report.object
AI agents for pharmacovigilance and medical affairs
Expert-reviewed from public records, platform data, primary sources, and private research beyond AI search. Use /api/v1/reports/RPT_LIFE_SCIENCES_AI_AGENTS_PHARMACOVIGILANCE_MED_AFFAIRS/sample before purchase, then download PDF, JSON, or Markdown after checkout.
page.markdown
AI agents for pharmacovigilance and medical affairs.md
# AI agents for pharmacovigilance and medical affairs
Pharmacovigilance and medical-affairs agents should preserve safety-to-label truth: AI-touched case facts, signal work items, label rationales, acceptor, downstream safety action and approved response boundary.
## Object
| field | value |
| ---------------- | --------------------------------------------------------- |
| report_id | RPT_LIFE_SCIENCES_AI_AGENTS_PHARMACOVIGILANCE_MED_AFFAIRS |
| schema_version | 2026-05-28 |
| last_updated_at | 2026-06-01 |
| category | AGENTS |
| sector | Life sciences |
| region | GLOBAL |
| confidence_score | 0.86 |
| source_count | 25 |
| evidence_count | 28 |
## Endpoints
| action | endpoint |
| ------------ | ------------------------------------------------------------------------------------ |
| summary | /api/v1/reports/RPT_LIFE_SCIENCES_AI_AGENTS_PHARMACOVIGILANCE_MED_AFFAIRS |
| sample | /api/v1/reports/RPT_LIFE_SCIENCES_AI_AGENTS_PHARMACOVIGILANCE_MED_AFFAIRS/sample |
| schema | /api/v1/reports/RPT_LIFE_SCIENCES_AI_AGENTS_PHARMACOVIGILANCE_MED_AFFAIRS/schema |
| quote | /api/v1/reports/RPT_LIFE_SCIENCES_AI_AGENTS_PHARMACOVIGILANCE_MED_AFFAIRS/quote |
| purchase | /checkout/RPT_LIFE_SCIENCES_AI_AGENTS_PHARMACOVIGILANCE_MED_AFFAIRS?view=agent |
| versions | /api/v1/reports/RPT_LIFE_SCIENCES_AI_AGENTS_PHARMACOVIGILANCE_MED_AFFAIRS/versions |
| changelog | /api/v1/reports/RPT_LIFE_SCIENCES_AI_AGENTS_PHARMACOVIGILANCE_MED_AFFAIRS/changelog |
| monitor | /api/v1/monitors?report_id=RPT_LIFE_SCIENCES_AI_AGENTS_PHARMACOVIGILANCE_MED_AFFAIRS |
| mcp_resource | colophon://reports/RPT_LIFE_SCIENCES_AI_AGENTS_PHARMACOVIGILANCE_MED_AFFAIRS/sample |
## Coverage
| field | value |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| category | AGENTS |
| sector | Life sciences |
| region | GLOBAL |
| topics | pharmacovigilance AI, medical affairs AI, ICSR, FAERS, AEMS, E2B(R3), signal detection, labeling, medical information, MLR |
| source_classes | safety_reporting, public_data_api, fda_program, fda_research, safety_standard, safety_system, medical_product_communications_guidance, pharmacovigilance_guidance, labeling_guidance, labeling_standard, promotional_submission_guidance, fda_guidance, data_integrity_guidance, pv_platform_docs, vendor_directory |
| update_cadence | Weekly |
## Sample JSON
```json
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"schema_version": "2026-05-28",
"title": "AI agents for pharmacovigilance and medical affairs",
"abstract": "Pharmacovigilance and medical-affairs agents should preserve safety-to-label truth: AI-touched case facts, signal work items, label rationales, acceptor, downstream safety action and approved response boundary.",
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"geographic_coverage": 0.8,
"known_gaps": [
"PV standards are public, but vendor case-processing productivity and signal-quality telemetry is usually private.",
"Medical-affairs and MLR workflows vary by company, market and product lifecycle stage.",
"Literature-monitoring evidence needs local source lists, languages and duplicate-handling rules."
