International Journal of Medical Informatics最新文献

{"title":"Tracking provenance in clinical data warehouses for quality management","authors":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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}

{"title":"Cross-modal similar clinical case retrieval using a modular model based on contrastive learning and k-nearest neighbor search","authors":"","doi":"10.1016/j.ijmedinf.2024.105680","DOIUrl":"10.1016/j.ijmedinf.2024.105680","url":null,"abstract":"<div><h3>Objective</h3><div>Electronic health record systems have made it possible for clinicians to use previously encountered similar cases to support clinical decision-making. However, most studies for similar case retrieval were based on single-modal data. The existing studies on cross-modal clinical case retrieval were limited. We aimed to develop a CRoss-Modal Retrieval (CRMR) model to retrieve similar clinical cases recorded in different data modalities.</div></div><div><h3>Materials and methods</h3><div>The publically available Medical Information Mart for Intensive Care-Chest X-ray (MIMIC-CXR) dataset was used for model development and testing. The CRMR model was designed as a modular model containing two feature extraction models, two feature transformation models, one feature transformation optimization model, and one case retrieval model. The ability to retrieve similar clinical cases recorded in different data modalities was facilitated by the use of contrastive deep learning and <em>k</em>-nearest neighbor search.</div></div><div><h3>Results</h3><div>The average retrieval precision, denoted as AP@<em>k</em>, of the developed CRMR model, were 76.9 %@5, 76.7 %@10, 76.5 %@20, 76.3 %@50, and 77.9 %@100, respectively. Here <em>k</em> is the number of similar cases returned after retrieval. The average retrieval time varied from 0.013 ms to 0.016 ms with <em>k</em> varying from 5 to 100. Moreover, the model can retrieve similar cases with the same multiple radiographic manifestations as the query case.</div></div><div><h3>Discussion</h3><div>The CRMR model has shown promising cross-modal retrieval performance in clinical case analysis, with the potential for future scalability and improvement in handling diverse disease types and data modalities. The CRMR model has promising potential to aid clinicians in making optimal and explainable clinical decisions.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579092","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":"Expert opinion elicitation for assisting deep learning based Lyme disease classifier with patient data","authors":"","doi":"10.1016/j.ijmedinf.2024.105682","DOIUrl":"10.1016/j.ijmedinf.2024.105682","url":null,"abstract":"<div><h3>Background</h3><div>Diagnosing erythema migrans (EM) skin lesion, the most common early symptom of Lyme disease, using deep learning techniques can be effective to prevent long-term complications. Existing works on deep learning based EM recognition only utilizes lesion image due to the lack of a dataset of Lyme disease related images with associated patient data. Doctors rely on patient information about the background of the skin lesion to confirm their diagnosis. To assist deep learning model with a probability score calculated from patient data, this study elicited opinions from fifteen expert doctors. To the best of our knowledge, this is the first expert elicitation work to calculate Lyme disease probability from patient data.</div></div><div><h3>Methods</h3><div>For the elicitation process, a questionnaire with questions and possible answers related to EM was prepared. Doctors provided relative weights to different answers to the questions. We converted doctors' evaluations to probability scores using Gaussian mixture based density estimation. We exploited formal concept analysis and decision tree for elicited model validation and explanation. We also proposed an algorithm for combining independent probability estimates from multiple modalities, such as merging the EM probability score from a deep learning image classifier with the elicited score from patient data.</div></div><div><h3>Results</h3><div>We successfully elicited opinions from fifteen expert doctors to create a model for obtaining EM probability scores from patient data.</div></div><div><h3>Conclusions</h3><div>The elicited probability score and the proposed algorithm can be utilized to make image based deep learning Lyme disease pre-scanners robust. The proposed elicitation and validation process is easy for doctors to follow and can help address related medical diagnosis problems where it is challenging to collect patient data.