Pharmacovigilance AI: where manual report processing breaks
The flow of adverse event reports is never uniform: patient inquiries, physician reports, letters, completed forms, calls to support, publications, and partner exchanges arrive in free form and in different languages.
For each one, you need to find the active substance, reaction, outcome, concomitant medications, and administration timing — and then assess the seriousness of the case.
It is at this step that the flow stops moving: manual case processing is limited by the number of specialists, and the bottleneck turns out to be not analytics but processing.
While the volume is small, coding errors are almost invisible: one specialist keeps interpretations in mind and codes terms consistently. As the flow grows, different people and shifts get involved, and the same case starts to be coded differently.
Duplicates are not filtered out, the "drug — reaction — outcome" link breaks when copied between systems, and the queue of unprocessed reports grows faster than it can be cleared.
The cost shows up in reporting delays: serious cases have a regulatory deadline measured in days, while time is spent on reading and tagging rather than on signal assessment.
A missed deadline and an incomplete report are no longer an internal metric but a risk to registration status. Worse, a rare "drug — reaction" association drowns in the overall mass and is discovered months later, when the case cohort has already accumulated.
We examine how this is addressed: entity extraction and dictionary-based term normalization remove routine work from humans, seriousness-class routing sends only disputed cases to manual processing, and quality control and deduplication keep coding errors from accumulating.
Measurable effects: speed, completeness, traceability
The first measurable shift is the initial case processing time. Previously, it was determined by how quickly a specialist could process the incoming flow: a letter, a feedback form, a call to medical support.
The model classifies the inquiry, extracts the adverse reaction, drug, dosage, and outcome, and the specialist verifies and confirms the result — time goes to verification, not manual processing.
Case data completeness stops depending on an individual's attentiveness. Required fields — reaction start date, seriousness criterion, concomitant therapy — are checked at intake, not at the stage of export to the regulatory report.
An unfilled field does not move forward: the system requests the missing information from the source or places the case in a queue for clarification.
Decision traceability is what auditors most often care about. Every step leaves a record: which model version provided the suggestion, what it proposed, what the specialist confirmed or corrected, and which version of the MedDRA dictionary was used for coding.
The answer to "why was the case classified as serious" is assembled from the log rather than reconstructed from memory.
The share of manual operations falls primarily in triage and initial processing — the highest-volume steps.
A class of failures related to human error on routine tasks also disappears: missed deadlines for cases with non-obvious seriousness, lost duplicates for the same patient, discrepancies between the E2B(R3) form and the internal case record. The team is left with a single set of metrics that shows exactly where refinement is needed.
Deployment options: cloud, client environment, hybrid
The choice of deployment scheme determines not operational convenience but which data physically leave the customer's perimeter.
For pharmacovigilance this is fundamental: adverse reaction reports contain patient personal data, and deadlines for submitting reports to the regulator are strict. Therefore, the scheme is chosen based on medical data isolation requirements, not team convenience.
Comparison of schemes
| Option | When it fits | Limitations |
|---|---|---|
| Cloud environment | Processing anonymized data outside the perimeter is acceptable, and fast launch is needed | Pseudonymization at intake; the communication channel becomes part of the environment — a buffer and retry on interruption are needed |
| Customer environment | Personal data do not leave the infrastructure, and there are in-house capacity and operations service | Model updates go through internal approvals; backups and access logs are on the customer side |
| Hybrid scheme | Data remain inside, while model training and updates happen outside | Two integration points, synchronization of model versions, access separation between environments |
We fix the scheme at the design stage and build it as a replaceable layer: report intake and the queue live at the perimeter boundary, so moving from cloud to customer environment does not require rewriting processing logic and case routing rules.
Implementation stages: from process audit to handover
The order of work here is the reverse of conventional development: first we analyze how cases are born and live in the organization, and only then do we write code.
Rules extracted from regulations and actual practice go into the system immediately rather than being reworked after the first acceptance.
- Pharmacovigilance process audit. We go through all sources — email, portals, literature, contact center inquiries — and record formats, volume, delays, and who makes decisions at each step. The output is a source map with data frequency and quality.
- Data flow scheme. We agree on the target scheme: what is extracted automatically, what goes to manual review, where control points sit, and how change history is maintained for each case.
- Dictionary setup. We connect medical dictionaries and reference guides, configure term mapping, and rules for determining seriousness, duplicates, and expectedness — using real examples from the customer's archive.
- Case routing rules. We specify who receives each case and within what time, how escalation works for missed deadlines, who is on duty, and what happens when an employee is unavailable.
- Acceptance checks. We run the system on historical cases with a known correct answer: we look at the share correctly processed, the number of false positives, and the time to handoff further along the process.
- Handover and training. We deliver configurations, schemes, and regulations, train the team, and launch parallel operation: the system and specialists work simultaneously, discrepancies are analyzed, and only then is the manual workflow switched off.
What remains with the customer: configurations and regulations
Implementation ends not with a demonstration of a working service but with the handover of a package that any team can use to run the system.
Everything listed is kept in a shared repository and internal knowledge base — without verbal agreements and "one engineer's knowledge."
- System configuration — parameters for development, acceptance, and production environments: limits on concurrent report processing, retry rules for exchange failures, queue settings.
- Data flow schemes — the path of a report from the source (mailbox, portal, file exchange) through normalization and coding to database entry and export, with marked manual review points.