]
},
"document": {
"object": "report_document",
"schemaVersion": "2026-05-28",
"title": "AI agents for pharmacovigilance and medical affairs",
"subtitle": "Safety AI protects product-risk truth from case intake to label and response boundaries.",
"abstract": "Pharmacovigilance and medical-affairs agents should preserve safety-to-label truth: AI-touched case facts, signal work items, label rationales, acceptor, downstream safety action and approved response boundary.",
"category": "AGENTS",
"sector": "Life sciences",
"region": "GLOBAL",
"topics": [
"pharmacovigilance AI",
"medical affairs AI",
"ICSR",
"FAERS",
"AEMS",
"E2B(R3)",
"signal detection",
"labeling",
"medical information",
"MLR"
],
"sources": [
{
"id": "S1",
"label": "FDA - Adverse Event Monitoring System E2B(R3) standards",
"publisher": "FDA",
"sourceClass": "safety_reporting",
"accessedAt": "2026-06-01",
"status": "verified",
"access": "sample",
"originalUrl": "https://www.fda.gov/drugs/fda-adverse-event-monitoring-system-aems/fda-adverse-event-monitoring-system-aems-e2br3-standards",
"transformChain": [],
"url": "https://www.fda.gov/drugs/fda-adverse-event-monitoring-system-aems/fda-adverse-event-monitoring-system-aems-e2br3-standards"
},
{
"id": "S2",
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"publisher": "FDA",
"sourceClass": "safety_reporting",
"accessedAt": "2026-06-01",
"status": "verified",
"access": "sample",
"originalUrl": "https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-electronic-submissions",
"transformChain": [],
"url": "https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-electronic-submissions"
},
{
"id": "S3",
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"publisher": "FDA",
"sourceClass": "public_data_api",
"accessedAt": "2026-06-01",
"status": "verified",
"access": "sample",
"originalUrl": "https://open.fda.gov/apis/drug/event/",
"transformChain": [],
"url": "https://open.fda.gov/apis/drug/event/"
},
{
"id": "S4",
"label": "FDA CDER - Emerging Drug Safety Technology Program",
"publisher": "FDA",
"sourceClass": "fda_program",
"accessedAt": "2026-06-01",
"status": "verified",
"access": "sample",
"originalUrl": "https://www.fda.gov/drugs/science-and-research-drugs/cder-emerging-drug-safety-technology-program-edstp",
"transformChain": [],
"url": "https://www.fda.gov/drugs/science-and-research-drugs/cder-emerging-drug-safety-technology-program-edstp"
},
{
"id": "S13",
"label": "EMA - GVP Module IX signal management",
"publisher": "EMA / HMA",
"sourceClass": "pharmacovigilance_guidance",
"accessedAt": "2026-06-01",
"status": "verified",
"access": "sample",
"originalUrl": "https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-good-pharmacovigilance-practices-gvp-module-ix-signal-management-rev-1_en.pdf",
"transformChain": [],
"provenanceNotes": "Pharmacovigilance guidance; use for signal, safety-plan or safety-communication evidence, then verify local governance records.",
"url": "https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-good-pharmacovigilance-practices-gvp-module-ix-signal-management-rev-1_en.pdf"
}
],
"evidence": [
{
"id": "E1",
"sourceId": "S1",
"claimIds": [
"C1",
"C2"
],
"summary": "FDA AEMS E2B(R3) materials provide instructions for electronic submission of individual case safety reports to FDA's adverse-event monitoring system.",
"status": "verified",
"confidence": 0.88,
"access": "sample"
},
{
"id": "E2",
"sourceId": "S2",
"claimIds": [
"C1",
"C2"
],
"summary": "FAERS electronic submission materials define the route for drug and biological product individual case safety reports.",
"status": "verified",
"confidence": 0.87,
"access": "sample"
},
{
"id": "E3",
"sourceId": "S3",
"claimIds": [],
"summary": "openFDA exposes public adverse-event data collected from FAERS. It is public exploration context, not a validated sponsor case-processing system.",
"status": "verified",
"confidence": 0.84,
"access": "sample"
},
{
"id": "E4",
"sourceId": "S4",
"claimIds": [],
"summary": "FDA's EDSTP focuses on AI and other emerging technologies in pharmacovigilance, including potential augmentation of human review of adverse-event reports.",
"status": "verified",
"confidence": 0.86,
"access": "sample"
},
{
"id": "E13",
"sourceId": "S13",
"claimIds": [],
"summary": "EMA GVP Module IX defines signal management as a continuous process and requires traceability and retrievability of documents related to safety-concern investigations.",
"status": "verified",
"confidence": 0.88,
"access": "sample"
}
],
"claims": [
{
"id": "C1",
"statement": "Pharmacovigilance AI must preserve the ICSR route because case intake, coding, seriousness, narrative and submission standards are formal safety records.",
"confidence": 0.88,
"evidenceIds": [
"E1",
"E2"
],
"access": "sample"
},
{
"id": "C2",
"statement": "Generative AI can support case narrative drafting and summarization, but accepted safety output requires human review, quality assessment and source traceability.",
"confidence": 0.84,
"evidenceIds": [
"E1",
"E2"
],
"access": "sample"
}
],
"colophon": {
"checkedAt": "2026-06-01",
"latestRevisionAt": "2026-06-01T00:00:00Z",
"reviewStandard": "expert_human_reviewed",
"kinetics": {
"speed": "moderate",
"impact": 0.88,
"momentum": 0.82,
"businessImpact": "PV and medical-affairs AI protects product-risk truth when case facts, signal rationale, labels and approved responses stay aligned.",
"rateOfChange": "Reviewer corrections, duplicate status, seriousness and MedDRA changes, signal handoffs and label-impact evidence are becoming the approval denominator.",
"from": "case intake automation, literature alerts and MI drafting",
"toward": "safety-to-label evidence records with reviewer corrections, signal impact and approved response boundaries"
},
"watch": [],
"gapAnalysis": "Safety-workflow telemetry linking AI-touched case facts, duplicate status, seriousness, MedDRA coding, causality support, narrative omissions, signal precision, label impact and reviewer correction rate."
