It may be a change in what is happening to patients. It may be a change in who is reporting, how aware they are, what prompted them, or how many duplicates arrived.
Both look identical in the data. Which is why characterising your own reporting stream comes before interpreting anything in it.
Adverse event volumes grow with exposure, with awareness, with channel expansion and with partner and literature obligations. The regulatory obligation applies to every case that arrives.
Most of the effort each case consumes is not medical. It is reading a document, finding the patient and product details, transcribing them into a database, hunting for the previous version of the same case, checking whether it arrived before through another route, and assembling the narrative from several sources.
That is the work to automate. The medical assessment inside each case is the work to protect, and it is the part currently squeezed.
The same case arrives through several routes
Patient, physician, partner, literature, each with different identifiers.
Duplicates corrupt the signal, not just the workload
One event counted twice has the shape of a trend.
Intake is manual, so the clock starts late
And the time lost at the front is the time unavailable at the end.
Safety, clinical and complaint systems do not reconcile
So the same event exists in three places with three statuses.
Signal screening is confused with signal evaluation
The first is a statistical flag. The second is medical work.
Literature screening is tuned for precision
When the only acceptable failure mode is not missing anything.
Timelines are managed as one number
When the obligation depends on jurisdiction, case characteristics and product status.
Benefit-risk is expected from a dashboard
When it is a structured judgement that data can only inform.
Measure your duplicate rate, then look at what it did to last year’s signals. Take a period of case intake and establish how many of those cases were duplicates of something already in the database, broken down by the route each arrived through. Then take the disproportionality outputs from the same period and ask how many were affected by the duplicate volume. Most organizations have never connected the two numbers, and the connection is the argument for investing in detection: a duplicate is not a wasted hour, it is a data integrity defect sitting inside your signal screening. A week of analysis on data you already hold.
The order matters more here than on any other page in our Life Sciences work. Analysis performed on an uncharacterised, undeduplicated stream produces findings that reflect the stream rather than the safety profile.
Step 1
Assess the sources and the routes
Step 2
Map the data to a common structure
Step 3
Standardize terminology and product identity
Step 4
Integrate safety, clinical, complaint and literature sources
Step 5
Cleanse and deduplicate before anything is analysed
Step 6
Automate the administration
Step 7
Analyze the operational picture
Step 8
Support detection with screening outputs
Step 9
Validate to intended use, against known cases
Step 10
Govern terminology, models and access
Step 11
Monitor accuracy, exceptions and timeliness
Step 12
Optimize continuously
Ask what proportion of an assessor’s day is medical work. Sit with somebody who processes cases and account for their time: reading source documents, locating product and patient detail, transcribing into the database, checking for duplicates, chasing missing information, assembling the narrative, and assessing the case medically. The last item is usually a small fraction of the total. That fraction is the measure worth improving, and it is improved by removing everything around it rather than by asking anybody to assess faster.
Grouped by where the work sits. The boundary below is not a disclaimer, it is the design principle the rest of the page follows.
Get the data in
Safety data integration across intake routes, with format, lag and volume characterised per route
Adverse event intake automation, including extraction from documents, forms and correspondence
Duplicate detection with candidate pairs presented for human confirmation
Product and terminology alignment, with dictionary version recorded against every coded value
Reconciliation with clinical and product complaint sources so one event is one event
Move the case
Case assembly, so an assessor opens a complete picture rather than gathering it
AI-assisted triage that proposes a priority order for qualified review
MedDRA and WHODrug coding assistance, proposing terms for a coder to accept or reject
Workflow automation for routing, chasing, follow-up requests and status tracking
Regulatory submission tracking and E2B exchange support
See the picture
ICSR analytics across volume, source, product, seriousness and completeness
Operational dashboards covering turnaround, quality, backlog and exception queues
Aggregate safety reporting analytics, assembling the data behind periodic reports
Cross-product and cross-market trend views, with the reporting stream shown alongside
Literature surveillance tooling tuned for recall, with ranking rather than filtering
Support the science
Signal screening outputs, including disproportionality, presented with their limitations
Stratification and subgroup views to support qualified evaluation
Masking and notoriety checks, so a screening result is read in context
Benefit-risk evidence assembly, bringing the inputs together for structured assessment
The third column is the honest one. On this page it matters more than the capability list.
| Use case | What we automate | What stays with your qualified people |
|---|---|---|
| Adverse event intake | Extraction, structuring, routing, acknowledgement, clock start | Whether the report is a valid case |
| ICSR processing | Assembly of the complete picture, follow-up request drafting | The medical assessment and every judgement in it |
| Serious event monitoring | Flagging, prioritisation, escalation routing, visibility | Determination of seriousness |
| Case prioritisation | A proposed order based on stated criteria | The decision on what is worked next |
| Duplicate identification | Candidate pair detection across routes and identifiers | Confirmation of every match, and any merge |
| Signal detection | Screening outputs with limitations and context attached | Evaluation, validation and the decision to act |
| Disproportionality analysis | Computation, stratification, masking and notoriety checks | Interpretation, and whether it means anything |
| Periodic safety reporting | Data assembly, consistency checks, reconciliation of inputs | Authorship, the assessment and the conclusions |
| Literature monitoring | Search, ranking for recall, grouping of near-duplicates | Relevance decisions and case identification |
| Complaint and safety reconciliation | Matching candidates across the two systems | Whether a complaint is also a safety event |
| Submission tracking | Status, destination, acknowledgement and exception visibility | Reportability and the submission decision |
| Post-marketing surveillance | Data integration, trend views, stream characterisation | Surveillance conclusions and regulatory action |
The validated safety system holds the case and is authoritative for it. Analytics reads from it and must never become a second place where a safety record is maintained.
