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.

A 40-year-old woman with bipolar disorder who was taking mirtazapine presented with mydriasis, abnormal diaphoresis, myoclonus and muscle rigidity after taking metocloplamide. Her medical history, which included the use of serotonergic agents, and the presence of symptoms including myoclonus and muscle rigidity were consistent with a diagnosis of serotonin syndrome (SS) according to the Hunter criteria. The symptoms diminished following three days of treatment with oral lorazepam and cyproheptadine and a reduced dose of mirtazapine. Metoclopramide is frequently used to various gastric symptom. Metoclopramide is not widely known to induce SS. This potentially fatal condition should be avoided by exercising care in the use of drugs that have the potential to cause drug-drug interactions.

Purpose: Artificial intelligence (AI) holds great potential for revolutionizing health care by providing clinicians with data-driven insights that support more accurate and efficient clinical decisions. However, applying AI in clinical settings is often challenging due to the complexity and vastness of medical information. This perspective article explores how AI development methodologies can be adapted to support clinicians in their decision-making processes, emphasizing the importance of a hybrid approach that combines AI capabilities with clinicians’ expertise. Patients and Methods: We developed a conceptual framework designed to integrate AI-driven hybrid intelligence into clinical practice to enhance decision-making. This framework focuses on adapting key AI concepts, such as backpropagation, quantization, and avoiding overfitting, to help clinicians better interpret complex medical data and improve diagnosis and treatment planning. Results: Several AI methodologies were adapted to enhance clinical decision-making. First, backpropagation allows clinicians to refine initial assessments by revisiting them as new data emerges, improving diagnostic accuracy over time. Second, quantization helps break down complex medical problems into manageable components, enabling clinicians to prioritize critical elements of care. Finally, avoiding overfitting encourages clinicians to balance rare diagnoses with more common explanations, reducing the risk of diagnostic errors and unnecessary complexity. Conclusion: The integration of AI-driven hybrid intelligence has the potential to enhance clinical decision-making. By adapting AI methodologies, clinicians can enhance their ability to analyze data, prioritize treatments, and make more accurate diagnoses while preserving the essential human aspect of health care. This framework highlights the importance of combining AI’s strengths with clinicians’ expertise for more effective and balanced decision-making in clinical practice. This perspective highlights the value of hybrid intelligence in achieving more balanced, effective, and patient-centered decision-making in health care.

Appendicitis is one of the most common causes of acute abdominal pain; yet the risk of delayed diagnosis remains despite recent advances in abdominal imaging. Understanding the factors associated with delayed diagnosis can lower the risk of diagnostic errors for acute appendicitis. These factors, including physicians’ specialty as a generalist or non-generalist, were evaluated through a retrospective, observational study of adult acute appendicitis cases at a single center, between April 1, 2014, and March 31, 2021. The main outcome was timely diagnosis, defined as "diagnosis at the first visit if the facility had computed tomography (CT) capability" or "referral to an appropriate medical institution promptly after the first visit for a facility without CT capability," with all other cases defined as delayed diagnosis. The frequency of delayed diagnosis was calculated and associated factors evaluated through multivariate and exploratory analyses. The overall rate of delayed diagnosis was 26.2% (200/763 cases). Multivariate analysis showed that tenderness in the right lower abdominal region, absence of diarrhea, a consultation of ≤6 h after symptom onset, and consultation with a generalist were associated with a decreased risk of delayed diagnosis of acute appendicitis. Exploratory analysis found that generalists performed more physical findings related to acute appendicitis, suggesting that this diagnostic approach may be associated with timely diagnosis. Future studies should adjust for other potential confounding factors, including patient complexity, consultation environment, number of physicians, diagnostic modality, and physician specialties.

The process of diagnostic errors among Japanese residents has not been previously studied. This descriptive study was conducted in June 2019 on junior residents at a single‐center educational hospital in Japan. Diagnosis Error Evaluation and Research taxonomy was used to measure the process of diagnostic error in the most memorable error cases. High frequency of diagnostic errors resulted from inaccurate/misinterpretation of history, failure/delay in eliciting physical examination findings, inaccurate/misinterpretation of physical examination, failure in weighting of physical examination, and failure/delay in considering the diagnosis. Residents made diagnostic errors mainly during history taking, physical examination, and assessment.

Lower gastrointestinal perforation is rare and challenging to diagnose in patients presenting with an acute abdomen. However, no study has examined the frequency and associated factors of diagnostic errors related to lower gastrointestinal perforation. This large-scale multicenter retrospective study investigated the frequency of diagnostic errors and identified the associated factors. Factors at the level of the patient, symptoms, situation, and physician were included in the analysis. Data were collected from nine institutions, between January 1, 2015 and December 31, 2019. Timely diagnosis was defined as diagnosis at the first visit in computed tomography (CT)-capable facilities or referral to an appropriate medical institution immediately following the first visit to a non-CT-capable facility. Cases not meeting this definition were defined as diagnostic errors that resulted in delayed diagnosis. Of the 439 cases of lower gastrointestinal perforation identified, delayed diagnosis occurred in 138 cases (31.4%). Multivariate logistic regression analysis revealed a significant association between examination by a non-generalist and delayed diagnosis. Other factors showing a tendency with delayed diagnosis included presence of fever, absence of abdominal tenderness, and unavailability of urgent radiology reports. Initial misdiagnoses were mainly gastroenteritis, constipation, and small bowel obstruction. In conclusion, diagnostic errors occurred in about one-third of patients with a lower gastrointestinal perforation.