Medidata Detect Patient Data Surveillance
Improve Clinical Data Management Efficiency by Automating Manual Data Review
Patient Data Surveillance (PDS) is a clinical data management workspace that accelerates data aggregation, review, and visualization across many sources – including Rave EDC and Non-Medidata Data. With PDS, clinical data managers and medical monitors can collaborate to review patient data more efficiently, resolve queries faster, and identify issues sooner.
Why Choose Patient Data Surveillance?
Save Time by Centralizing Data Review
Patient Data Surveillance aggregates and integrates many patient data sources upfront, saving significant time and eliminating the need for external applications, trackers, and spreadsheets.
PDS streamlines data management activities in an intuitive system where users can navigate between listings, patient profiles, and queries with a single click.
Reduce Reliance on Clinical Programming
Patient Data Surveillance is a no-code software application that allows data managers to configure the tool themselves without waiting for clinical programming resources.
PDS leverages simple drag-and-drop functionality and a library of operators and templates, allowing users to create complex cross-data listings and derived variables in a matter of minutes.
AI Data Reconciliation that Keeps Data Managers in the Loop
Manual data review processes cannot scale to the volume and variety of data needed in today’s clinical trials. Using AI in data management applications is a powerful way to de-risk activities that save time and reduce human error.
Patient Data Surveillance includes AI-driven data reconciliation that automatically flags inconsistencies among data sources. The algorithm is trained by user feedback and by Medidata’s industry-leading historical clinical data set.
Fast Setup, Fast Success
Get started in a matter of days and build listings and profiles in just a few hours. Patient Data Surveillance requires minimal configurations with templates provided out of the box.
Improve Data Quality Holistically
Patient Data Surveillance is part of Medidata Detect, a comprehensive data surveillance and risk management solution that holistically manages quality at the datapoint, patient, site, and study level.
Medidata Detect was recently named as a Leader in the IDC MarketScape “Worldwide Life Science R&D Risk-Based Monitoring Solutions 2022 Vendor Assessment.”
Key Features of Patient Data Surveillance?
Build Listings Across All your Data
Patient Data Surveillance provides a single tool to aggregate, review and analyze data, from all data sources. Data Managers can generate data outputs, including listings and exceptions, with simple drag-and-drop functionality and perform bulk query management with one click.
Simplify Complex Data Reconciliation with AI
AI-driven data reconciliation is a powerful way to de-risk data management activities by automating the identification of potential discrepancies across more complex data sets. Algorithms automatically develop relationships within and between data sets, driven by curated data from the historical clinical trials on the Medidata platform. This tool automatically provides a daily list of potential discrepancies for the data manager to review, reducing time to reconcile data and check consistency between data sources.
Visualize the aggregated patient narratives in one place to review safety and medical patient-level critical data. Data managers can focus patient profiles on areas of interest with graphs, side-by-side timeline views, and custom flags.
Medidata’s Rave EDC (Electronic Data Capture) is the most advanced, robust and secure EDC system for all clinical trial data capture and management. Rave EDC is the cornerstone of the Medidata Platform that connects processes, eliminates data reconciliation, and delivers cross-functional and cross-study data insights.
Patient Data Surveillance
Ensuring data quality and patient safety oversight during clinical trials requires multiple operational team members to aggregate, interrogate, and query data across EDC and other sources. View this infographic to see how patient data surveillance can be made easy.
Transformation of Clinical Data Management to Clinical Data Science
As the clinical trial data acquisition landscape expands, the industry requires new approaches to support the rapid pace and increasing diversity of data sources. Thus, it’s not surprising that the focus of clinical data management teams is shifting from reactive, exhaustive data cleaning to proactive, risk-based clinical data science.