Automations
This pillar addresses patient matching workflows that combine EHR, genomic, and clinical narrative data to identify trial-ready cohorts more precisely than manual screening approaches. Content should show how custom cohort workflows reduce enrollment delays, improve candidate quality, and support privacy-aware integrations across decentralized health data environments.
This page details a custom, end-to-end orchestration workflow that ingests and harmonizes EHR, genomic, and clinical narrative data to identify and rank trial-ready patient cohorts. It explains the architecture for multi-agent screening, matching logic, and physician review routing, delivering measurable reductions in enrollment delays and improved candidate quality for sponsors and CROs.
This page covers a custom automation workflow that unifies disparate EHR data from Epic, Cerner, and other systems into a structured, query-ready format for cohort discovery. It details the agentic pipeline for mapping, deduplication, and quality validation, enabling faster, more accurate patient stratification and eliminating manual data wrangling overhead.
This page explains a custom workflow for continuously ingesting and processing genomic sequencing and variant data from labs and biobanks. It outlines the streaming architecture, annotation agents, and integration with clinical data to enable genotype-driven cohort identification, accelerating biomarker study enrollment and reducing manual data handling.
This page details a multi-agent workflow that automates the extraction of key phenotypes, comorbidities, and outcomes from unstructured physician notes and reports. It covers NLP orchestration, entity linking, and structured data output, turning narrative bloat into actionable cohort criteria and saving hundreds of hours of manual chart review.
This page outlines a custom workflow architecture that executes cohort queries across decentralized hospital networks without moving patient data. It explains the agentic coordination, privacy-preserving record linkage (PPRL) techniques, and result aggregation, enabling scalable patient finding while maintaining strict HIPAA and GDPR compliance.
This page describes an agentic workflow that automates the evaluation of patient records against complex, multi-parameter clinical trial protocols. It details the rule orchestration, temporal logic handling, and exception flagging, drastically reducing manual screening time and improving consistency in eligibility determination.
This page covers a custom workflow that analyzes a patient's entire medical history across time to assess eligibility, checking for prior conditions, treatments, and lab trends. It explains the temporal reasoning agents, data fusion logic, and implementation with EHR systems to replace error-prone manual timeline reviews.
This page details an automation workflow that scores and ranks potentially eligible patients based on fit-to-protocol, geographic feasibility, and predicted enrollment likelihood. It outlines the scoring model integration, real-time ranking engine, and dashboard outputs, enabling sites to prioritize the highest-value candidates first.
This page explains a custom workflow that uses NLP and ontology-based agents to identify patients matching complex phenotypic descriptions from published literature or novel study designs. It covers the semantic search architecture, phenotype logic, and integration with EHR data, accelerating research cohort building for rare or complex diseases.
This page outlines an automation workflow that matches patients with specific genomic variants to open trials targeting those biomarkers. It details the variant annotation pipeline, trial registry integration, and notification system, creating a scalable engine for precision oncology and genetic disorder recruitment.
This page describes a multi-agent workflow that automates the identification of patients positive for specific biomarkers from lab, pathology, and genomic reports. It explains the data ingestion, result interpretation logic, and patient list generation, streamlining enrollment for targeted therapy and companion diagnostic trials.
This page details a custom workflow for discovering ultra-rare disease patients by orchestrating queries across registries, specialty clinics, and research networks. It covers the federated search architecture, natural language phenotyping agents, and caregiver outreach coordination, turning a years-long search into a systematic, automated process.
This page explains an automation workflow that models expected enrollment rates for a trial protocol against real-world patient populations. It details the simulation agents, gap analysis reporting, and recommendations for protocol amendment or site selection, de-risking trial planning and improving feasibility confidence.
This page covers a multi-agent workflow that evaluates a single patient's profile against dozens of open trials to identify all potential study opportunities. It outlines the parallel screening logic, conflict checking, and consent management, maximizing site efficiency and patient options while maintaining governance.
This page describes a custom workflow that continuously monitors trial data and external evidence to re-stratify or re-randomize patients within an ongoing study. It explains the adaptive design logic, safety checks, and communication triggers, enabling more responsive and scientifically rigorous trial conduct.
This page details an automation workflow that uses cryptographic hashing and tokenization to link patient records across institutions without exposing identities. It covers the agentic orchestration of matching algorithms, audit trails, and governance controls, essential for multi-site studies operating under strict data use agreements.
