The Three-System Problem

Every clinical trial runs on three systems that don't talk to each other.

EDC captures patient data. CTMS manages sites and monitors. eTMF holds regulatory documents. Three vendors. Three logins. Three data models. Three support contracts. And one operations team trying to keep them synchronized through spreadsheets, manual exports, and weekly reconciliation meetings that consume 15-20% of your clinical operations budget.

The median Phase 3 trial takes 6-8 weeks longer than planned to reach database lock—not because of science, but because of data reconciliation. Query backlogs. Missing documents. Deviation logs that don't match between systems. You're paying for that delay in extended site costs, delayed revenue, and competitive disadvantage.

The consequences compound. When a site coordinator enters a patient visit in EDC, the CTMS has no idea it happened. Someone has to manually update the enrollment tracker. When monitors check site performance, they're looking at yesterday's data. When enrollment slips, you find out late.

When a monitor identifies a protocol deviation during a site visit, they log it in their trip report in CTMS. But understanding the impact on data analysis requires checking EDC. The deviation exists in two places, described differently, with no guaranteed link between them. When the FDA asks for a complete deviation history, someone spends days reconciling reports.

The Trial Master File is supposed to be the regulatory record of the trial. In practice, it's an archive where documents go to die. Site essential documents generated in CTMS get manually uploaded to eTMF weeks later. Patient safety reports captured in EDC exist separately from the safety documentation in the TMF. The file is always incomplete, always catching up, always a liability.

This isn't a technology problem. It's an architecture problem. These systems were built by different companies for different purposes. Integration is always an afterthought—API connections that break, file exports that lag, mappings that drift. The fundamental assumption is that these domains are separate. They're not.

One Platform

Seal Clinical is one platform where EDC, CTMS, and eTMF share a single data model.

A patient visit isn't entered in EDC and then synced to CTMS. The visit is one object, visible in both contexts. When the monitor reviews the visit during their trip, they see the same data the data manager sees. When someone needs to understand what happened, they don't reconcile—they look.

A protocol deviation isn't a note in a trip report that someone later copies into a tracking spreadsheet. The deviation is a first-class entity linked to the visit, the subject, the site, and the protocol version. When you query for deviations, you get all of them—regardless of which workflow created them.

The TMF isn't an archive. It's a living reflection of trial operations. When a site is activated in CTMS, the essential documents file automatically. When a serious adverse event is reported in EDC, the safety narrative links directly. TMF completeness isn't a metric you chase at inspection time—it's a byproduct of doing the work.

Electronic Data Capture

Seal EDC is built for the modern trial. Sites don't want clunky forms that feel like they were designed in 2005. They want something that works like the software they use everywhere else—fast, intuitive, mobile-friendly.

Forms are designed visually. Drag fields, set edit checks, define skip logic. No programming required for standard forms. When you need complex derivations or cross-form validations, the expression language handles it. Preview your forms exactly as sites will see them before you go live.

Edit checks fire in real-time as data is entered. Sites see validation errors immediately, not days later in a query. The goal is clean data at the point of capture, not data cleaning campaigns after the fact. When an edit check does generate a query, sites respond in context—they see the question next to the data, not in a separate query management module.

Randomization and drug supply integrate directly. When a subject is randomized, the treatment assignment flows through to the dispensing log. No manual transcription. No mismatches between what EDC says and what the site dispensed.

Medical coding happens continuously. As adverse events and medications are entered, coding suggestions appear. Coders review in batches rather than facing a mountain at database lock. The MedDRA and WHO Drug dictionaries update automatically.

Clinical Trial Management

CTMS in Seal isn't a separate module bolted onto EDC. It's a different view of the same trial.

Site management starts with feasibility. Track potential sites through qualification, selection, and activation. Document the regulatory submissions—IRB approvals, contracts, budgets. When a site activates, their essential documents flow to eTMF automatically.

Monitoring happens in context. Monitors see their assigned sites with current enrollment, open queries, and pending issues. Trip reports capture findings linked directly to the subjects and visits reviewed. When a monitor identifies a protocol deviation, it creates the same deviation record the data manager will see. No translation layer. No reconciliation.

Study metrics are real-time because they derive from actual operations. Enrollment curves, screen failure rates, query cycle times—all calculated from the data as it exists right now. No ETL jobs. No dashboard refresh schedules. The number you see is the number that's true.

Trial Master File

The eTMF in Seal follows the TMF Reference Model structure. Zone, Section, Artifact. The taxonomy is there because inspectors expect it. But the filing isn't manual.

