論文

It has been observed that CRCs, pivotal figures in clinical research, often grapple with a substantial mental burden. This study, which investigates the resilience factors of CRCs, aims to provide practical suggestions for their education and resilience development, empowering them with actionable knowledge. Ten CRCs, who had been working as CRCs for many years, were interviewed individually. They were asked about their challenging experiences while working as CRCs, how they successfully adapted to those challenges, and the factors involved in the process. We then inductively coded the factors involved in successfully adapting to challenges as the analysis theme. The qualitative analysis reveals that the factors of resilience of CRCs were classified into four groups(: 1)individual characteristics and ways of thinking,(2)meaning, goals, and motivation,(3)ways of dealing with work, and(4)support from those around them. Among the factors, the following were considered to be particularly important for CRCs: meaning, continuing attempt to find meaning in one's work, even in the face of adversity; metacognition, handling reflection on one's own and others' cognitive activities from a bird's-eye view; and balancing professional and patient perspectives that maintains the rigor of carrying out clinical research as a medical professional with empathy for patients' feelings.

Patient-reported symptoms provide valuable insights into patient experiences and can enhance healthcare quality; however, effectively capturing them remains challenging. Although natural language processing (NLP) models have been developed to extract adverse events and symptoms from medical records written by healthcare professionals, limited studies have focused on models designed for patient-generated narratives. This study developed an NLP model to extract patient-reported symptoms from pharmaceutical care records and validated its effectiveness in analyzing diverse patient-generated narratives. The target dataset comprised “Subjective” sections of pharmaceutical care records created by community pharmacists for patients prescribed anticancer drugs. Two annotation guidelines were applied to develop robust ground-truth data, which was used to develop and evaluate a new transformer-based named entity recognition model. Model performance was compared with that of an existing tool for Japanese clinical texts and tested on external patient-generated blog data to evaluate generalizability. The newly developed BERT-CRF model significantly outperformed the existing model, achieving an F1 score > 0.8 on pharmaceutical care records and extracting > 98% of physical symptom entries from patient blogs, with a 20% improvement over the existing tool. These findings highlight the importance of fine-tuning models using patient-specific narrative data to capture nuanced and colloquial symptom expressions.

Tablets and capsules are widely used forms of oral medication, but some patients experience difficulty handling them, which can reduce medication adherence and affect health outcomes. This study aimed to identify factors contributing to perceived handling difficulty, using data from harmo®, a nationwide electronic medication notebook system. A questionnaire was distributed to adult users who had been prescribed oral medications, and the responses were linked with personal health records to analyze medication characteristics and patient backgrounds. Among the 1,230 respondents, 24% reported difficulty with small tablets or capsules. A size threshold was identified: a combined long and short diameter of 13.3 mm or less was most associated with handling problems (ROC-AUC = 0.834). Binomial logistic regression analysis revealed that difficulty in applying force with the hands (OR = 2.64), prescription of small tablets or capsules (OR = 2.52), and medical histories of hypertension (OR = 1.69) and osteoporosis (OR = 4.99) were significantly associated with reported difficulty. These results suggest that both the physical characteristics of formulations and individual patient factors influence medication usability. Our results provide evidence to inform more patient-centered approaches to oral formulation design and prescribing practices, ultimately supporting better adherence and medication safety.

Denosumab is used to reduce skeletal-related events such as fractures in cancer patients with bone metastasis, but may cause severe hypocalcemia. We previously developed and updated a risk prediction model for≥grade 2 denosumab-induced hypocalcemia from a hospital-based administrative database and clinical datasets from two facilities. The final risk-scoring system using only calcium, albumin, and alkaline phosphatase levels provided high performance. Here, we aimed to externally validate the scoring system’s performance using an independent clinical dataset from Gunma Prefectural Cancer Center. Clinical data (May 2017–November 2023) were retrospectively collected and the discriminant performance of the previously developed model (sensitivity, specificity, positive predictive value, negative predictive value and receiver operating characteristic-area under the curve (ROC-AUC)) was evaluated. For 161 cases analyzed, the model demonstrated a sensitivity of 85.7%, specificity of 72.1%, positive predictive value of 12.2%, and negative predictive value of 99.1%. ROC-AUC was 0.813. All performance parameters were comparable to those in the previous study. The results strongly support the generalizability of the scoring system. This straightforward, easily interpretable, high-performance risk prediction system is expected to enhance the safety of denosumab treatment.