This page outlines a workflow that automatically checks every cohort query and data access action against active Data Use Agreements and patient consent stipulations. It explains the rule engine integration with IRB systems, real-time compliance validation, and violation alerting, reducing legal and regulatory risk in data-driven research.
This page describes a custom workflow that dynamically governs data access based on evolving patient consent preferences and study authorization. It details the policy engine, API gateways, and audit logging, creating a patient-centric data architecture that is both compliant and operationally efficient.
This page explains an automation workflow that generates statistically realistic synthetic patient datasets to model cohort size and characteristics before querying real PHI. It covers the generative AI models, fidelity validation, and integration with trial design tools, allowing for risk-free feasibility analysis and protocol stress-testing.
This page details a workflow that continuously monitors all data ingress, processing, and egress within a cohort stratification platform for compliance with HIPAA, GDPR, and other regulations. It outlines the agentic audit trail generation, anomaly detection, and reporting automation, providing defensible evidence for regulators and institutional review boards.
This page covers a workflow that automates the creation of site feasibility questionnaires and activation packets tailored to a specific trial protocol and site profile. It explains the document assembly agents, data pulling from CTMS, and delivery orchestration, cutting weeks off the site activation timeline.
This page describes an agentic workflow that identifies and ranks potential principal investigators based on publication history, past trial performance, and therapeutic expertise. It details the data aggregation, scoring logic, and automated outreach sequence, optimizing site selection and investigator engagement.
This page outlines a custom workflow that predicts future enrollment rates for clinical trial sites using historical performance, local epidemiology, and site capacity data. It explains the forecasting model integration, alerting for underperforming sites, and recommendation engine for corrective actions, improving overall trial enrollment reliability.
This page details a workflow that uses bots and agents to proactively request, track, and validate essential regulatory documents (1572s, CVs, lab certs) from clinical sites. It covers the communication orchestration, document parsing, and TMF filing integration, reducing administrative delays and ensuring inspection readiness.
This page explains a custom workflow that aggregates enrollment data from EDC, CTMS, and site sources into a live dashboard with automated alerts for milestones, screen failures, and enrollment gaps. It details the data pipeline, visualization agents, and notification logic, giving study managers proactive control over recruitment.
This page describes a multi-agent workflow that automates the scheduling of patient trial visits based on protocol windows, site calendars, and patient preferences. It covers the coordination logic, reminder generation (SMS/email), and rescheduling handling, reducing no-show rates and site coordinator burden.
This page outlines a workflow that continuously monitors EDC, lab, and patient-reported data for potential Serious Adverse Events. It explains the detection algorithms, initial triage logic, and automated routing to pharmacovigilance teams, accelerating regulatory reporting and enhancing patient safety oversight.
This page details a custom workflow that predicts drug and kit demand by site based on real-time enrollment data and patient visit schedules. It covers the integration with IVRS/IWRS and ERP systems, automated purchase order triggering, and exception handling, preventing stockouts and reducing wasted inventory.
This page describes an automation workflow where AI agents review entered clinical data for discrepancies, outliers, and protocol deviations, automatically generating queries for site resolution. It explains the rule and ML model orchestration, query drafting, and integration with EDC systems, dramatically reducing manual data management effort.
This page explains a workflow that automates the analysis of RWE datasets (claims, EHR) to estimate prevalence, incidence, and standard of care for a proposed trial protocol. It details the data querying agents, analytic pipeline, and report generation, providing data-driven feasibility assessments to inform go/no-go decisions.
This page outlines a custom workflow that automatically scores a draft clinical trial protocol for operational complexity, enrollment risk, and cost drivers. It details the NLP agents that parse protocol text, the rule-based scoring engine, and the integration with planning systems, enabling proactive protocol optimization before finalization.
This page covers a workflow that automates the creation of comprehensive feasibility reports by pulling data from RWE platforms, site databases, and competitive intelligence. It explains the document assembly agents, narrative generation, and distribution logic, delivering stakeholder-ready insights in hours instead of weeks.
This page details a workflow that uses simulation agents to model how changes to inclusion/exclusion criteria, endpoints, or visit schedules would impact cohort size and enrollment speed. It covers the digital twin of the patient population, scenario testing, and reporting, allowing for data-driven protocol design iterations.
