Automated Red-Flag Triggers from Surveillance Reports and Field Investigator Notes (Workers Compensation & Auto)

Surveillance Coordinators in Workers Compensation and Auto live at the intersection of truth and paperwork. You field thousands of pages of surveillance reports, investigation notes, IME reports, and field investigation correspondence—often accompanied by hours of video. The challenge is simple to state but hard to solve: find the contradictions, fast. Which observations, statements, or timestamps meaningfully undermine alleged limitations? Which behaviors, jobs, or activities conflict with claimed injuries? Missing any one of these red flags can drive leakage, prolong litigation, and dilute SIU referral quality.

Nomad Data’s Doc Chat for Insurance was built for exactly this scenario. Doc Chat performs end-to-end, AI analysis of surveillance notes insurance teams rely on every day, ingesting entire claim files—thousands of pages at once—and surfacing actionable red flags in minutes. It reads every page and cross-checks details across disparate sources to find contradictions from investigations and flag activity inconsistent with injury claim narratives, all with page-level citations and a defensible audit trail. For Surveillance Coordinators supporting Workers Compensation and Auto claims, this means less hunting and more deciding.

The nuanced problem Surveillance Coordinators face in Workers Compensation and Auto

In Workers Compensation and Auto bodily injury, evidence lives everywhere. A single claim can include FNOL and FROI packages, employer statements, recorded statements, police crash reports, ISO ClaimSearch reports, medical records, IME opinions, return-to-work slips, wage statements, and a multi-day series of surveillance logs with second-by-second notations, maps, and stills. Vendors deliver different report formats; field investigator narratives vary widely; timestamps don’t always align; and video is frequently summarized differently by separate reviewers. Meanwhile, claimants’ self-reported limitations evolve with time and context, making true contradictions harder to isolate without exhaustive cross-reference.

For Workers Compensation, nuance often hinges on restrictions and job tasks. Can a claimant with a 10-pound lifting limit be observed repeatedly carrying a 24-pack of water? Does a “no repetitive overhead reach” restriction conflict with grooming dogs, installing ceiling fixtures, or loading roof racks? Does the IME’s measured range of motion square with the ease of loading heavy bags into a trunk, crawling under a truck, or power-washing a driveway?

In Auto, the patterns differ. An allegedly debilitating cervical strain might conflict with a weekend pick-up game, kickboxing class, or a six-hour rideshare shift. Claimants who deny seatbelt use may be contradicted by airbag and restraint data in the police crash report. Pain scales reported at medical visits may be at odds with observed activities of daily living (ADLs). These nuances require more than “reading” files. They demand integrated inference across medical chronology, surveillance logs, and claim statements.

How the process is handled manually today

Today, Surveillance Coordinators and SIU partners painstakingly assemble timelines by hand. They scan PDF surveillance reports and investigation notes, copy timestamps into spreadsheets, and annotate notable behaviors frame-by-frame. They compare the observations with IME reports, treatment notes, work status forms, and restrictions. They re-open the FNOL and recorded statements, and match prior complaints or limitations to observed activities. They check police crash reports for restraint use, airbag deployment, and vehicle damage patterns. They search the ISO report for prior claims that could explain current complaints. They chase down inconsistencies and create internal memos that attempt to tie it all together.

This is slow, repetitive work. Fatigue creeps in as video hours stack up. Important contradictions—like a right-shoulder restriction paired with a left-handed lift in video—are easily missed. Vendor report styles differ. Some observations use colloquial phrasing; others include medical misnomers that complicate matches. And because large claims carry the largest documentation, the place where you need the most scrutiny is often the place where manual review breaks down.

The result is predictable: backlogs, inconsistent SIU referrals, and delayed decisions. Potentially defensible denials may instead become costly settlements. Litigation risk rises because the best evidence is buried and surfaced late. And any post-close audit must reconstruct the logic from scattered notes and emails—hardly ideal.

How Doc Chat automates AI analysis of surveillance notes in insurance claims

Doc Chat changes the equation. It ingests your surveillance PDFs, narrative logs, still-image appendices, and investigator field investigation correspondence alongside IME reports, treating notes, police crash reports, ISO claim reports, demand letters, wage records, and more—all at once. From there, it performs multi-document reasoning to automatically find contradictions from investigations and flag activity inconsistent with injury claim statements. Because Doc Chat is trained on your internal playbooks, red-flag definitions, and escalation thresholds (the Nomad Process), it applies your exact investigative standards every time.

Here is what automation looks like in practice for a Surveillance Coordinator in Workers Compensation and Auto:

1) End-to-end ingestion at scale
Doc Chat ingests an entire claim file—hundreds or even thousands of pages—so you don’t need to “parcel” review. It recognizes common insurance artifacts: FNOL/FROI forms, recorded statement transcripts, treatment plans, work status forms, disability slips, IME narratives, surveillance logs, still images, and emails. It normalizes dates, claim numbers, names, and locations to anchor the file.

