International Journal of Medical Informatics最新文献

{"title":"Healthy nutrition and weight management for a positive pregnancy experience in the antenatal period: Comparison of responses from artificial intelligence models on nutrition during pregnancy","authors":"Emine Karacan","doi":"10.1016/j.ijmedinf.2024.105663","DOIUrl":"10.1016/j.ijmedinf.2024.105663","url":null,"abstract":"<div><h3>Background</h3><div>As artificial intelligence AI-supported applications become integral to web-based information-seeking, assessing their impact on healthy nutrition and weight management during the antenatal period is crucial.</div></div><div><h3>Objective</h3><div>This study was conducted to evaluate both the quality and semantic similarity of responses created by AI models to the most frequently asked questions about healthy nutrition and weight management during the antenatal period, based on existing clinical knowledge.</div></div><div><h3>Methods</h3><div>In this study, a cross-sectional assessment design was used to explore data from 3 AI models (GPT-4, MedicalGPT, Med-PaLM). We directed the most frequently asked questions about nutrition during pregnancy, obtained from the American College of Obstetricians and Gynecologists (ACOG) to each model in a new and single session on October 21, 2023, without any prior conversation. Immediately after, instructions were given to the AI models to generate responses to these questions. The responses created by AI models were evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) scale. Additionally, to assess the semantic similarity between answers to 31 pregnancy nutrition-related frequently asked questions sourced from the ACOG and responses from AI models we evaluated cosine similarity using both WORD2VEC and BioLORD-2023.</div></div><div><h3>Results</h3><div>Med-PaLM outperformed GPT-4 and MedicalGPT in response quality (mean = 3.93), demonstrating superior clinical accuracy over both GPT-4 (p = 0.016) and MedicalGPT (p = 0.001). GPT-4 had higher quality than MedicalGPT (p = 0.027).</div><div>The semantic similarity between ACOG and Med-PaLM is higher with WORD2VEC (0.92) compared to BioLORD-2023 (0.81), showing a difference of +0.11. The similarity scores for ACOG–MedicalGPT and ACOG–GPT-4 are similar across both models, with minimal differences of −0.01. Overall, WORD2VEC has a slightly higher average similarity (0.82) than BioLORD-2023 (0.79), with a difference of +0.03.</div></div><div><h3>Conclusions</h3><div>Despite the superior performance of Med-PaLM, there is a need for further evidence-based research and improvement in the integration of AI in healthcare due to varying AI model performances.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"193 ","pages":"Article 105663"},"PeriodicalIF":3.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine-learning-based algorithm for bone marrow cell differential counting","authors":"Ta-Chuan Yu ,&nbsp;Cheng-Kun Yang ,&nbsp;Wei-Han Hsu ,&nbsp;Cheng-An Hsu ,&nbsp;Hsiao-Chun Wang ,&nbsp;Hsin-Jung Hsiao ,&nbsp;Hsiao-Ling Chao ,&nbsp;Han-Peng Hsieh ,&nbsp;Jia-Rong Wu ,&nbsp;Yen-Chun Tsai ,&nbsp;Yi-Mei Chiang ,&nbsp;Poshing Lee ,&nbsp;Che-Pin Lin ,&nbsp;Ling-Ping Chen ,&nbsp;Yung-Chuan Sung ,&nbsp;Ya-Yun Yang ,&nbsp;Chin-Ling Yu ,&nbsp;Chih-Kang Lin ,&nbsp;Chia-Pin Kang ,&nbsp;Che-Wei Chang ,&nbsp;Wen-Chien Chou","doi":"10.1016/j.ijmedinf.2024.105692","DOIUrl":"10.1016/j.ijmedinf.2024.105692","url":null,"abstract":"<div><h3>Background</h3><div>Differential counting (DC) of different cell types in bone marrow (BM) aspiration smears is crucial for diagnosing hematological diseases. However, a clinically applicable method for automatic DC has yet to be developed.</div></div><div><h3>Objective</h3><div>This study developed and validated an artificial intelligence (AI)-based algorithm for identifying and classifying nucleated cells in BM smears.</div></div><div><h3>Methods</h3><div>In the development phase, a mask region–based convolutional neural network (Mask R-CNN)-based AI model was trained to detect and classify individual BM cells. We used a large data set of expert-annotated images representing a variety of disease categories. The BM slides were stained with Liu’s stain or Wright–Giemsa stain. Consensus meetings were held to ensure experts from different institutes applied consistent criteria in classifying cells. Subsequently, the performance of the AI algorithm in identifying cell images and determining cell ratios was evaluated using a multinational clinical dataset.