},
"body": {
"sections": [
{
"id": "executive-summary",
"title": "Executive summary",
"access": "sample",
"claimIds": [
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"C1"
],
"evidenceIds": [
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],
"blocks": [
{
"id": "executive-summary-narrative",
"evidenceIds": [
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],
"type": "section_narrative",
"lead": "Pharmacovigilance AI is worth funding when reviewer corrections preserve one product-risk story from case intake to signal, labeling and approved responses; otherwise faster intake just creates signal distortion, label rework and inspection exposure.",
"body": [
"The commercial trap is accelerating case intake and medical-information drafting separately while weakening the company's single product-risk story. Safety AI is useful only if it closes the loop from adverse-event source to accepted ICSR, signal work item, labeling rationale and approved response boundary. E2B(R3) standards make the case record structured [E7]. FDA narrative-summarization research shows fluent AI output still needs quality assessment [E6].",
"The failure mode is concrete: a duplicate decision shifts case counts, a seriousness or MedDRA change moves the signal set, a weak narrative hides causality context and a label implication constrains later approved responses.",
"The operating denominator is accepted safety-to-label work: accepted ICSR rate, reviewer correction minutes, narrative omissions, signal false positives, label-impact handoffs and response-grounding defects by product and market.",
"The first proof object is a blinded replay of archived cases and approved response assets. It should show whether AI improves quality-adjusted safety capacity before it is allowed to draft across PV operations, labeling, medical information or MLR."
],
"detail": [
"Budget should move from generic assistants to workflows that preserve product-risk truth across PV operations, safety physicians, labeling, medical information and MLR.",
"Procurement should price the denominator as quality-adjusted safety capacity: accepted cases, reviewed signals, label-impact decisions and approved responses net of corrections, omissions and compliance defects."
]
},
{
"id": "board-decision-table",
"title": "The buying decision",
"evidenceIds": [
"E13"
],
"type": "table",
"columns": [
{
"key": "question",
"label": "Question",
"align": "left"
},
{
"key": "answer",
"label": "Answer",
"align": "left"
},
{
"key": "move",
"label": "Action",
"align": "left"
}
],
"rows": [
{
"question": "What is the object of approval?",
"answer": "Accepted ICSRs, signal work items, label rationales and approved-response boundaries, with reviewer correction as the denominator.",
"move": "Approve spend only when each AI-touched fact can be traced to reviewer correction and signal, label or response consequence."
},
{
"question": "Where is the first production lane?",
"answer": "High-volume case narrative plus duplicate, seriousness, MedDRA and causality support tied to signal and label handoff.",
"move": "Run a blinded replay against archived accepted cases and known label outcomes before expanding to medical-affairs drafting."
},
{
"question": "What should remain blocked?",
"answer": "Autonomous case acceptance, signal escalation, label-change rationale or medical-affairs claims without source text, reviewer correction and approved-response boundaries.",
"move": "Keep final acceptance with PV, safety physicians, labeling and MLR until the evidence record proves quality-adjusted capacity."