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586729","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":"A literature-based approach to predict continuous hospital length of stay in adult acute care patients using admission variables: A single university center experience","authors":"","doi":"10.1016/j.ijmedinf.2024.105678","DOIUrl":"10.1016/j.ijmedinf.2024.105678","url":null,"abstract":"<div><h3>Purpose</h3><div>To review the existing literature on predicting length of stay (LOS) and to apply the findings on a Real World Data example in a single hospital.</div></div><div><h3>Methods</h3><div>Performing a literature review on PubMed and Embase, focusing on adults, acute conditions, and hospital-wide prediction of LOS, summarizing all the variables and statistical methods used to predict LOS. Then, we use this set of variables on a single university hospital and run an XGBoost model with Survival Cox regression on the LOS, as well as a logistic regression on binary LOS (cut-off at 4 days). Model metrics are the concordance index (c-index) and area under the curve (AUC).</div></div><div><h3>Results</h3><div>After applying the search strategy and exclusion criteria, 57 articles are included in the study. The list of variables is long, but mostly non-clinical data are used in the existing literature. A wide range of statistical methods are used, with a recent trend toward machine learning models. The XGBoost model results for the Cox regression in a C-index of 0.87, and the logistic regression on binary LOS has an AUC of 0.94.</div></div><div><h3>Conclusions</h3><div>Many variables identified in the literature are not available at the time of admission, yet they are still used in models for predicting LOS. Machine learning has become the preferred statistical approach in recent studies, though mainly for binary LOS predictions. Based on the current literature, it remains challenging to derive a practical and high performing model for continuous LOS prediction.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549025","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":"Post-Cardiac arrest outcome prediction using machine learning: A systematic review and meta-analysis","authors":"","doi":"10.1016/j.ijmedinf.2024.105659","DOIUrl":"10.1016/j.ijmedinf.2024.105659","url":null,"abstract":"<div><h3>Background</h3><div>Early and reliable prognostication in post-cardiac arrest patients remains challenging, with various factors linked to return of spontaneous circulation (ROSC), survival, and neurological results. Machine learning and deep learning models show promise in improving these predictions. This systematic review and <em>meta</em>-analysis evaluates how effective these approaches are in predicting clinical outcomes at different time points using structured data.</div></div><div><h3>Methods</h3><div>This study followed PRISMA guidelines, involving a comprehensive search across PubMed, Scopus, and Web of Science databases until March 2024. Studies aimed at predicting ROSC, survival (or mortality), and neurological outcomes after cardiac arrest through the application of machine learning or deep learning techniques with structured data were included. Data extraction followed the guidelines of the CHARMS checklist, and the bias risk was evaluated using PROBAST tool. Models reporting the AUC metric with 95 % confidence intervals were incorporated into the quantitative synthesis and <em>meta</em>-analysis.</div></div><div><h3>Results</h3><div>After extracting 2,753 initial records, 41 studies met the inclusion criteria, yielding 97 machine learning and 16 deep learning models. The pooled AUC for predicting favorable neurological outcomes (CPC 1 or 2) at hospital discharge was 0.871 (95 % CI: 0.813 – 0.928) for machine learning models and 0.877 (95 % CI: 0.831–0.924) across deep learning algorithms. For survival prediction, this value was found to be 0.837 (95 % CI: 0.757–0.916). Considerable heterogeneity and high risk of bias were observed, mainly attributable to inadequate management of missing data and the absence of calibration plots. Most studies focused on pre-hospital factors, with age, sex, and initial arrest rhythm being the most frequent features.</div></div><div><h3>Conclusion</h3><div>Predictive models utilizing AI-based approaches, including machine and deep learning models exhibit enhanced effectiveness compared to previous regression algorithms, but significant heterogeneity and high risk of bias limit their dependability. Evaluating state-of-the-art deep learning models tailored for tabular data and their clinical generalizability can enhance outcome prediction after cardiac arrest.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552267","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":"Evaluating the Effectiveness of advanced large language models in medical Knowledge: A Comparative study using Japanese national medical examination","authors":"","doi":"10.1016/j.ijmedinf.2024.105673","DOIUrl":"10.1016/j.ijmedinf.2024.105673","url":null,"abstract":"<div><div>Study aims and objectives.