When a document is created in operations, it knows where it belongs. Site regulatory submissions file to the site section. Protocol amendments file to the trial section. Monitoring visit reports file with the date and site. You don't organize documents—you generate them in context and they organize themselves.

Completeness tracking is automatic. The system knows which artifacts are expected based on the trial phase, the active sites, and the visits conducted. Missing documents surface immediately. When an inspection is scheduled, the completeness report shows exactly what needs attention.

Document QC happens before filing. Metadata validation, naming convention checks, placeholder detection. Reviewers approve documents through a workflow, and the audit trail captures every version. When the inspector wants to see the approval history of a protocol amendment, you show them—without digging through email archives.

Safety Integration

Adverse events captured in EDC flow directly to safety workflows. When a serious adverse event meets reporting criteria, the system initiates the appropriate pathway. Expedited reports to regulators. Notifications to investigators. Updates to the Investigator's Brochure.

The safety database isn't a separate system. The same adverse event record that the site entered appears in aggregate safety analyses. When you need to understand a signal, you drill from the aggregate directly to the source. No case matching. No reconciliation against the clinical database.

Pharmacovigilance requirements extend beyond the trial. Post-marketing safety reports follow the same structure. The transition from clinical to post-market isn't a data migration—it's a configuration change.

The Inspection

When the FDA arrives, they're not auditing your systems. They're auditing your trial. They want to understand what happened, why it happened, and whether you maintained control.

In a fragmented world, answering their questions requires pulling data from three systems, reconciling discrepancies, and hoping nothing falls through the cracks. In Seal, the answer to "show me all deviations for this site" is one query. The answer to "show me the training records for everyone who touched this subject's data" is a click.

The TMF is always ready because it was built through operations. The edit check history exists because it was captured at the time. The audit trail is complete because every action was logged in one system.

You don't prepare for inspections. You run your trial well, and inspection readiness is the result.

Scales With Your Pipeline

You don't need enterprise clinical software for a Phase 1 with ten sites. But you need something that doesn't break when Phase 3 has two hundred.

Start with EDC and basic site management. Add eTMF when regulatory rigor matters. Add safety database integration when aggregate reporting requirements mature. The data model supports expansion because it was designed for the full lifecycle from the start.

You're not buying a point solution you'll rip out later. You're buying the platform that grows with your pipeline.

Getting Started

You've seen clinical system implementations drag on for years. Requirements gathering. Vendor negotiations. Configuration. Validation. UAT. Training. By the time you go live, the trial you designed it for is half over.

Seal works differently. A simple Phase 1 trial—ten sites, fifty subjects, standard forms—can be live in weeks. AI generates the study structure from your protocol. Your team reviews and adjusts. Validation runs against pre-validated platform components. Sites access through a web browser with no installation. You're collecting data before the traditional implementation would finish requirements.

For complex registrational trials, allow two to three months—still faster than the six to twelve months typical of enterprise EDC implementations. The difference is that configuration is conversation, not programming. You describe what you need; the system builds it. Your clinical team validates the trial-specific configuration, not the underlying platform.

What about existing trials? We support mid-study transitions for trials that need to move from legacy systems. Data migrates with audit trails preserved. Sites experience a new interface, not a new workflow. The sponsor gains unified data and operational visibility without disrupting the science.

AI That Builds Your Trial

Trial setup traditionally takes months—form design, edit check programming, randomization configuration, TMF structure creation. AI compresses this dramatically.

Describe your trial: "Phase 2 oncology, 15 sites, 120 subjects, six treatment visits plus follow-up, primary endpoint is tumor response by RECIST 1.1." AI generates the study structure—visit schedule, form templates with CDASH-compliant fields, standard edit checks, TMF artifact expectations. You review and refine rather than building from blank.

CRF design becomes conversational. "Add a vitals form with blood pressure, heart rate, temperature, and respiratory rate. Flag if systolic BP exceeds 180." AI creates the form with appropriate fields, validation rules, and query triggers. Review, approve, deploy.

Every AI proposal is transparent. When AI generates a form or suggests an edit check, you see exactly what it created. You modify and approve. New forms go through your standard review process. Configuration changes follow change control. AI accelerates study build; your team controls the protocol.

And AI works throughout the trial. Medical coding happens continuously—AI suggests MedDRA and WHO Drug codes as data enters, coders review rather than code from scratch. Query generation transforms—AI identifies data inconsistencies and drafts query text, data managers review and send. Site monitoring focuses where needed—AI flags sites trending toward problems before they become crises.

TMF completeness stays current because AI tracks expected artifacts against actual filings. "Site 205 activated three weeks ago but essential documents haven't filed." The TMF is inspection-ready because AI ensures it stays complete.