</div></div><div><h3>Results</h3><div>The AI model was trained on 542 slides (85.1 % stained with Liu’s stain and 14.9 % with Wright–Giemsa stain) containing 597,222 annotated cells. It achieved an accuracy of 0.94 for the testing dataset containing 26,170 cells. The performance of the AI model was further validated using another multinational real-world dataset (data obtained from three centers in Taiwan and one in the United States) comprising 200,639 cells. The AI model achieved an accuracy of 0.881 in classifying individual cells and demonstrated high precision in classifying blasts (0.927), bands and polymorphonuclear neutrophils (0.955), plasma cells (0.930), and lymphocytes (0.789). When the differential counting percentage of each cell type was assessed, a strong correlation (ρ &gt; 0.8) between the AI and manual methods was observed for most cell categories.</div></div><div><h3>Conclusions</h3><div>In this study, an AI algorithm was developed and clinically validated using large, multinational datasets. Our algorithm can locate and classify BM cells simultaneously and has potential clinical applicability for automating BM differential counting.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"194 ","pages":"Article 105692"},"PeriodicalIF":3.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supporting the care to breast cancer patients with unique needs: Evidence from online community members’ responses","authors":"Anqi Xu ,&nbsp;Yuanyuan Gao","doi":"10.1016/j.ijmedinf.2024.105695","DOIUrl":"10.1016/j.ijmedinf.2024.105695","url":null,"abstract":"<div><h3>Background</h3><div>Breast cancer is the most common cancer diagnosed in women globally. Online cancer communities (OCCs) provide platforms for breast cancer patients to connect, share experiences, and support each other. These communities facilitate discussions on a range of health- and non-health-related topics. However, posts discussing unique topics may receive varying levels of attention and support. This study aims to devise a method for identifying and supporting such posts, enhancing community response and support strategies.</div></div><div><h3>Methods</h3><div>We propose a Uniqueness Score Extraction Framework to compute health- and non-health-related uniqueness scores for online community posts. The framework utilizes deep learning-based natural language processing models to identify the topics discussed in OCCs and calculates the health- and non-health-related uniqueness scores of a post based on the uniqueness of the topics identified by the BERTopic model. We further employ econometric models to assess how the uniqueness scores of posts affect community members’ responses to those posts.</div></div><div><h3>Results</h3><div>Our study reveals that posts with a higher concentration of unique health-related topics in OCCs elicit quicker, more frequent, but shorter responses. Conversely, posts containing more unique non-health-related topics in the entire post prompt faster and longer responses, unless these topics become overly dominant, in which case the number of replies decreases, and response times are prolonged.</div></div><div><h3>Conclusion</h3><div>Our research develops a framework to identify posts with high uniqueness scores in OCCs, and sheds light on community member responses to these discussions. The findings indicate that while members are supportive, particularly regarding health-related topics, the post-content’s nature and focus greatly affect their engagement. These discoveries could enhance our understanding of community dynamics in OCCs, offering valuable implications for researchers, OCC facilitators, and medical professionals in supporting patients within online platforms.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"193 ","pages":"Article 105695"},"PeriodicalIF":3.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced NSCLC subtyping and staging through attention-augmented multi-task deep learning: A novel diagnostic tool","authors":"Runhuang Yang ,&nbsp;Weiming Li ,&nbsp;Siqi Yu ,&nbsp;Zhiyuan Wu ,&nbsp;Haiping Zhang ,&nbsp;Xiangtong Liu ,&nbsp;Lixin Tao ,&nbsp;Xia Li ,&nbsp;Jian Huang ,&nbsp;Xiuhua Guo","doi":"10.1016/j.ijmedinf.2024.105694","DOIUrl":"10.1016/j.ijmedinf.2024.105694","url":null,"abstract":"<div><h3>Objectives</h3><div>The objective of this study is to develop a novel multi-task learning approach with attention encoders for classifying histologic subtypes and clinical stages of non-small cell lung cancer (NSCLC), with superior performance compared to currently popular deep-learning models.