostic errors in referred patients with scheduled visit to the outpatient department than in patients with urgent visit to the outpatient department (OR 4.08, p=0.03), but no difference between patients with emergency and urgent visit to the emergency room and outpatient department, respectively. CONCLUSION: Contextual factors such as consultation type may affect diagnostic errors; however, since the differences in the prevalence of diagnostic errors were small, the effect of contextual factors on diagnostic accuracy may be small in physicians working in different care settings.

Five tips to guide beginners and young general physicians on writing clinical image reports.

We propose the following five tips as important processes for writing clinical image reports: select a suitable case for the clinical image report; take appropriate images; select a journal for submission; prepare models of clinical image reports; and create templates for structuring clinical image reports in advance. We hope that these five tips will help beginners and young general physicians write clinical image reports.

Incidence of Diagnostic Errors Among Unexpectedly Hospitalized Patients Using an Automated Medical History-Taking System With a Differential Diagnosis Generator: Retrospective Observational Study.

BACKGROUND: Automated medical history-taking systems that generate differential diagnosis lists have been suggested to contribute to improved diagnostic accuracy. However, the effect of these systems on diagnostic errors in clinical practice remains unknown. OBJECTIVE: This study aimed to assess the incidence of diagnostic errors in an outpatient department, where an artificial intelligence (AI)-driven automated medical history-taking system that generates differential diagnosis lists was implemented in clinical practice. METHODS: We conducted a retrospective observational study using data from a community hospital in Japan. We included patients aged 20 years and older who used an AI-driven, automated medical history-taking system that generates differential diagnosis lists in the outpatient department of internal medicine for whom the index visit was between July 1, 2019, and June 30, 2020, followed by unplanned hospitalization within 14 days. The primary endpoint was the incidence of diagnostic errors, which were detected using the Revised Safer Dx Instrument by at least two independent reviewers. To evaluate the effect of differential diagnosis lists from the AI system on the incidence of diagnostic errors, we compared the incidence of these errors between a group where the AI system generated the final diagnosis in the differential diagnosis list and a group where the AI system did not generate the final diagnosis in the list; the Fisher exact test was used for comparison between these groups. For cases with confirmed diagnostic errors, further review was conducted to identify the contributing factors of these errors via discussion among three reviewers, using the Safer Dx Process Breakdown Supplement as a reference. RESULTS: A total of 146 patients were analyzed. A final diagnosis was confirmed for 138 patients and was observed in the differential diagnosis list from the AI system for 69 patients. Diagnostic errors occurred in 16 out of 146 patients (11.0%, 95% CI 6.4%-17.2%). Although statistically insignificant, the incidence of diagnostic errors was lower in cases where the final diagnosis was included in the differential diagnosis list from the AI system than in cases where the final diagnosis was not included in the list (7.2% vs 15.9%, P=.18). CONCLUSIONS: The incidence of diagnostic errors among patients in the outpatient department of internal medicine who used an automated medical history-taking system that generates differential diagnosis lists seemed to be lower than the previously reported incidence of diagnostic errors. This result suggests that the implementation of an automated medical history-taking system that generates differential diagnosis lists could be beneficial for diagnostic safety in the outpatient department of internal medicine.

Effects of a Differential Diagnosis List of Artificial Intelligence on Differential Diagnoses by Physicians: An Exploratory Analysis of Data from a Randomized Controlled Study.

A diagnostic decision support system (DDSS) is expected to reduce diagnostic errors. However, its effect on physicians' diagnostic decisions remains unclear. Our study aimed to assess the prevalence of diagnoses from artificial intelligence (AI) in physicians' differential diagnoses when using AI-driven DDSS that generates a differential diagnosis from the information entered by the patient before the clinical encounter on physicians' differential diagnoses. In this randomized controlled study, an exploratory analysis was performed. Twenty-two physicians were required to generate up to three differential diagnoses per case by reading 16 clinical vignettes. The participants were divided into two groups, an intervention group, and a control group, with and without a differential diagnosis list of AI, respectively. The prevalence of physician diagnosis identical with the differential diagnosis of AI (primary outcome) was significantly higher in the intervention group than in the control group (70.2% vs. 55.1%, p < 0.001). The primary outcome was significantly >10% higher in the intervention group than in the control group, except for attending physicians, and physicians who did not trust AI. This study suggests that at least 15% of physicians' differential diagnoses were affected by the differential diagnosis list in the AI-driven DDSS.

Efficacy of Artificial-Intelligence-Driven Differential-Diagnosis List on the Diagnostic Accuracy of Physicians: An Open-Label Randomized Controlled Study.

BACKGROUND: The efficacy of artificial intelligence (AI)-driven automated medical-history-taking systems with AI-driven differential-diagnosis lists on physicians' diagnostic accuracy was shown. However, considering the negative effects of AI-driven differential-diagnosis lists such as omission (physicians reject a correct diagnosis suggested by AI) and commission (physicians accept an incorrect diagnosis suggested by AI) errors, the efficacy of AI-driven automated medical-history-taking systems without AI-driven differential-diagnosis lists on physicians' diagnostic accuracy should be evaluated. OBJECTIVE: The present study was conducted to evaluate the efficacy of AI-driven automated medical-history-taking systems with or without AI-driven differential-diagnosis lists on physicians' diagnostic accuracy. METHODS: This randomized controlled study was conducted in January 2021 and included 22 physicians working at a university hospital. Participants were required to read 16 clinical vignettes in which the AI-driven medical history of real patients generated up to three differential diagnoses per case. Participants were divided into two groups: with and without an AI-driven differential-diagnosis list. RESULTS: There was no significant difference in diagnostic accuracy between the two groups (57.4% vs. 56.3%, respectively; p = 0.91). Vignettes that included a correct diagnosis in the AI-generated list showed the greatest positive effect on physicians' diagnostic accuracy (adjusted odds ratio 7.68; 95% CI 4.68-12.58; p < 0.001). In the group with AI-driven differential-diagnosis lists, 15.9% of diagnoses were omission errors and 14.8% were commission errors. CONCLUSIONS: Physicians' diagnostic accuracy using AI-driven automated medical history did not differ between the groups with and without AI-driven differential-diagnosis lists.

Clozapine-associated severe eosinophilia following lithium rebound neutropenia: A case report.

BACKGROUND: Clozapine use is complicated by an increased risk of hematological adverse effects such as neutropenia and, rarely, eosinophilia. CASE PRESENTATION: We present the case of a 48-year-old man with treatment-resistant schizophrenia. On day 12 after clozapine initiation, he had a cough with a temperature of 39.8°C. On day 16, his leukocyte count had increased to 9320 cells/mm3 (neutrophils 7550 cells/mm3 and eosinophils 680 cells/mm3 ). We discontinued lithium because of neutrophilia and damage to renal function on day 20. His eosinophil count increased until day 29, reaching 6750 cells/mm3 . We suspected a drug-induced reaction and discontinued clozapine on day 30. His eosinophil count gradually decreased, reaching the normal range by day 40. However, his leukocyte and neutrophil counts also gradually decreased to below than the normal range by day 40. His leukocytes and neutrophil counts had recovered by day 55. CONCLUSION: We concluded that this patient had clozapine-associated severe eosinophilia following lithium rebound neutropenia.