BMC Medical Informatics and Decision Making最新文献

{"title":"Reusing routine electronic health record data for nationwide COVID-19 surveillance in nursing homes: barriers, facilitators, and lessons learned.","authors":"Y Wieland-Jorna, R A Verheij, A L Francke, R Coppen, S C de Greeff, A Elffers, M G Oosterveld-Vlug","doi":"10.1186/s12911-024-02818-3","DOIUrl":"https://doi.org/10.1186/s12911-024-02818-3","url":null,"abstract":"<p><strong>Background: </strong>At the beginning of the COVID-19 pandemic in 2020, little was known about the spread of COVID-19 in Dutch nursing homes while older people were particularly at risk of severe symptoms. Therefore, attempts were made to develop a nationwide COVID-19 repository based on routinely recorded data in the electronic health records (EHRs) of nursing home residents. This study aims to describe the facilitators and barriers encountered during the development of the repository and the lessons learned regarding the reuse of EHR data for surveillance and research purposes.</p><p><strong>Methods: </strong>Using inductive content analysis, we reviewed 325 documents written and saved during the development of the COVID-19 repository. This included meeting minutes, e-mails, notes made after phone calls with stakeholders, and documents developed to inform stakeholders. We also assessed the fitness for purpose of the data by evaluating the completeness, plausibility, conformity, and timeliness of the data.</p><p><strong>Results: </strong>Key facilitators found in this study were: 1) inter-organizational collaboration to create support; 2) early and close involvement of EHR software vendors; and 3) coordination and communication between partners. Key barriers that hampered the fitness of EHR data for surveillance were: 1) changes over time in national SARS-CoV-2 testing policy; 2) differences between EHR systems; 3) increased workload in nursing homes and lack of perceived urgency; 4) uncertainty regarding the legal requirements for extracting EHR data; 5) the short notice at which complete and understandable information about the repository had to be developed; and 6) lack of clarity about the differences between various COVID-19 monitors.</p><p><strong>Conclusions: </strong>Despite the urgent need for information on the spread of SARS-CoV-2 among nursing home residents, setting up a repository based on EHR data proved challenging. The facilitators and barriers found in this study affected the extent to which the data could be used. We formulated nine lessons learned for developing future repositories based on EHR data for surveillance and research purposes. These lessons were in three main areas: legal framework, contextual circumstances, and quality of the data. Currently, these lessons are being applied in setting up a new registry in the nursing home sector.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"408"},"PeriodicalIF":3.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusion-driven semi-supervised learning-based lung nodules classification with dual-discriminator and dual-generator generative adversarial network.","authors":"Ahmed Saihood, Wijdan Rashid Abdulhussien, Laith Alzubaid, Mohamed Manoufali, Yuantong Gu","doi":"10.1186/s12911-024-02820-9","DOIUrl":"https://doi.org/10.1186/s12911-024-02820-9","url":null,"abstract":"<p><strong>Background: </strong>The detection and classification of lung nodules are crucial in medical imaging, as they significantly impact patient outcomes related to lung cancer diagnosis and treatment. However, existing models often suffer from mode collapse and poor generalizability, as they fail to capture the complete diversity of the data distribution. This study addresses these challenges by proposing a novel generative adversarial network (GAN) architecture tailored for semi-supervised lung nodule classification.</p><p><strong>Methods: </strong>The proposed DDDG-GAN model consists of dual generators and discriminators. Each generator specializes in benign or malignant nodules, generating diverse, high-fidelity synthetic images for each class. This dual-generator setup prevents mode collapse. The dual-discriminator framework enhances the model's generalization capability, ensuring better performance on unseen data. Feature fusion techniques are incorporated to refine the model's discriminatory power between benign and malignant nodules. The model is evaluated in two scenarios: (1) training and testing on the LIDC-IDRI dataset and (2) training on LIDC-IDRI, testing on the unseen LUNA16 dataset and the unseen LUNGx dataset.</p><p><strong>Results: </strong>In Scenario 1, the DDDG-GAN achieved an accuracy of 92.56%, a precision of 90.12%, a recall of 95.87%, and an F1 score of 92.77%. In Scenario 2, the model demonstrated robust performance with an accuracy of 72.6%, a precision of 72.3%, a recall of 73.82%, and an F1 score of 73.39% when testing using Luna16 and an accuracy of 71.23%, a precision of 67.56%, a recall of 73.52%, and an F1 score of 70.42% when testing using LungX. The results indicate that the proposed model outperforms state-of-the-art semi-supervised learning approaches.