2) Cross-evidence contradiction detection
Doc Chat applies your red-flag taxonomy to automatically surface contradictions. It aligns observed activities with restriction profiles in IME reports and provider notes. If a claimant reports “cannot drive more than 10 minutes,” but surveillance logs show two hours behind the wheel, Doc Chat highlights the discrepancy and cites source pages and times. In Auto, it can align police crash data (seatbelt use, airbag deployment) with reported injury mechanisms and video-captured behaviors that undermine claimed limitations.

3) Evidence normalization and timeline building
Doc Chat constructs a defensible timeline of events: initial injury, FNOL, first medical visit, IME, recorded statements, surveillance windows, observed tasks, and return-to-work directives. It links each timeline entry to source pages and timestamps, so SIU and defense counsel can validate quickly.

4) Real-time Q&A
Ask Doc Chat anything across the entire file and get instant answers with citations: “List all observed items carried over 10 lbs.” “Show every mention of seatbelt use.” “Summarize every return-to-work restriction and align with surveillance observations.” “Provide all references to medications prescribed post-loss.” Real-time Q&A turns complex files into a queryable database.

5) Presets for red-flag summaries
Doc Chat generates standardized “Red-Flag Surveillance Summaries” that conform to your internal templates: a table of alleged limitations, observed activities, contradictions, and recommended next steps. This consistency eliminates reviewer variance and accelerates referrals.

6) Seamless SIU handoff
When Doc Chat’s thresholds are met, it drafts an SIU referral with the relevant citations and exhibits, including page links for quick verification. The referral can be pushed into your claim system or SIU case management tool, reducing swivel-chair work and getting investigators into action faster.

What Doc Chat flags: concrete examples for Workers Compensation and Auto

To support Surveillance Coordinators, Doc Chat focuses on the specific red flags most predictive of exaggeration, inconsistency, or fraud—grounded in your playbook. Two quick reference lists follow.

Workers Compensation red flags Doc Chat can surface

• Observed lifting, carrying, squatting, kneeling, crawling, ladder climbing, pushing/pulling that exceed stated work restrictions in IME or provider records.
• ADLs inconsistent with claimed impairment (e.g., mowing, snow shoveling, roofing, heavy yard work), time-stamped against reported pain flare-ups and medication schedules.
• Work activity while on total temporary disability: side jobs, on-site presence, vehicle with employer branding, observed cash transactions.
• Inconsistent mobility: prolonged standing or walking after claims of limited tolerance; jogging, cycling, or gym activity despite lower extremity or back restrictions.
• Contradictory statements across recorded statements, FROI, and treatment notes (e.g., cause of injury shifting, symptom onset varying, mechanism differing from surveillanceable reality).
• Mismatch with durable medical equipment use (e.g., brace or cane absent during strenuous activity yet present in clinic visits).
• Return-to-work directive conflicts (e.g., “no repetitive overhead reach” contradicted by observed overhead stocking or manual labor).

Auto claim red flags Doc Chat can surface

• ADLs inconsistent with alleged whiplash, concussion, or shoulder/knee injuries (e.g., contact sports, heavy lifting, extended driving or rideshare gig shifts).
• Seatbelt use contradictions between police crash reports, medical narratives, and claimant statements.
• Activity levels misaligned with pain scales recorded at clinical encounters (e.g., 9/10 pain claims with same-day strenuous activity).
• Pre-existing or overlapping injuries identified in ISO reports or prior medical records that conflict with proximate cause narratives.
• Demand package statements inconsistent with earlier recorded statements or surveillance observations.
• Contradictions between IME impairment ratings and observed functional capacity.

Business impact for Surveillance Coordinators and SIU

When contradictions surface early and cleanly, outcomes improve across the board—especially in high-exposure Workers Compensation and Auto BI claims:

Time savings: Surveillance Coordinators often spend hours or days reconciling multi-source narratives for a single claim. Doc Chat reduces review from days to minutes, as documented in real-world settings where teams have seen thousand-page claims answered in seconds. For a carrier example, see how Great American Insurance Group accelerated complex claims with AI in this case study.

Cost reduction: Faster contradiction discovery curbs leakage and shrinks loss-adjustment expense. Files needing outside counsel or specialty reviews are targeted, not blanket-referred. Teams avoid overtime during surge events because Doc Chat scales instantly.

Accuracy and consistency: Humans tire; AI doesn’t. Doc Chat applies your red-flag taxonomy consistently across every file, every time. Page-level citations support compliance, reinsurance audits, and litigation defense.

