Case Intake Automation
Case intake automation extracts patient, product, event, and reporter elements from unstructured narratives, reducing manual data entry that consumes most safety operations capacity.
Custom Software
AI Adverse Event Detection Software Development
AI adverse event detection software applies machine learning and clinical NLP to case narratives, clinical records, and safety databases to surface potential safety signals and triage incoming reports. It supports pharmacovigilance only: causality assessment is performed by qualified safety physicians, and no regulatory submission is ever filed automatically.
Pharmacovigilance runs on volume that grows faster than safety teams do, and most of that volume is case intake and triage rather than scientific judgment. Taction Software builds AI adverse event detection that automates the processing layer while leaving causality assessment, seriousness determination, and regulatory decisions entirely with qualified safety personnel.
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What Is AI Adverse Event Detection Software
AI adverse event detection refers to machine learning and natural language processing applied to safety data: extracting structured case elements from narratives, triaging incoming reports by seriousness and expectedness, surfacing disproportionality signals across case series, and preparing regulatory documentation. Detection is not causality. A statistical signal indicates a pattern warranting review, and only a qualified safety physician determines whether a causal relationship is plausible. This work sits inside our broader healthcare AI practice, where validation and regulatory alignment are engineering deliverables.
Report Triage Support
Triage support proposes seriousness and expectedness classification for safety reviewer confirmation, prioritizing cases against regulatory reporting timelines.
Signal Detection Analytics
Signal detection applies disproportionality methods across case series, surfacing patterns for evaluation. Statistical signals are not findings of causality.
Clinical Narrative Processing
Clinical NLP extracts event terms and temporal relationships, drawing on our clinical NLP development practice for medical language handling.
Regulatory Documentation Preparation
Tooling prepares MedWatch and ICSR documentation from case data, which qualified personnel review and submit. Submission is never automated.
Decision Support Boundaries
Every output carries decision support framing. The software does not determine causality, assign seriousness, file reports, or make any regulatory determination.
Core AI Adverse Event Detection Services
Our AI adverse event detection services cover case processing automation, signal detection, workflow integration, regulatory documentation, and validated system delivery. Pharmacovigilance software carries obligations most healthcare AI does not, since safety systems supporting regulatory reporting fall under Part 11 expectations and inspection scrutiny. We scope validation from the start rather than retrofitting. Engagements typically open with a review of case volume, current intake process, and safety database configuration. Deliverables are structured so safety physicians, pharmacovigilance operations, and quality assurance can review independently.
01
Narrative Extraction Development
We build narrative extraction for case elements with confidence scoring, so low-confidence extractions route to manual review rather than entering silently.
02
Safety Database Integration
Integration connects with your safety database for case creation and update, handling the structured field mapping that intake automation requires.
03
Triage Workflow Design
Triage routing respects reporting timelines, prioritizing expedited cases so automation supports compliance rather than obscuring deadline exposure.
04
Signal Detection Implementation
We implement disproportionality analysis with configurable thresholds, presenting signals for evaluation with underlying case series always accessible.
05
Literature and RWE Monitoring
Signal work extends to published and real world sources, connecting with real world evidence platform capabilities for post-market surveillance.
06
Part 11 Validated Delivery
Safety systems require validation. Our 21 CFR Part 11 for AI work covers audit trails, electronic signatures, and computer system validation.
Benefits of AI Adverse Event Detection Software
The benefits of AI adverse event detection concentrate in intake capacity, triage consistency, and earlier signal visibility. Case processing is largely mechanical work that scales poorly with headcount, and safety teams routinely spend more effort on data entry than on scientific evaluation. Automating extraction reallocates qualified attention toward assessment. We publish no figures on processing volume, detection speed, or compliance rates, because those depend entirely on your case mix, product portfolio, and current process maturity.
Reallocated Reviewer Capacity
Automating case processing shifts qualified safety physician time from data entry toward causality assessment, which only they can perform.
