Data Science and Analytics
Turning investigator history into usable signals
Data science and analytics teams are often tasked with modeling trial performance during the clinical trial planning phase. That work depends on having data that is broad enough to be meaningful and credible enough to stand behind.
SunshineMD supports these teams by providing structured investigator data that can be incorporated into internal analysis without starting from scratch.
Why modeling site performance is so difficult
One of the hardest variables to model in clinical trials is investigator performance.
Enrollment data is fragmented, while sponsors only see what happened in their own trials. Patient counts are rarely available outside closed systems. As teams expand into new therapeutic areas or adjust protocols, historical context is limited.
This makes it difficult to build models that reflect how sites are likely to perform in the real world.
Where existing data sources break down
Many data sources offer volume without clarity.
Some rely on opaque methodologies. Others aggregate signals that are difficult to validate or explain internally. In many cases, teams are asked to trust outputs without being able to trace how they were derived.
For analytics teams, that lack of transparency creates friction when models are reviewed, shared or challenged.
How SunshineMD supports data science and analytics teams
SunshineMD serves as a foundational data source that analytics teams can combine with other internal and external inputs.
Adjusted payment signals within the dataset correlate strongly with actual enrollment activity, giving teams a transparent and defensible input for modeling site performance.
Teams use SunshineMD data to:
- Add investigator performance context to internal models
- Compare site behavior across studies and sponsors
- Ground forecasts in documented historical activity
- Support analyses with data that can be clearly explained
Rather than offering a black-box prediction, SunshineMD provides signals [LINK TO SUNSHINE SCORE PAGE] teams can interpret and defend.
What changes for analytics teams
With access to structured investigator history, analytics teams can:
- Spend less time sourcing and cleaning raw data
- Build models with broader historical context
- Communicate assumptions more clearly to stakeholders
- Increase confidence in the inputs that drive forecasting and planning
See SunshineMD in practice
To explore how SunshineMD data can support your analytics workflows, request a demo.
See how investigator performance data can be incorporated into models used for trial planning and site evaluation.