Automated Red-Flag Triggers from Surveillance Reports and Field Investigator Notes for Claims Managers in Workers Compensation and Auto

Claims managers in Workers Compensation and Auto lines juggle an overwhelming volume of documentation every day. Surveillance reports, field investigator notes, IME reports, and correspondence accumulate quickly as a claim evolves, and the most consequential details are often buried inside long narratives, time-stamped observation logs, or inconsistent provider summaries. The challenge is simple to state but hard to solve at scale: surface contradictions in seconds, not days, so you can make defensible, timely decisions. That is exactly where Nomad Data’s Doc Chat excels.

Doc Chat is a suite of purpose-built, AI-powered document agents that performs end-to-end analysis across entire claim files, including surveillance reports and investigation notes. It automatically extracts red-flag indicators, highlights activity inconsistent with stated restrictions, and cross-references IME findings against observed behavior. With real-time Q&A and audit-ready page citations, claims managers can quickly find contradictions from investigations and bring focus to the next best action. Learn more about Doc Chat for insurance at nomad-data.com/doc-chat-insurance.

The problem: contradictions are scattered across thousands of pages

In Workers Compensation and Auto claims, material contradictions rarely present as a single sentence on a single page. They emerge from the intersection of surveillance footage or narrative logs, claimant or witness statements, medical records, and policy language. A surveillance note might document a claimant loading heavy equipment into a truck. An IME report might record lifting restrictions of no more than 10 pounds. Field investigation correspondence might include a neighbor statement that the claimant works weekends under the table. Any one note alone may not be decisive, but the totality of evidence, tracked across time and sources, becomes a powerful red flag. The difficulty is that human reviewers are forced to read line by line and remember links across pages, dates, and sources.

For a claims manager, this is not an academic frustration. It is a daily operational barrier that inflates loss-adjustment expense, extends cycle time, and increases leakage risk. In Workers Compensation, each week spent combing investigation notes can mean prolonged temporary total disability payments, delayed return-to-work planning, and compounding reserves. In Auto bodily injury, the longer it takes to confirm inconsistent activity, the more negotiating leverage erodes and the greater the chance of litigation escalation or an unnecessary settlement premium.

What makes Workers Compensation and Auto especially nuanced for claims managers

Workers Compensation and Auto have distinct evidentiary patterns and documentation rhythms that complicate red-flag discovery:

• Workers Compensation nuances: frequent IMEs and peer reviews, functional capacity evaluations, nurse case manager notes, PT and OT daily treatment notes, RTW forms, work restrictions, job descriptions, wage statements, and employer HR correspondence. Surveillance reports often include multi-day time logs capturing lifting, bending, ladder use, or extended driving, which must be cross-walked to the latest restrictions.
• Auto nuances: police reports with diagrams, PIP or MedPay bills, recorded statements, demand packages, property damage photos, EDR or telematics extracts, and occasional dashcam or third-party video. Surveillance may capture strenuous recreational activity inconsistent with claimed whiplash or back injury, or extended travel patterns undermining severity claims.

Across both lines, claims managers must correlate date-stamped observations with the claim’s chronological record: FNOL details, ISO claim reports, statements, IME dates, treatment milestones, and coverage or endorsement specifics. It is highly manual, cognitively demanding work.

How the process is handled manually today

Most organizations still piece this together by hand. Claims managers or senior examiners gather PDFs and emails from adjuster diaries, SIU investigators, surveillance vendors, nurse case managers, TPAs, and defense counsel. They scan and rescan documents, relying on memory and sticky-note systems, and track possible contradictions in a spreadsheet. A typical routine looks like this:

• Open the latest surveillance report and skim narrative logs for strenuous activity, lifting weights, durations of activity, or long-distance driving. Copy potential red flags into a spreadsheet.
• Flip to IME reports to see if the physician’s restrictions conflict with the observed behaviors. Note page and paragraph for possible SIU referral or EUO planning.
• Check recorded statements and interview notes for prior inconsistent statements, work capability claims, or pain level narratives. Manually compare language against surveillance observations.
• Scan field investigation correspondence for employer or neighbor statements that corroborate or contradict the claimant’s version of events.
• Reconcile dates and times across sources. Verify whether the surveillance occurred before or after an IME, before or after an increase in claimed pain, or on days with therapy appointments.
• Draft an SIU referral, attach evidence, export screenshots, and build a timeline in a Word doc or PowerPoint.