</p><p><strong>Conclusions: </strong>The DDDG-GAN model mitigates mode collapse and improves generalizability in lung nodule classification. It demonstrates superior performance on both the LIDC-IDRI and the unseen LUNA16 and LungX datasets, offering significant potential for improving diagnostic accuracy in clinical practice.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"403"},"PeriodicalIF":3.3,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142881400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A nomogram to distinguish noncardiac chest pain based on cardiopulmonary exercise testing in cardiology clinic.","authors":"Mingyu Xu, Rui Li, Bingqing Bai, Yuting Liu, Haofeng Zhou, Yingxue Liao, Fengyao Liu, Peihua Cao, Qingshan Geng, Huan Ma","doi":"10.1186/s12911-024-02813-8","DOIUrl":"https://doi.org/10.1186/s12911-024-02813-8","url":null,"abstract":"<p><strong>Background: </strong>Psychological disorders, such as anxiety and depression, are considered to be one of the causes of noncardiac chest pain (NCCP). And these patients can be challenging to differentiate from coronary artery disease (CAD), leading to a considerable number of patients still undergoing angiography. We aim to develop a practical prediction model and nomogram using cardiopulmonary exercise testing (CPET), to help identify these patients.</p><p><strong>Methods: </strong>1,531 eligible patients' electronic medical record data were obtained from Guangdong Provincial People's Hospital. They were randomly divided into a training dataset (N = 918) and a testing dataset (N = 613) at a ratio of 6:4, and 595 cases without missing data were also selected from testing dataset to form a complete dataset. The training set is used to build the model, and the testing set and the complete set are used for internal validation. Eight machine learning (ML) methods are used to build the model and the best model is finally adopted.</p><p><strong>Results: </strong>The model built by logistic regression performed the best, and among the 29 parameters, six parameters were determined to be valuable parameters for establishing the diagnostic equation and nomogram. The nomogram showed favorable calibration and discrimination with an area under the receiver operating characteristic curve (AUC) of 0.857 in the training set, 0.851 in the testing set, and 0.848 in the complete set. Meanwhile, decision curve analysis demonstrated the clinical utility of the nomogram.</p><p><strong>Conclusions: </strong>A nomogram using CPET to distinguish anxiety/depression from CAD was developed. It may optimize the disease management and improve patient prognosis.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"405"},"PeriodicalIF":3.3,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the accuracy and quality of artificial intelligence (AI) chatbot-generated responses in making patient-specific drug-therapy and healthcare-related decisions.","authors":"Meron W Shiferaw, Taylor Zheng, Abigail Winter, Leigh Ann Mike, Lingtak-Neander Chan","doi":"10.1186/s12911-024-02824-5","DOIUrl":"https://doi.org/10.1186/s12911-024-02824-5","url":null,"abstract":"<p><strong>Background: </strong>Interactive artificial intelligence tools such as ChatGPT have gained popularity, yet little is known about their reliability as a reference tool for healthcare-related information for healthcare providers and trainees. The objective of this study was to assess the consistency, quality, and accuracy of the responses generated by ChatGPT on healthcare-related inquiries.</p><p><strong>Methods: </strong>A total of 18 open-ended questions including six questions in three defined clinical areas (2 each to address \"what\", \"why\", and \"how\", respectively) were submitted to ChatGPT v3.5 based on real-world usage experience. The experiment was conducted in duplicate using 2 computers. Five investigators independently ranked each response using a 4-point scale to rate the quality of the bot's responses. The Delphi method was used to compare each investigator's score with the goal of reaching at least 80% consistency. The accuracy of the responses was checked using established professional references and resources. When the responses were in question, the bot was asked to provide reference material used for the investigators to determine the accuracy and quality. The investigators determined the consistency, accuracy, and quality by establishing a consensus.</p><p><strong>Results: </strong>The speech pattern and length of the responses were consistent within the same user but different between users. Occasionally, ChatGPT provided 2 completely different responses to the same question. Overall, ChatGPT provided more accurate responses (8 out of 12) to the \"what\" questions with less reliable performance to the \"why\" and \"how\" questions. We identified errors in calculation, unit of measurement, and misuse of protocols by ChatGPT. Some of these errors could result in clinical decisions leading to harm. We also identified citations and references shown by ChatGPT that did not exist in the literature.