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

Claims leaders in Workers Compensation and Auto face an uncomfortable reality: the evidence you need to control leakage is often buried in thousands of pages of surveillance reports, field investigator notes, IME findings, EUO transcripts, and correspondence scattered across email and claim systems. Teams spend hours hunting for contradictions—only to miss a detail that materially changes the settlement strategy. Nomad Data’s Doc Chat solves this bottleneck by performing AI analysis of surveillance notes and investigative files at portfolio scale, automatically surfacing red flags such as observed activity inconsistent with reported restrictions, mismatches between recorded statements and surveillance, and inconsistencies across treating notes and IME reports. Reviews that took days now take minutes, with page-level citations and structured outputs tuned to your playbook.

Doc Chat is a suite of purpose-built, AI-powered agents for insurance document intelligence. It ingests entire claim files—including surveillance reports, investigation notes, IME reports, field investigation correspondence, recorded statements, EUO transcripts, police reports, medical bills, demand packages, FNOL forms, and ISO claim reports—then extracts, cross-checks, and explains the findings in real time. With a direct focus on claims management outcomes, Doc Chat helps your team find contradictions from investigations and reliably flag activity inconsistent with injury claims, without adding headcount.

Why this problem is so nuanced for Claims Managers in Workers Compensation and Auto

In Workers Compensation and Auto, contradictions rarely appear as a single smoking gun sentence. Instead, they emerge from the intersection of timelines, restrictions, and observed behaviors spread across multiple documents. The investigator may note an eight-minute video of a claimant loading mulch bags; the treating physician’s note from the same week may state “no lifting over 10 lbs”; an IME could document full range of motion; a recorded statement might limit activity to short walks. Each alone is inconclusive. Together, they reshape liability, degree-of-disability, and reserve strategy. The challenge is volume and variability: different PI vendors write in different styles, investigators use shorthand, and surveillance footage summaries vary in time stamps and detail. Meanwhile, medical narratives, ICD/CPT details, and work restrictions evolve visit-to-visit. For a Claims Manager responsible for oversight, QA, and leakage control, ensuring every desk uniformly catches these intersections across hundreds of active files is extremely hard using manual review alone.

Workers Compensation adds further complexity: vocational reports, job descriptions, FROI/SROI filings, nurse case manager notes, and employer correspondence must be reconciled against activity logs and surveillance. Return-to-work plans hinge on precise restrictions (e.g., sit/stand tolerance, lifting thresholds, repetitive motion limits). Small inconsistencies—like driving long distances while certified for temporary total disability—can be material. In Auto BI/PD, contradictions often involve ADLs (activities of daily living), repair timelines, rental usage, and pain narratives juxtaposed with police reports, PT notes, EUO answers, and social-media-adjacent observations summarized by a field investigator. For Claims Managers, the risk is twofold: paying more than warranted due to missed red flags, or mismanaging customer experience and litigation exposure due to late discovery of issues.

How the manual process works today—and why it breaks

Most organizations still rely on adjusters and SIU partners to read surveillance summaries line-by-line, pull key facts into spreadsheets, and attempt to reconcile them with medical records and restrictions from both treaters and IMEs. Investigators send PDFs of narrative reports, stills, and video summaries; adjusters skim for highlights, add notes to the claim, and share excerpts with supervisors or defense counsel. When time allows, they compare against recorded statements and EUO transcripts, search the claim notes for prior admissions, and re-open IME opinions to check for alignment. In theory this is thorough; in practice, it is brittle. Human attention declines across long packets; vendor formats vary; and real-world workloads limit cross-document comparisons to only what fits in the day. Busy teams triage to the obvious and defer deep comparisons, leaving subtle but consequential contradictions undiscovered until a mediation or deposition forces a scramble.

Common pain points we hear from Claims Managers include: inconsistent desk-level practices; difficulty standardizing red-flag definitions across states and LOBs; delayed SIU referrals because potential contradictions weren’t evident early; and rework when late findings alter reserves or litigation posture. Each issue traces back to the same root cause: the manual approach can’t reliably connect every dot across surveillance reports, investigation notes, IMEs, and correspondence—at the speed and scale you need.