Consistent Triage Application
Proposed seriousness classification applies criteria uniformly for reviewer confirmation, reducing variation driven by case volume and reviewer fatigue.
Earlier Signal Visibility
Systematic disproportionality monitoring surfaces patterns across case series earlier than periodic manual review of accumulating reports.
Better Timeline Compliance
Timeline-aware triage makes expedited reporting deadlines visible in workflow, reducing exposure created by cases sitting unrecognized in a queue.
Structured Safety Data
Consistent extraction improves data quality in the safety database, which strengthens every downstream analysis and periodic report.
Incident Reporting Continuity
Clinical adverse events connect with facility reporting through incident reporting software for event documentation continuity.
Our AI Adverse Event Detection Process
We deliver AI adverse event detection projects in gated phases so safety and quality stakeholders approve direction before engineering cost accumulates. Discovery establishes intended use, case volume, validation requirements, and the boundary between automated processing and qualified assessment. Development is iterative with safety physician review. Where systems support regulatory reporting, validation documentation is produced alongside development, and extraction accuracy is verified against manual case processing before any automation reaches production.
Discovery and Validation Assessment
Discovery defines intended use, assesses Part 11 applicability, and documents which steps remain with qualified personnel before any development.
Data and Narrative Assessment
We evaluate narrative quality and case volume patterns, since extraction accuracy depends heavily on how reports arrive and how complete they are.
Extraction Development and Verification
Extraction is verified against manual processing on a validation set, with confidence thresholds calibrated so uncertain cases route to human review.
Hallucination Controls
Where models draft narrative text, we require grounded generation with source traceability, since fabricated case detail in a safety record is unacceptable.
Validated Deployment
Deployment follows computer system validation with documented testing, audit trails, change control, and inspection-ready documentation.
Rollout and Ongoing Support
Rollout expands by case type with accuracy monitoring, safety governance review, and revalidation as products, terminology, and regulations change.
Technology and Compliance
Adverse event software handles patient data and produces records subject to regulatory inspection. Taction holds ISO 27001 certification and follows HIPAA-aligned engineering practice. Part 11 applies where systems support regulatory reporting, requiring audit trails, electronic signature controls, and validation. Pharmacovigilance also operates under mandatory reporting timelines where automation must make deadlines visible rather than obscure them. The specific risk requiring attention is generative text: fabricated or omitted detail in a case narrative is a regulatory and patient safety problem, not merely a quality issue.
HIPAA-Aligned Engineering
Builds apply encryption in transit and at rest, role-based access, and complete audit logging. Our HIPAA compliance software development practice defines these controls.
21 CFR Part 11 Compliance
Part 11 requires audit trails, electronic signatures, and validation for systems supporting regulatory reporting, which shapes architecture from the start.
Generative Text Controls
Hallucination risk is treated as a safety issue. Any drafted narrative stays grounded in source data with traceability, and human review is mandatory.
Causality Assessment Boundary
Causality determination remains with qualified safety physicians. Statistical signals are presented as patterns requiring evaluation, never as conclusions.
Reporting Timeline Transparency
Expedited timelines are surfaced explicitly in workflow, so automation improves deadline visibility rather than hiding exposure inside a queue.
Deployment Security
Deployments run on-premise, in your cloud tenancy, or hybrid, with network segmentation, signed container images, and documented penetration testing before release.
Why Choose Taction Software
Taction Software was founded in 2013 and has spent over 12 years building healthcare software, delivering more than 200 healthcare projects from four US offices in Chicago, Cheyenne, Austin, and Sacramento, with ISO 27001 certification. Our relevant discipline here is separating processing from judgment. Safety automation delivers real value in extraction and triage, and none at all in causality assessment, and vendors blurring that line create inspection exposure for their clients. Our leadership brings more than 20 years of personal experience in the field.
01
Processing Versus Judgment
We automate case processing and leave causality assessment entirely with qualified personnel, which is both correct and inspection-defensible.
