The Site Feasibility Workbench is an open-source application developed by Databricks designed to streamline clinical trial site selection through machine learning and integrated data analytics. The platform operates entirely within the Databricks workspace environment, eliminating the need for external API calls while providing comprehensive site evaluation capabilities for clinical operations teams 1)
Clinical trial site selection represents a critical phase in trial planning, requiring evaluation of numerous operational, demographic, and logistical factors across potential research centers. The Site Feasibility Workbench addresses this challenge by combining machine learning-driven scoring mechanisms with integrated data management capabilities. As the first public release of the broader Clinical Operations Intelligence Hub, the application demonstrates Databricks' commitment to bringing specialized healthcare analytics tools to the lakehouse architecture 2)
The workbench enables clinical operations teams to systematically evaluate candidate sites against weighted criteria including patient population characteristics, site infrastructure, past trial performance, and operational capacity constraints. By automating this evaluation process, organizations can reduce selection bias, accelerate feasibility assessments, and optimize trial site networks for improved recruitment and retention outcomes.
The Site Feasibility Workbench implements a six-step guided workflow that structures the site selection process through both human expertise and algorithmic analysis. The platform integrates three core technical components working in concert within the Databricks ecosystem.
The first architectural element uses ML-driven site scoring, which applies machine learning models to historical trial data and operational metrics to generate quantitative feasibility scores for candidate sites. These models learn patterns from past trial outcomes, including recruitment velocity, patient retention rates, protocol compliance, and adverse event reporting quality at various research centers.
The second component leverages Lakebase for maintaining operational state and managing the evolving data landscape throughout the feasibility assessment process. Lakebase provides centralized governance, data cataloging, and version control capabilities essential for maintaining data quality and audit trails in clinical contexts where regulatory compliance and data integrity are paramount.
The third technical component incorporates AI/BI Genie for natural language access to site data and analysis results. This conversational analytics interface allows clinical operations staff to query feasibility metrics, generate site comparison reports, and explore what-if scenarios using natural language queries, reducing technical friction and democratizing access to sophisticated analytical capabilities across the trial team 3)
The workbench addresses specific pain points in clinical trial site selection workflows. Traditional site selection relies heavily on manual research, institutional relationships, and subjective evaluation criteria that may not scale effectively across large site networks or diverse therapeutic areas. By systematizing the selection process through data-driven scoring and transparent workflows, the platform enables:
* Systematic site evaluation against weighted, measurable criteria * Rapid feasibility modeling to test site network scenarios and projected recruitment curves * Regulatory-compliant documentation with audit trails for site selection decisions * Scalable assessment across hundreds of candidate sites simultaneously * Predictive insights into likely site performance based on historical patterns
The no-code workflow design allows clinical operations professionals without data science backgrounds to independently conduct feasibility assessments and iterate on site selection strategies without requiring specialized technical support or external consultants.
By operating entirely within the Databricks workspace, the Site Feasibility Workbench exemplifies lakehouse-native application architecture for healthcare analytics. This approach consolidates disparate data sources—including site performance histories, patient demographics, geographic and logistical data, and protocol requirements—into unified Delta Lake tables accessible across analytics, machine learning, and governance tools 4)
The architecture eliminates data movement between external systems, reducing security risks, minimizing operational latency, and ensuring that all stakeholders work from a single source of truth. Integration with Databricks' AI/BI capabilities provides seamless transitions between exploratory natural language queries and sophisticated machine learning model training on the same underlying data platforms.
The Site Feasibility Workbench represents the initial public release of a larger Clinical Operations Intelligence Hub initiative aimed at systematizing multiple aspects of clinical trial operations through lakehouse-native analytics. Beyond site selection, the hub framework envisions integrated applications for patient recruitment optimization, site performance monitoring, protocol deviation analysis, and supply chain logistics across multi-site trials.