That boundary sounds obvious and is breached routinely, usually by a well-intentioned extract that somebody starts correcting.
| Layer | What belongs there | The constraint |
|---|---|---|
| Safety database | The case, its versions, assessments, submissions and audit trail | Authoritative. Validated. Never duplicated as a working copy |
| Intake and extraction | Getting structured data out of documents, forms and correspondence | Assistive, verified, with confidence routed by consequence |
| Terminology services | MedDRA and WHODrug with version control | Verbatim retained, version recorded against every coded value |
| Exchange | E2B(R3) and partner data exchange | Format and acknowledgement handling, with exceptions owned |
| Analytics layer | Operational and screening analytics across time and products | Read-only on safety data. Not a record. Not a correction route |
| Literature pipeline | Search, ranking, grouping and reviewer workflow | Tuned for recall, with measured performance against a held-out set |
| AI components | Extraction, matching, ranking, drafting, summarisation | Bounded, logged, attributable, with a human decision point |
Check whether anybody is correcting data in your analytics layer. Find out whether any safety figure that reaches a report or a submission has been adjusted anywhere other than in the validated safety database. The honest answer is sometimes yes, usually in a spreadsheet, usually for a defensible reason under time pressure. That is a second record of safety data outside the validated system, and it is the kind of finding that is far better discovered internally than during an inspection. The fix is to make the analytics layer read-only and route every correction back to the source.
Which frameworks apply, how a system is classified, what validation is required and what constitutes acceptable practice are determined by your pharmacovigilance, quality and regulatory functions.
Frameworks where they apply
- Good pharmacovigilance practice and applicable GxP requirements
- ICH E2 guidelines and ICH E2B(R3) for case data and exchange
- FDA and EMA pharmacovigilance requirements as they apply to your products and markets
- FDA 21 CFR Part 11 and EU Annex 11 where applicable, with validation scoped by your quality function
Data integrity
- ALCOA plus principles applied to the safety record
- Verbatim terms retained permanently alongside codes
- Dictionary version recorded against every coded value
- Controlled correction with no silent overwrite, and safety record retention to your requirements
Privacy
- GDPR and applicable privacy requirements for patient and reporter personal data
- Minimisation, because a safety case needs what the assessment requires and not more
- Reporter and patient identity handled to the standard your privacy function sets
- Cross-border transfer and partner data sharing within agreed terms
AI oversight
- Defined and approved use cases, with an explicit list of what each component may do
- Human decision point on everything medical, regulatory or irreversible
- Model, prompt and threshold versions under change control, because all three change the output
- Performance monitored in production, with drift treated as a change requiring assessment
Pharmacovigilance operations are well instrumented on turnaround and submission timeliness, because those are visible and reported.
Case quality, duplicate performance, coding consistency and automation accuracy are measured less often, and they are what determines whether the timely output was right.
| Failure | How it presents |
|---|---|
| Duplicates are entering undetected | Volume and signal counts both inflate, and nobody connects the two |
| Extraction accuracy fell with the source mix | A partner switched to scans and nothing failed |
| The verification queue is not worked | Low-confidence values enter the record because the queue was ignored |
| Coding drifts between coders or over versions | Trends move and no clinical change occurred |
| Screening output goes unreviewed | The list is produced, nobody has capacity, and it quietly stops being read |
| Literature recall degraded | The ranking still looks healthy and relevant items stopped surfacing |
| Case quality falls with workload | Turnaround holds and completeness does not |
| A correction was made outside the safety database | A figure in a report does not match the validated record |
Pharmacovigilance programmes report cases processed and timeliness achieved. The outcome that matters is whether qualified people spend their time on medical work, and whether the data underneath their conclusions is clean.
| Outcome | How to tell |
|---|---|
| Administration reduced | The share of an assessor’s day spent on medical work has risen |
| Processing efficiency improved | Turnaround improved without completeness or amendment rate worsening |
| Safety data quality strengthened | Duplicate rate measured and falling, coding consistency sampled and stable |
| Reporting supported | Submission exceptions are owned and the tracking is trusted |
| Signal identification accelerated | Screening output is produced, reviewed, and reaches qualified evaluation |
| Inspection readiness improved | An automated case trail reads as evidence, with attribution clear |
| Operations more scalable | Volume growth is absorbed without the quality measures moving |
We build the data and automation layer underneath pharmacovigilance: integration across intake routes, duplicate detection, case assembly, coding assistance, screening support, literature tooling and operational analytics. Every medical and regulatory judgement stays with your qualified people, and we say so on the page rather than in a contract.
CaliberFocus builds the data and automation layer underneath pharmacovigilance: integration across intake routes, duplicate detection, case assembly, coding assistance, signal screening support, literature tooling and operational analytics. We automate the transcription and the assembly so qualified assessors spend their time on medical work, and every medical and regulatory judgement stays with your people.
- Duplicate detection with every match confirmed by a person
- Case assembly that frees assessors for medical work
- Literature surveillance tuned for recall, with ranking not filtering
- Read-only analytics beside the validated safety database
Security & Compliance
