This issue tracks a coordinated effort to evaluate and expand AI capabilities throughout the Data2Evidence (D2E) platform, with a focus on identifying high-impact opportunities to enhance user workflows, automation, and accessibility. Effectively integrating AI across the platform can dramatically reduce manual workload, enable smarter automation, lower barriers for new users, and unlock new ways of interacting with healthcare data and analytics.
D2E already leverages AI in select areas, such as NLP pipelines and coding assistance. This initiative seeks to systematically explore and implement improvements wherever AI can streamline or transform the user experience, empowering users to onboard data, define cohorts, and design analyses more quickly, intuitively, and with less manual intervention.
Key areas of focus include:
Enhancing the coding assistant for notebooks to help define and generate Strategus analyses, including enabling the creation of analysis definitions directly from natural language descriptions.
Improving automated data mapping and NLP pipelines, combining efforts to advance both structured and unstructured data onboarding with greater automation and accuracy.
Enabling AI-driven creation of cohort definitions in D2E Cohorts from natural language descriptions, making cohort building more accessible to a broader range of users.
By addressing these areas in a unified way, D2E will further reduce manual effort, foster innovation, and make powerful analytic tools more accessible and efficient for users across the OHDSI community.
This issue tracks a coordinated effort to evaluate and expand AI capabilities throughout the Data2Evidence (D2E) platform, with a focus on identifying high-impact opportunities to enhance user workflows, automation, and accessibility. Effectively integrating AI across the platform can dramatically reduce manual workload, enable smarter automation, lower barriers for new users, and unlock new ways of interacting with healthcare data and analytics.
D2E already leverages AI in select areas, such as NLP pipelines and coding assistance. This initiative seeks to systematically explore and implement improvements wherever AI can streamline or transform the user experience, empowering users to onboard data, define cohorts, and design analyses more quickly, intuitively, and with less manual intervention.
Key areas of focus include:
Enhancing the coding assistant for notebooks to help define and generate Strategus analyses, including enabling the creation of analysis definitions directly from natural language descriptions.
Improving automated data mapping and NLP pipelines, combining efforts to advance both structured and unstructured data onboarding with greater automation and accuracy.
Enabling AI-driven creation of cohort definitions in D2E Cohorts from natural language descriptions, making cohort building more accessible to a broader range of users.
By addressing these areas in a unified way, D2E will further reduce manual effort, foster innovation, and make powerful analytic tools more accessible and efficient for users across the OHDSI community.