</p><p><strong>Conclusions: </strong>ChatGPT is not ready to take on the coaching role for either healthcare learners or healthcare professionals. The lack of consistency in the responses to the same question is problematic for both learners and decision-makers. The intrinsic assumptions made by the chatbot could lead to erroneous clinical decisions. The unreliability in providing valid references is a serious flaw in using ChatGPT to drive clinical decision making.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"404"},"PeriodicalIF":3.3,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and evaluation of a clinical nursing decision support system for the prevention of neonatal hypoglycaemia.","authors":"Qiaoyan Liu, Lulu Sun, Jie Yang, Wei Yin, Songmei Cao","doi":"10.1186/s12911-024-02826-3","DOIUrl":"https://doi.org/10.1186/s12911-024-02826-3","url":null,"abstract":"<p><strong>Background: </strong>Hypoglycaemia is one of the most common complications during the neonatal period. Recurrent hypoglycaemia episodes can result in neurodevelopmental deficits and even sudden death. Available evidence indicates that healthcare professionals ought to promptly assess the risk of hypoglycaemia in newborns immediately following birth and formulate the most suitable preventive strategies. Consequently, this study was designed to develop a clinical nursing decision support system for neonatal hypoglycaemia prevention based on the prediction model for neonatal hypoglycaemia risk that was developed in a previous study, and to evaluate its efficacy.</p><p><strong>Methods: </strong>Nursing process as the theoretical framework, based on evidence-based nursing, standardized nursing language, and clinical decision support technology, the neonatal hypoglycaemia prevention nursing decision support system was developed.This system was implemented in the neonatology department of a tertiary grade A general hospital from September 1st to 30th, 2023.The application efficacy of the system was assessed and compared through the examination of the incidence of neonatal hypoglycemia, adverse outcomes associated with neonatal hypoglycemia, and the experiences of nurses following the implementation of the system.</p><p><strong>Results: </strong>The incidence of neonatal hypoglycaemia decreased after the system was implemented, and the difference was statistically significant (X² = 4.522, P = 0.033). None of the neonates experienced adverse outcomes during hospitalization. The rate of hypoglycaemia risk assessment in neonates after system implementation was 92.16%. The total Clinical Nursing Information System Effectiveness Evaluation Scale score was 104.36 ± 1.96.</p><p><strong>Conclusion: </strong>The neonatal hypoglycaemia prevention nursing decision support system realizes neonatal hypoglycaemia risk assessment, intelligent decision-making, and effect evaluation, effectively diminishes the incidence of neonatal hypoglycaemia, and enhances the standardization of neonatal hypoglycaemia management.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"400"},"PeriodicalIF":3.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142881394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting in-hospital mortality in patients with heart failure combined with atrial fibrillation using stacking ensemble model: an analysis of the medical information mart for intensive care IV (MIMIC-IV).","authors":"Panpan Chen, Junhua Sun, Yingjie Chu, Yujie Zhao","doi":"10.1186/s12911-024-02829-0","DOIUrl":"https://doi.org/10.1186/s12911-024-02829-0","url":null,"abstract":"<p><strong>Background: </strong>Heart failure (HF) and atrial fibrillation (AF) usually coexist and are associated with a poorer prognosis. This study aimed to develop a model to predict in-hospital mortality in patients with HF combined with AF.</p><p><strong>Methods: </strong>Patients with HF and AF were obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database from 2008 to 2019. Feature selection was based on the Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression model. Random Forest, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), K-Nearest Neighbor (KNN) models, and their stacked model (the stacking ensemble model) were established. The area under of the curve (AUC) with 95% confidence interval (CI), sensitivity, specificity, as well as accuracy were applied to assess the performance of the predictive models.</p><p><strong>Results: </strong>A total of 5,998 patients with HF combined with AF were included, of which 4,198 patients were assigned to the training set and 1,800 to the testing set (7:3). Among these 4,198 patients, 624 (14.86%) died in-hospital and 3,574 (85.14%) survived. Twenty-two features were used to construct the predictive model. Among these four single models, the AUC was 0.747 (95%CI: 0.717-0.777) for the Random Forest model, 0.755 (95%CI: 0.725-0.785) for the XGBoost model, 0.754 (95%CI: 0.724-0.784) for the LGBM model, and 0.746 (95%CI: 0.716-0.776) for the KNN model in the testing set. The stacking ensemble model had the highest AUC compared to the four single models, with AUCs of 0.837 (95%CI: 0.821-0.852) and 0.768 (95%CI: 0.740-0.796) for the training set and testing set, respectively.