Narratives posted on the internet by patients contain a vast amount of information about various concerns. This study aimed to extract multiple concerns from interviews with breast cancer patients using the natural language processing (NLP) model bidirectional encoder representations from transformers (BERT). A total of 508 interview transcriptions of breast cancer patients written in Japanese were labeled with five types of concern labels: "treatment," "physical," "psychological," "work/financial," and "family/friends." The labeled texts were used to create a multi-label classifier by fine-tuning a pre-trained BERT model. Prior to fine-tuning, we also created several classifiers with domain adaptation using (1) breast cancer patients’ blog articles and (2) breast cancer patients’ interview transcriptions. The performance of the classifiers was evaluated in terms of precision through 5-fold cross-validation. The multi-label classifiers with only fine-tuning had precision values of over 0.80 for "physical" and "work/financial" out of the five concerns. On the other hand, precision for "treatment" was low at approximately 0.25. However, for the classifiers using domain adaptation, the precision of this label took a range of 0.40–0.51, with some cases improving by more than 0.2. This study showed combining domain adaptation with a multi-label classifier on target data made it possible to efficiently extract multiple concerns from interviews.

Background: Medication compliance, which refers to the extent to which patients correctly adhere to prescribed regimens, is influenced by various psychological, behavioral, and demographic factors. When analyzing these factors, challenges such as multicollinearity and variable selection often arise, complicating the interpretation of results. To address the issue of multicollinearity and better analyze the importance of each factor, machine learning methods are considered to be useful. Objective: This study aimed to identify key factors influencing medication compliance by applying regularized logistic regression and LightGBM. Methods: A questionnaire survey was conducted among 638 adult patients in Japan who had been continuously taking medications for at least 3 months. The survey collected data on demographics, medication habits, psychological adherence factors, and compliance. Logistic regression with regularization was used to handle multicollinearity, while LightGBM was used to calculate feature importance. Results: The regularized logistic regression model identified significant predictors, including "using the drug at approximately the same time each day" (coefficient 0.479; P=.02), "taking meals at approximately the same time each day" (coefficient 0.407; P=.02), and "I would like to have my medication reduced" (coefficient -0.410; P=.01). The top 5 variables with the highest feature importance scores in the LightGBM results were "Age" (feature importance 179.1), "Using the drug at approximately the same time each day" (feature importance 148.4), "Taking meals at approximately the same time each day" (feature importance 109.0), "I would like to have my medication reduced" (feature importance 77.48), and "I think I want to take my medicine" (feature importance 70.85). Additionally, the feature importance scores for the groups of medication adherence-related factors were 77.92 for lifestyle-related items, 52.04 for awareness of medication, 20.30 for relationships with health care professionals, and 5.05 for others. Conclusions: The most significant factors for medication compliance were the consistency of medication and meal timing (mean of feature importance), followed by the number of medications and patient attitudes toward their treatment. This study is the first to use a machine learning model to calculate and compare the relative importance of factors affecting medication adherence. Our findings demonstrate that, in terms of relative importance, lifestyle habits are the most significant contributors to medication compliance among the general patient population. The findings suggest that regularization and machine learning methods, such as LightGBM, are useful for better understanding the numerous adherence factors affected by multicollinearity.

