DISQOVER for determining relevant clinical data sets
Accessing the correct information is a prerequisite for effective clinical development: from trial design hypothesis validation to portfolio management. But so is having the time to process and manage all the data – and here's where many research departments fall short. Scientists need to pre-process the data: aggregate the bits and pieces of information they need and bundle them for future processing and analysis.
Data re-use best practices
With its powerful data-modeling and formatting capabilities, DISQOVER allows study designers and other users to 're-use' data generated for other primary purposes, e.g., unsuccessful trials. In some cases, the combined and re-usable data from previous studies can generate new hypotheses or insights without setting up a new clinical trial.
In cohort selection, for example, real-world data can be re-used in powerful ways to design a study with as little variability as possible. Non-clinical data can provide essential insights into how the trial should be designed to avoid adverse effects – for example, through more precise identification of the patient population subset where the treatment would have the most beneficial impact. Preliminary providing potential partner sites with these inclusion/exclusion criteria can significantly optimize recruitment. By integrating internal and external clinical data and real-world data, clinical scientists can quickly identify relevant datasets to support their hypotheses.
Capturing metadata and making it searchable
Clinical trials result in complex and multidimensional data sets. By capturing the generated metadata, DISQOVER allows users to
narrow down on specific perspectives or criteria;
perform downstream analysis;
create new subsets according to various criteria.
"By re-using clinical trial data, we maximize the contributions of trial participants, which ultimately benefits future patients as well."
