Based on over a decade of deep data extraction and domain experience coupled with the latest artificial technology, Doctor Evidence has created a proprietary software in its DOC Search™ platform. DOC Search retrieves relevant medical documents from multiple sources and instantly performs dynamic analyses of the data contained in those sources.

Some of the distinguished features of DOC Search include:

Structured for flexibility to incorporate multiple and federated data sources:

The machine learning architecture of the system dynamically links unstructured natural language, such as PubMed abstracts, with semi-structured information from other sources like clinical trial registrations and even news articles.

Powerful ontology system:

Enables inclusion of vast taxonomies that can manage synonyms and differences in jargon across the heterogeneous data sources.

Indexing of entities + the full pass of natural language processing on all documents:

Enables high-level analytics, such as co-occurrences and publication type detection.

State-of-the-art programming methodologies:

Functional programming, combined with industry-standard database technologies, provides the best of both worlds: a reliable foundation in an extremely flexible environment.

Focus on interactivity:

Enables users to playfully explore the breadth and depth of a topic and provides a more enjoyable experience during a search session, while also performing as a highly functional tool for tasks such as protocol feasibility assessments and extracting search protocol elements.

Use cases of DOC Search include:

Landscape Analytics:

Users can export a DOC Search Feasibility Report to obtain an overall summary of an evidence base and make the determination of whether there is sufficient data available to warrant a particular literature review.


Automatic identification and linking together associated publications for the same trials (i.e., post-hocs, follow-up/extension studies, or subgroup analyses), including timelines.

Monitoring & signal detection:

Semi-automates the process of determining when a review should be updated. The “Signals” feature allows users to save a search and monitor for new literature of interest. Users can save an array of “Signals” and receive targeted alerts as soon as new articles are published, providing a real-time indication of whether a literature review should be updated.

KOL identification:

Top thought leaders and/or influential authors (including collaboration networks) are identified by a powerful algorithm. Users can drill down on a specific author to retrieve that author’s contact information, affiliations, key interests, and top interventions reported in their publications.