Automations
This pillar focuses on post-market safety workflows that monitor internal and external data streams for adverse event signals, verify patterns, and route compliant reporting actions automatically. Pages should connect the business case of faster detection and lower reporting overhead with the architecture required for signal ingestion, case review, escalation, and regulatory submission.
This foundational workflow orchestrates continuous signal ingestion from EMRs, social media, and literature, automated case triage, and regulatory submission routing to compress detection-to-action timelines. The page details a multi-agent architecture using LangGraph for state management, integration with legacy PV systems like ARISg, and the controls needed for audit trails and medical review, delivering measurable reductions in manual monitoring overhead and faster compliance.
This workflow deploys specialized agents to continuously scan PubMed, EMBASE, and preprint servers, extract adverse event mentions, and normalize them against product portfolios. It eliminates manual literature review, reducing the risk of missed signals. The page covers agent orchestration, entity linking, confidence scoring, and integration with signal management systems to provide a scalable, always-on surveillance layer for global safety teams.
Automates the intake of unstructured adverse event reports from emails, faxes, and web forms, using LLMs to extract patient demographics, drug details, and event narratives into standardized ICSR fields. This workflow cuts data-entry time by over 70% and improves data quality. The page explains the document processing pipeline, validation rules, human-in-the-loop correction queues, and integration with safety databases like Oracle Argus or Veeva Safety.
Implements a real-time pipeline to monitor de-identified electronic health record streams for adverse event patterns, using NLP to interpret clinical notes and lab alerts. This enables proactive signal detection before formal reporting. The page details the FHIR/HL7 integration patterns, privacy-preserving techniques, anomaly detection models, and the alerting logic that routes potential cases to PV associates for validation.
Orchestrates agents to monitor patient support forums, call center transcripts, and social media for potential adverse events, performing sentiment analysis and initial clinical assessment. This surfaces early warning signals from direct patient feedback. The architecture combines real-time API polling, conversation threading, MedDRA term suggestion, and priority scoring to route high-risk mentions to case processing teams.
Replaces manual case sorting with an AI agent that evaluates incoming ICSRs for seriousness, expectedness, and reporting deadlines to assign priority and route to the appropriate reviewer. This slashes triage time and prevents deadline misses. The page covers the rule-based and ML scoring logic, integration with case management systems, and the configurable routing rules that adapt to product-specific requirements and team capacity.
Deploys a collaborative agent system to identify potential duplicate cases across global databases using fuzzy matching on patient, drug, and event data, then proposes merges for reviewer approval. This eliminates redundant work and ensures data integrity. The implementation details include matching algorithms, confidence thresholds, workflow orchestration for merge proposals, and audit logging for compliance.
Uses LLM agents to synthesize coherent, chronological case narratives from fragmented source documents like medical records, lab reports, and correspondence. This saves medical writers hours per case and improves narrative quality. The page explains the retrieval-augmented generation (RAG) architecture, template adherence, fact-checking steps, and the medical reviewer approval loop required for production use.
Extends narrative automation to include summarization of complex patient histories from longitudinal EHR data, highlighting relevant medical events for causality assessment. This accelerates case processing for chronic therapies. The workflow integrates with clinical data lakes, uses structured prompts for consistency, and includes a side-by-side comparison tool for reviewer validation.
Orchestrates agents to retrieve relevant patient history, drug timelines, and dechallenge/rechallenge information, applying the WHO-UMC causality framework to propose an assessment score. This brings standardization and speed to a subjective, time-consuming task. The page details the evidence-gathering steps, reasoning logic, explanation generation, and the mandatory pharmacovigilance physician review gate.
Implements a production-grade workflow where LLM agents suggest the most precise MedDRA PT and LLT codes for verbatim terms, leveraging the hierarchical dictionary and historical coding patterns. This reduces coder workload and improves consistency. The architecture includes a coding validation agent, an escalation path for ambiguous terms, and seamless integration with coding platforms for batch application.
Automates the coding of drug names, including investigational compounds and trade names, to standardized WHO-DD entries, handling disambiguation and dose form normalization. This accelerates a critical data standardization step. The page covers the drug name recognition model, mapping logic to the dictionary, handling of 'not codeable' scenarios, and integration with the case processing flow.