Morale and retention: Offloading rote review to AI lets Surveillance Coordinators and SIU focus on strategic investigation and negotiation. As noted in Nomad’s perspective on data entry automation, refocusing talent on higher-value work improves satisfaction and outcomes. Read more in AI’s Untapped Goldmine: Automating Data Entry.

Why Doc Chat: built for complexity, designed around your playbooks

Surveillance contradictions rarely sit neatly on one page. They emerge from inference—connecting dots across surveillance reports, investigation notes, IME reports, police records, policy language, and claimant statements. Most generic tools struggle here because the answers aren’t “located” in a single field; they’re synthesized. As Nomad explains in Beyond Extraction, document intelligence is about inference, not location. That is exactly where Doc Chat excels.

Doc Chat advantages for Surveillance Coordinators in Workers Compensation and Auto include:

Volume: Ingest entire claim files—thousands of pages—in one shot. Reviews that used to take days complete in minutes.

Complexity: Endorsements, restrictions, and nuanced language are normalized and cross-checked. Doc Chat pulls out exclusions, IME trigger language, and subtle contradictions that hide in inconsistent formats.

The Nomad Process: We train Doc Chat on your red-flag taxonomy, SIU referral criteria, and investigative standards. You get a personalized system that reflects how your Surveillance Coordinators actually work.

Real-time Q&A: Ask questions like “Show all contradictions between observed lifting and the IME’s 10-pound limit,” and get answers with page citations—across every document in the file.

Thorough and complete: Doc Chat surfaces every reference to coverage, liability, or damages relevant to the contradiction at hand, so no key detail slips through the cracks.

Your partner in AI: Nomad delivers white-glove service with 1–2 week implementations. You are not buying a one-size-fits-all tool; you’re gaining a partner that co-creates with your team and evolves the solution as your investigative playbooks mature.

From manual grind to automated precision: a day-in-the-life with Doc Chat

Here is how a Surveillance Coordinator in Workers Compensation or Auto would use Doc Chat on a typical file:

1) Intake: Drag and drop the full file: surveillance reports, investigation notes, IME reports, field investigation correspondence, FNOL, recorded statements, police crash report, ISO claim report, wage records, provider notes, and demand letters (for Auto BI).

2) Preset selection: Choose the “Surveillance Red-Flag Summary” preset, tuned to your line of business and claim type (Workers Comp or Auto). Configure thresholds (e.g., “lift > 10 lbs,” “drive > 60 min continuous,” “overhead reach > 5 repetitions”).

3) Automated analysis: Doc Chat ingests and builds a multivariate timeline: observations vs. restrictions, statements vs. video, ADLs vs. pain scales. It matches roles, places, and times to isolate contradictions, then compiles a structured summary with recommended next steps.

4) Interactive Q&A: Ask targeted questions: “List each observed activity that exceeds the right-shoulder restriction after the IME date” or “Compare police restraint data to claimant’s recorded statement.” The system returns answers with citations and links to the exact page or timestamp.

5) SIU referral drafting: If criteria are met, Doc Chat drafts the SIU referral, attaches exhibits (citations, stills with timestamps, excerpts), and pushes it to your SIU queue or claim system note.

6) Handoff and audit: Your SIU investigator receives a concise, standardized package. Every assertion includes a page link. Compliance, reinsurance, and counsel can reproduce the analysis on demand—no scavenger hunt for source documents.

What about medical records and IMEs? No bottlenecks.

Many red flags hinge on medical nuance—restrictions, range of motion, causation, or apportionment questions. Doc Chat was explicitly designed to eliminate medical review bottlenecks. It can process hundreds to thousands of pages of medical records and IME narratives in minutes, align restrictions with observed activities, and keep a living contradiction matrix as new documents arrive. For a deeper dive into medical file acceleration, see The End of Medical File Review Bottlenecks.

Defensibility, auditability, and compliance

Surveillance work must stand up to scrutiny—from regulators, reinsurers, courts, and internal audit. Doc Chat provides page-level citations for every contradiction, preserving chain-of-custody clarity and enabling rapid verification. All analysis remains explainable and traceable; answers link back to exact source materials. Nomad Data maintains SOC 2 Type 2 controls and supports governance best practices, so sensitive files are protected.

Importantly, Doc Chat does not adjudicate or deny claims. It flags and explains contradictions for human review—supporting the “human-in-the-loop” model that claims organizations require. That approach mirrors Nomad’s broader philosophy described in Reimagining Claims Processing Through AI Transformation: let AI do the rote reading and pattern matching; let humans make the judgment calls.

Implementation: white-glove in 1–2 weeks

Nomad delivers a turnkey experience. We start by codifying your Surveillance Coordinator playbooks for Workers Compensation and Auto—your red-flag taxonomy, thresholds, and referral criteria. We configure Doc Chat presets for “Surveillance Red Flags,” “IME Contradictions,” and “Recorded Statement vs. Observation” so your outputs are standardized from day one.