</div></div><div><h3>Material and methods</h3><div>Data were collected from six publicly available datasets in The Cancer Imaging Archive (TCIA). Following the inclusion and exclusion criteria, a total of 4548 CT slices from 758 cases were allocated. We evaluated multiple multi-task learning models that integrate attention mechanisms to resolve challenges in NSCLC subtype classification and clinical staging. These models utilized convolution-based modules in their shared layers for feature extraction, while the task layers were dedicated to histological subtype classification and staging. Each branch sequentially processed features through convolution-based and attention-based modules prior to classification.</div></div><div><h3>Results</h3><div>Our study evaluated 758 NSCLC patients (mean age, 66.2 years ± 10.3; 473 men), spanning ADC and SCC cases. In the classification of histological subtypes and clinical staging of NSCLC, the MobileNet-based multi-task learning model enhanced with attention mechanisms (MN-MTL-A) demonstrated superior performance, achieving Area Under the Curve (AUC) scores of 0.963 (95 % CI: 0.943, 0.981) and 0.966 (95 % CI: 0.945, 0.982) for each task, respectively. The model significantly surpassed its counterparts lacking attention mechanisms and those configured for single-task learning, as evidenced by P-values of 0.01 or less for both tasks, according to DeLong’s test.</div></div><div><h3>Conclusions</h3><div>The integration of attention encoder blocks into our multi-task learning network significantly enhanced the accuracy of NSCLC histological subtyping and clinical staging. Given the reduced reliance on precise radiologist annotation, our proposed model shows promising potential for clinical application.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"193 ","pages":"Article 105694"},"PeriodicalIF":3.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review","authors":"Yusuf Abas Mohamed ,&nbsp;Bee Ee Khoo ,&nbsp;Mohd Shahrimie Mohd Asaari ,&nbsp;Mohd Ezane Aziz ,&nbsp;Fattah Rahiman Ghazali","doi":"10.1016/j.ijmedinf.2024.105689","DOIUrl":"10.1016/j.ijmedinf.2024.105689","url":null,"abstract":"<div><h3>Objective</h3><div>Explainable Artificial Intelligence (XAI) is increasingly recognized as a crucial tool in cancer care, with significant potential to enhance diagnosis, prognosis, and treatment planning. However, the holistic integration of XAI across all stages of cancer care remains underexplored. This review addresses this gap by systematically evaluating the role of XAI in these critical areas, identifying key challenges and emerging trends.</div></div><div><h3>Materials and methods</h3><div>Following the PRISMA guidelines, a comprehensive literature search was conducted across Scopus and Web of Science, focusing on publications from January 2020 to May 2024. After rigorous screening and quality assessment, 69 studies were selected for in-depth analysis.</div></div><div><h3>Results</h3><div>The review identified critical gaps in the application of XAI within cancer care, notably the exclusion of clinicians in 83% of studies, which raises concerns about real-world applicability and may lead to explanations that are technically sound but clinically irrelevant. Additionally, 87% of studies lacked rigorous evaluation of XAI explanations, compromising their reliability in clinical practice. The dominance of post-hoc visual methods like SHAP, LIME and Grad-CAM reflects a trend toward explanations that may be inherently flawed due to specific input perturbations and simplifying assumptions. The lack of formal evaluation metrics and standardization constrains broader XAI adoption in clinical settings, creating a disconnect between AI development and clinical integration. Moreover, translating XAI insights into actionable clinical decisions remains challenging due to the absence of clear guidelines for integrating these tools into clinical workflows.</div></div><div><h3>Conclusion</h3><div>This review highlights the need for greater clinician involvement, standardized XAI evaluation metrics, clinician-centric interfaces, context-aware XAI systems, and frameworks for integrating XAI into clinical workflows for informed clinical decision-making and improved outcomes in cancer care.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"193 ","pages":"Article 105689"},"PeriodicalIF":3.