Background: Wearable devices (WDs) have evolved beyond simple fitness trackers to sophisticated health monitors capable of measuring vital signs, such as heart rate and blood oxygen levels. Their application in health care, particularly medication management, is an emerging field poised to significantly enhance patient adherence to treatment regimens. Despite their widespread use and increasing incorporation into clinical trials, a comprehensive review of WDs in terms of medication adherence has not been conducted. Objective: This study aimed to conduct a comprehensive scoping review to evaluate the impact of WDs on medication adherence across a variety of diseases, summarizing key research findings, outcomes, and challenges encountered. Methods: Adhering to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, a structured search was conducted across MEDLINE, Web of Science, and Embase databases, covering the literature from January 1, 2010, to September 30, 2022. The search strategy was based on terms related to WDs and medication adherence, specifically focusing on empirical studies to ensure the inclusion of original research findings. Studies were selected based on their relevance to medication adherence, usage of WDs in detecting medication-taking actions, and their role in integrated medication management systems. Results: We screened 657 articles and identified 18 articles. The identified studies demonstrated the diverse applications of WDs in enhancing medication adherence across diseases such as Parkinson disease, diabetes, and cardiovascular conditions. The geographical distribution and publication years of these studies indicate a growing interest in this research area. The studies were divided into three types: (1) studies reporting a correlation between data from WDs or their usage and medication adherence or drug usage as outcomes, (2) studies using WDs to detect the act of medication-taking itself, and (3) studies proposing an integrated medication management system that uses WDs in managing medication. Conclusions: WDs are increasingly being recognized for their potential to enhance medication management and adherence. This review underscores the need for further empirical research to validate the effectiveness of WDs in real-life settings and explore their use in predicting adherence based on activity rhythms and activities. Despite technological advancements, challenges remain regarding the integration of WDs into routine clinical practice. Future research should focus on leveraging the comprehensive data provided by WDs to develop personalized medication management strategies that can improve patient outcomes.

Pemetrexed is a folate analog inhibitor for the treatment of non-small-cell lung cancer (NSCLC). Prophylactic supplementation with vitamin B12 and folic acid reduces hematotoxicity associated with pemetrexed. Metformin, the antidiabetic agent, has been associated with the potential side effect of vitamin B12 deficiency. This retrospective observational study aimed to evaluate the effect of concomitant metformin use on hematologic adverse events in patients with NSCLC undergoing pemetrexed-based chemotherapy using the Medical Data Vision Database. Patients with stage III or higher NSCLC who received pemetrexed from April 2008 to May 2021 were categorized into metformin-treated (MTF) and non-metformin-treated (non-MTF) groups. The primary outcome was the proportion of granulocyte colony-stimulating factor (G-CSF) administration during cycle (C) 1 to C2 or C2 to C3 of pemetrexed therapy. Propensity score matching (PSM) was used to balance the baseline characteristics between the groups. A total of 1174 patients met the inclusion criteria (54 in MTF and 1120 in non-MTF). After PSM, 52 patients were included in each group. The median metformin dosage in the MTF group was 500 mg/d before and 625 mg/d after PSM. There were no significant differences between the MTF and non-MTF groups in G-CSF administration (15.4 vs. 21.2%, p=0.446). Multivariate logistic regression analysis also showed that metformin use did not significantly affect hematologic toxicity (odds ratio: 1.208, 95% CI: 0.554–2.634). This suggests that the concomitant use of a relatively low dose of metformin is unlikely to significantly increase the risk of hematotoxicity in Japanese patients with NSCLC receiving pemetrexed-based chemotherapy.