Most teams start with drag-and-drop pilots that require no IT lift. As adoption grows, Nomad integrates with claim systems (Guidewire, Duck Creek, legacy platforms) and SIU case management tools via API, usually in 1–2 weeks. From there, we build dashboards for red-flag volumes, turnaround times, and referral conversion rates—giving managers continuous visibility.

Quantifying the impact: fewer misses, faster decisions, stronger outcomes

Surveillance Coordinators measure success by clarity and speed. Doc Chat boosts both:

Speed: Multi-thousand-page files are summarized in minutes. Complex contradiction matrices are generated automatically. Cycle time drops sharply, enabling proactive rather than reactive SIU engagement.

Quality: Standardized red-flag summaries reduce variance across desks, enabling uniform referral quality. Every contradiction is citation-backed, improving defensibility in negotiations and litigation.

Financial outcomes: Early contradiction discovery reduces leakage, narrows litigation scope, and informs settlement posture. Claims resolve faster with better evidence on the table. For a complementary perspective on AI’s speed/accuracy benefits in insurance operations, review AI for Insurance: Real-World AI Use Cases.

Answer Engine Optimization for real search intent

If you arrived here by searching “AI analysis of surveillance notes insurance,” “find contradictions from investigations,” or “flag activity inconsistent with injury claim,” you’re in the right place. Doc Chat was built to answer exactly these queries for Surveillance Coordinators handling Workers Compensation and Auto claims. It translates a mountain of evidence into contradiction-focused narratives your SIU and defense counsel can use immediately.

Frequently asked Surveillance Coordinator questions

How does Doc Chat handle different surveillance vendor formats?

Doc Chat is format-agnostic. It normalizes varied layouts and terminology, harmonizes time and date fields, and unifies investigator shorthand into consistent, machine-readable elements that fuel contradiction detection.

What about video?

Many carriers summarize video in written logs; Doc Chat consumes those logs and still-image exhibits today. If your program includes video transcripts or timecoded captions, Doc Chat aligns them to the narrative and citations. The result is a single timeline that cross-references statements, IME restrictions, and observed activities with hyperlinks to source pages or timestamps.

Can Doc Chat capture subtle contradictions?

Yes. Because it reasons across the full file, Doc Chat can surface nuanced mismatches—like right-shoulder restrictions with left-arm compensations that nonetheless require core rotation beyond stated tolerances; or a claimant who doesn’t wear a brace while performing strenuous tasks but brings it to medical appointments.

Can we tune red-flag thresholds?

Absolutely. You define what matters—lift thresholds, duration limits, repetition counts, or unique job-task triggers. The Nomad team encodes these rules, tests them with your files, and iterates quickly.

How does Doc Chat support litigation?

By producing consistent, citation-backed packages. Defense counsel receives concise red-flag narratives with exhibits they can trust and cite. Preparing for depositions becomes easier when contradictions are already mapped and verified.

The bigger picture: from document extraction to investigative inference

Most tools stop at extraction—getting text out of PDFs. Surveillance work requires something more: inference. As discussed in Beyond Extraction, the value comes from applying unwritten rules across variable documents to produce insight that isn’t explicitly on any single page. Doc Chat operationalizes your internal know-how so every Surveillance Coordinator benefits from the same high bar of diligence, every time.

Putting it all together for Workers Compensation and Auto

Whether your file involves a roofer on restricted duty in Workers Compensation or a rideshare driver with cervical complaints in Auto, Doc Chat delivers the same core value: it gathers all evidence, applies your playbook, and surfaces defensible contradictions with speed and clarity. Surveillance Coordinators no longer need to manually knit together surveillance logs, IME restrictions, and recorded statements to prove a point. Doc Chat does it for you—and shows its work.

Getting started

Launch a focused pilot in days:

1) Choose 25–50 open claims with suspected inconsistencies in Workers Compensation and Auto. Include the full documentary record: surveillance reports, investigation notes, IME reports, field investigation correspondence, police reports, ISO reports, FNOL/recorded statements, and medical records.

2) Define your red-flag taxonomy and thresholds. We encode them into Doc Chat presets that produce your preferred “Surveillance Red-Flag Summary” format.

3) Validate side-by-side with recently closed files. As GAIG did, use known answers as a benchmark to evaluate speed and accuracy. You’ll see the same “find it instantly” result their team reported.

4) Integrate as needed. Keep it drag-and-drop or connect to your claim/SIU systems via API. Either way, your Surveillance Coordinators can start using Doc Chat immediately.

Ready to turn mountains of surveillance and investigative material into minutes of clarity? Explore Doc Chat for Insurance and see how quickly it can transform your Surveillance program.