How Doc Chat automates red-flag detection from surveillance and investigator notes

Doc Chat operationalizes what your best examiners and SIU investigators do—then scales it. The system ingests complete claim files (thousands of pages at a time), normalizes surveillance and field notes, extracts activities and time-stamps, cross-references those against medical restrictions and claimant statements, and generates contradiction alerts with page-level citations. Because it’s trained on your rules, thresholds, and state-specific standards, Doc Chat behaves like your organization’s seasoned expert—consistent, fast, and explainable. It is designed for AI analysis of surveillance notes insurance carriers rely on to act decisively and defensibly.

Here is the typical automated flow from upload to action:

1) Ingest and normalize — Upload surveillance reports, investigator narratives, IME reports, treating notes, EUO transcripts, recorded statements, police reports, demand letters, FNOL, ISO claim reports, wage records, and employer correspondence. Doc Chat handles wildly varied formats and styles, consolidating them into a unified, queryable corpus.

2) Extract activities and restrictions — The agent identifies activities (e.g., walking, ladder climbing, lifting, driving, squatting), quantifies duration and load where available (e.g., “carried two 40-lb bags for approximately 50 feet”), and extracts explicit restrictions from IME/treating records (e.g., “no lifting >10 lbs, sit/stand as tolerated, avoid bending”). It also captures ADL claims made in statements or demand packages.

3) Temporal and cross-document reasoning — Doc Chat aligns dates across surveillance, medical visits, and statements to determine contemporaneity. It catches when a strenuous activity occurs within days of a clinical note asserting severe limitation, or when the same pain narrative persists despite observed improvement over time.

4) Contradiction and anomaly detection — Using your rulebook, Doc Chat triggers alerts—e.g., “Observed activity inconsistent with injury claim: lifting 40 lbs on 5/12 conflicts with 5/10 restriction of no lifting >10 lbs.” It also flags suspicious repetition (boilerplate phrasing across providers), timing anomalies (gym visits before therapy attendance), and geographic inconsistencies (travel while claiming driving intolerance).

5) Structured output and citations — Findings are delivered as a standardized summary with sections for Activities, Restrictions, Contradictions, Missing Documents, and Suggested Actions. Every assertion links back to source page numbers for defensibility and audit readiness.

6) Real-time Q&A and follow-ups — Claims Managers and examiners can ask questions like “find contradictions from investigations” or “flag activity inconsistent with injury claim from 5/1–5/31” and get instant, cited answers—even across 10,000+ pages. This lets the team pivot from reading to decision-making.

7) Workflow integration — Alerts feed your triage queues, SIU referral workflows, defense counsel packets, and reserve recommendation processes. With APIs, Doc Chat updates claim notes and attaches structured summaries to the file so your core system remains the system of record.

Concrete examples of red-flag triggers Doc Chat can surface

Workers Compensation example: A claimant with a lumbar strain is on TTD with a 10-lb restriction from a 6/3 IME. Surveillance on 6/7 documents lifting three 40-lb salt bags into an SUV over 12 minutes. Treating notes from 6/8 reiterate severe limitation with prolonged standing. Doc Chat triangulates the three sources, then generates an alert: “Observed lifting 40 lbs (6/7) contradicts IME restriction (6/3) and conflicts with reported standing intolerance (6/8). Consider modified RTW or additional investigation.” The alert links to the exact pages in the surveillance report, the IME summary, and the treating note.

Auto example: A claimant reports inability to drive due to neck pain and confirms in a recorded statement. Field investigator notes document a 2.5-hour round-trip highway drive to a sports event, with surveillance stills showing head rotations while parking. PT notes during the same week report severe limitation in cervical rotation. Doc Chat flags: “Driving duration and observed head rotation (6/14) inconsistent with recorded statement (6/10) and PT note (6/13). Evaluate credibility; consider independent medical evaluation and EUO follow-up.”

AI analysis of surveillance notes insurance teams can trust

Adjusters and managers are rightly careful about explainability. Doc Chat is built for regulated insurance environments. Every red flag includes a rationale and citation back to the source page and paragraph. Your QA reviewers, defense counsel, reinsurers, and regulators can verify the evidence in seconds—mirroring results referenced in real-world deployments by major carriers. In fact, one carrier publicly shared that AI-powered review enabled their adjusters to find facts “instantly” with links back to pages, transforming cycle times and confidence; see their experience in this case study.

Behind the scenes, Doc Chat does more than keyword search. It interprets concepts across inconsistent documents and then applies your institutional rules to produce actionable insight—a discipline we’ve written about extensively. If you’ve been told “document scraping is just web scraping for PDFs,” you’ll appreciate why that’s wrong after reading Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs. Claims contradictions aren’t a field on a page; they’re an inference across pages, dates, and narratives.