Even with experienced staff, this process is slow, error-prone, and hard to scale during surge volumes. The practical result: contradictions are found late or missed altogether, negotiations drag on, and leakage rises. Backlogs force tradeoffs that disadvantage both the insurer and the policyholder waiting for resolution.

AI analysis of surveillance notes insurance: what good looks like

There is increasing demand for AI analysis of surveillance notes insurance teams can trust. What does good look like for a claims manager?

First, it means end-to-end ingestion of the entire file, not a sampling. Second, it means the system can reason across documents and dates to assemble events into a coherent timeline. Third, it means the AI maps observed activity against claimed restrictions and medical limitations, then plainly flags activity inconsistent with injury claim narratives. Finally, good looks like audit-ready transparency: every assertion should link back to the precise page and timestamp where the fact was found.

Doc Chat delivers on these requirements at enterprise scale. It does not simply summarize documents. It finds contradictions from investigations by cross-referencing surveillance logs with IME restrictions, treatment notes, and recorded statements, producing a high-confidence red-flag list with source citations and suggested next actions.

How Doc Chat automates red-flag detection across surveillance and investigations

Doc Chat by Nomad Data ingests full claim files, including surveillance reports, investigation notes, IME reports, field investigation correspondence, nurse case manager notes, police reports, demand packages, and more. It then applies purpose-built claims logic trained on your organization’s playbooks and standards. The approach draws on the principles described in Beyond Extraction: Why Document Scraping Isnt Just Web Scraping for PDFs, where value comes from inference across unstructured documents, not from extracting a single field. Read more here: Beyond Extraction.

Key automation capabilities tailored for claims managers:

• Restriction-to-observation mapping: Extracts restrictions from IME reports and treating provider notes, then aligns them to surveillance actions such as lifting weights, ladder use, extended driving, or landscaping work observed during specific time windows.
• Contradiction detection across sources: Identifies inconsistencies between recorded statements and field investigation correspondence, or between police reports and later narratives. Flags shifts in pain levels or functional claims that do not track with observed activity.
• Chronological alignment: Builds a precise timeline that anchors surveillance timestamps to medical appointments, therapy sessions, or IME dates to evaluate credibility.
• Activity consistency grading: Scores the degree of inconsistency between observed behavior and stated limitations, with rationales and citations.
• SIU referral pack generation: Automatically compiles a page-cited dossier with excerpts, screenshots, and a narrative timeline suitable for SIU, EUO planning, or defense counsel.
• Real-time Q&A: Allows a claims manager to ask questions like list every instance the claimant lifted more than 10 pounds, or where does the IME say the claimant cannot sit for more than 30 minutes, and receive instant, cited answers across thousands of pages.

Examples of auto-generated red-flag triggers

Doc Chat’s red-flag engine is customizable to your thresholds and state-specific rules, and can include triggers like:

• Observed heavy lifting that exceeds IME restrictions within 14 days of exam.
• Extended driving or commuting inconsistent with reported inability to sit for more than X minutes.
• Recreational sports participation during active PT or while reporting high pain scores.
• Multiple days of yard work, remodeling, or construction work during TTD status in Workers Compensation.
• Carrying a child, pushing heavy carts, or handling pets that exceed weight limits stated in restrictions.
• Temporal contradictions, such as strenuous activity on the same day as a treatment escalation or shortly after a request for benefit extension.
• Discrepancies between sworn statements and field investigator notes or neighbor statements.
• Inconsistencies between demand letters and earlier recorded statements or police reports in Auto bodily injury claims.

Because every claim and every jurisdiction is different, we encode your organization’s red-flag definitions and playbook into the agent so the outputs align with your practices. The Nomad process tailors AI behavior to your desk-level standards, reinforcing consistent decision quality across your team.

