野田 和敬

BACKGROUND: Patient awareness surveys on polypharmacy have been reported previously, but no previous study has examined the effects of sending feedback to health professionals on reducing medication use. Our study aimed to conduct a patient survey to examine factors contributing to polypharmacy, feedback the results to health professionals, and analyze the resulting changes in the number of polypharmacy patients and prescribed medications. METHODS: After conducting a questionnaire survey of patients in Study 1, we provided its results to the healthcare professionals, and then surveyed the number of polypharmacy patients and oral medications using a before-after comparative study design in Study 2. In Study 1, we examined polypharmacy and its contributing factors by performing logistic regression analysis. In Study 2, we performed a t-test and a chi-square test. RESULTS: In the questionnaire survey, significant differences were found in the following 3 items: age (odds ratio (OR) = 3.14; 95% confidence interval (CI) = 2.01-4.91), number of medical institutions (OR = 2.34; 95%CI = 1.50-3.64), and patients' difficulty with asking their doctors to deprescribe their medications (OR = 2.21; 95%CI = 1.25-3.90). After the feedback, the number of polypharmacy patients decreased from 175 to 159 individuals and the mean number of prescribed medications per patient decreased from 8.2 to 7.7 (p < 0.001, respectively). CONCLUSIONS: Providing feedback to health professionals on polypharmacy survey results may lead to a decrease in the number of polypharmacy patients. Factors contributing to polypharmacy included age (75 years or older), the number of medical institutions (2 or more institutions), and patients' difficulty with asking their physicians to deprescribe their medications. Feedback to health professionals reduced the percentage of polypharmacy patients and the number of prescribed medications. TRIAL REGISTRATION: UMIN. Registered 21 June 2020 - Retrospectively registered, https://www.umin.ac.jp/ctr/index-j.htm.

Objectives: We examined whether problem-based learning tutorials using patient-simulated videos showing daily life are more practical for clinical learning, compared with traditional paper-based problem-based learning, for the consideration rate of psychosocial issues and the recall rate for experienced learning. Methods: Twenty-two groups with 120 fifth-year students were each assigned paper-based problem-based learning and video-based problem-based learning using patient-simulated videos. We compared target achievement rates in questionnaires using the Wilcoxon signed-rank test and discussion contents diversity using the Mann-Whitney U test. A follow-up survey used a chi-square test to measure students' recall of cases in three categories: video, paper, and non-experienced. Results: Video-based problem-based learning displayed significantly higher achievement rates for imagining authentic patients (p=0.001), incorporating a comprehensive approach including psychosocial aspects (p<0.001), and satisfaction with sessions (p=0.001). No significant differences existed in the discussion contents diversity regarding the International Classification of Primary Care Second Edition codes and chapter types or in the rate of psychological codes. In a follow-up survey comparing video and paper groups to non-experienced groups, the rates were higher for video (χ2=24.319, p<0.001) and paper (χ2=11.134, p=0.001). Although the video rate tended to be higher than the paper rate, no significant difference was found between the two. Conclusions: Patient-simulated videos showing daily life facilitate imagining true patients and support a comprehensive approach that fosters better memory. The clinical patient-simulated video method is more practical and clinical problem-based tutorials can be implemented if we create patient-simulated videos for each symptom as teaching materials.

BACKGROUND: Telediagnosis (TD), which uses information and communications technology, has recently undergone rapid development. Since no studies have compared the diagnostic precision of TD to that of face-to-face diagnosis (FD), we examined and compared the diagnostic accuracy of these diagnostic approaches among general medicine outpatients. METHODS: Data of 97 patients (45 men and 52 women with a mean age of 52 years) who underwent initial examinations at a regional hospital were analyzed. Two fully trained general medicine physicians were selected from a group of three physicians to perform FD and TD. Levels of agreement (as κ coefficients) were determined between TD and FD diagnoses as well as between final diagnoses and TD and FD diagnoses. RESULTS: The κ coefficients were 0.75 for TD and FD and 0.81 for both, the final diagnoses and the TD and FD diagnoses, revealing a sufficiently high level of diagnostic agreement. CONCLUSIONS: TD can provide the same level of diagnostic accuracy as FD among general medicine outpatients for adults. The help of medical assistants and the utilization of physical examination devices might enable medical staff to provide TD care similar in quality to FD. TD could be a useful diagnostic tool when medical work force is limited (e.g., in remote areas, during natural disasters, and in at-home care).