What changes for a Claims Manager day-to-day

When red-flag detection is automated and explainable, Claims Managers gain consistent oversight across every desk and file. New hires immediately benefit from institutionalized rules; senior examiners get the time and context to negotiate, set reserves more accurately, and manage litigated files strategically. SIU referrals are data-driven and earlier. QA audits move from spot-checks to complete coverage. And because Doc Chat standardizes outputs, peer review and handoffs are cleaner, freeing leaders to focus on training and strategy instead of rework.

How Doc Chat stacks up against manual review

Nomad Data’s platform regularly shifts work from days to minutes by processing claim packets at scale and surfacing the exact contradictions adjusters would otherwise need to hunt down manually. In medical-file-heavy claims, our clients report summarization that previously took 5–10 hours now takes about a minute, and 10,000–15,000-page files can be processed in well under two minutes, with consistent quality, no fatigue, and standard formats. See the transformation in medical record review speed and consistency in The End of Medical File Review Bottlenecks.

For surveillance and investigative materials, the gains are similar: Doc Chat reads every page with identical rigor, never misses a time stamp, and never forgets a restriction from an IME 200 pages earlier. Real-time Q&A lets examiners ask “Which activities exceeded lifting restrictions last week?” or “Where did the claimant describe ADLs that conflict with observed behavior?” and receive instant answers plus citations.

From evidence to action: how contradictions flow into claim decisions

Doc Chat’s red-flag triggers are delivered in a structured output you define. Claims Managers typically configure sections such as Activities, Restrictions, Contradictions, Missing Evidence, Suggested Actions, and Litigation Signals. Suggested actions might include requesting additional surveillance, scheduling an IME, ordering a functional capacity evaluation (FCE), initiating an EUO in Auto BI, updating reserves, or referring to SIU. Because the format is standardized, teams can compare files apples-to-apples, benchmark desk performance, and build predictable escalations across Workers Compensation and Auto lines.

Examples of contradictions Doc Chat routinely identifies

— Surveillance shows claimant loading heavy equipment shortly after a treating note restricts lifting and bending.
— Field investigator logs note consistent gym visits ahead of scheduled PT the claimant missed.
— EUO transcript asserts inability to drive more than 10 minutes, while parking-lot surveillance documents multi-hour driving the same week.
— Claim notes indicate cane usage; surveillance shows extended ambulation without assistive devices.
— IME finds near-normal range of motion; demand letter issued later repeats severe limitation claims verbatim from older provider notes, suggesting boilerplate or copy-forward issues.
— Police report details minimal vehicle damage inconsistent with ongoing severe pain narratives; Doc Chat prompts to reconcile with medical documentation and CPT utilization.

Business impact: time, cost, accuracy, and leakage

Automating contradiction detection yields measurable benefits at the file and portfolio level. Claims Managers typically quantify impact along four axes:

Cycle time — Intake-to-evaluation compresses dramatically. Reviews that once required multiple sessions now conclude in one sitting. Claims triage becomes proactive, with earlier SIU engagement and faster movement to strategy (settle, defend, or seek additional documentation).

Expense — Less manual page-turning and fewer outside vendor reviews save hours per file and shrink loss-adjustment expense (LAE). When ad hoc investigative requests are targeted by AI evidence, they become higher-yield, lowering overall spend while raising efficacy.

Accuracy — Machines do not tire. Doc Chat maintains consistent extraction accuracy across every page and every file, minimizing misses that lead to leakage. Page-level citations bolster defensibility with auditors, reinsurers, and courts.

Leakage reduction — The earliest possible detection of contradictions shifts negotiation leverage, improves reserve accuracy, and reduces overpayment risk—especially in soft-tissue claims where surveillance, IME alignment, and timeline precision materially affect outcomes.

Why Nomad Data and Doc Chat are different

Doc Chat is not a generic summarizer. It’s a purpose-built insurance document intelligence platform trained on your playbooks, forms, and standards. It ingests complete claim files at scale, performs deep inference across inconsistent documents, and returns structured outputs with citations. Our clients choose Nomad for four reasons:

1) Depth and scale — Doc Chat processes hundreds of thousands of pages per minute across large portfolios without adding headcount. It handles the toughest formats and nested attachments to deliver complete reviews.