Business impact for claims managers: speed, accuracy, and defensibility

Nomad Data’s clients routinely report transformation when shifting surveillance and investigation analysis to Doc Chat. As highlighted in our piece The End of Medical File Review Bottlenecks, AI can process hundreds of thousands of pages per minute and maintain unwavering attention from page 1 to page 1,500. See details: The End of Medical File Review Bottlenecks. For claims managers, this translates into:

• Time savings: Initial contradiction sweeps go from days to minutes. Real-time Q&A eliminates back-and-forth digging and manual keyword searches.
• Cost reduction: Fewer manual touchpoints, lower overtime during surges, and reduced spend on external specialists for document review.
• Accuracy gains: Consistent extraction of dates, restrictions, observed activities, and contradictions with page-level citations and linked context.
• Cycle time reductions: Faster SIU referrals and earlier coverage or compensability determinations, which stabilize reserves and curb leakage.
• Higher SIU yield: Better-quality, better-documented referrals improve outcomes when pursuing EUOs, subrogation, or litigation defenses.

Great American Insurance Group publicly discussed how Nomad accelerated complex claim reviews, compressing tasks that once took days into minutes with source-linked answers for oversight and audit. Read the workflow transformation story: GAIG accelerates complex claims with AI.

Why organizations miss contradictions in surveillance and notes

Two root causes drive misses. First, volume and fatigue make it impossible for human reviewers to maintain perfect recall across thousands of pages. Second, many contradictions are inference problems, not keyword problems. For example, a surveillance narrative may describe repeated trips up and down stairs carrying storage totes. The IME may not use the same exact vocabulary and instead states no stair climbing while carrying more than 5 pounds. Humans not only need to remember both facts but also normalize the language and units to compare them. The Beyond Extraction article explains this complexity gap and why an inference-first approach is required.

Doc Chat bridges that gap. It normalizes activity descriptions and medical restrictions, harmonizing synonyms and disparate phrasing, so contradictions become visible and actionable.

Deep dive: how Doc Chat works under the hood for claims managers

Doc Chat is more than a summarizer. It is a set of insurance-trained document agents built for end-to-end claim review. While technical details are abstracted from end users, the engine follows a transparent, auditable flow:

1. Full-file ingestion and classification

Doc Chat ingests the entire claim file, including surveillance reports, IME and peer review reports, field investigation correspondence, nurse case manager notes, recorded statements, police reports, ISO claim reports, FNOL forms, demand letters, EOBs, therapy notes, and adjuster diary entries. It classifies each document type and applies tailored extraction logic accordingly.

2. Entity and fact extraction

The agent extracts core facts such as claimant identity, employer, injuries, restrictions, medications, observed activities, dates and times, locations, distances driven, weights lifted, and relationships between observers and the claimant. It recognizes narrative patterns typical in surveillance reports such as X observed claimant exiting residence at 06:45, carrying large black trash bag down 12 exterior steps.

3. Normalization and alignment

Doc Chat normalizes units and phrases so IME restriction cannot lift more than 10 lbs and surveillance tote lifting approximately 25 lbs align. It syncs timestamps with medical visit calendars and appointment logs to contextualize observations.

4. Contradiction and red-flag scoring

Using your playbook, Doc Chat evaluates contradictions against thresholds and state-specific nuances. It produces a red-flag list with rationale, risk level, and linked citations. Patterns across multiple days of surveillance or multiple IMEs receive elevated weight.

5. Evidence pack and next-best action

The agent compiles SIU-ready evidence packs that include excerpts, screenshots or page crops, timelines, and suggested actions such as request EUO on topics X and Y, verify employer timesheets for dates A–C, obtain RTW form update, or schedule follow-up IME focused on lumbar flexion.

6. Real-time Q&A and export

Claims managers can ask targeted questions across the full corpus and export structured outputs to claim systems or share with counsel. Because every answer includes page-level citations, oversight and compliance reviews are straightforward.

Examples by line of business

Workers Compensation

Workers Comp files often hinge on capacity and restrictions. Doc Chat compares surveillance against IME restrictions, FCE findings, and treating provider notes to flag contradictions like manual materials handling above approved limits, ladder or roof work inconsistent with restrictions, prolonged kneeling or squatting during TTD status, or heavy yard work during therapy cycles. It also highlights misalignments between nurse case manager summaries and underlying provider notes, and cross-checks field investigation correspondence, such as supervisor statements, for inconsistencies with recorded statements.

