WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

WEBINAR

Pharmacovigilance With AI Beyond the FAERS Spreadsheet

Design faster, cleaner, more defensible signal detection workflows

April 22, 2026

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

You will build from pain point to pilot plan

1
Why spreadsheet-led review plateaus early
2
Where AI fits without taking over judgment
3
Methods behind modern detection
4
How to evaluate signals beyond model scores
5
Governance, failure modes, and a 90-day pilot

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Spreadsheet review breaks on volume before intent

The first plateau is operational. Teams still care deeply, but the work arrives faster than humans can sort it consistently by hand.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Manual triage creates quiet queues and loud consequences

A spreadsheet can store rows, but it cannot reliably show which rows deserve scarce expert attention first.

ICSR volume grows faster than review capacity
Literature surveillance adds unstructured evidence
Queues hide risk until prioritization is late
Urgency varies by reviewer, product, and week

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Spreadsheets preserve data, but workflows preserve judgment

Section 1: Why Spreadsheets Plateau Early

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

A late priority signal starts as ordinary rows

Presentation

A safety team reviews 4,800 quarterly ICSRs for an oncology product. Several serious hepatic narratives use different verbatim terms, three countries, and mixed MedDRA coding. No single PT crosses the manual review threshold.

Which workflow change would most directly reduce the risk of late prioritization?

AAdd another spreadsheet tab for serious cases only
BCluster related narratives and route clusters to clinical review✓
CWait for the next quarterly aggregate report
DEscalate only cases with the same preferred term

Teaching point

Signals often appear first as related evidence, not identical rows. Clustering can surface clinical patterns before a single coding bucket looks large.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Figure 1AI-assisted PV task map from intake to escalation

flowchart LR
 A[Case and literature intake] --> B[Structure and code evidence]
 B --> C[Cluster and rank items]
 C --> D[Human triage review]
 D --> E{Escalate?}
 E -->|Yes| F[Clinical signal evaluation]
 E -->|No| G[Document rationale]
 F --> H[Governed action or monitoring]

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Use AI to focus attention, not to outsource accountability

The safest AI use cases reduce search burden while keeping safety decisions owned by qualified people.

Rank cases by review priority
Group near-duplicate or clinically similar narratives
Summarize evidence for reviewer verification
Suggest codes or concepts with confidence and provenance

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Table 1Structured and unstructured PV data touchpoints

Touchpoint	Typical data	Useful AI support	Human check
ICSR intake	Narratives, dates, labs	Extraction, duplicate hints	Source verification
MedDRA coding	Verbatim terms	Code suggestions	Coder approval
Literature	Abstracts, full text	Screening, summaries	Safety sign-off
Aggregate review	Counts, trends	Ranking, clustering	Clinical context
Escalation	Evidence packets	Draft rationale	Decision owner

Match the model to the data and the decision, not to the buzzword.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Ranking is assistance; decision-making is accountability

Section 2: Where AI Actually Fits

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

NLP clustering can shrink the queue without shrinking judgment

Presentation

A top-20 pharma team applies NLP clustering to FAERS narratives for a marketed oncology product. Similar clinical narratives are batched for reviewer triage. Queue time drops 35% over two quarters.

What is the strongest interpretation of this result?

AThe NLP model has replaced signal evaluation
BThe workflow improved prioritization while preserving human review✓
CFAERS data no longer need clinical context
DAll future signals should be detected by clustering alone

Teaching point

Queue reduction is a workflow win, not proof of autonomous signal detection. The value comes from better grouping before human assessment.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Disproportionality remains the baseline, not the whole answer

Classic measures ask whether a drug-event pair appears more often than expected in a reporting database.

Proportional reporting ratio

PRR = \frac{a/(a+b)}{c/(c+d)}

Reporting odds ratio

ROR = \frac{a/c}{b/d}

Clinical rule

Signal \neq Score

The two-by-two table depends on reported counts, not true incidence.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Bayesian shrinkage reduces false confidence in sparse cells

Sparse event counts can look dramatic until uncertainty is accounted for.

