Platform / Literature monitoring
Literature monitoring without the backlog
Run your search strategies on schedule, screen every article with AI, and extract reportable safety information, with experts confirming what matters.
Ranked by relevance and risk
Valid cases sent to case processing
Everything literature monitoring needs, in one place
Search strategies
Your literature search strategies run automatically on schedule, versioned and documented, so you can show an inspector exactly what was searched and when.
- Search terms per product, substance and market
- Weekly or custom schedules
- Every strategy version-controlled with approval
- Run history kept for inspection
| Strategy | Sources | Schedule | Version |
|---|---|---|---|
| Product [A] · global | [Databases] | Weekly | v[__] |
| Product [B] · global | [Databases] | Weekly | v[__] |
| Substance [C] · local | [Local journals] | Monthly | v[__] |
Placeholder strategies, configured with your team.
Auto-Query Builder
Writing and maintaining literature search strings is slow, specialist work. The Auto-Query Builder generates complete search queries from your product information, so every product is covered with the right names, synonyms and terms.
- Builds queries from substance names, INNs, trade names, local brand names and synonyms
- Adds safety terms and exclusions agreed with your team
- Converts each query into the syntax of every database you search
- Tests recall against a set of known relevant articles before approval
- Every generated query versioned, reviewed and approved before use
INN: [inn-a]
Trade names: [Brand 1], [Brand 2]
Local names: [Local brand]
Scope: adverse events, pregnancy, overdose
AND (adverse OR “side effect” OR toxicity OR pregnancy OR overdose)
NOT (animal model)
Recall check: [__] of [__] known articles found
Global and local sources
International bibliographic databases are searched alongside the local journals each market requires, so local reporting obligations are met as well as global ones.
- Major international biomedical databases
- Local and regional journals per market
- Conference abstracts where your SOPs require them
- Coverage reviewed with your team at setup
AI screening
Every article is screened and ranked by relevance, with the reason for each decision shown. Reviewers spend their time on the articles that matter.
- Titles, abstracts and full texts screened
- Ranked by relevance and potential reportability
- Reason shown for every decision
- Borderline articles always go to a reviewer
Article 1 · case reportPotential ICSR0.96Article 2 · cohort studySafety-relevant0.88Article 3 · in vitro studyNot relevant0.94Article 4 · review articleBorderline · reviewer0.58Example results and scores.
Safety extraction
Patients, products, events and outcomes are extracted from relevant articles and checked against the minimum criteria for a valid case.
- Patients, products, events and outcomes extracted
- Minimum criteria for a valid ICSR checked
- Citation and author details captured as the reporter
- Every value linked to the text
Duplicate checks
The same patient can appear in an article, a spontaneous report and a partner case. Articles are checked against existing cases and other hits before anything is created.
- Compared with existing cases from every source
- Same article from several searches recognised
- Follow-up articles linked to the original case
- Possible duplicates sent to a reviewer
Straight to cases
Once an expert confirms an article is reportable, a case is created in case processing with everything already extracted and linked.
- Case created directly in case processing
- Article attached as the source document
- Clock starts from the right day zero
- Monthly literature reports compiled automatically
Four steps, one continuous flow
Search
Strategies run on your schedule
Screen
AI ranks and explains relevance
Extract
Safety information with sources
Confirm
Expert review, then case creation
AI does the work. Your experts decide.
- Running searches on schedule
- Screening and ranking articles
- Extracting safety information
- Duplicate checks
- Translation of non-English articles
- Drafting case entries
in the
loop
- Confirming reportability
- Assessing causality
- Deciding on follow-up
- Approving search strategy changes
- Reviewing borderline articles
- Signing off monthly reports
vGenix Ai vs other approaches
How literature monitoring works today in most teams, with point tools, and with vGenix Ai.
| Area | Manual process | Point-tool automation | vGenix Ai |
|---|---|---|---|
| Searching | Run by hand on a schedule | Saved searches, run separately | Versioned strategies run automatically |
| Sources | Mostly international databases | Limited source coverage | International and local journals per market |
| Screening | Every abstract read in full | Keyword filtering | AI ranking with the reason for every decision |
| Extraction | Copied out by hand | Partial extraction | Safety data extracted and source-linked |
| Duplicates | Checked manually against cases | Not connected to cases | Checked against every case and literature hit |
| Hand-off to cases | Re-keyed into case processing | Exported and re-imported | Case created directly, clock started |
| Inspection evidence | Search logs in folders | Logs in separate tools | Every run, decision and version recorded |
“Point-tool automation” describes separate tools that each automate one step. It does not refer to any named vendor.
What changes, and how we measure it
Every engagement starts with your baseline. These are the measures we track for literature monitoring.
Illustrative scenario: a mid-size pharma company handling about 40,000 ICSRs a year across 3 regions. These figures are for discussion, not client results. In every engagement we agree baselines with you before go-live and measure results from platform data.
- Screening effort per week
- Time from publication to case
- Backlog size
- Reportable articles missed in QC sampling
- Search strategy coverage
Part of one platform
Literature monitoring shares its data model, rules and audit trail with every other module, so nothing is re-keyed between them.
Intelligent safety should be one connected operation
Automate the work. Keep the decisions with your experts. Keep the evidence traceable. See it on your own data in six weeks.
6-week pilot on literature monitoring
- Baselines agreed up front
- Configured to your SOPs
- Measured on your own data
- Validation evidence from day one