Diagnosis is one of the crucial tasks performed by primary care physicians; however, primary care is at high risk of diagnostic errors due to the characteristics and uncertainties associated with the field. Prevention of diagnostic errors in primary care requires urgent action, and one of the possible methods is the use of health information technology. Its modes such as clinical decision support systems (CDSS) have been demonstrated to improve the quality of care in a variety of medical settings, including hospitals and primary care centers, though its usefulness in the diagnostic domain is still unknown. We conducted a scoping review to confirm the usefulness of the CDSS in the diagnostic domain in primary care and to identify areas that need to be explored. Search terms were chosen to cover the three dimensions of interest: decision support systems, diagnosis, and primary care. A total of 26 studies were included in the review. As a result, we found that the CDSS and reminder tools have significant effects on screening for common chronic diseases; however, the CDSS has not yet been fully validated for the diagnosis of acute and uncommon chronic diseases. Moreover, there were few studies involving non-physicians.

Objectives: Coronavirus disease (COVID-19) blindness, that is, the excessive consideration of the disease in diagnosis, has reportedly led to delayed diagnosis of some diseases. We compared several clinical measures between patients admitted for bacteremia during the two months of the COVID-19 pandemic and those admitted during the same period in 2019. We hypothesized that the pandemic has led to delayed treatment of bacteremia. Methods: This retrospective observational study compared several measures undertaken for patients who visited the emergency unit in two hospitals between March 1 and May 31, 2020, during the COVID-19 pandemic and whose blood cultures tested positive for bacteremia with those for corresponding patients treated during the same period in 2019. The primary measure was time from consultation to blood culture/antimicrobials. Results: We included 29 eligible patients from 2020 and 26 from 2019. In 2020, the time from consultation to antimicrobial administration was significantly longer than in 2019 (mean [range], 222 [145–309] min vs. 139 [102–179] min, p=0.002). The frequency of chest computed tomography (CT) was significantly higher in 2020 (96.6 vs. 73.1%, p=0.021). Significant differences were not observed in the time to blood culture or chest CT preceding the blood culture between the two periods. Conclusions: Our findings suggested that due to the COVID-19 epidemic/pandemic, focusing on the exclusion of its infection using CT scans leads to an overall delay in the diagnosis and treatment of bacteremia. Medical providers must be aware of COVID-19 blindness and evaluate patients objectively based on rational criteria and take appropriate action.

Objectives: To assess the usefulness of case reports as sources for research on diagnostic errors in uncommon diseases and atypical presentations. Content: We reviewed 563 case reports of diagnostic error. The commonality of the final diagnoses was classified based on the description in the articles, Orphanet, or epidemiological data on available references; the typicality of presentation was classified based on the description in the articles and the judgment of the physician researchers. Diagnosis Error Evaluation and Research (DEER), Reliable Diagnosis Challenges (RDC), and Generic Diagnostic Pitfalls (GDP) taxonomies were used to assess the factors contributing to diagnostic errors. Summary and outlook: Excluding three cases in that commonality could not be classified, 560 cases were classified into four categories: typical presentations of common diseases (60, 10.7 %), atypical presentations of common diseases (35, 6.2 %), typical presentations of uncommon diseases (276, 49.3 %), and atypical presentations of uncommon diseases (189, 33.8 %). The most important DEER taxonomy was “Failure/delay in considering the diagnosis” among the four categories, whereas the most important RDC and GDP taxonomies varied with the categories. Case reports can be a useful data source for research on the diagnostic errors of uncommon diseases with or without atypical presentations.

