Information Technology (IT) and specialized systems could have a prominent role towards the support of drug safety processes, both in the clinical context but also beyond that. PVClinical project aims to build an IT platform, enabling the investigation of potential Adverse Drug Reactions (ADRs). In this paper, we outline the utilization of Observational Medical Outcomes Partnership - Common Data Model (OMOP-CDM) and the openly available Observational Health Data Sciences and Informatics (OHDSI) software stack as part of PVClinical platform. OMOP-CDM offers the capacity to integrate data from Electronic Health Records (EHRs) (e.g., encounters, patients, providers, diagnoses, drugs, measurements and procedures) via an accepted data model. Furthermore, the OHDSI software stack provides valuable analytics tools which could be used to address important questions regarding drug safety quickly and efficiently, enabling the investigation of potential ADRs in the clinical environment.
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http://dx.doi.org/10.3233/SHTI210232 | DOI Listing |
Front Netw Physiol
November 2024
Department of Mathematics, Northeastern University, Boston, MA, United States.
In this mini review, we propose the use of the Julia programming language and its software as a strong candidate for reproducible, efficient, and sustainable physiological signal analysis. First, we highlight available software and Julia communities that provide top-of-the-class algorithms for all aspects of physiological signal processing despite the language's relatively young age. Julia can significantly accelerate both research and software development due to its high-level interactive language and high-performance code generation.
View Article and Find Full Text PDFOpen Res Eur
July 2024
Discovery Research ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology, Hamburg, Germany.
Objective: The European Health Data Space (EHDS) shapes the digital transformation of healthcare in Europe. The EHDS regulation will also accelerate the use of health data for research, innovation, policy-making, and regulatory activities for secondary use of data (known as EHDS2). The Integration of heterogeneous Data and Evidence towards Regulatory and HTA Acceptance (IDERHA) project builds one of the first pan-European health data spaces in alignment with the EHDS2 requirements, addressing lung cancer as a pilot.
View Article and Find Full Text PDFStud Health Technol Inform
August 2024
Associate Professor, Faculdade de Medicina da Universidade do Porto, Portugal.
One approach to enriching the Learning Health System (LHS) is leveraging vital signs and data from wearable technologies. Blood oxygen, heart rate, respiration rates, and other data collected by wearables (like sleep and exercise patterns) can be used to monitor and predict health conditions. This data is already being collected and could be used to improve healthcare in several ways.
View Article and Find Full Text PDFStud Health Technol Inform
August 2024
Clinical Informatics Service, Hospital Clínic de Barcelona. 08036 - Barcelona, Spain.
Common Data Models (CDMs) enhance data exchange and integration across diverse sources, preserving semantics and context. Transforming local data into CDMs is typically cumbersome and resource-intensive, with limited reusability. This article compares OntoBridge, an ontology-based tool designed to streamline the conversion of local datasets into CDMs, with traditional ETL methods in adopting the OMOP CDM.
View Article and Find Full Text PDFJMIR Med Inform
August 2024
Univ Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des Technologies de santé et des, Pratiques médicales, 2 Place de Verdun, Lille, F-59000, France.
Background: Patient-monitoring software generates a large amount of data that can be reused for clinical audits and scientific research. The Observational Health Data Sciences and Informatics (OHDSI) consortium developed the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to standardize electronic health record data and promote large-scale observational and longitudinal research.
Objective: This study aimed to transform primary care data into the OMOP CDM format.
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