2) Your rules, institutionalized — We capture your best examiners’ unwritten rules and thresholds and encode them into the agent. This standardizes decision support across desks, shifts, and geographies.

3) Real-time Q&A plus explainability — Ask complex questions across the entire file and get instant answers with citations back to the exact page and paragraph—crucial for QA, SIU, and litigation.

4) White glove delivery with rapid time-to-value — Our team handles onboarding, tuning, and integration. Most Claims teams are live in 1–2 weeks, with early users productive on day one via drag-and-drop uploads. As adoption grows, APIs integrate with your claim system to automate alerts and notes.

Learn more about our insurance-specific capabilities and see examples at Doc Chat for Insurance.

Security, compliance, and defensibility

Doc Chat is designed for regulated use. It provides transparent audit trails for every answer and alert, including time stamps, source citations, and versioning of rules. The platform supports enterprise security controls and is built to align with rigorous standards that insurance IT and compliance teams expect. Because every alert cites the underlying page, reviewers can quickly validate and, when appropriate, share excerpts with counsel or regulators. This transparency has helped carriers accelerate adoption and trust, as highlighted in our client stories, including the Great American Insurance Group webinar recap linked above.

How it integrates with your claims workflow

Doc Chat meets you where you work. Early pilots start with simple drag-and-drop ingestion and export of structured summaries. As you scale, our team connects Doc Chat to your claim platform for automated note entries, task creation for SIU referrals, reserve recommendation routing, and dashboarding. Alerts can be sent to queues by severity (e.g., hard contradiction vs. soft inconsistency). We also support batch portfolio reviews—useful for quarterly leakage audits, reinsurance submissions, and defense counsel portfolio triage.

Answering high-intent questions from Claims Managers

“How does Doc Chat perform AI analysis of surveillance notes insurance teams can use in real time?”

Doc Chat extracts activities, durations, loads, and locations from surveillance narratives; maps them against contemporaneous medical restrictions and statements; then applies your thresholds to generate red flags with citations. Teams can query the file in natural language—“List all observed lifting over 10 lbs within 10 days of an IME restriction”—and get immediate, verifiable answers.

“Can Doc Chat find contradictions from investigations even when the wording differs?”

Yes. Because it reasons over concepts instead of simple keywords, Doc Chat detects contradictions despite paraphrasing or vendor-specific phrasing. It aligns entities and times across reports, notes, and transcripts, so a “moving multiple sacks of soil” observation will be compared against lifting restrictions even if the numeric weight is implied rather than explicit.

“Will Doc Chat reliably flag activity inconsistent with injury claim for Workers Comp and Auto?”

That’s the core use case. For Workers Comp, it flags activity inconsistent with medical restrictions and RTW plans. For Auto BI, it flags inconsistencies between claimed limitations and observed ADLs, driving tolerance, or recreational activity. Alerts are tuned to your definitions of materiality and can differ by jurisdiction or injury type.

The manual-to-automated journey: from pilot to scale

Successful Claims leaders move quickly but carefully. Here’s a pragmatic adoption pattern we see yielding fast ROI and strong internal buy-in:

Phase 1 — Prove it on your toughest files. Start with 10–20 active files heavy on surveillance and investigative notes. Upload complete packets (surveillance reports, investigation notes, IME reports, treating records, statements, EUO, police reports). Compare Doc Chat’s contradiction summary to your team’s findings. Expect near-instant “aha” moments as missed details surface with clear citations.

Phase 2 — Tuning and thresholds. We capture your desk-level rules—what counts as material, when to auto-refer to SIU, what severity levels appear on dashboards—and encode them. This institutionalizes your best practices and ensures consistency.

Phase 3 — Workflow integration. Connect Doc Chat outputs to your claims system. Push alerts and summaries to notes, create auto-tasks for SIU or IME scheduling, and add a dashboard for manager oversight. This tends to take 1–2 weeks and transforms daily operations.

Phase 4 — Portfolio reviews. Run batch evaluations across segments (e.g., soft-tissue Auto BI, long-duration TTD in Workers Comp) to find systemic opportunities, adjust reserves, and standardize negotiation posture.

