Development of a FHIR Layer on Top of the OMOP Common Data Model for the CAPABLE Project

Gabetta, Matteo; Alloni, Anna; Polce, Francesca; Lanzola, Giordano; Parimbelli, Enea; Barbarini, Nicola

doi:10.3233/shti210804

Cited by 4 publications

(1 citation statement)

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“…It allows different systems and applications to exchange and use data effectively. To achieve interoperability, cancer centers can adopt standards such as FHIR and OMOP [ 42 ]. Adopting these standards enables cancer centers to ensure that their data is compatible with that of other institutions and researchers, facilitating the exchange and integration of data and improving the quality and relevance of cancer research.…”

Section: Discussionmentioning

confidence: 99%

From data strategy to implementation to advance cancer research and cancer care: A French comprehensive cancer center experience

Heudel,

Crochet,

Durand

et al. 2023

PLOS Digit Health

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In a comprehensive cancer center, effective data strategies are essential to evaluate practices, and outcome, understanding the disease and prognostic factors, identifying disparities in cancer care, and overall developing better treatments. To achieve these goals, the Center Léon Bérard (CLB) considers various data collection strategies, including electronic medical records (EMRs), clinical trial data, and research projects. Advanced data analysis techniques like natural language processing (NLP) can be used to extract and categorize information from these sources to provide a more complete description of patient data. Data sharing is also crucial for collaboration across comprehensive cancer centers, but it must be done securely and in compliance with regulations like GDPR. To ensure data is shared appropriately, CLB should develop clear data sharing policies and share data in a controlled, standardized format like OSIRIS RWD, OMOP and FHIR. The UNICANCER initiative has launched the CONSORE project to support the development of a structured and standardized repository of patient data to improve cancer research and patient outcomes. Real-world data (RWD) studies are vital in cancer research as they provide a comprehensive and accurate picture of patient outcomes and treatment patterns. By incorporating RWD into data collection, analysis, and sharing strategies, comprehensive cancer centers can take a more comprehensive and patient-centered approach to cancer research. In conclusion, comprehensive cancer centers must take an integrated approach to data collection, analysis, and sharing to enhance their understanding of cancer and improve patient outcomes. Leveraging advanced data analytics techniques and developing effective data sharing policies can help cancer centers effectively harness the power of data to drive progress in cancer research.

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Section: Discussionmentioning

confidence: 99%

From data strategy to implementation to advance cancer research and cancer care: A French comprehensive cancer center experience

Heudel,

Crochet,

Durand

et al. 2023

PLOS Digit Health

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HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) in digital healthcare ecosystems for chronic disease management: Scoping review

Gazzarata,

Almeida,

Lindsköld

et al. 2024

International Journal of Medical Informatics

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Machine Learning–Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review

Balch¹,

Ruppert²,

Loftus³

et al. 2023

JMIR Med Inform

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Background Machine learning–enabled clinical information systems (ML-CISs) have the potential to drive health care delivery and research. The Fast Healthcare Interoperability Resources (FHIR) data standard has been increasingly applied in developing these systems. However, methods for applying FHIR to ML-CISs are variable. Objective This study evaluates and compares the functionalities, strengths, and weaknesses of existing systems and proposes guidelines for optimizing future work with ML-CISs. Methods Embase, PubMed, and Web of Science were searched for articles describing machine learning systems that were used for clinical data analytics or decision support in compliance with FHIR standards. Information regarding each system’s functionality, data sources, formats, security, performance, resource requirements, scalability, strengths, and limitations was compared across systems. Results A total of 39 articles describing FHIR-based ML-CISs were divided into the following three categories according to their primary focus: clinical decision support systems (n=18), data management and analytic platforms (n=10), or auxiliary modules and application programming interfaces (n=11). Model strengths included novel use of cloud systems, Bayesian networks, visualization strategies, and techniques for translating unstructured or free-text data to FHIR frameworks. Many intelligent systems lacked electronic health record interoperability and externally validated evidence of clinical efficacy. Conclusions Shortcomings in current ML-CISs can be addressed by incorporating modular and interoperable data management, analytic platforms, secure interinstitutional data exchange, and application programming interfaces with adequate scalability to support both real-time and prospective clinical applications that use electronic health record platforms with diverse implementations.

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Development of a FHIR Layer on Top of the OMOP Common Data Model for the CAPABLE Project

Cited by 4 publications

References 0 publications

From data strategy to implementation to advance cancer research and cancer care: A French comprehensive cancer center experience

From data strategy to implementation to advance cancer research and cancer care: A French comprehensive cancer center experience

HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) in digital healthcare ecosystems for chronic disease management: Scoping review

Machine Learning–Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review

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