Auto

In Auto bodily injury and PIP claims, Doc Chat aligns surveillance logs with pain diaries, demand letters, and police reports. It can flag recreational sports or strenuous activities inconsistent with claimed limitations, driving duration that contradicts alleged seat-intolerance, or travel patterns undermining the necessity of attendant care. It also checks consistency between early recorded statements and later demand narratives, and synthesizes contradictions for effective EUO or negotiation strategy.

From manual to automated: measurable transformation

Our research and client outcomes show dramatic improvements when moving from manual contradiction hunts to Doc Chat’s automated approach. As outlined in our article Reimagining Claims Processing Through AI Transformation, teams have cut document review time by orders of magnitude while improving consistency. Explore more: Reimagining Claims Processing Through AI Transformation.

Typical results for claims managers include:

• 70 to 90 percent faster initial contradiction discovery on surveillance and notes, with page-cited evidence ready for SIU in minutes.
• 20 to 40 percent reduction in loss-adjustment expense due to fewer manual touchpoints and faster cycle times.
• Meaningful reduction in leakage via earlier, better-documented coverage or compensability decisions and higher-quality negotiations.
• Lower burnout and higher retention as claims professionals shift from tedious reading to focused investigation and strategy.

These gains compound when combined with automated intake and structured extraction benefits described in AI’s Untapped Goldmine: Automating Data Entry. Document intelligence eliminates rote data entry and lets staff concentrate on the exceptions and the analysis that truly move outcomes. Read more: Automating Data Entry.

Governance, explainability, and audit readiness

Claims managers need outputs they can defend to internal audit, regulators, reinsurers, and courts. Doc Chat is designed for enterprise-grade transparency. Every extracted fact and red flag is tied to a page or timestamp with a direct citation link. When you send a referral to SIU or share a pack with defense counsel, they can click straight to the source within seconds.

Security and privacy are non-negotiable. Nomad Data maintains strong controls, and outputs stay within your governance framework. Crucially, the system is engineered to reduce risk rather than create it: it systematizes your playbook, enforces consistency across the team, and keeps humans in the loop for the final decision. This model aligns with the principle that AI acts like a capable junior analyst whose work is always verified by the claims manager before determinations are made.

Why Nomad Data is the best partner for red-flag automation

Generic summarizers cannot deliver the depth required for claims. Nomad Data’s Doc Chat was built for insurance and claims document complexity. Key differentiators:

• Volume at speed: Review entire claim files in minutes, including long surveillance reports and multi-IME medical histories.
• Complexity by design: Doc Chat is trained to surface exclusions, endorsements, restrictions, and trigger language that often hide in dense, inconsistent documents.
• The Nomad process: We embed your playbooks and SIU criteria, so the red-flag engine reflects your standards desk by desk and jurisdiction by jurisdiction.
• Real-time Q&A with citations: Ask questions across the whole file and get instant, auditable answers, eliminating blind spots.
• White glove service: Dedicated team guides discovery, configuration, validation, and rollout.
• Fast implementation: Initial deployment and value typically in 1 to 2 weeks, then iterated with your feedback.

We have repeatedly proven these capabilities in production with leading carriers. The GAIG case study shows exactly how page-linked explainability builds trust with adjusters and compliance alike.

Embedding your SIU thresholds and jurisdictional nuances

Each carrier has unique investigative thresholds. For Workers Compensation, that might include precise weight limits, frequency of activity required to trigger SIU review, or rules for corroboration before escalation. For Auto, it might include standards for when to request EUO, telematics verification, or social media OSINT. We configure Doc Chat to match these thresholds and continuously tune the model as your playbook evolves. The result is a fit-for-purpose agent that flags exactly what your SIU and claims teams consider actionable, reducing noise and increasing follow-through.

