Data directory is a centralized repository for managing data, which contains relevant information describing data assets. In the healthcare field, medical data faces difficulties in management and usage due to its large and complex nature. Therefore, applying data catalogs in the healthcare field can greatly help institutions manage data more conveniently and effectively. However, in the healthcare field, data catalogs have always faced some challenges, such as dictionary mismatch and user interests dismatch. There is a data catalog overcame these challenges through an integrated metadata model. But the original metadata model only supports three medical information standards: VCF, DICOM and REDCap. In order to optimize this metadata model to cover a wider range of medical data types, the FHIR standard will be integrated into this model. At the same time, in order to better integrate FHIR with this metadata model, FHIR data analysis tools are added to this model, so that it can automatically generate metadata values about the FHIR patients dataset. To achieve this integration, the study identifies the need for a descriptive analysis tool capable of processing FHIR data. Tools such as DOMO, Pathling, Pyre and FHIR-dbt-analytics are evaluated based on their ability to traverse FHIR datasets and generate essential statistical metadata. DOMO emerges as the preferred tool due to its user-friendly user experience, high processing speed, and high-quality FHIR analysis ability.It not only effectively analyzes the FHIR dataset and generates the required metadata values, but also supports the analysis of OMOP data, allowing the model to integrate both FHIR and OMOP standards, further improving the compatibility of the metadata model. In summary, this study provides a detailed comparison and analysis of descriptive tools based on FHIR, ultimately determining that DOMO is the only tool that meets the requirements.
Il data directory è un repository centralizzato per la gestione dei dati, che contiene informazioni rilevanti che descrivono le risorse di dati. Nel campo sanitario, i dati medici incontrano difficoltà nella gestione e nell'uso a causa della loro natura grande e complessa. Pertanto, l'applicazione dei cataloghi dei dati nel campo sanitario può aiutare notevolmente le istituzioni a gestire i dati in modo più conveniente ed efficace. Tuttavia, nel campo sanitario, i cataloghi dei dati hanno sempre affrontato alcune sfide, come la mancata corrispondenza dei dizionari e la discrepanza degli interessi degli utenti. C'è un catalogo dei dati che ha superato queste sfide attraverso un modello di metadati integrato. Ma il modello originale di metadati supporta solo tre standard di informazioni mediche: VCF, DICOM e REDCap. Per ottimizzare questo modello di metadati per coprire un'ampia gamma di tipi di dati medici, lo standard FHIR verrà integrato in questo modello. Allo stesso tempo, per integrare meglio FHIR con questo modello di metadati, sono stati aggiunti strumenti di analisi dei dati FHIR a questo modello, così che possa generare automaticamente i valori dei metadati riguardanti il dataset dei pazienti FHIR. Per raggiungere questa integrazione, lo studio identifica la necessità di uno strumento di analisi descrittiva in grado di elaborare i dati FHIR. Strumenti come DOMO, Pathling, Pyre e FHIR-dbt-analytics sono valutati sulla base della loro capacità di attraversare i dataset FHIR e generare metadati statistici essenziali. DOMO emerge come lo strumento preferito grazie alla sua esperienza utente amichevole, alla sua alta velocità di elaborazione e alla sua elevata capacità di analisi FHIR. Non solo analizza efficacemente il dataset FHIR e genera i valori di metadati richiesti, ma supporta anche l'analisi dei dati OMOP, consentendo al modello di integrare sia gli standard FHIR che OMOP, migliorando ulteriormente la compatibilità del modello di metadati. In sintesi, questo studio fornisce un confronto dettagliato e un'analisi degli strumenti descrittivi basati su FHIR, determinando infine che DOMO è l'unico strumento che soddisfa i requisiti.
Tools of metadata generation for improving metadata model
Sun, Yixuan
2023/2024
Abstract
Data directory is a centralized repository for managing data, which contains relevant information describing data assets. In the healthcare field, medical data faces difficulties in management and usage due to its large and complex nature. Therefore, applying data catalogs in the healthcare field can greatly help institutions manage data more conveniently and effectively. However, in the healthcare field, data catalogs have always faced some challenges, such as dictionary mismatch and user interests dismatch. There is a data catalog overcame these challenges through an integrated metadata model. But the original metadata model only supports three medical information standards: VCF, DICOM and REDCap. In order to optimize this metadata model to cover a wider range of medical data types, the FHIR standard will be integrated into this model. At the same time, in order to better integrate FHIR with this metadata model, FHIR data analysis tools are added to this model, so that it can automatically generate metadata values about the FHIR patients dataset. To achieve this integration, the study identifies the need for a descriptive analysis tool capable of processing FHIR data. Tools such as DOMO, Pathling, Pyre and FHIR-dbt-analytics are evaluated based on their ability to traverse FHIR datasets and generate essential statistical metadata. DOMO emerges as the preferred tool due to its user-friendly user experience, high processing speed, and high-quality FHIR analysis ability.It not only effectively analyzes the FHIR dataset and generates the required metadata values, but also supports the analysis of OMOP data, allowing the model to integrate both FHIR and OMOP standards, further improving the compatibility of the metadata model. In summary, this study provides a detailed comparison and analysis of descriptive tools based on FHIR, ultimately determining that DOMO is the only tool that meets the requirements.| File | Dimensione | Formato | |
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2024_07_SUN.pdf
solo utenti autorizzati a partire dal 23/06/2027
Descrizione: Data directory is a centralized repository for managing data, which contains relevant information describing data assets. In the healthcare field, medical data faces difficulties in management and usage due to its large and complex nature. Therefore, applying data catalogs in the healthcare field can greatly help institutions manage data more conveniently and effectively. However, in the healthcare field, data catalogs have always faced some challenges, such as dictionary mismatch and user interests dismatch. There is a data catalog overcame these challenges through an integrated metadata model. But the original metadata model only supports three medical information standards: VCF, DICOM and REDCap. In order to optimize this metadata model to cover a wider range of medical data types, the FHIR standard will be integrated into this model. At the same time, in order to better integrate FHIR with this metadata model, FHIR data analysis tools are added to this model, so that it can automatically generate metadata values about the FHIR patients dataset. To achieve this integration, the study identifies the need for a descriptive analysis tool capable of processing FHIR data. Tools such as DOMO, Pathling, Pyre and FHIR-dbt-analytics are evaluated based on their ability to traverse FHIR datasets and generate essential statistical metadata. DOMO emerges as the preferred tool due to its user-friendly user experience, high processing speed, and high-quality FHIR analysis ability.It not only effectively analyzes the FHIR dataset and generates the required metadata values, but also supports the analysis of OMOP data, allowing the model to integrate both FHIR and OMOP standards, further improving the compatibility of the metadata model. In summary, this study provides a detailed comparison and analysis of descriptive tools based on FHIR, ultimately determining that DOMO is the only tool that meets the requirements.
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2.24 MB
Formato
Adobe PDF
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https://hdl.handle.net/10589/223060