- Access policies — roles based on least privilege, user action logs, separate environments for personal data, and rules for external contractors.
- Configured dictionaries — adverse event terminology, mapping to internal reference guides, rules for merging duplicates and repeat reports of the same case.
- Documentation package — architecture description, operator instructions, acceptance checklist, and a "requirement — check — report" traceability matrix.
- Support regulations — frequency of retraining and recognition quality checks on a labeled sample, dictionary update procedure, conditions and procedure for rollback to the previous version, and incident escalation scheme.
The package removes the main risk of such projects: when the model behaves differently from the demo, there is something and someone to investigate with.
Case study: before and after on a report flow
Before implementation, the flow of adverse event reports was processed manually: letters, feedback forms, and medical representative inquiries went into one queue.
For each case, a specialist determined the substance, reaction, seriousness, and link to an already open case from scratch, and on peak days initial processing time reached four hours.
What was changed
Text parsing in Russian and English relies on an industry terminology reference; we added deduplication rules by key fields and confidence scoring for each attribute. Only cases below the confidence threshold go to manual review.
The result is assembled into a structure compatible with E2B(R3) — export to the regulatory environment did not require separate assembly.
Measurements on the same flow: median initial processing time dropped from four hours to forty minutes, and peak days no longer went into overtime. The share of cases without manual edits rose from about half to nine out of ten.
The number of escalations for review fell by almost three times, and specialists began handling disputed and serious cases instead of mechanical tagging.
Every automated decision keeps a trace: which fields were extracted, with what confidence, and by which rule duplicates were merged. This makes it possible to investigate any discrepancy and further train the model on accumulated examples without reworking integrations.
How does signal management differ from case processing?
Individual case processing is the handling of one report: validation, coding terms with MedDRA, assessment of seriousness and causality, and timely submission to the regulator.
The unit of work here is the report, and verification comes down to the completeness and correctness of a specific record.
Signal management is structured differently: it does not process a report but aggregates similar events across the entire database, compares observed frequency with expected frequency, and separates statistical noise from a new risk.
The unit of work is a signal, not a case, so the data, timelines, and verification logic here are different.
| Workflow | Data | Timelines | What AI checks |
|---|---|---|---|
| Individual case processing | one report | deadline by seriousness criterion | missing fields, coding discrepancies |
| Signal aggregation | entire database for a period | recalculation cycle | frequency increase, unexpected combinations |
| Periodic reporting | confirmed signals and summary | calendar period | completeness and consistency of the summary |
The third workflow — periodic reporting — assembles already confirmed signals and aggregate data for a period into a regular document.
The workflows cannot be mixed: a model trained to find rare combinations across the entire database produces false positives on a single report, and validation rules for one case do not see frequency increases.
Therefore, in the architecture these are separate processing branches with shared data normalization and a single terminology reference.
Common concerns: pharmacovigilance, AI, and audits
Technical concerns about pharmacovigilance rarely sound like "do we believe in AI" — more often it is a question of how the system behaves at the edges: an ambiguous case, atypical wording, an accumulated database. Below are answers to what is usually raised before launch.
How does the system determine case seriousness?
Seriousness criteria are set by rules derived from regulatory requirements, while the model extracts signs from the text: outcome, hospitalization, life threat, drug discontinuation.
If signs conflict or confidence is below the threshold, the case does not move forward automatically — it enters the manual queue with a note explaining why. Every decision is recorded in the log, so the processing is reproducible during an audit.
What happens to unrecognized reports?
We build recognition quality control on a labeled sample: we measure the share of automatically processed reports and accuracy for each field type. Anything that does not pass the threshold is not lost — it remains in the manual queue together with the source file. We show which wordings the model gets wrong and further train it on those examples.
How are duplicate reports filtered out?
A single case arrives from different sources in different wordings. We match versions by a combination of attributes — drug, dates, reaction, patient data — and by text similarity.
Matching ones are merged into one case with links to the primary reports, and disputed pairs go to manual confirmation.
How does validation work, and what should be done with the existing database?
Validation consists of documented requirements, test scenarios, and a verification protocol on a pre-labeled set, plus a version log of models and rules.
We connect the existing database in stages: we map fields, output questionable records in a separate report, and verify the result after loading. The output is the same data, but with a ready queue and decision history.
Let's discuss the task: where to start in your environment
Effort estimation relies on data, not a process description — so we start the analysis with your environment. Below is the minimum set that covers most initial questions: we clarify the rest as the discussion proceeds.
- Intake channels: a list of sources of adverse reaction reports — spontaneous reports, clinical programs, literature — and the form in which they arrive today.
- Data samples: 30–50 anonymized case records and 2–3 fully processed signals — we use them to check achievable extraction completeness.
- Benchmark: if specialists have already labeled cases manually, we need "input — verified answer" pairs; otherwise there is nothing to measure quality against.
- Access: a test environment, read-only accounts, drug and medical terminology reference guides, and personal data processing rules.
- Regulatory environment: which reports are generated now and within what timelines, who approves changes, and how system validation is structured.
- Integration point: where we return the result — to the existing safety system, to electronic document management, or via file exchange.
Next, we agree on a pilot launch: we fix one source and one signal class, define metrics on the benchmark sample, and test them on your data rather than demo data.
Based on the results, we will return with an effort estimate and implementation plan — with stages, roles, and what you get after each. Describe the task and the current process: that is enough to propose a concrete starting scheme.