02
Validated System Delivery
We build Part 11 audit trails, validation documentation, and change control from the start rather than assembling them before an inspection.
03
Grounded Generation Discipline
We treat hallucination in safety narratives as unacceptable rather than a tolerable error rate, with source traceability required for any generated text.
04
Established Healthcare Focus
Founded in 2013, we have concentrated on healthcare rather than treating it as one vertical among several, producing depth in regulated systems.
05
Clinical Language Experience
Our clinical NLP work handles medical terminology and narrative structure, which general-purpose extraction handles poorly in safety contexts.
06
Certified Security Posture
ISO 27001 certification and HIPAA-aligned engineering mean security controls are documented and auditable, supporting sponsor and inspection readiness.
Pricing
AI adverse event detection pricing depends on scope, case volume, validation requirements, and whether signal detection analytics are included. An intake extraction module costs considerably less than a platform adding signal detection, multi-product monitoring, and full Part 11 validation. Validation documentation is a material cost component in this category and we scope it explicitly rather than folding it into development. Discovery produces an itemized, fixed-scope estimate with phase-level breakdown. Safety database licensing and cloud infrastructure are separate from engineering.
MVP or Single Module
An MVP delivering case intake extraction with reviewer confirmation typically runs $40,000 to $80,000, validating accuracy before wider automation.
Full Platform Build
A full platform with triage workflow, signal detection, and regulatory documentation preparation typically falls between $80,000 and $200,000.
Enterprise Deployment
Enterprise engagements covering multi-product portfolios, full Part 11 validation, and multi-client segregation start at $200,000.
Discovery Phase Scoping
Discovery is a paid, time-boxed phase producing an itemized estimate, architecture plan, and validation requirement assessment. It is separable so you can evaluate our work first.
Cost Drivers to Expect
Validation scope, case volume, safety database integration depth, and signal analytics breadth are the largest variables, identified during discovery.
Ongoing Support Costs
Post-launch revalidation, terminology updates, regulatory change management, and support are quoted separately as a retainer sized to case volume.
Get Started
If you are evaluating AI adverse event detection for case intake automation, triage support, or signal detection, the fastest next step is a discovery call with our team. We will review case volume, safety database configuration, and validation requirements, then return an itemized, fixed-scope estimate. Contact us to schedule that conversation.
FAQs
Frequently Asked Questions
Safety departments evaluating AI adverse event detection usually ask whether automation touches causality, how Part 11 applies, and how generated narrative text is controlled. The answers below reflect how we scope these projects. If your systems support regulatory reporting, expect validation to be a substantial and explicitly scoped cost component.
No. Causality assessment requires medical judgment and remains entirely with qualified safety physicians. The software extracts case elements, proposes triage classification for confirmation, and surfaces statistical signals. A disproportionality signal indicates a pattern warranting evaluation, not a causal conclusion.
No. It prepares documentation from case data, and qualified personnel review and submit every report. Automated regulatory submission would remove accountable human review from a filing that carries legal weight, and would create serious inspection exposure. Preparation is automated; submission is not.
Through grounded generation with source traceability, confidence thresholds that route uncertain extractions to manual review, and mandatory human review of any drafted text. Fabricated or omitted detail in a safety narrative is a regulatory and patient safety problem, so we treat it as a hard constraint rather than an error rate.
Where the system supports regulatory reporting, yes, requiring audit trails, electronic signature controls, and computer system validation. This materially affects architecture and cost, and we scope it explicitly. Systems used purely for internal analytics carry lighter requirements, which we assess during discovery.
Good for structured elements such as product, dose, and dates, more variable for complex temporal relationships and event characterization. We verify against manual processing on your own cases and set confidence thresholds so uncertain extractions route to reviewers rather than entering the database silently.
Yes, with different configuration. Device reporting operates under distinct rules and event definitions, so intake logic, triage criteria, and documentation templates differ from pharmaceutical adverse event processing. We build to the applicable framework rather than adapting a pharmaceutical configuration.