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy of machine learning in diagnosing microsatellite instability in gastric cancer: A systematic review and meta-analysis","authors":"Yuou Ying ,&nbsp;Ruyi Ju ,&nbsp;Jieyi Wang ,&nbsp;Wenkai Li ,&nbsp;Yuan Ji ,&nbsp;Zhenyu Shi ,&nbsp;Jinhan Chen ,&nbsp;Mingxian Chen","doi":"10.1016/j.ijmedinf.2024.105685","DOIUrl":"10.1016/j.ijmedinf.2024.105685","url":null,"abstract":"<div><h3>Background</h3><div>Significant challenges persist in the early identification of microsatellite instability (MSI) within current clinical practice. In recent years, with the growing utilization of machine learning (ML) in the diagnosis and management of gastric cancer (GC), numerous researchers have explored the effectiveness of ML methodologies in detecting MSI. Nevertheless, the predictive value of these approaches still lacks comprehensive evidence. Accordingly, this study was carried out to consolidate the accuracy of ML in the prompt detection of MSI in GC.</div></div><div><h3>Methods</h3><div>PubMed, the Cochrane Library, the Web of Science, and Embase were retrieved up to March 20, 2024. The risk of bias in the encompassed studies was evaluated utilizing a risk assessment tool for predictive models. Models were then subjected to subgroup analysis based on the modeling variables.</div></div><div><h3>Results</h3><div>A total of 12 studies, encompassing 11,912 patients with GC, satisfied the predefined inclusion criteria. ML models established in these studies were primarily based on pathological images, clinical features, and radiomics. The results suggested that in the validation sets, the pathological image-based models had a synthesized c-index of 0.86 [95 % CI (0.83–0.89)], with sensitivity and specificity being 0.86 [95 % CI (0.76–0.92)] and 0.83 [95 % CI (0.78–0.87)], respectively; radiomics feature-based models achieved respective values of 0.87 [95 % CI (0.81–0.92)], 0.77 [95 % CI (0.70–0.83)] and 0.81 [95 % CI (0.74–0.87)]; radiomics feature-based models + clinical feature-based models achieved respective values of 0.87 [95 % CI (0.81–0.93)], 0.78 [95 % CI (0.70–0.84)] and 0.79 [95 % CI (0.69–0.86)].</div></div><div><h3>Conclusions</h3><div>ML has demonstrated optimal performance in detecting MSI in GC and could serve as a prospective early adjunctive detection tool for MSI in GC. Future research should contemplate minimally invasive or non-invasive, readily collectible, and efficient predictors to augment the predictive accuracy of ML.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"193 ","pages":"Article 105685"},"PeriodicalIF":3.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking provenance in clinical data warehouses for quality management","authors":"Marco Johns,&nbsp;Lena Baum,&nbsp;Fabian Prasser","doi":"10.1016/j.ijmedinf.2024.105690","DOIUrl":"10.1016/j.ijmedinf.2024.105690","url":null,"abstract":"<div><h3>Introduction</h3><div>Data provenance, which documents the origin, history, and transformations of data, can enhance the reproducibility of processing workflows and help to address errors and quality issues. In this work, we focus on tracking and utilizing provenance information as part of quality management in Extract-Transform-Load (ETL) processes used to build clinical data warehouses.</div></div><div><h3>Methods</h3><div>We designed and implemented a framework that automatically tracks how data flows through an ETL process and detects errors and quality problems during processing. This information is then reported against an Application Programming Interface (API) that stores the issues along with contextual information on their location within the data being transformed and the overall workflow. We further designed a dashboard that supports health data engineers with inspecting the encountered issues and tracing them back to their root causes.</div></div><div><h3>Results</h3><div>The framework was implemented in Java using the Spring Framework and integrated into ETL processes for Informatics for Integrating Biology and the Bedside (i2b2). The dashboard was realized using Grafana. We evaluated our approach on three different ETL processes for real-world datasets used to integrate them into our i2b2 clinical data warehouse. Using the provenance dashboard, we were able to identify frequent error patterns and link them to specific data points from the sources as well as ETL process steps. Provenance tracking increased the execution times of loading processes with an impact depending on the number of identified issues.