Two sample playbooks you can run immediately

Workers Compensation: Modified duty acceleration

Objective: identify evidence supporting earlier RTW/modified duty. Inputs: IME/Treater restrictions, surveillance activities, job description, employer correspondence, vocational reports. Doc Chat output: list of activities exceeding restrictions with dates, suggested IME addendum questions, potential modified tasks, and missing documentation (e.g., outdated job descriptions). Result: faster RTW discussions, more defensible TTD transitions, reduced indemnity.

Auto BI: Credibility assessment to guide negotiation

Objective: target contradictions tied to ADLs and driving tolerance. Inputs: surveillance summaries, field notes, recorded statements, EUO transcript, PT notes, demand letter, police report. Doc Chat output: contradictions table with citations; severity ranking; suggested negotiation talking points and investigative steps (e.g., supplemental PT records, pharmacy fill checks consistent with reported usage). Result: earlier, sounder settlement strategies and fewer surprises at mediation.

What Claims Managers should measure

Operationalizing AI is easier when you track the right metrics. Common Claims Manager KPIs for Doc Chat include reduction in days-to-disposition, percentage of files with contradictions identified pre-litigation, LAE per claim, reserve accuracy variance, SIU conversion rate, percent of alerts with action taken within SLA, and audit exceptions per 100 files. Because Doc Chat standardizes outputs, creation of these dashboards is straightforward, and portfolio-level insights emerge in weeks, not quarters.

Addressing common concerns

Will AI “hallucinate” contradictions? In our document-grounded approach, alerts are anchored to page-level citations. If the evidence isn’t in the file, Doc Chat doesn’t conjure it. Reviewers always see the source page before acting.

How does this affect staffing and morale? Doc Chat eliminates rote page-turning and frees adjusters and SIU to do higher-value work—investigation, negotiation, mentoring. Teams report less burnout and higher job satisfaction once the document grind goes away.

Is this just for medical files? No. While medical records are a major input, Doc Chat is equally effective on surveillance reports, investigator notes, EUO transcripts, police reports, demand packages, repair estimates, loss run reports, and coverage documents. The engine is built for heterogeneous, unstructured claim files—the exact problem space described in our perspective on modern document intelligence: Reimagining Claims Processing Through AI Transformation.

The Nomad Process: white glove and fast

Nomad’s delivery model is simple: we co-create with you. Our team interviews your Claims Managers, SIU, and QA to capture the unwritten rules that drive your best outcomes. We configure Doc Chat to your workflows, thresholds, and output formats, then validate on real files. Most teams are live in 1–2 weeks. Users get immediate value via drag-and-drop uploads; IT can take a beat before wiring APIs into production. Because we handle the heavy lifting, your team avoids DIY risk and sees results quickly.

The bigger picture: reclaiming time, standardizing quality, lowering leakage

The biggest risk now is inaction. Competitors implementing AI document intelligence are reducing costs and shortening cycle times while raising quality and defensibility. As claims documentation grows in volume and complexity, relying on manual review alone will widen the gap. Doc Chat offers a pragmatic, defensible, and fast path to modernize contradiction detection across Workers Compensation and Auto without disrupting your core systems or culture.

Key takeaways for Claims Managers

• Doc Chat automatically surfaces contradictions across surveillance reports, investigation notes, IME/treating records, EUO transcripts, statements, and more—with page-level citations.
• Reviews shift from days to minutes; cycle times compress, reserves stabilize earlier, and SIU referrals become timely and evidence-based.
• Outputs are standardized to your playbook, institutionalizing consistency across desks and geographies.
• Implementation is white glove and fast—typically 1–2 weeks to go live, with immediate value from day one.
• Security, auditability, and explainability are built in, enabling confident adoption in regulated environments.

Business outcomes you can expect

• 30–70% reduction in time spent on document review and red-flag detection.
• Material reduction in LAE by targeting outside services and investigations where they yield the most leverage.
• Improved reserve accuracy and earlier negotiations due to faster, clearer evidence.
• Lower leakage driven by consistent, portfolio-wide detection of contradictions that change liability and value.
• Higher adjuster and SIU satisfaction as rote work is automated and expertise is amplified.

Get started

If your team is ready to move beyond manual page-turning and make contradiction detection a consistent, data-driven capability, learn more about Doc Chat for Insurance and schedule a working session with our team. Bring real files heavy with surveillance and field investigation notes. In under an hour, you’ll see how quickly Doc Chat can find contradictions from investigations, flag activity inconsistent with injury claims, and equip your Claims Managers to lead with speed, accuracy, and confidence.