We are working within the framework of the LUCIA Project – Lung Cancer-related risk factors and their Impact Assessment on the Implementation of Keycloak and OMOP for safety and Standardized data in Lung Cancer.
We believe that in the world of health and medical research, data security and standardization are crucial.
Keycloak is an identity and access management (IAM) tool that provides us with:
- Centralized authentication: Allows users to authenticate once and access multiple applications without needing to re-enter their credentials.
- Role-based authorization: Defines roles and permissions to control access to resources and functionalities within an application.
- Integration with existing systems: Keycloak integrates easily with web applications, REST services, databases, and other systems.
- Network security: Provides an additional layer of security by protecting communications between applications and users.
- User and group administration: Allows you to manage users, groups and their attributes centrally.
The Common Data Model (CDM) is a widely used standard in medical research. It provides a consistent structure for storing clinical and health data, facilitating analysis and comparison across different studies. However, like any approach, it also presents certain risks and limitations in the context of lung cancer. Below, we detail some of these risks:
- Limitations in the representation of lung cancer-specific data:
OMOP is designed to be generic and applicable to a wide variety of medical conditions. This may result in a lack of details specific to lung cancer, such as molecular markers, histological subtypes, or characteristics specific to patients with this disease.
- Complexity in adapting surveys and questionnaires:
It does not provide a specific structure for surveys or questionnaires related to lifestyle or specific lung cancer risk factors. Adapting these tools to the OMOP structure can be complicated and require significant adjustments.
- Lack of data on environmental exposures and lifestyle habits:
It focuses primarily on clinical and healthcare data. It does not include detailed information on environmental exposures such as air pollution or lifestyle habits over time such as physical activity or diet.
- Challenges in capturing longitudinal data:
OMOP is based on transactional data, meaning that events are recorded at specific times (e.g., doctor visits). Capturing longitudinal data over time (e.g., lifestyle changes) can be challenging within the OMOP framework.
- Limitations in the representation of specific treatments:
It doesn't always capture specific details about lung cancer treatments, such as targeted therapies or immunotherapy. This can make it difficult to analyze the effectiveness of specific treatments.
Although OMOP offers a standardized structure for medical data, it is important to consider its limitations and carefully adapt it to the context of lung cancer.
The LUCIA project receives funding from the European Union's Horizon Europe research and innovation programme under grant agreement 101096473 .