</div></div><div><h3>Conclusions</h3><div>Provenance tracking can be a valuable tool for implementing continuous quality management for ETL processes. Relevant information can be collected from existing ETL workloads using dedicated APIs and visualized through dashboards, which support the identification of frequent patterns of problems together with their root causes, providing valuable information for improvements.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"193 ","pages":"Article 105690"},"PeriodicalIF":3.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of the openEHR reference model for PGHD: A case study on the DH-Convener initiative","authors":"Somayeh Abedian ,&nbsp;Sten Hanke ,&nbsp;Rada Hussein","doi":"10.1016/j.ijmedinf.2024.105686","DOIUrl":"10.1016/j.ijmedinf.2024.105686","url":null,"abstract":"<div><h3>Objectives</h3><div>Patient-Generated Health Data (PGHD) is increasingly influential in therapy and diagnostic decisions. PGHD should be integrated into electronic health records (EHR) to maximize its utility. This study evaluates the openEHR Reference Model (RM) compatibility with the DH-Convener initiative’s modules (Data Collection Module and Data Connector Module) as a potential concept for standardizing PGHD across wearable health devices, focusing on achieving interoperability.</div></div><div><h3>Materials and Methods</h3><div>The study analyzes various types of PGHD, assessing the data formats and structures used by wearable tools. We evaluate openEHR RM specification with our initiative, DH-Convenor, focusing on PGHD semantic interoperability challenges. We evaluated current Archetypes and Templates that are now created and exist on openEHR Clinical Knowledge Management (CKM) and mapped them to our requirements. The DH-Convener modules are examined for their compatibility in standardizing PGHD integration into openEHR clinical workflows and compared with other existing standards for flexibility, scalability, and interoperability.</div></div><div><h3>Results</h3><div>The findings indicate that the diversity in data formats across wearable tools and openEHR shows strong potential as unifying data models based on the DH-Convener’s modules. It supports a wide range of PGHD types in existing archetypes and aligns well with our initiative’s requirements for storing PGHD, enabling more seamless integration into EHR systems.</div></div><div><h3>Discussion</h3><div>Integrating PGHD into EHR is crucial for personalized healthcare, but inconsistent device formats hinder interoperability. The DH-Convener leverages openEHR to provide a strong solution, though stakeholder collaboration remains essential. Our initiative demonstrates openEHR’s ability to ensure consistency, particularly in Europe.</div></div><div><h3>Conclusion</h3><div>We aligned the openEHR layers with the DH-Convener modules, demonstrating openEHR’s compatibility for storing PGHD and supporting interoperability goals, such as standardized storage and seamless data transfer to Austria’s national EHR. Future efforts should prioritize promoting these models and ensuring their adaptability to emerging wearable devices.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"193 ","pages":"Article 105686"},"PeriodicalIF":3.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Healthcare professionals’ cross-organizational access to electronic health records: A scoping review","authors":"Øivind Skeidsvoll Solvang ,&nbsp;Sonja Cassidy ,&nbsp;Conceição Granja ,&nbsp;Terje Solvoll","doi":"10.1016/j.ijmedinf.2024.105688","DOIUrl":"10.1016/j.ijmedinf.2024.105688","url":null,"abstract":"<div><h3>Background</h3><div>Cross-organizational access to shared electronic health records can enhance integrated, people-centered health services. However, a gap remains between these potential benefits and the limited support currently offered by electronic health records. The Valkyrie research project aims to bridge this gap by developing a technical prototype of an architecture to promote healthcare service coordination.</div></div><div><h3>Objective</h3><div>To inform the Valkyrie project, we aimed to evaluate approaches for healthcare professionals’ access to electronic health records across healthcare providers and identify factors influencing the success and failure of these approaches.</div></div><div><h3>Materials and methods</h3><div>Using the Joanna Briggs Institute guidance for scoping reviews, searches were conducted in six research databases and grey literature, without limitations on year or language. Papers selected for full-text review were analyzed, and data was extracted using standardized forms that reflected the population, concept, and context framework and the categorization model used in the qualitative analysis of the barriers and facilitators reported in the included papers.