Current high-throughput sequencing technologies allow us to acquire entire genomes in a very short time and at a relatively sustainable cost, thus resulting in an increasing diffusion of genetic test capabilities, in specialized clinical laboratories and research centers. In contrast, it is still limited the impact of genomic information on clinical decisions, as an effective interpretation is a challenging task. From the technological point of view, genomic data are big in size, have a complex granular nature and strongly depend on the computational steps of the generation and processing workflows. This article introduces our work to create the openEHR Genomic Project and the set of genomic information models we developed to catch such complex structure and to preserve data provenance efficiently in a machine-readable format. The models support clinical actionability of data, by improving their quality, fostering interoperability and laying the basis for re-usability.
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http://dx.doi.org/10.3233/SHTI200199 | DOI Listing |
Stud Health Technol Inform
May 2021
Institute of Medical Informatics, University of Lübeck, Germany.
Precision medicine is an emerging and important field for health care. Molecular tumor boards use a combination of clinical and molecular data, such as somatic tumor mutations to decide on personalized therapies for patients who have run out of standard treatment options. Personalized treatment decisions require clinical data from the hospital information system and mutation data to be accessible in a structured way.
View Article and Find Full Text PDFStud Health Technol Inform
June 2020
Clinical Model program, openEHR Foundation.
Current high-throughput sequencing technologies allow us to acquire entire genomes in a very short time and at a relatively sustainable cost, thus resulting in an increasing diffusion of genetic test capabilities, in specialized clinical laboratories and research centers. In contrast, it is still limited the impact of genomic information on clinical decisions, as an effective interpretation is a challenging task. From the technological point of view, genomic data are big in size, have a complex granular nature and strongly depend on the computational steps of the generation and processing workflows.
View Article and Find Full Text PDFInt J Med Inform
December 2018
Center for Advanced Studies, Research and Development in Sardinia (CRS4), Loc. Piscina Manna, Ed.1, 09010 Pula, CA, Italy.
Purpose: The increasing usage of high throughput sequencing in personalized medicine brings new challenges to the realm of healthcare informatics. Patient records need to accommodate data of unprecedented size and complexity as well as keep track of their production process. In this work we present a solution for integrating genomic data into electronic health records via openEHR archetypes.
View Article and Find Full Text PDFIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. HiGHmed brings together 24 partners from academia and industry, aiming at improvements in care provision, biomedical research and epidemiology. By establishing a shared information governance framework, data integration centers and an open platform architecture in cooperation with independent healthcare providers, the meaningful reuse of data will be facilitated.
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June 2018
CINTESIS, Porto, Portugal.
Since the Human Genomic Project discovered the sequencing of human genome, the interest about genome content in clinical practice has increased. Genetic information has become a key point to understand diseases or improve treatments, for example, the nutrigenomic and nutrigenetics. However, the huge amount of data generated raises the need for Electronic Health Record (EHR) improvements as it becomes increasingly necessary that it includes more specific genetic information.
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