Provides a targeted automation solution for remediating large backlogs of uncoded or poorly coded historical cases, applying modern NLP to suggest codes at scale with human reviewer oversight. This unlocks trapped data for aggregate analysis. The implementation focuses on batch processing pipelines, progress dashboards, and audit trails to support one-time cleanup projects.
Orchestrates agents to autonomously execute the complex data queries from multiple safety databases required for PSUR/PBRER generation, adhering to the defined review period and product list. This eliminates days of manual query building and data reconciliation. The page details the query agent logic, data aggregation, discrepancy flagging, and the handoff to the report drafting workflow.
Operationalizes continuous signal detection by automating the calculation of disproportionality measures (PRR, ROR) on incoming case data, running against a dynamic comparator database. This shifts signal detection from periodic batch runs to a real-time monitoring capability. The architecture covers data pipeline orchestration, statistical agent implementation, alert thresholding, and integration with signal management meetings.
Automates the labor-intensive compilation of data packs for signal detection meetings, pulling the latest cases, literature findings, and analysis results into a structured, review-ready document. This ensures committees have consistent, up-to-date information. The workflow combines data agents, document assembly logic, and secure distribution to meeting participants, saving significant pre-meeting preparation time.
Builds a compliant workflow where agents draft expedited regulatory reports (CIOMS I, MedWatch 3500A) by populating templates with case data, performing validity checks, and routing for sign-off. This reduces reporting lag and prevents formatting errors. The page details the template logic, business rule validation, integration with E2B generation, and the electronic submission handoff.
Focuses on the technical generation and validation of E2B R3 XML messages from enriched case data, ensuring strict compliance with ICH and regional validator rules before submission. This is a critical, error-prone step in the reporting chain. The implementation covers schema validation, terminology mapping, and the orchestration between the safety database and regulatory gateway.
Manages the complex logistics of submitting safety reports to dozens of global health authorities (FDA, EMA, PMDA, etc.), each with unique gateway protocols and acknowledgment requirements. This prevents submission errors and automates tracking. The workflow uses agents to handle gateway APIs, manage encryption, parse acknowledgments, and update submission status in the tracking system.
Implements an intelligent deadline management system that ingests case intake dates, calculates country-specific reporting clocks (7-day, 15-day), and proactively alerts teams of upcoming due dates and risks of delay. This provides a single source of truth for compliance timelines. The page covers calendar integration, exception handling for holidays, and escalation workflows to prevent missed reports.
Automates the extraction, tabulation, and formatting of cumulative safety data, line listings, and summary tabulations from the safety database into the precise layouts required for PSURs. This eliminates weeks of manual copy-paste and reformatting work. The architecture involves data transformation agents, template engines, and quality checks to ensure numerical consistency and traceability.
Deploys a suite of automated QC agents that run predefined validation rules on processed cases—checking for data completeness, coding accuracy, and narrative consistency—before finalization. This shifts QC from a sample-based audit to a 100% check, improving data integrity. The page details rule configuration, exception routing for remediation, and integration with case workflow states.
Uses AI to continuously monitor system audit trails for unusual patterns—like bulk deletions, unauthorized access, or atypical workflow jumps—that could indicate process deviations or integrity issues. This strengthens compliance posture. The implementation includes log ingestion, behavioral baselining, alert generation, and integration with quality management systems for investigation.
Assists medical writers by generating draft versions of Direct Healthcare Professional Communications (DHPCs) or Dear Doctor Letters based on new safety data, approved labeling changes, and regulatory guidance. This accelerates critical risk communication. The workflow uses RAG over internal documents and regulatory templates, includes legal and medical review gates, and manages versioning through to publication.
Orchestrates a cross-functional workflow where agents analyze new safety signals, propose specific labeling changes (e.g., new warnings, contraindications), and route the change proposal through regulatory and medical affairs for review. This systematizes a complex, document-heavy process. The page covers change impact assessment, document comparison, and integration with labeling management systems like Veeva Vault or CSC.