This page describes an agentic workflow that generates and delivers personalized recruitment messages (email, letter, portal) to potentially eligible patients, based on their clinical profile and communication preferences. It explains the content generation, omnichannel orchestration, and response tracking, improving recruitment conversion rates.
This page outlines a multi-agent workflow that guides a patient through the pre-screening, education, and electronic informed consent process. It details the interactive bots, document presentation, signature capture, and integration with eConsent and EDC platforms, streamlining the enrollment pathway and improving the participant experience.
This page explains a custom workflow that triggers personalized check-ins, educational content, and visit preparation messages to trial participants based on their study stage and engagement signals. It covers the logic for timing and channel selection, reducing dropout rates and improving protocol adherence.
This page details a workflow deploying a 24/7 conversational AI agent to answer participant questions about trial procedures, side effects, and logistics. It explains the integration with protocol documents, EHR for context, and escalation paths to human coordinators, reducing site call volume and improving participant support.
This page describes a workflow that analyzes patient records and external data to identify potential barriers to trial participation (transportation, financial, literacy). It outlines the data fusion, risk flagging, and routing to patient navigation services, enabling proactive support to improve diversity and retention in clinical trials.
This page covers a custom workflow that automatically synchronizes key data—such as investigator leads, site status, and enrollment metrics—between CRM systems (e.g., Veeva CRM) and Clinical Operations platforms (e.g., Veeva Vault CTMS). It details the API orchestration and data mapping agents, breaking down silos between business development and clinical teams.
This page explains a workflow that creates a real-time bridge between Electronic Data Capture (EDC) systems and automated patient screening engines. It details the API agents that push candidate data from EHRs into EDC for pre-screening and pull screening results back, creating a seamless data flow that eliminates duplicate entry and accelerates startup.
This page describes a workflow that integrates Interactive Voice/Web Response Systems (IVRS/IWRS) with the core patient stratification engine to automate patient randomization and drug kit assignment based on real-time biomarker or stratification results. It explains the event-driven architecture and fail-safe controls for high-stakes adaptive trials.
This page details a multi-agent workflow that governs and automates the secure exchange of cohort lists, screening data, and monitoring reports between CRO and sponsor systems. It covers the data transformation, validation, and audit trail generation, ensuring smooth collaboration and regulatory compliance in outsourced trials.
This page explains a specialized workflow for oncology trials that automates the stratification of patients based on complex biomarker panels, tumor mutational burden, and PD-L1 status from genomic and pathology reports. It details the integration with sequencing pipelines and clinical data, enabling rapid enrollment into precision oncology study arms.
This page outlines a custom workflow for CNS trials that uses NLP and cognitive assessment data to phenotype patients for disorders like Alzheimer's, Parkinson's, and depression. It describes the multimodal data fusion, symptom trajectory analysis, and cohort matching logic required for these complex, symptom-driven studies.
This page details a workflow specifically designed to find rare disease patients by applying advanced natural language phenotyping agents to clinical narratives across hospital networks. It explains the ontology-driven search, family history analysis, and proband identification, tackling the fundamental challenge of rare disease trial recruitment.
This page describes an automation workflow that screens patient records for specific immune profiles (e.g., lymphocyte counts, prior immunotherapy response, autoimmune history) relevant to immunotherapy trial eligibility. It covers the lab data interpretation logic and integration with flow cytometry results, streamlining enrollment for this growing therapeutic area.
This page explains a workflow that automates the complex task of pediatric cohort stratification, accounting for age bands, weight-based dosing, and guardian consent dynamics. It details the specialized logic for querying pediatric EHR data, feasibility forecasting, and matching patients to appropriate age-stratified study arms.
This page outlines a governance workflow that continuously monitors patient screening and matching algorithms for demographic, geographic, or socioeconomic bias. It explains the automated audit agents, fairness metric calculation, and feedback loops for model retraining, ensuring equitable cohort selection and regulatory compliance.
This page describes an automation workflow that generates human-readable, audit-ready explanation reports every time an AI agent flags a patient as eligible or ineligible. It details the evidence retrieval, rationale synthesis, and report formatting, building essential trust and facilitating physician review of automated decisions.
This page outlines a workflow that automates the assembly, redaction, and packaging of all data, logs, and decisions related to cohort identification for a regulatory audit or inspection. It details the agentic querying of audit trails, document compilation, and secure delivery, turning a panic-driven manual effort into a routine, controlled process.
How We Work
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.
Read moreThe first call is a practical review of your use case and the right next step.
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