PURPOSE: To develop a clinical score to discriminate patients with somatic symptom disorder (SSD) from those with medical disease (MD) for complaints of non-acute pain. METHODS: We retrospectively examined the clinical records of consecutive patients with pain for a duration of ≥1 month in our department from April 2003 to March 2015. We divided the subjects according to the diagnoses of definite SSD (as diagnosed and tracked by psychiatrists in our hospital), probable SSD (without evaluation by psychiatrists in our hospital), matched MD (randomly matched two patients by age, sex, and pain location for each definite SSD patient), unmatched MD, other mental disease, or functional somatic syndrome (FSS). We investigated eight clinical factors for definite SSD and matched MD, and developed a diagnostic score to identify SSD. We subsequently validated the model with cases of probable SSD and unmatched MD. RESULTS: The number of patients with definite SSD, probable SSD, matched MD, unmatched MD, other mental disease, and FSS was 104 (3.5%), 214 (7.3%), 197 (6.7%), 742 (25%), 708 (24%), and 978 (33%), respectively. In a conditional logistic regression analysis, the following five factors were included as independent predictors of SSD: Analgesics ineffective, Mental disorder history, Unclear provocative/palliative factors, Persistence without cessation, and Stress feelings/episodes (A-MUPS). The area under the receiver operating characteristic curve (AUC) of the model was 0.900 (95% CI: 0.864-0.937, p<0.001), and the McFadden's pseudo-R-squared was 0.709. For internal validation, the AUC between probable SSD and unmatched MD was 0.930 (95% CI: 0.910-0.950, p<0.001). The prevalence and the likelihood ratio of SSD increased as the score increased. CONCLUSION: The A-MUPS score was useful for discriminating patients with SSD from those with MD for complaints of non-acute pain, although external validation and refinement should be needed.

BACKGROUND: This study aimed to clarify the influence of predicting a correct diagnosis from the history on physical examination by comparing the diagnostic accuracy of auscultation with and without clinical information. METHODS: The participants were 102 medical students from the 2013 clinical clerkship course. Auscultation was performed with a cardiology patient simulator. Participants were randomly assigned to two groups. Each group listened to a different simulated heart murmur and then made a diagnosis without clinical information. Next, a history suggesting a different murmur was provided to each group and they predicted the diagnosis. Finally, the students listened to a murmur corresponding to the history provided and again made a diagnosis. Correct and incorrect diagnosis rates of auscultation were compared between students with and without clinical information, between students predicting a correct or incorrect diagnosis from the history (correct and incorrect prediction groups, respectively), and between students without clinical information and those making an incorrect prediction. RESULTS: For auscultation with or without clinical information, the correct diagnosis rate was 62.7% (128/204 participants) versus 54.4% (111/204 participants), showing no significant difference (P=0.09). After receiving clinical information, a correct diagnosis was made by 102/117 students (87.2%) in the correct prediction group versus 26/87 students (29.9%) in the incorrect prediction group, showing a significant difference (P=0.006). The correct diagnosis rate was also significantly lower in the incorrect prediction group than when the students performed auscultation without clinical information (54.4% versus 29.9%, P<0.001). CONCLUSION: Obtaining a history alone does not improve the diagnostic accuracy of physical examination. However, accurately predicting the diagnosis from the history is associated with higher diagnostic accuracy of physical examination, while incorrect prediction is associated with lower diagnostic accuracy of examination.

PURPOSE: We hypothesized that even with appropriate clinical information, abnormal CT findings can still be missed if correct diagnostic predictions are not made. MATERIALS AND METHODS: Of 388 total students (97 5th-year medical students × 4), students who detected abnormalities without clinical information were eliminated. The remaining students (hereafter, subjects) obtained clinical information, made diagnostic predictions, and reevaluated images. The proportion of failures in detecting abnormalities was compared between the correct prediction group and the incorrect prediction group. In the correct prediction group, the relationship between failures of detection and the ranking of the correct diagnosis was also examined. RESULTS: A total of 341 subjects were assessed. The proportion of subjects who failed to detect abnormalities in the correct prediction group (47.7 %, 93/195) was significantly lower (P < 0.001) than in the incorrect prediction group (85.6 %, 125/146). In the correct prediction group, the proportion of subjects who failed to detect abnormalities was significantly lower (P = 0.004) when the correct diagnosis was ranked first (38.5 %, 42/109) compared with lower rankings (59.3 %, 51/86). CONCLUSION: Making appropriate diagnostic predictions and estimating the possibility of them based on clinical information is important to avoid missing abnormal CT findings.

PURPOSE: To compare the diagnostic accuracy of diseases predicted from patient responses to a simple questionnaire completed prior to examination by doctors with different levels of ambulatory training in general medicine. PARTICIPANTS AND METHODS: Before patient examination, five trained physicians, four short-term-trained residents, and four untrained residents examined patient responses to a simple questionnaire and then indicated, in rank order according to their subjective confidence level, the diseases they predicted. Final diagnosis was subsequently determined from hospital records by mentor physicians 3 months after the first patient visit. Predicted diseases and final diagnoses were codified using the International Classification of Diseases version 10. A "correct" diagnosis was one where the predicted disease matched the final diagnosis code. RESULTS: A total of 148 patient questionnaires were evaluated. The Herfindahl index was 0.024, indicating a high degree of diversity in final diagnoses. The proportion of correct diagnoses was high in the trained group (96 of 148, 65%; residual analysis, 4.4) and low in the untrained group (56 of 148, 38%; residual analysis, -3.6) (χ (2)=22.27, P<0.001). In cases of correct diagnosis, the cumulative number of correct diagnoses showed almost no improvement, even when doctors in the three groups predicted ≥4 diseases. CONCLUSION: Doctors who completed ambulatory training in general medicine while treating a diverse range of diseases accurately predicted diagnosis in 65% of cases from limited written information provided by a simple patient questionnaire, which proved useful for diagnosis. The study also suggests that up to three differential diagnoses are appropriate for diagnostic prediction, while ≥4 differential diagnoses barely improved the diagnostic accuracy, regardless of doctors' competence in general medicine. If doctors can become able to predict the final diagnosis from limited information, the correct diagnostic outcome may improve and save further consultation hours.

