DOC Data is a powerful environment for structuring data and generating analysis from full-text clinical studies and other epidemiology sources. DOC Data enables finger-tip ready review and analysis of data. The methodologies incorporated into the DOC Data platform meet the highest global evidence-based medicine standards and practices. Data analytics are directly integrated into the R Project for Statistical Computing modules so there is no denigration tied to data portability issues. The centralization and structuring of clinical data into a single, quality-controlled data hub and networked repository streamlines access and integration into an organization’s best-practices workflows.
Each DOC Data project is developed using Doctor Evidence's proprietary DOC—Digital Outcome Conversion—data extraction process, in which data from full-text articles are curated and organized into highly structured data sets. DOC Data includes a powerful term ontology management system—DOC OS—that enables versatility to various users of clinical data regarding the management of medical terms. DOC OS uses advanced machine learning principles that delivers agility to multiple users to perform specific analysis with linked traceability back to the original source document. The system has a multilayered ontology learning structure to tokenize term associations at various levels: the enterprise, departments, geographies, functions, or user id.
DOC Data outputs encompass three main features:
- Study Summaries. View critical data points, tables and figures from a study in a single convenient webpage.
- Table and Chart Wizard Tools. Summarize key information from multiple studies into summary and table analysis.
- Analysis. Create meta-analyses, Bayesian and other EBM analysis on multiple outcomes in minutes; supplement existing direct data or create new analyses using indirect comparisons of two interventions with a common comparator.