Solves the operational headache of reconciling adverse event cases between licensor and licensee, using agents to match cases, identify discrepancies, and generate reconciliation reports automatically. This ensures contractual compliance and data alignment. The architecture includes secure data exchange protocols, matching logic, and a collaborative dashboard for resolving differences.
Automates the detection of SUSARs from clinical trial data by continuously comparing serious adverse events against the reference safety information (RSI/IB), flagging unexpected cases for expedited reporting. This reduces manual review burden for study teams. The workflow integrates with EDC systems, applies causality and expectedness logic, and triggers the appropriate reporting pathway.
Builds a real-time safety monitoring layer for modern trial designs, ingesting data from wearables, ePRO, and remote sites to detect potential safety signals early and adjust trial parameters. This enables proactive risk management. The page details the data fusion architecture, adaptive signal detection algorithms, and alerting to data monitoring committees (DMCs).
Automates the front-end of medical information services, using an AI agent to classify incoming inquiries, retrieve approved response content from a knowledge base, and draft a response for medical reviewer approval. This improves response time and consistency. The implementation includes intent classification, RAG over product monographs, and integration with CRM systems like Veeva CRM or Salesforce.
Applies NLP to analyze free-text medical inquiries from HCPs and patients to identify unreported adverse events or product quality complaints, converting service data into potential safety cases. This turns a cost center into a signal detection asset. The workflow details text mining, case conversion logic, and routing to the pharmacovigilance intake system.
Implements a high-velocity surveillance workflow tailored for vaccines, performing near real-time rapid cycle analysis on large-scale administration data to detect safety signals post-launch. This is critical for public health programs. The architecture covers massive data ingestion, statistical monitoring methods like PRISM, and integration with public health agency reporting frameworks.
Builds a specialized workflow for monitoring the complex safety profiles of oncology drugs, handling unique AE terminologies (e.g., CTCAE), managing expected high rates of serious events, and tracking long-term sequelae. This provides tailored efficiency for oncology PV teams. The page covers therapy-specific triage rules, specialized coding support, and integration with oncology registries.
Adapts pharmacovigilance principles to medical devices, automating the ingestion of complaint data, MDR report generation, and trend analysis for device malfunctions or patient injuries. This addresses the distinct regulatory requirements of device vigilance. The workflow details integration with complaint handling systems (e.g., SAP CRM), device-specific coding (UDI), and reporting to FDA MAUDE or EUDAMED.
Creates an automated system to manage the long-term follow-up (LTFU) obligations for advanced therapies, tracking patients over years, sending reminders for data collection, and analyzing delayed adverse events. This ensures compliance with risk management plans. The architecture includes patient registry integration, timeline management, and specialized reporting for delayed effects like secondary malignancies.
Designs the overarching orchestration layer (using tools like LangGraph) that coordinates specialized agents across the fragmented PV tech stack—from clinical data warehouses to regulatory gateways—ensuring seamless data flow and process handoffs. This page is for architects building the central nervous system of an automated PV operation.
Automates the critical interface between Pharmacovigilance and Quality systems, ensuring that potential product quality complaints identified in safety cases are automatically logged as deviations in the QMS (e.g., TrackWise, Veeva QMS) for investigation. This closes a common compliance gap and accelerates root cause analysis.
Connects safety signal detection with supply chain operations by automatically correlating adverse event clusters with specific drug lot numbers and triggering investigations or potential recalls. This enables rapid, targeted risk mitigation. The workflow details integration between the safety database and ERP/SCM systems like SAP for real-time lot traceability.
Goes beyond reactive detection by using AI to model pharmacological pathways and predict potential adverse drug interactions before they appear in significant numbers in spontaneous reports. This supports proactive risk management. The page covers the use of biomedical knowledge graphs, simulation agents, and the workflow for validating and acting on predictive alerts.
Monitors the volume and nature of incoming case reports by country, reporter type, and product to detect unusual spikes or drops that could indicate under-reporting, a media event, or a data feed failure. This provides operational intelligence. The implementation involves time-series analysis, alerting to PV operations managers, and dashboards for oversight.
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One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
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