}
]
}
]
},
{
"id": "what-changed",
"title": "What changed",
"access": "sample",
"dek": "Public evidence is now structured enough to define private approval tests.",
"claimIds": [
"C2",
"C1"
],
"evidenceIds": [
"E13"
],
"blocks": [
{
"id": "what-changed-narrative",
"evidenceIds": [
"E13"
],
"type": "section_narrative",
"lead": "Safety-to-label consistency is the pressure: accepted ICSRs, signal work, label rationale and approved responses must stay aligned as AI touches more of the safety record.",
"body": [
"Case-level AI errors are not isolated clerical defects. A missed duplicate, changed seriousness, weak MedDRA code or omitted narrative detail can alter signal work and labeling, then constrain approved medical responses. The current public record supports augmentation, but FAERS summarization research still shows fluent AI summaries need quality assessment [E6].",
"Require vendors to return a per-product evidence record with source text, AI-extracted facts, duplicate, seriousness, coding and causality deltas, reviewer corrections and signal or label handoff; use archived cases and approved response assets as the private eval.",
"For safety and medical-affairs workflows, the approval record has to connect source case or claim evidence, accepted output, reviewer correction, escalation path, label or response boundary and retained audit evidence before capacity claims matter."
]
},
{
"id": "trigger-table",
"title": "Triggers that change approval timing",
"evidenceIds": [
"E1",
"E4",
"E13"
],
"type": "table",
"columns": [
{
"key": "trigger",
"label": "Trigger",
"align": "left"
},
{
"key": "why_now",
"label": "Why it matters",
"align": "left"
},
{
"key": "buyer_action",
"label": "Decision use",
"align": "left"
}
],
"rows": [
{
"trigger": "AEMS and E2B(R3)",
"why_now": "Electronic case transmission and standards define the safety data route [E1, E7].",
"buyer_action": "Align intake and narrative agents to ICSR data elements and submission rules."
},
{
"trigger": "FDA EDSTP",
"why_now": "FDA is explicitly learning where AI can augment PV review [E4, E5].",
"buyer_action": "Track accepted use cases and design reviewer-in-the-loop evidence."
},
{
"trigger": "Generative AI narrative research",
"why_now": "Case narrative summarization is concrete, but quality assessment remains decisive [E6].",
"buyer_action": "Run private narrative, coding and seriousness evals."
},
{
"trigger": "Signal and pharmacovigilance-plan controls",
"why_now": "Signal work items and PV plans have auditable objects beyond case-processing automation [E13, E14].",
"buyer_action": "Separate signal validation, prioritization and label-impact fields from case drafting."
},
{
"trigger": "Label, SIUU and Form 2253 routes",
"why_now": "Medical-affairs outputs can cross approved-label communications [E12], SIUU and promotional-submission routes [E18, E19], and safety-communication boundaries [E20].",
"buyer_action": "Classify the communication route before allowing an agent to draft or approve response text."
}
]
},
{
"id": "route-definition-callout",
"title": "Route definition",
"evidenceIds": [
"E13"
],
"type": "callout",
"tone": "neutral",
"body": "For this report, route means the governed ICSR processing path, signal-management work item, labeling-change rationale, SIUU or promotional-communication classification, and approved-response boundary that carry source text, reviewer correction and audit evidence."
}
]
},
{
"id": "decision-map",
"title": "Decision map",
"access": "sample",
"dek": "Workflow approval changes by authority, source boundary and proof burden.",
"claimIds": [],
"evidenceIds": [
"E13"
],
"blocks": [
{
"id": "decision-map-narrative",
"evidenceIds": [
"E13"
],
"type": "section_narrative",
"lead": "Fund PV and medical-affairs AI when it preserves the accepted safety-to-label evidence record across narratives, signals and response boundaries.",
"body": [
"The decision map should start with an ICSR-to-signal-to-label evidence record with source text, accepted case fields, reviewer corrections, signal or label impact and retained evidence: what source record is read, what output is drafted, who approves it, where it is stored and what event forces reapproval.",
"A system can pass a low-risk support test and still fail production work: source evidence, accepted output, integration state, exception handling, reviewer burden and downstream rework have to hold together.",
"The useful vendor question is whether the system can make the named task inspectable, reversible and measurable inside the operating record."
