In this group, you will find the recording of the DCI Network Sept 2024 Workshop - Defining the Data Ecosystem for a Cancer Learning Health Systems https://www.dcinetwork.org/workshops/september-2024
This also serves as the home of:
Working Group 1 – LUMINA - Improve Collaborative Data Sharing and Learning Networks
L - Longitudinal database
U - Unified multi-model data
M - Multi-stakeholder collaborations
I - Integration of diverse data sources
N - Navigation and patient support
A – Analytics for improved clinical decision and for RWE
Pronunciation: loo-MEE-nuh
Derived from the Latin word for "light," LUMINA suggests illumination and clarity in cancer research and care. It encompasses the group's focus on longitudinal studies, data integration, and providing clear guidance for patients throughout their cancer journey
The DCI Network Working Group 1 is focused on is a collaborative initiative to address the critical need for harmonized data capture and integration across the entire patient journey. The primary objective of this working group is to develop a comprehensive reference architecture that enables the collection, storage, and analysis of multi-modal data from various sources to support real-world evidence generation and longitudinal studies in oncology.
The working group brings together experts from leading pharmaceutical companies, including pharmaceutical and technology companies, and renowned cancer centers such as Beth Israel Cancer Center and Dana-Farber Cancer Center. We are also connected to leaders in the FDA and The National Cancer to coordinate our efforts with their efforts and standardization, interoperability, and data transparency.
The working group's goal is to digitally track in a standardized way the entire patient journey, starting from disease screening and diagnosis, progressing through treatment selection and outcomes comparison, and extending to long-term outcome tracking using remote patient monitoring and wearable devices. By creating standardized data models across this continuum, the group aims to facilitate seamless data capture, integration, and analysis, enabling more efficient and effective real-world evidence generation.
The reference architecture developed by the working group will incorporate data from traditional clinical sources, such as electronic medical records and genomic sequencing, and non-traditional sources, including patient-reported outcomes and public biomedical data repositories. This multi-modal approach ensures a comprehensive view of the patient journey, capturing clinical outcomes, quality of life measures, and social determinants of health.
Key components of the reference architecture will include:
- Standardized data models and ontologies for capturing patient data across various journey stages, ensuring interoperability and consistency across datasets.
- Data integration pipelines and APIs for ingesting and harmonizing data from disparate sources, including clinical systems, research databases, and patient-generated health data.
- Secure data storage and management solutions, leveraging cloud-based platforms to enable scalable and efficient data access and analysis.
- Advanced analytics and machine learning tools for deriving insights from the integrated data, supporting hypothesis generation, cohort identification, and treatment outcome prediction.
- Visualization and reporting interfaces for presenting actionable insights to healthcare providers, researchers, and patients, facilitating data-driven decision-making and personalized care delivery.
By aligning the data models used by healthcare providers and public databases into this reference architecture, participating cancer centers will be better equipped to contribute to and benefit from cloud-based disease consortiums. These collaborative networks will enable the sharing of data, insights, and best practices across institutions, accelerating the pace of discovery and improving patient outcomes.
The working group will also strongly emphasize data governance, privacy, and security, ensuring that all data is handled in compliance with relevant regulations and ethical guidelines. This includes implementing robust access controls, data de-identification techniques, and secure data-sharing protocols.
The DCI Network Working Group on Standardized Data Models for Patient Journey and Real-World Evidence will drive the development and adoption of this transformative reference architecture through regular meetings, workshops, and pilot projects. By fostering collaboration across industry, academia, and government, the group aims to accelerate innovation in oncology research and care delivery, ultimately improving outcomes for cancer patients worldwide.