The safety picture is distributed across evidence types.
Individual cases, literature, clinical trial findings, real-world evidence, class effects, toxicology, complaints, and external intelligence are reviewed in separate operating systems.
Safety & pharmacovigilance intelligence
Preserve the chain from evidence to safety action.
StromaOS connects case evidence, literature, trial data, real-world evidence, class effects, signal detection, aggregate analysis, medical assessment, regulatory obligations, and lifecycle follow-up in one governed execution environment.
Connected safety evidence
Governed signal execution
Preserved medical judgment
Traceable regulatory action
The safety operating problem
Signals are not decisions. Decisions are not actions. Actions are not closure.
Pharmacovigilance depends on a chain of evidence, medical judgment, regulatory obligation, operational execution, and lifecycle follow-up. That chain is frequently split across case systems, signal tools, literature workflows, aggregate reports, spreadsheets, committees, documents, and local processes.
Assessment context is lost between reviews.
Signal history, prior rationale, dissent, threshold changes, dismissed hypotheses, outstanding evidence, and follow-up actions are difficult to carry across committees and reporting cycles.
Recommendations and downstream execution separate.
Monitoring, mitigation, label, communication, study, submission, training, or market actions may be managed outside the signal record that triggered them.
AI can accelerate review without carrying medical authority.
Models can classify, summarize, extract, trend, and draft, but they cannot be allowed to collapse evidence assembly, medical evaluation, regulatory judgment, and release into one uncontrolled action.
How the solution works
Safety becomes one governed signal-to-action lifecycle.
StromaOS coordinates incoming evidence, detection methods, medical assessment, quality gates, regulatory obligations, accountable review, action management, and closure in one execution chain.
STEP 01
Ingest and classify evidence
Cases, literature, trial findings, RWE, complaints, class effects, and external safety intelligence.
STEP 02
Detect and prioritize
Trends, disproportionality, clusters, severity, expectedness, exposure, novelty, and rule-based triggers.
STEP 03
Assemble the assessment
Case series, mechanism, class context, trial evidence, RWE, alternative explanations, uncertainty, and gaps.
STEP 04
Apply medical authority
Signal validation, benefit-risk interpretation, escalation, committee review, decision, and rationale.
STEP 05
Execute safety action
Investigate, monitor, communicate, mitigate, amend, report, update label, or close with evidence.
STEP 06
Preserve and learn
Methods, versions, evidence, rationale, approvals, obligations, follow-up, effectiveness, and lifecycle memory.
Signal control
Every signal remains connected to its evidence, decision, action, and follow-up.
A signal management process should show more than status. StromaOS preserves why a signal was opened, which evidence changed the assessment, which uncertainties remain, who had authority, what action was approved, and what evidence is required for closure.
Exposure and comparator populations for safety studies come from approved cohort packages, so the people behind an assessment are defined once and traceable to the individual.
The result is a continuous safety record rather than a sequence of disconnected committee artifacts.
Model a signal workflow
Six stages, each producing a named artifact the next stage can use. A refused action is recorded with the same standing as a completed one.
Governance throughout safety
AI can accelerate evidence work without inheriting medical judgment.
StromaOS separates classification, extraction, detection, synthesis, recommendation, evaluation, approval, reporting, release, and closure. Named medical, safety, regulatory, quality, and governance authorities retain accountable decisions.
Explore the Governance Spine01
Evidence governance
Every signal assertion remains linked to the cases, analyses, literature, trial findings, real-world evidence, class context, extraction, interpretation, date, and confidence that support it.
02
Detection and threshold control
Disproportionality, trend, seriousness, expectedness, exposure, novelty, frequency, mechanism, and escalation criteria are explicit and versioned.
03
Human medical authority
AI may assemble and analyze evidence. Accountable medical and safety authorities retain signal validation, causality, benefit-risk, action, and closure decisions.
04
Regulatory and release authority
Drafting, medical review, regulatory review, approval, submission, communication, and label release remain separate governed permissions.
05
Evidence Data Trace
Inputs, methods, models, prompts, versions, thresholds, dissent, decisions, approvals, obligations, actions, effectiveness, and closure remain reconstructable.
Start with one signal workflow, aggregate process, or safety evidence bottleneck.
Connect the safety sources and specialist systems already in use. Define the evidence standard, thresholds, medical authority, quality gates, regulatory obligations, and closure criteria. Then run the work as one governed safety operation.