]
},
{
"id": "route-selection-table",
"title": "Workflow selection",
"evidenceIds": [
"E13"
],
"type": "table",
"columns": [
{
"key": "route",
"label": "Workflow",
"align": "left"
},
{
"key": "accepted_output",
"label": "Accepted work",
"align": "left"
},
{
"key": "gate",
"label": "Approval gate",
"align": "left"
},
{
"key": "buyer_rule",
"label": "Approval rule",
"align": "left"
}
],
"rows": [
{
"route": "Safety case intake",
"accepted_output": "Triage, duplicate check, seriousness assessment and structured case fields.",
"gate": "Primary source, reporter, product, event, patient, timestamp and reviewer action.",
"buyer_rule": "Measure accepted cases and correction rate, not extracted fields alone."
},
{
"route": "Narrative and ICSR processing",
"accepted_output": "Reviewer-approved narrative, MedDRA coding and E2B-ready case packet.",
"gate": "Source trace, coding validation, causality support and quality review.",
"buyer_rule": "Keep final case acceptance with safety professionals."
},
{
"route": "Signal and labeling",
"accepted_output": "Signal work item, label-change evidence or safety question for review.",
"gate": "Case series, disproportionality context, medical review and governance committee [E13, E15]. Label-change routes need safety-label and SPL evidence [E16, E17].",
"buyer_rule": "Do not let exploratory signal scores become label decisions."
},
{
"route": "Medical affairs and MLR",
"accepted_output": "Approved response, literature summary, claims check or review-ready material.",
"gate": "Label and approved-claims checks [E12], SIUU boundary [E18], promotional-submission route [E19], citation, reviewer and market-specific rules.",
"buyer_rule": "Require source-approved claims and audit trail for every generated answer."
}
]
},
{
"id": "workflow-authority-matrix",
"title": "Write authority changes the proof burden",
"caption": "Use the matrix to decide what proof is required before each workflow becomes accepted work.",
"evidenceIds": [
"E13"
],
"type": "visual_matrix",
"columns": [
"Source path",
"Reviewer gate",
"System action",
"Retest trigger"
],
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{
"label": "Safety case intake",
"note": "Triage, duplicate check, seriousness assessment and structured case fields.",
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{
"label": "Source named",
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"tone": "info",
"strength": "medium"
},
{
"label": "Human gate",
"detail": "Measure accepted cases and correction rate, not extracted fields alone.",
"tone": "blocked",
"strength": "gate"
},
{
"label": "Accepted work",
"detail": "Triage, duplicate check, seriousness assessment and structured case fields.",
"tone": "success",
"strength": "high"
},
{
"label": "Reapproval",
"detail": "Retest when policy, workflow, model, data access or system write rights change.",
"tone": "warning",
"strength": "medium"
}
]
},
{
"label": "Narrative and ICSR processing",
"note": "Reviewer-approved narrative, MedDRA coding and E2B-ready case packet.",
"cells": [
{
"label": "Source named",
"detail": "Source trace, coding validation, causality support and quality review.",
"tone": "info",
"strength": "medium"
},
{
"label": "Human gate",
"detail": "Keep final case acceptance with safety professionals.",
"tone": "blocked",
"strength": "gate"
},
{
"label": "Accepted work",
"detail": "Reviewer-approved narrative, MedDRA coding and E2B-ready case packet.",
"tone": "success",
"strength": "high"
},
{
"label": "Reapproval",
"detail": "Retest when policy, workflow, model, data access or system write rights change.",
"tone": "warning",
"strength": "medium"
}
]
},
{
"label": "Signal and labeling",
"note": "Signal work item, label-change evidence or safety question for review.",
"cells": [
{
"label": "Source named",
"detail": "Case series, disproportionality context, medical review and governance committee [E13, E15]. Label-change routes need safety-label and SPL evidence [E16, E17].",
"tone": "info",
"strength": "medium"
},
{
"label": "Human gate",
"detail": "Do not let exploratory signal scores become label decisions.",
"tone": "blocked",
"strength": "gate"
},
{
"label": "Accepted work",
"detail": "Signal work item, label-change evidence or safety question for review.",
"tone": "success",
"strength": "high"
},
{
"label": "Reapproval",
"detail": "Retest when policy, workflow, model, data access or system write rights change.",
"tone": "warning",
"strength": "medium"
}
]
},
{
"label": "Medical affairs and MLR",
"note": "Approved response, literature summary, claims check or review-ready material.",
"cells": [
{
"label": "Source named",
"detail": "Label and approved-claims checks [E12], SIUU boundary [E18], promotional-submission route [E19], citation, reviewer and market-specific rules.",
"tone": "info",
"strength": "medium"
},
{
"label": "Human gate",
"detail": "Require source-approved claims and audit trail for every generated answer.",
"tone": "blocked",
"strength": "gate"
},
{
"label": "Accepted work",
"detail": "Approved response, literature summary, claims check or review-ready material.",
"tone": "success",
"strength": "high"
},
{
"label": "Reapproval",
"detail": "Retest when policy, workflow, model, data access or system write rights change.",
"tone": "warning",
"strength": "medium"
}
]
}
],
"legend": [
{
"label": "Private eval can start here",
"tone": "success",
"strength": "high"
},
{