Background: Effective symptom identification, a key responsibility for community pharmacists, requires patients to describe their symptoms accurately and comprehensively. However, current practices in pharmacies may be insufficient in capturing patient-reported symptoms comprehensively, potentially affecting the quality of pharmaceutical care and patient safety. Objective: This study aimed to construct a new, hierarchical symptom list derived from the Patient-Friendly Term List of the Medical Dictionary for Regulatory Activities (MedDRA) and to develop and evaluate a mobile app incorporating this list for facilitating symptom reporting by patients in pharmacy settings. The study also aimed to assess the usability and acceptance of this app among potential users. Methods: Subjective symptom-related terms were extracted from the Patient-Friendly Term List version 23.0 of the MedDRA. These terms were systematically consolidated and organized into a hierarchical, user-friendly symptom list. A mobile app incorporating this list was developed for pharmacy settings, featuring a symptom selection interface and a free-text input field for additional symptoms. The app included an instructional video explaining the importance of symptom reporting and guidance on navigation. Usability tests and semistructured interviews were conducted with participants aged >20 years. Interview transcripts were analyzed using the Unified Theory of Acceptance and Use of Technology (UTAUT) model to evaluate factors influencing the acceptance of technology. Results: From the initial 1440 terms in the Patient-Friendly Term List, 795 relevant terms were selected and organized into 40 site-specific subcategories, which were then grouped into broader site categories (mental, head, trunk, upper limb, lower limb, physical condition, and others). These terms were further consolidated into 211 patient-friendly symptom terms, forming a hierarchical symptom list. The app’s interface design limited options to 10 items per screen to assist with decision-making. A total of 5 adults participated in the usability test. Participants found the interface intuitive and easy to use, requiring minimal effort, and provided positive feedback regarding the potential utility of the app in pharmacy settings. The UTAUT analysis identified several facilitating factors, including ease of use and the potential for enhanced pharmacist-patient communication. However, concerns were raised about usability for older adults and the need for simplified technical terminology. Conclusions: The user-friendly app with a hierarchically structured symptom list and complementary free-text entry has potential benefits for improving the accuracy and efficiency of symptom reporting in pharmacy settings. The positive user acceptance and identified areas for improvement provide a foundation for further development and implementation of this technology to enhance communication between patients and pharmacists. Future improvements should focus on addressing usability for older adults and simplifying technical terminology.

Background: Androgen receptor axis-targeting reagents (ARATs) have become key drugs for patients with castration-resistant prostate cancer (CRPC). ARATs are taken long term in outpatient settings, and effective adverse event (AE) monitoring can help prolong treatment duration for patients with CRPC. Despite the importance of monitoring, few studies have identified which AEs can be captured and assessed in community pharmacies, where pharmacists in Japan dispense medications, provide counseling, and monitor potential AEs for outpatients prescribed ARATs. Therefore, we anticipated that a named entity recognition (NER) system might be used to extract AEs recorded in pharmaceutical care records generated by community pharmacists. Objective: This study aimed to evaluate whether an NER system can effectively and systematically identify AEs in outpatients undergoing ARAT therapy by reviewing pharmaceutical care records generated by community pharmacists, focusing on assessment notes, which often contain detailed records of AEs. Additionally, the study sought to determine whether outpatient pharmacotherapy monitoring can be enhanced by using NER to systematically collect AEs from pharmaceutical care records. Methods: We used an NER system based on the widely used Japanese medical term extraction system MedNER-CR-JA, which uses Bidirectional Encoder Representations from Transformers (BERT). To evaluate its performance for pharmaceutical care records by community pharmacists, the NER system was first applied to 1008 assessment notes in records related to anticancer drug prescriptions. Three pharmaceutically proficient researchers compared the results with the annotated notes assigned symptom tags according to annotation guidelines and evaluated the performance of the NER system on the assessment notes in the pharmaceutical care records. The system was then applied to 2193 assessment notes for patients prescribed ARATs. Results: The F1-score for exact matches of all symptom tags between the NER system and annotators was 0.72, confirming the NER system has sufficient performance for application to pharmaceutical care records. The NER system automatically assigned 1900 symptom tags for the 2193 assessment notes from patients prescribed ARATs; 623 tags (32.8%) were positive symptom tags (symptoms present), while 1067 tags (56.2%) were negative symptom tags (symptoms absent). Positive symptom tags included ARAT-related AEs such as “pain,” “skin disorders,” “fatigue,” and “gastrointestinal symptoms.” Many other symptoms were classified as serious AEs. Furthermore, differences in symptom tag profiles reflecting pharmacists’ AE monitoring were observed between androgen synthesis inhibition and androgen receptor signaling inhibition. Conclusions: The NER system successfully extracted AEs from pharmaceutical care records of patients prescribed ARATs, demonstrating its potential to systematically track the presence and absence of AEs in outpatients. Based on the analysis of a large volume of pharmaceutical medical records using the NER system, community pharmacists not only detect potential AEs but also actively monitor the absence of severe AEs, offering valuable insights for the continuous improvement of patient safety management.