</p><p><strong>Conclusion: </strong>The stacking ensemble model showed a good predictive effect in predicting in-hospital mortality in patients with HF combined with AF and may provide clinicians with a reference tool for early identification of mortality risk.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"402"},"PeriodicalIF":3.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142881404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated redaction of names in adverse event reports using transformer-based neural networks.","authors":"Eva-Lisa Meldau, Shachi Bista, Carlos Melgarejo-González, G Niklas Norén","doi":"10.1186/s12911-024-02785-9","DOIUrl":"https://doi.org/10.1186/s12911-024-02785-9","url":null,"abstract":"<p><strong>Background: </strong>Automated recognition and redaction of personal identifiers in free text can enable organisations to share data while protecting privacy. This is important in the context of pharmacovigilance since relevant detailed information on the clinical course of events, differential diagnosis, and patient-reported reflections may often only be conveyed in narrative form. The aim of this study is to develop and evaluate a method for automated redaction of person names in English narrative text on adverse event reports. The target domain for this study was case narratives from the United Kingdom's Yellow Card scheme, which collects and monitors information on suspected side effects to medicines and vaccines.</p><p><strong>Methods: </strong>We finetuned BERT - a transformer-based neural network - for recognising names in case narratives. Training data consisted of newly annotated records from the Yellow Card data and of the i2b2 2014 deidentification challenge. Because the Yellow Card data contained few names, we used predictive models to select narratives for training. Performance was evaluated on a separate set of annotated narratives from the Yellow Card scheme. In-depth review determined whether (parts of) person names missed by the de-identification method could enable re-identification of the individual, and whether de-identification reduced the clinical utility of narratives by collaterally masking relevant information.</p><p><strong>Results: </strong>Recall on held-out Yellow Card data was 87% (155/179) at a precision of 55% (155/282) and a false-positive rate of 0.05% (127/ 263,451). Considering tokens longer than three characters separately, recall was 94% (102/108) and precision 58% (102/175). For 13 of the 5,042 narratives in Yellow Card test data (71 with person names), the method failed to flag at least one name token. According to in-depth review, the leaked information could enable direct identification for one narrative and indirect identification for two narratives. Clinically relevant information was removed in less than 1% of the 5,042 processed narratives; 97% of the narratives were completely untouched.</p><p><strong>Conclusions: </strong>Automated redaction of names in free-text narratives of adverse event reports can achieve sufficient recall including shorter tokens like patient initials. In-depth review shows that the rare leaks that occur tend not to compromise patient confidentiality. Precision and false positive rates are acceptable with almost all clinically relevant information retained.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"401"},"PeriodicalIF":3.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142881334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A software tool for applying Bayes' theorem in medical diagnostics.","authors":"Theodora Chatzimichail, Aristides T Hatjimihail","doi":"10.1186/s12911-024-02721-x","DOIUrl":"https://doi.org/10.1186/s12911-024-02721-x","url":null,"abstract":"<p><strong>Background: </strong>In medical diagnostics, estimating post-test or posterior probabilities for disease, positive and negative predictive values, and their associated uncertainty is essential for patient care.</p><p><strong>Objective: </strong>The aim of this work is to introduce a software tool developed in the Wolfram Language for the parametric estimation, visualization, and comparison of Bayesian diagnostic measures and their uncertainty.</p><p><strong>Methods: </strong>This tool employs Bayes' theorem to estimate positive and negative predictive values and posterior probabilities for the presence and absence of a disease. It estimates their standard sampling, measurement, and combined uncertainty, as well as their confidence intervals, applying uncertainty propagation methods based on first-order Taylor series approximations. It employs normal, lognormal, and gamma distributions.</p><p><strong>Results: </strong>The software generates plots and tables of the estimates to support clinical decision-making. An illustrative case study using fasting plasma glucose data from the National Health and Nutrition Examination Survey (NHANES) demonstrates its application in diagnosing diabetes mellitus. The results highlight the significant impact of measurement uncertainty on Bayesian diagnostic measures, particularly on positive predictive value and posterior probabilities.