Publications by authors named "M Vaterkowski"
Article Synopsis
- Recruiting cancer patients for clinical trials is challenging due to the complex eligibility assessment process, which involves interactions among healthcare professionals and patients.
- A study in France revealed that while technological support like Clinical Trial Recruitment Support Systems (CTRSS) could enhance pre-screening, manual verification remains crucial for ensuring accuracy in patient-trial matching.
- The success of recruitment efforts relies on understanding the interplay of material, organizational, and human factors, as well as the motivations of all stakeholders involved.
From Syntactic to Semantic Interoperability Using a Hyperontology in the Oncology Domain.
Stud Health Technol Inform
August 2024
Interoperability is crucial to overcoming various challenges of data integration in the healthcare domain. While OMOP and FHIR data standards handle syntactic heterogeneity among heterogeneous data sources, ontologies support semantic interoperability to overcome the complexity and disparity of healthcare data. This study proposes an ontological approach in the context of the EUCAIM project to support semantic interoperability among distributed big data repositories that have applied heterogeneous cancer image data models using a semantically well-founded Hyperontology for the oncology domain.
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- Electronic Health Record (EHR) systems are essential digital tools in clinical practice that store patient health information and can enhance research studies, particularly platform trials, which utilize multiple treatment options under one protocol.
- Despite their potential, challenges like incomplete records and complex eligibility criteria must be addressed for effective use of EHRs in research.
- The EU-PEARL project aims to develop methods and tools for better assessing EHR protocol feasibility, selecting clinical sites, and efficiently pre-screening patients for platform trials, using a consensus-based readiness survey and interoperable queries.
Purpose: To compare the computability of Observational Medical Outcomes Partnership (OMOP)-based queries related to prescreening of patients using two versions of the OMOP common data model (CDM; v5.3 and v5.4) and to assess the performance of the Greater Paris University Hospital (APHP) prescreening tool.
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