Background: A challenge in updating systematic reviews is the workload in screening the articles. Many screening models using natural language processing technology have been implemented to scrutinize articles based on titles and abstracts. While these approaches show promise, traditional models typically treat abstracts as uniform text. We hypothesize that selective training on specific abstract components could enhance model performance for systematic review screening. Objective: We evaluated the efficacy of a novel screening model that selects specific components from abstracts to improve performance and developed an automatic systematic review update model using an abstract component classifier to categorize abstracts based on their components. Methods: A screening model was created based on the included and excluded articles in the existing systematic review and used as the scheme for the automatic update of the systematic review. A prior publication was selected for the systematic review, and articles included or excluded in the articles screening process were used as training data. The titles and abstracts were classified into 5 categories (Title, Introduction, Methods, Results, and Conclusion). Thirty-one component-composition datasets were created by combining 5 component datasets. We implemented 31 screening models using the component-composition datasets and compared their performances. Comparisons were conducted using 3 pretrained models: Bidirectional Encoder Representations from Transformer (BERT), BioLinkBERT, and BioM- Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA). Moreover, to automate the component selection of abstracts, we developed the Abstract Component Classifier Model and created component datasets using this classifier model classification. Using the component datasets classified using the Abstract Component Classifier Model, we created 10 component-composition datasets used by the top 10 screening models with the highest performance when implementing screening models using the component datasets that were classified manually. Ten screening models were implemented using these datasets, and their performances were compared with those of models developed using manually classified component-composition datasets. The primary evaluation metric was the F10-Score weighted by the recall. Results: A total of 256 included articles and 1261 excluded articles were extracted from the selected systematic review. In the screening models implemented using manually classified datasets, the performance of some surpassed that of models trained on all components (BERT: 9 models, BioLinkBERT: 6 models, and BioM-ELECTRA: 21 models). In models implemented using datasets classified by the Abstract Component Classifier Model, the performances of some models (BERT: 7 models and BioM-ELECTRA: 9 models) surpassed that of the models trained on all components. These models achieved an 88.6% reduction in manual screening workload while maintaining high recall (0.93). Conclusions: Component selection from the title and abstract can improve the performance of screening models and substantially reduce the manual screening workload in systematic review updates. Future research should focus on validating this approach across different systematic review domains.

Objectives: Factors affecting denosumab-induced hypocalcemia in male patients with osteoporosis remain unclear because of the small patient population. Nevertheless, it is important to explore male-specific risk factors. This study aimed to identify the factors affecting the development of denosumab-induced hypocalcemia in male patients with osteoporosis and compare them with those in female patients with osteoporosis. Study design: This retrospective, observational study used data from a hospital-based administrative claims database. Patients with osteoporosis undergoing denosumab treatment between April 2008 and May 2020 were enrolled. Data on age, inpatient/outpatient status, laboratory data, drug use, and disease status were collected. Main outcome measures: Patients were classified as those with hypocalcemia and without hypocalcemia, and variables with P < 0.2 and missing proportions <30 % on univariate analysis were entered into multivariate logistic regression analysis. The multiple imputation method was used to compensate for missing data. Results: Overall, 175 males and 1335 females were included in the analysis, and hypocalcemia developed in 52 and 247 patients, respectively. In the multivariate analysis, prior use of preventive medicine (calcium supplements or activated vitamin D) decreased the risk of osteoporosis only in male patients. Low corrected levels of calcium and albumin and low estimated glomerular filtration rates increased the risk in both sexes. Conclusions: Prior use of preventive medicine may be a factor that decreases the risk of denosumab-induced hypocalcemia in male patients. Low corrected levels of calcium and albumin, and low estimated glomerular filtration rates are common risk factors in both male and female patients.