Shrinkage pulls tiny counts toward the background until evidence accumulates.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Table 2Method families used in AI-enabled signal detection

Method	Best fit	Common output	Validation focus
Disproportionality	Reported count imbalance	Drug-event score	Threshold behavior
Bayesian statistics	Sparse reports	Shrunken estimate	Calibration
Supervised ML	Known triage labels	Priority class	Generalization
NLP	Narratives, articles	Concepts, clusters	Extraction quality
LLMs	Drafting, summaries	Text output	Faithfulness

Do not validate a summarizer as if it were a signal detector.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Supervised triage models learn your history, including its flaws

Historical labels are useful only when they reflect the decision you want the model to support now.

Define the label before collecting training data
Check reviewer agreement on past decisions
Hold out products, time periods, or regions
Test performance on rare but important events

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Figure 2NLP and LLMs should separate extraction from interpretation

flowchart TD
 A[Source narrative or article] --> B[Extract clinical entities]
 B --> C[Link spans to source text]
 C --> D[Cluster or summarize]
 D --> E[Reviewer verifies output]
 E --> F[Structured rationale captured]

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

LLM literature support works only with sign-off discipline

Presentation

A mid-sized biotech pilots LLM-assisted literature surveillance for rare hepatic events. The model screens abstracts and drafts summaries. Every summary routes to a safety physician before inclusion in signal review.

Which control is most important for this pilot?

AAllow the LLM to exclude articles without review
BRequire physician sign-off and source-linked summaries✓
CUse only articles with positive causality statements
DMeasure success only by articles processed per hour

Teaching point

LLMs can reduce reading burden, but literature relevance and clinical meaning require accountable review. Speed alone is not a safety metric.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Precision and recall pull against each other in rare-event work

Signal triage is a tradeoff between finding relevant evidence and avoiding reviewer overload.

How many flagged items matter

Precision = \frac{True\ Positives}{True\ Positives + False\ Positives}

How many relevant items are found

Recall = \frac{True\ Positives}{True\ Positives + False\ Negatives}

Rare-event caution

PPV \downarrow\ when\ prevalence\ is\ low

For rare events, small false-positive rates can still create large review queues.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Reporting artifacts can fool both spreadsheets and models

A pattern in reports may reflect changing attention rather than changing patient risk.

Label updates stimulate reporter attention
Media coverage changes what gets submitted
Coding practices shift with MedDRA versions
Comparator products can mask or inflate scores

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

A false-positive spike can arrive right after a label update

Presentation

After a label update and media attention, reports of a known neurologic event triple for a marketed drug. Disproportionality rises, and an ML triage model assigns high priority to many new cases.

What is the best first interpretation?

AThe product risk has definitely increased
BStimulated reporting is plausible and must be assessed✓
CThe ML model is defective and should be retired
DThe cases should be ignored because the event is labeled

Teaching point

Label and media effects can raise report counts without changing true incidence. The right response is contextual review, not automatic dismissal.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Table 3Evidence package for escalation review

Element	What to include	Why it matters
Case pattern	Counts, severity, timing	Shows clinical shape
Data quality	Duplicates, missing fields	Tests reliability
Context	Label, media, exposure	Checks artifacts
Clinical plausibility	Mechanism, risk factors	Supports interpretation
Decision trail	Reviewer rationale	Creates audit record

The package should help reviewers challenge the signal, not just confirm it.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Figure 3Escalation should be a review path, not a score threshold

flowchart TD
 A[Model or metric flags pattern] --> B[Check data quality]
 B --> C[Add reporting context]
 C --> D[Clinical review]
 D --> E{Escalate signal?}
 E -->|Yes| F[Open signal evaluation]
 E -->|No| G[Monitor with rationale]
 G --> H[Reassess on trigger]

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Governance starts with intended use, not model admiration

Regulators are more likely to tolerate innovation when the use case, limits, and controls are legible.

State the decision the tool supports
Define allowed and prohibited uses
Validate against that intended use
Document human override and exception handling

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Table 4Inspection-ready AI documentation set

Document	Purpose	Owner
Intended use	Defines supported decision	Safety lead
Validation report	Shows tested performance	Data science
SOP or work instruction	Controls daily use	PV operations
Change log	Tracks versions and updates	QA or system owner
Exception log	Records overrides and issues	Process owner
Training record	Shows user readiness	Functional manager

Ownership should be named before go-live, not reconstructed during inspection.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Figure 4Change control must include model, data, and dictionary shifts

flowchart LR
 A[Proposed change] --> B{Change type}
 B --> C[Model or prompt]
 B --> D[Data source]
 B --> E[MedDRA version]
 B --> F[Product or label]
 C --> G[Impact assessment]
 D --> G
 E --> G
 F --> G
 G --> H[Approve, test, release]

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

AI governance is a shared operating model

Section 5: Governance That Survives Audit

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Rare events make ordinary accuracy misleading

In PV, the most dangerous failures are often mundane: sparse events, weak labels, drift, leakage, and over-trust.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

A vendor model can pass history and fail the next quarter

Presentation

A vendor AI triage model performs well on historical ICSRs. After a MedDRA version change and a new product launch, high-priority case capture drops and reviewers notice odd rankings.