RATIONALE, AIMS AND OBJECTIVES: To investigate the subsequent behaviour of doctor-shopping patients (defined as those attending multiple hospitals for the same complaint) who consulted our department and factors related to cessation of doctor shopping. METHODS: Patients who presented without referral to the Department of General Medicine at Chiba University Hospital in Japan (our department) completed a questionnaire at their first visit. A follow-up questionnaire was also sent to them in order to assess doctor shopping after 3 months. Then items in the questionnaires were investigated for significant differences between patients who continued or stopped doctor shopping. Logistic regression analysis was performed with items showing a significant difference between patients who stopped doctor shopping and those who continued it, in order to identify independent determinants of the cessation of shopping. RESULTS: A total of 978 patients who presented spontaneously to our department consented to this study, and 929 patients (95.0%) completed questionnaires correctly. Among them, 203 patients (21.9%) were identified as doctor shoppers. The follow-up survey was completed correctly by 138 patients (68.0%). Among them, 25 patients (18.1%) were found to have continued doctor shopping, which was a significantly lower rate than before (P < 0.001). Logistic regression analysis selected the following factors as independent determinants of the cessation of doctor shopping: 'confirmation of the diagnosis' (odds ratio: 8.12, 95% confidence interval: 1.46-45.26), and 'satisfaction with consultation' (odds ratio: 2.07, 95% confidence interval: 1.42-3.01). CONCLUSION: Doctor shopping decreased significantly after patients consulted our department, with 'confirmation of the diagnosis' and 'satisfaction with consultation' being identified as contributing factors.

OBJECTIVE: Carnett's test is a simple clinical test in which abdominal tenderness is evaluated while the patient tenses the abdominal muscles. It is useful for differentiating abdominal wall pain from intra-abdominal pain. However, no study has reported its association with psychogenic abdominal pain. We evaluated its diagnostic usefulness in psychogenic abdominal pain. METHODS: Two physicians performed Carnett's test on each patient, but only one received the medical history. The other physician only conducted the test. Based on the final diagnosis, patients were categorized into 3 groups: psychogenic pain, abdominal wall pain, or intra-abdominal pain. Each group was analyzed in association with the results of Carnett's test conducted by the blinded physician. PATIENTS: A total of 130 outpatients with the chief complaint of abdominal pain who had abdominal tenderness. RESULTS: There were 22 patients with psychogenic abdominal pain, 19 with abdominal wall pain and 62 with intra-abdominal pain. In patients with psychogenic pain or abdominal wall pain, Carnett's test was usually positive, whereas the test was usually negative in patients with intra-abdominal pain (p<0.001, respectively). The positive likelihood ratio of Carnett's test for psychogenic abdominal pain was 2.91 (95% confidence interval [CI], 2.71-3.13), while the negative likelihood ratio was 0.19 (95% CI, 0.11-0.34). The corresponding values for abdominal wall pain were 2.62 (95% CI, 2.45-2.81) and 0.23 (95% CI, 0.13-0.41), respectively. CONCLUSION: Carnett's test may be useful for ruling in and ruling out psychogenic abdominal pain in addition to distinguishing between abdominal wall pain and intra-abdominal pain.

OBJECTIVE: Patient medical history is important for making a diagnosis of causes of dizziness, but there have been no studies on the diagnostic value of individual items in the history. This study was performed to identify and validate useful questions for suspecting a diagnosis of benign paroxysmal positional vertigo (BPPV). METHODS: Construction and validation of a disease prediction model was performed at the outpatient clinic in the Department of General Medicine of Chiba University Hospital. Patients with dizziness were enrolled (145 patients for construction of the disease prediction model and 61 patients for its validation). This study targeted BPPV of the posterior semicircular canals only with a positive Dix-Hallpike test (DHT + BPPV) to avoid diagnostic ambiguity. Binomial logistic regression analysis was performed to identify the items that were useful for diagnosis or exclusion of DHT + BPPV. RESULTS: Twelve patients from the derivation set and six patients from the validation set had DHT + BPPV. Binomial logistic regression analysis selected a "duration of dizziness ≤15 seconds" and "onset when turning over in bed" as independent predictors of DHT + BPPV with an odds ratio (95% confidence interval) of 4.36 (1.18-16.19) and 10.17 (2.49-41.63), respectively. Affirmative answers to both questions yielded a likelihood ratio of 6.81 (5.11-9.10) for diagnosis of DHT + BPPV, while negative answers to both had a likelihood ratio of 0.19 (0.08-0.47). CONCLUSION: A "duration of dizziness ≤15 seconds" and "onset when turning over in bed" were the two most important questions among various historical features of BPPV.