Iodine-based contrast agents can induce various acute hypersensitivity reactions ranging from mild itching or vomiting shortly after administration to severe hypotension or loss of consciousness. In Japan, steroid premedication is commonly used to prevent acute hypersensitivity reactions. However, little clear evidence supporting its efficacy is available. In this study, we evaluated the effectiveness of premedication for acute hypersensitivity reactions induced by nonionic iodine contrast agents using propensity score matching. The participants included patients who were administered nonionic iodine contrast agents at Yokohama City Minato Red Cross Hospital between April 1, 2016 and March 31, 2022. Only first-time patients with no history of hypersensitivity reactions to contrast agents were included. The patients were classified into premedication and non-premedication groups, and the incidence proportions of acute hypersensitivity reactions were compared after matching. Of the 19976 patients, 422 (211 in each group) were matched. In the premedication group, 7 cases (3.32%) of hypersensitivity reactions occurred. In contrast, only 2 cases (0.95%) were observed in the non-premedication group. The odds ratio for the occurrence of hypersensitivity reactions in the premedication group was 3.500 (95% confidence interval, 0.727–16.848), with no significant difference. Therefore, premedication did not demonstrate the efficacy in preventing acute hypersensitivity reactions induced by nonionic iodine contrast agents. Based on the results of this study and guidelines, a recommendation for premedication was not reached for patients with a history of allergies, including asthma and atopy, as well as those with a history of drug or food allergies.

Background: Although hypertension is a common side effect of tyrosine kinase inhibitor (TKI) treatment for renal cell carcinoma (RCC), there is limited evidence regarding its occurrence and related risk factors. Preliminary studies suggest that proton pump inhibitors (PPIs) may mitigate the risk of TKI-induced hypertension; however, their clinical effectiveness remains unclear. Aims: In this study, we examined the prevalence of TKI-induced hypertension and the patterns of antihypertensive prescriptions among patients with RCC in Japan. Additionally, we investigated factors associated with TKI-induced hypertension to assess the potential impact of PPIs. Methods and Results: Data from patients diagnosed with RCC who were prescribed TKIs between April 2008 and July 2021 were retrospectively gathered from a Japanese administrative database. TKI-induced hypertension was detected following the diagnosis of hypertension and subsequently the prescription of an antihypertensive agent during TKI therapy. The prescription details for antihypertensive agents were organized in a tabular format. Cox proportional hazards regression analysis was conducted to examine factors contributing to TKI-induced hypertension. Among the 225 patients analyzed, 36.4% experienced hypertension, and calcium channel blockers were the most prescribed antihypertensive agents. Pre-existing hypertension was identified as a risk factor for TKI-induced hypertension, while the concurrent use of PPIs did not show a tendency to reduce the risk of TKI-induced hypertension. Conclusions: These findings indicate the importance of blood pressure management in patients with elevated baseline blood pressure.

Purpose: To evaluate the accuracy of diagnostic coding for acute kidney injury (AKI) in Japan. Methods: The data analyzed were obtained from the JMDC hospital-based administrative claims database from cases registered between April 2014 and August 2022. Only patients who underwent serum creatinine measurements two or more times with intervals of 7 days or less were eligible for inclusion. AKIs were identified by International Classification of Diseases 10th Revision (ICD-10) codes N14 and N17. These were assessed according to the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Results: A total of 467 019 patients (median age, 74 [range, 20–99] years; male, 50.9%) were eligible for inclusion. Among these patients, 1849 (0.4%) were assigned ICD-10 codes for AKI. Among these 1849 patients, the code was assigned within 7 days of the occurrence of AKI (as defined by the KDIGO criteria) in 212 patients, within 14 days in 294 patients, and within 30 days in 386 patients. The positive predictive values and 95% confidence intervals of the ICD-10 code for AKI at these timepoints were as follows: within 7 days, 11.5% (10.1%–13.0%); within 14 days, 15.9% (14.3%–17.6%); and within 30 days, 20.9% (19.1%–22.8%). Conclusions: The ICD-10 codes for AKI showed poor positive predictive values for AKI as defined by the KDIGO criteria, suggesting that it may be difficult to identify AKI using ICD-10 codes alone in the Japanese context.