</p><p><strong>Conclusion: </strong>The software tool enhances the estimation and facilitates the comparison of Bayesian diagnostic measures, which are critical for medical practice. It provides a framework for their uncertainty quantification and assists in understanding and applying Bayes' theorem in medical diagnostics.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"399"},"PeriodicalIF":3.3,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142870827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and alpha-testing of a patient decision aid for patients with chronic myeloid leukemia regarding dose reduction.","authors":"D N Lokhorst, M F Djodikromo, R P M G Hermens, N M A Blijlevens, C L Bekker","doi":"10.1186/s12911-024-02806-7","DOIUrl":"https://doi.org/10.1186/s12911-024-02806-7","url":null,"abstract":"<p><strong>Background: </strong>Dose reduction of tyrosine kinase inhibitors (TKIs) is an option for some chronic myeloid leukemia (CML) patients to minimize side effects while maintaining efficacy. Shared decision-making (SDM) and patient decision aids (PDAs) are advocated to make informed choices such as reducing the dose of TKIs. This paper describes the development and alpha-testing of a PDA for patients with CML receiving TKI dose reduction.</p><p><strong>Methods: </strong>The PDA was iteratively developed following IPDAS guidelines. First, a needs assessment with semi-structured interviews was conducted to understand the needs and preferences of patients and healthcare providers. Second, through feedback cycles with the project team and steering group the scope, content, and format were defined. Third, three rounds of alpha-testing were performed via individual \"think aloud\" sessions with patients (round 1) and healthcare providers (round 2) to qualitatively assess the comprehensibility, acceptability, and desirability of the PDA. Round 3 included quantitative evaluation via an acceptability and usability questionnaire. Qualitative data were categorized, and quantitative data were descriptively analyzed.</p><p><strong>Results: </strong>The majority valued the development of the PDA during the needs assessment (n = 30). The PDA included disease and treatment information, information about dose reduction, knowledge questions, and a value clarification section. During alpha-testing, the PDA was considered clear, balanced, and helpful for decision-making. A total of 76% of the patients (n = 17) and 100% of the healthcare providers (n = 9) recommended it with overall mean scores of 7.4 and 7.8, respectively. The above average usability score was 68.1.</p><p><strong>Conclusion: </strong>A well-accepted online PDA for chronic phase CML patients to consider TKI dose reduction was developed.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"398"},"PeriodicalIF":3.3,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital monitoring of medication safety in children: an investigation of ADR signalling techniques in Malaysia.","authors":"Beldona Hema Rekha, Shairyzah Ahmad Hisham, Izyan A Wahab, Norleen Mohamed Ali, Khang Wen Goh, Long Chiau Ming","doi":"10.1186/s12911-024-02801-y","DOIUrl":"https://doi.org/10.1186/s12911-024-02801-y","url":null,"abstract":"<p><strong>Background: </strong>Digital solutions can help monitor medication safety in children who are often excluded in clinical trials. The lack of reliable safety data often leads to either under- or over-dose of medications during clinical management which make them either not responding well to treatment or susceptible to adverse drug reactions (ADRs).</p><p><strong>Aim: </strong>This study investigated ADR signalling techniques to detect serious ADRs in Malaysian children aged from birth to 12 years old using an electronic ADRs' database.</p><p><strong>Methods: </strong>Four techniques (Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN) and Multi-item Gamma Poisson Shrinker (MGPS)) were tested on ADR reports submitted to the National Pharmaceutical Regulatory Agency between 2016 and 2020. Sensitivity, Specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of the techniques were compared.</p><p><strong>Results: </strong>A total of 31 medicine-Important Medical Event pairs were found and examined among the 3152 paediatric ADR reports. Three techniques (PRR, ROR, MGPS) signalled oculogyric crisis and dystonia for metoclopramide. BCPNN and MGPS signalled angioedema for paracetamol, amoxicillin and ibuprofen. Similar performances were found for PRR, ROR and BCPNN (sensitivity of 12%, specificity of 100%, PPV of 100% and NPV of 21%). MGPS revealed the highest sensitivity (20%) and NPV (23%), as well as similar specificity and PPV (100%).</p><p><strong>Conclusions: </strong>This study suggests that medication safety signalling techniques could be applied on electronic health records to monitor medication safety issues in children. Clinicians and medication safety specialist could prioritise the signals for further clinical consideration and prompt response.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"395"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142852890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}