"label": "Requires named owner or reviewer",
"tone": "warning",
"strength": "medium"
},
{
"label": "Hard approval gate before scale",
"tone": "blocked",
"strength": "gate"
}
]
}
]
},
{
"id": "signature-proof",
"title": "Signature proof",
"access": "locked",
"dek": "The decisive proof is whether the workflow survives evidence, audit and rework.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "signature-proof-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "provider-workflow-landscape",
"title": "Provider lanes",
"access": "locked",
"dek": "Providers should be compared by workflow authority.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "provider-workflow-landscape-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "use-case-route-selection",
"title": "Workflow selection",
"access": "locked",
"dek": "Production starts with the lane where output can be accepted or rejected visibly.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "use-case-route-selection-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "evidence-and-benchmarks",
"title": "Evidence fit",
"access": "locked",
"dek": "Public evidence maps to local accepted work.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "evidence-and-benchmarks-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "private-eval-design",
"title": "Private eval design",
"access": "locked",
"dek": "Public evidence chooses the tests; local cases decide production.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "private-eval-design-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "risks-and-controls",
"title": "Risks and controls",
"access": "locked",
"dek": "The main risk is ungoverned action.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "risks-and-controls-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "operating-model",
"title": "Operating model",
"access": "locked",
"dek": "Production ownership crosses clinical, technical, compliance and finance teams.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "operating-model-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "economics",
"title": "Economics",
"access": "locked",
"dek": "Price the accepted work.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "economics-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "implementation-signals",
"title": "Public claim tests",
"access": "locked",
"dek": "Vendor pages seed diligence; local telemetry supplies operating proof.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "implementation-signals-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "vendor-directory",
"title": "Diligence directory",
"access": "locked",
"dek": "Representative vendors and platform RFI anchors to include in diligence.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "vendor-directory-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "recommended-next-steps",
"title": "Recommended next steps",
"access": "locked",
"dek": "A practical workflow can be approved in phases without pretending the whole market is ready.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "recommended-next-steps-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "known-unknowns",
"title": "Known unknowns",
"access": "locked",
"dek": "These gaps should shape diligence rather than delay all buying.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "known-unknowns-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "source-ledger",
"title": "Source ledger",
"access": "locked",
"dek": "Primary public sources and evidence rows behind the workflow analysis.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "source-ledger-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "glossary",
"title": "Glossary",
"access": "sample",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "glossary-table",
"title": "Terms used in this report",
"caption": "Definitions are reader aids, not separate evidence claims.",
"evidenceIds": [],
"type": "table",
"columns": [
{
"key": "term",
"label": "Term",
"align": "left"
},
{
"key": "meaning",
"label": "Plain meaning",
"align": "left"
}
],
"rows": [
{
"term": "Route",
"meaning": "The approved path from work to accepted output: source set, data boundary, model or vendor channel, tools, reviewer, evidence record and reapproval trigger."
},
{
"term": "API",
"meaning": "Application programming interface: a governed software connection used by an app or workflow to call a model or service."
}
]
}
]
}
]
}
},
"gap_analysis": "Safety-workflow telemetry linking AI-touched case facts, duplicate status, seriousness, MedDRA coding, causality support, narrative omissions, signal precision, label impact and reviewer correction rate.",
"kinetics": {
"speed": "moderate",
"impact": 0.88,
"momentum": 0.82,
"businessImpact": "PV and medical-affairs AI protects product-risk truth when case facts, signal rationale, labels and approved responses stay aligned.",
"rateOfChange": "Reviewer corrections, duplicate status, seriousness and MedDRA changes, signal handoffs and label-impact evidence are becoming the approval denominator.",
"from": "case intake automation, literature alerts and MI drafting",
"toward": "safety-to-label evidence records with reviewer corrections, signal impact and approved response boundaries"
},
"watch_signals": [],
"sample_findings": [
"The decisive PV and medical-affairs AI control is accepted safety-to-label evidence, not case-drafting speed."
]
}
```