</div></div><div><h3>Results</h3><div>Among the 290 identified papers, five were deemed eligible for full-text review. The included papers were heterogeneous in country, year of publication, study setting, implementation level, and access approaches to electronic health records, highlighting various techniques, from federated to centralized, for accessing shared electronic health records.</div></div><div><h3>Discussion and conclusion</h3><div>The review did not identify one single superior access approach. However, a hybrid approach incorporating components from the different approaches combined with emerging technologies may benefit the Valkyrie project. The key facilitators were identified as improved information quality and flexible and easy access. In contrast, lack of trust and poor information quality were significant barriers to successful cross-organizational access to electronic health records. Future research should explore alternative access approaches, considering information quality, user training, and collegial trust across healthcare providers.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"193 ","pages":"Article 105688"},"PeriodicalIF":3.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acute myocardial infarction risk prediction in emergency chest pain patients: An external validation study","authors":"Ching-Hung Chang ,&nbsp;Phung-Anh Nguyen ,&nbsp;Chien-Cheng Huang ,&nbsp;Chung-Feng Liu ,&nbsp;Septi Melisa ,&nbsp;Chia-Jung Chen ,&nbsp;Chien-Chin Hsu ,&nbsp;Hung-Jung Lin ,&nbsp;Min-Huei Hsu ,&nbsp;Chun-Ming Shih ,&nbsp;Ju-Chi Liu ,&nbsp;Hung-Yu Yang ,&nbsp;Jason C. Hsu","doi":"10.1016/j.ijmedinf.2024.105683","DOIUrl":"10.1016/j.ijmedinf.2024.105683","url":null,"abstract":"<div><h3>Background</h3><div>Chest pain is a common symptom that presents to the emergency department (ED), and its causes range from minor illnesses to serious diseases such as acute coronary syndrome. Accurate and timely diagnosis is essential for the efficient management and treatment of these patients.</div></div><div><h3>Objective</h3><div>This study aims to expand on a model previously developed by the Chi Mei Medical Group (CMMG) Emergency Department in 2020 to predict adverse cardiac events in patients with chest pain. The main goal is to evaluate the accuracy and generalizability of the model through external validation using data from other hospitals.</div></div><div><h3>Methods</h3><div>The initial model for this study was developed using data from three CMMG-affiliated hospitals in southern Taiwan. We utilized four supervised machine learning algorithms, namely random forest, logistic regression, support-vector clustering, and K-nearest neighbor, to predict the risk of acute myocardial infarction within a one month for emergency chest pain patients. The study used the model with the best area under the curve (AUC), recall and precision for external validation. The external validated data source was data collected from three hospitals associated with Taipei Medical University (TMU) in northern Taiwan. <strong>Results:</strong> The original best model constructed by CMMG exhibited an AUC of 0.822, an accuracy of 0.740, a recall of 0.741, a precision of 0.566, a specificity of 0.740, and an NPV of 0.861. Subsequently, during the external validation phase, CMMG’s top-performing model demonstrated acceptable validation result with TMU’s data, achieving an AUC of 0.63, an accuracy of 0.661, a recall of 0.593, a precision of 0.243, a specificity of 0.691, and an NPV of 0.900. While the results indicate that the model’s performance varied across different datasets and are not outstanding, the model is still acceptable for clinical application as a preliminary decision-support tool.</div></div><div><h3>Conclusion</h3><div>This study highlights the importance of external validation to confirm the applicability of the previously developed predictive model in other hospital settings. Although the model shows potential in assessing chest pain patients in the ED, its broad clinical application requires further validation to ensure it can improve patient outcomes and optimize healthcare resource allocation.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"193 ","pages":"Article 105683"},"PeriodicalIF":3.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}