</div><div>This study aims to evaluate the accuracy of medical knowledge in the most advanced LLMs (GPT-4o, GPT-4, Gemini 1.5 Pro, and Claude 3 Opus) as of 2024. It is the first to evaluate these LLMs using a non-English medical licensing exam. The insights from this study will guide educators, policymakers, and technical experts in the effective use of AI in medical education and clinical diagnosis.</div></div><div><h3>Method</h3><div>Authors inputted 790 questions from Japanese National Medical Examination into the chat windows of the LLMs to obtain responses. Two authors independently assessed the correctness. Authors analyzed the overall accuracy rates of the LLMs and compared their performance on image and non-image questions, questions of varying difficulty levels, general and clinical questions, and questions from different medical specialties. Additionally, authors examined the correlation between the number of publications and LLMs’ performance in different medical specialties.</div></div><div><h3>Results</h3><div>GPT-4o achieved highest accuracy rate of 89.2% and outperformed the other LLMs in overall performance and each specific category. All four LLMs performed better on non-image questions than image questions, with a 10% accuracy gap. They also performed better on easy questions compared to normal and difficult ones. GPT-4o achieved a 95.0% accuracy rate on easy questions, marking it as an effective knowledge source for medical education. Four LLMs performed worst on “Gastroenterology and Hepatology” specialty. There was a positive correlation between the number of publications and LLM performance in different specialties.</div></div><div><h3>Conclusions</h3><div>GPT-4o achieved an overall accuracy rate close to 90%, with 95.0% on easy questions, significantly outperforming the other LLMs. This indicates GPT-4o’s potential as a knowledge source for easy questions. Image-based questions and question difficulty significantly impact LLM accuracy. “Gastroenterology and Hepatology” is the specialty with the lowest performance. The LLMs’ performance across medical specialties correlates positively with the number of related publications.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549026","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":"Enhancing real world data interoperability in healthcare: A methodological approach to laboratory unit harmonization","authors":"","doi":"10.1016/j.ijmedinf.2024.105665","DOIUrl":"10.1016/j.ijmedinf.2024.105665","url":null,"abstract":"<div><h3>Objective</h3><div>The primary aim of this study is to address the critical issue of non-standardized units in clinical laboratory data, which poses significant challenges to data interoperability and secondary usage. Despite UCUM (Unified Code for Units of Measure) offering a unique representation for laboratory test units, nearly 60% of laboratory codes in healthcare organizations use non-standard units. We sought to design, implement and test a methodology for the harmonization of units to the UCUM standards across a large research network.</div></div><div><h3>Methods</h3><div>Using dimensional analysis and a curated equivalence table, the proposed methodology harmonizes disparate units to UCUM standards. The process focused on identifying and converting non-UCUM conforming units, with the goal of enhancing data comparability and interoperability across different systems.</div></div><div><h3>Results</h3><div>The methodology successfully achieved over 90% coverage of laboratory data with units in UCUM standards across the TriNetX research network, a significant improvement from baseline measurements. This enhancement in unit standardization directly contributed to increased interoperability of laboratory data, facilitating more reliable and comparable data analysis across various healthcare organizations.</div></div><div><h3>Conclusion</h3><div>The successful harmonization of laboratory data units to UCUM standards represents a significant advancement in the field of biomedical informatics. By demonstrating a practical and effective approach to overcoming the challenges of non-standardized units, our study contributes to the broader efforts to improve data interoperability and usability for secondary purposes such as research and observational studies. Future work will focus on addressing the remaining gaps in unit standardization and exploring the implications of this methodology on clinical outcomes and research capabilities.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579093","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}