Background Manual chart review using validated assessment tools is a standardised methodology for detecting diagnostic errors. However, this requires considerable human resources and time. ChatGPT, a recently developed artificial intelligence chatbot based on a large language model, can effectively classify text based on suitable prompts. Therefore, ChatGPT can assist manual chart reviews in detecting diagnostic errors. Objective This study aimed to clarify whether ChatGPT could correctly detect diagnostic errors and possible factors contributing to them based on case presentations. Methods We analysed 545 published case reports that included diagnostic errors. We imputed the texts of case presentations and the final diagnoses with some original prompts into ChatGPT (GPT-4) to generate responses, including the judgement of diagnostic errors and contributing factors of diagnostic errors. Factors contributing to diagnostic errors were coded according to the following three taxonomies: Diagnosis Error Evaluation and Research (DEER), Reliable Diagnosis Challenges (RDC) and Generic Diagnostic Pitfalls (GDP). The responses on the contributing factors from ChatGPT were compared with those from physicians. Results ChatGPT correctly detected diagnostic errors in 519/545 cases (95%) and coded statistically larger numbers of factors contributing to diagnostic errors per case than physicians: DEER (median 5 vs 1, p<0.001), RDC (median 4 vs 2, p<0.001) and GDP (median 4 vs 1, p<0.001). The most important contributing factors of diagnostic errors coded by ChatGPT were ‘failure/delay in considering the diagnosis’ (315, 57.8%) in DEER, ‘atypical presentation’ (365, 67.0%) in RDC, and ‘atypical presentation’ (264, 48.4%) in GDP. Conclusion ChatGPT accurately detects diagnostic errors from case presentations. ChatGPT may be more sensitive than manual reviewing in detecting factors contributing to diagnostic errors, especially for ‘atypical presentation’.

Aspiration pneumonia has a high recurrence rate, and oral care by dentists has been found effective in preventing its onset; however, this has not been evaluated using prospective studies. Therefore, we conducted a prospective study to evaluate the effectiveness of professional oral care by dentists in reducing aspiration pneumonia recurrence in older adult patients. In this prospective cohort study, we evaluated a dental oral care intervention, using a historical control group (control group). It was conducted at a single-centre regional core hospital in Japan that serves a large number of patients aged older than 80 years. Patients who were hospitalised for aspiration pneumonia were included in this study. During the study period (1 April 2021 to 31 March 2022), the clinical group received weekly professional cleaning by a dentist and the control group received standard oral care by a nurse as usual from 1 April 2020 to 31 March 2021. The dentist oral care group received weekly professional oral care from a dentist and was followed prospectively for 1 year. A Kaplan-Meier analysis was used to evaluate the timing of recurrent aspiration pneumonia or death. A Cox proportional hazards model was used to obtain a hazard ratio and determine the 95% confidence intervals. There were 91 participants in the clinical group and 94 in the control group. The mean age of participants was 85 years, and 75 (40.5%) were female. The recurrence rate was 27.5% in the clinical group and 44.7% in the control group (P = .005). Professional cleaning by a dentist reduced the risk of recurrence of aspiration pneumonia by approximately 50% (adjusted hazard ratio, 0.465; 95% confidence interval, 0.278-0.78). Professional cleaning by a dentist was associated with a lower rate of aspiration pneumonia recurrence than nurse-provided conventional oral care.

Few studies have assessed the associations between hyperuricemia and lifestyle-related diseases after adjusting for waist circumference (WC) and sex. This cross-sectional study included 33,498 Japanese individuals, and was conducted at the Center for Preventive Medicine, NTT Kanto Medical Center, Tokyo, from May 2006 to March 2015. Hyperuricemia was defined as a uric acid level of >7 mg/dl in men; >6 mg/dl in women. Metabolic syndrome (Mets) components were defined using the Japanese criteria for Mets. The subjects were stratified into quartiles according to their WC as follows: males: <78.4, 78.4 to <83.5, 83.5 to <89, and ≥89 cm; females: <71.6, 71.6 to <77, 77 to 7 mg/dl (odds ratio [OR]: 1.70, 95% confidence interval [CI]: 1.57–1.83), Females: a uric acid level of >6 mg/dl (OR: 2.35, 95% CI 1.83–2.99). Hyperuricemia was found to be an independent predictor of several lifestyle-related diseases, even after adjusting for WC which is closely related with insulin resistance. Hyperuricemia might require greater attention during the prevention of lifestyle-related diseases and future cardiovascular disease.