What is the most likely governance gap?

AThe model should never have used historical data
BValidation did not test change impact and post-launch drift✓
CReviewers should follow the model rankings without question
DMedDRA changes are irrelevant to AI triage

Teaching point

Historical validation is necessary but not sufficient. Dictionary changes and new product patterns can shift inputs enough to degrade performance.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

The common AI failures are detectable if you look early

Most failures leave clues before they become quality events.

Bad labels teach the model the wrong target
Leakage makes validation look unrealistically strong
Drift changes input patterns after deployment
Automation bias lowers reviewer challenge

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Table 5Failure modes and practical controls

Failure mode	Early warning	Practical control
Class imbalance	High accuracy, low recall	Rare-event test set
Coding inconsistency	Odd term patterns	Coder review sample
Data leakage	Too-good validation	Feature audit
Drift	Output mix shifts	Monitoring dashboard
Automation bias	Few overrides	Low-score sampling
Prompt instability	Summary style changes	Prompt version control

Controls should be active during the pilot, not invented after failure.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

A first pilot should improve one decision point

Do not start with a transformation program. Start with a narrow workflow where evidence can change a decision.

Pick a painful queue with clear ownership
Choose one output: rank, cluster, extract, or summarize
Define the human decision before the model
Limit scope by product, event, region, or source

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Table 6Metrics and QA gates for a 90-day pilot

Dimension	Metric	QA gate
Speed	Time to first qualified review	No missed serious cases in sample
Quality	Reviewer agreement	Documented rationale complete
Safety	False-negative sample rate	Medical review of misses
Usability	Override rate and reasons	Training completed
Stability	Output drift over time	Trigger review if threshold crossed

Agree on baseline, target, and stopping rules before go-live.

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Figure 5A 90-day pilot can be simple and inspection-aware

timeline
 title 90-day AI signal detection pilot
 Days 1-15 : Select use case and baseline
 Days 16-30 : Configure tool and validation set
 Days 31-45 : Train reviewers and QA checks
 Days 46-75 : Run parallel workflow
 Days 76-85 : Review errors and metrics
 Days 86-90 : Go, revise, or stop decision

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

A pilot earns expansion only when it improves a defined decision under controlled conditions.
— Section 7: Blueprint For A First Pilot

WEBINARPharmacovigilance With AI: Signal Detection Beyond the FAERS Spreadsheet

Thanks for watching

Run one narrow 90-day pilot where evidence can change a decision

Choose one queue or decision point this week
Write the intended use and prohibited uses
Set baseline, success metric, QA gate, and owner
Review pilot evidence at day 90: go, revise, or stop

Pharmacovigilance With AI Beyond the FAERS Spreadsheet

You will build from pain point to pilot plan

Spreadsheet review breaks on volume before intent

Manual triage creates quiet queues and loud consequences

Spreadsheets preserve data, but workflows preserve judgment

A late priority signal starts as ordinary rows

Use AI to focus attention, not to outsource accountability

Ranking is assistance; decision-making is accountability

NLP clustering can shrink the queue without shrinking judgment

Disproportionality remains the baseline, not the whole answer

Bayesian shrinkage reduces false confidence in sparse cells

Supervised triage models learn your history, including its flaws

LLM literature support works only with sign-off discipline

Precision and recall pull against each other in rare-event work

Reporting artifacts can fool both spreadsheets and models

A false-positive spike can arrive right after a label update

Governance starts with intended use, not model admiration

AI governance is a shared operating model

Rare events make ordinary accuracy misleading

A vendor model can pass history and fail the next quarter

The common AI failures are detectable if you look early

A first pilot should improve one decision point

Run one narrow 90-day pilot where evidence can change a decision

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