ランダム抽出した全国の病院2000施設を対象に、医薬品使用に関連したインシデント・アクシデントのインシデントレポートの作成から分析、対策案の立案に至るまでのプロセスの実態とその課題についてアンケート調査を行った。有効回答409施設について集計した結果、患者への影響度にかかわらず、多くのインシデント・アクシデントの対応は、当事者や事例が発生した各部署内において医療安全管理委員会と共同して行われており、病床数や加算算定状況によらず、インシデントレポートの作成と分析は9割以上の病院で実施されていた。患者の影響レベルに関わらず半数近くは分析手法を活用しており、その奥が根本原因分析法であった一方で、影響レベルが低い事例では、あまり分析手法が活用されていなかった。全国の病院において、分析手法が有効に活用されるために改善の余地があることが示された。

全国の大学薬学部・薬科大学77校を対象に、薬学生への副作用報告に関連する教育の現状について質問紙調査を行い、45校より回答を得た。その結果、「副作用報告制度」に関する授業は、医薬品情報学や薬事・法規など複数の授業科目を通じて、ほぼ全て(94%)の大学で実施されており、その大半が4年次(実務実習前)までに実施されていた。授業の特徴や工夫として、症例を用いた副作用の因果関係評価や副作用報告の模擬実習などが行われており、これらの授業に医療現場の薬剤師が講師として参画しているケースもみられた。副作用評価における副作用の因果関係評価や重篤度判定は、副作用報告用紙に症例ごとの評価結果を記載する上で重要であり、今回の調査では、副作用の因果関係評価に関する授業は40校(83%)、副作用の重篤度の判定基準等に関する授業は33校(69%)で実施されていたが、「副作用報告用紙への記載方法」に関する授業を実施している大学は18校(38%)に留まっていた。これらは主に、実務実習事前学習の中で、演習・実習形式で実施されていた。追加で実施した薬学の実務実習生の受け入れが年1回以上ある病院や薬局の薬剤師を対象とした調査(有効回答22名)では、副作用報告に関して「大学でどの程度学習して実習に臨んでいるのかを知りたい」といった意見や、「副作用報告の経験が少ないため指導が難しい」といった指導方法への戸惑い、薬剤師自身の副作用報告に対する研鑽が必要であるといった意見が寄せられた。

日本での過量服薬(OD)の実態把握においてソーシャルネットワーキングサービス(SNS)の利用可能性があるかどうか検討した。Twitter APIを利用して取得した2023年1月10日～2月8日の投稿データのうち、日本語で記載され「OD」という単語を含むリツイート以外の投稿29980件を解析対象とした。解析対象のOD関連投稿に対してガイドラインに従って研究者2名がアノテーションを行った後、ラベルの一致度を確認した。さらに、自然言語処理技術を用いて、収集した投稿データのODに関する投稿を分類する分類器の構築を試みた。アノテーションの一致度に関して、全体の重みづけをした際のκ係数は0.81であった。それぞれのラベルについてκ係数を算出した結果、「ODをしている投稿」のκ係数は0.81、「ODをしていない投稿」のκ係数は0.94といずれも評価者間で高い一致度を示し、「ODをしていた投稿」のκ係数は0.33と評価者間で評価が分かれていた。「ODをしている投稿」のラベルが付与された投稿5283件に対し、医薬品名やそれに類する表現の記述が含まれる投稿は1543件であった。SNSの投摘に基づいて、受診を要さない軽症の事例やこれからODをしようとしている・過去にODをしていたとする、いわゆるODハイリスク群による投稿を精度よく抽出することが可能であった。