Operationalizing outputs in your ecosystem

Doc Chat integrates with modern claims platforms and collaboration tools. Teams typically start with a simple drag-and-drop workflow to validate value, then connect via API to accelerate at scale. Exports include:

• Red-flag timeline with citations for SIU.
• EUO question outlines informed by contradictions.
• Provider outreach and RTW form request templates.
• Defense counsel packs with exhibits.
• Structured contradiction fields for dashboards and trend monitoring.

This is how claims managers turn insight into consistent action. Instead of one-off wins, you institutionalize an always-on investigative layer that raises your baseline performance on every surveillance report and investigation note.

Answering high-intent needs head-on

Use case: AI analysis of surveillance notes insurance teams can trust

Doc Chat reads narrative surveillance notes end to end, normalizes activity descriptions, and compares them to medical restrictions and statements. It generates a contradiction list with severity scoring and page-level citations so managers can quickly approve SIU referrals and instruct the desk on next steps.

Use case: find contradictions from investigations without manual spreadsheets

Field investigation correspondence, neighbor interviews, or employer HR emails often contradict statements buried elsewhere. Doc Chat cross-checks these sources, highlights discrepancies, and consolidates them into a timeline that is far easier to defend than a personal spreadsheet.

Use case: flag activity inconsistent with injury claim fast enough to impact strategy

Timing matters. When an auto-generated alert shows strenuous activity inconsistent with current restrictions within days of an IME, you can act before negotiations harden. Early, defensible insight improves outcomes and reduces litigation risk.

Human in the loop: the right balance for decisions

Doc Chat produces summarized contradictions and recommendations, but the claims manager makes the call. This approach preserves human judgment, captures nuance in jurisdictional rules, and ensures decisions align with policy language and case law. Our article on AI transformation in claims describes how teams calibrate trust and maintain oversight while realizing massive time savings.

Quantifying the ROI for Workers Compensation and Auto

Every organization starts from a different baseline, but the math is consistent. Reviewing surveillance reports and investigation notes manually consumes significant hours per claim. With Doc Chat, that time compresses to minutes, allowing teams to:

• Handle more claims per FTE without sacrificing quality.
• Accelerate compensability and coverage decisions, stabilizing reserves earlier.
• Reduce leakage by catching contradictions before settlement posture solidifies.
• Improve SIU hit rates by sending complete, well-documented referrals.
• Boost morale by eliminating tedious reading and data entry, lowering turnover risk.

As our clients have found, faster and more consistent review does not just cut costs; it enables better strategic outcomes at every stage of a claim, from triage to litigation management.

Implementation in 1 to 2 weeks, not months

We make adoption straightforward. Most carriers begin with a focused pilot on Workers Compensation and Auto claims that feature surveillance and field notes. The white glove Nomad team works with your claims managers and SIU leaders to encode your playbook, define red-flag thresholds, and validate outputs against known cases. Because Doc Chat can operate with a drag-and-drop interface and then integrate via API, you get immediate value and a clear path to scale without disruption.

Beyond contradictions: building a proactive, intelligent claims operation

Contradiction detection is one piece of a broader transformation. Doc Chat can also automate completeness checks, flag missing medical evidence, create demand response summaries, and surface policy triggers and endorsements. By standardizing complex workflows that once lived in individual heads, you improve consistency and resiliency across the claims organization. For a broader view of how AI is reshaping insurance, see AI for Insurance: Real-World Use Cases.

What makes Doc Chat different from generic AI tools

Many teams have tried consumer-grade AI or off-the-shelf summarizers and been disappointed. Those tools are not built for the inference-heavy work of claims contradiction detection. Doc Chat is insurance-native. It reads like your best examiner, cross-references like your best SIU investigator, and always points back to the source. It is trained on your rules, speaks your language, and scales to your volumes. And it will evolve with you as your playbook changes.

Get started

If your organization wants to automate red-flag triggers from surveillance reports, investigation notes, IME reports, and field investigation correspondence, Doc Chat provides a proven, auditable path forward. Claims managers in Workers Compensation and Auto can move from reactive, manual hunts to proactive, consistent detection with minutes of effort. Explore Doc Chat for insurance at nomad-data.com/doc-chat-insurance and see how quickly you can operationalize contradiction detection and transform claim outcomes.