Automated Red-Flag Triggers from Surveillance Reports and Field Investigator Notes – SIU Investigator Guide for Workers Compensation and Auto

Special Investigations Units (SIU) in Workers Compensation and Auto claims are inundated with surveillance reports, field investigator notes, IME reports, recorded statements, police reports, and endless email correspondence. The problem isn’t a lack of data—it’s that crucial contradictions and activity inconsistent with alleged injuries hide across thousands of pages and dozens of sources. Miss one inconsistency or timeline conflict and leakage, litigation, and reserve drift follow. Nomad Data’s Doc Chat changes that calculus, turning hours or days of manual review into minutes and ensuring nothing material falls through the cracks.

Doc Chat is a suite of insurance-specific, AI-powered document agents that perform deep analysis across entire claim files—reading surveillance narratives, cross-checking IME restrictions against daily activities, aligning field investigation correspondence with recorded statements, and surfacing precise red-flag triggers with page-level citations. SIU investigators can ask natural-language questions like “List observed activities that exceed IME lifting restrictions” or “Show all contradictory statements regarding work status,” and get defensible answers instantly. Learn more at Doc Chat for Insurance.

Why SIU Needs Automation Now in Workers Compensation and Auto

In Workers Compensation and Auto, claim documentation volume and complexity have exploded. A single litigated WC file can span IME reports, FCEs, PT/OT notes, pharmacy records, wage statements, surveillance logs, investigator affidavits, and employer communications. Auto BI files compound the complexity with police reports, EUO transcripts, medical demand packages, repair estimates, EDR downloads, and social media captures. For SIU investigators, the job is to stitch together a timeline, reconcile conflicting accounts, and isolate suspicious patterns. Manually, that’s slow and error-prone.

Adjusters and SIU teams frequently ask for “AI analysis of surveillance notes insurance,” hoping to find contradictions from investigations without reading every word. The mission, however, isn’t just keyword search—it’s inference: Does the activity described in a surveillance report violate IME restrictions? Does a recorded statement’s timeline conflict with employer attendance logs or the FNOL time of loss? Are pain-scale claims compatible with observed activity? That’s where Doc Chat excels.

Where Red Flags Hide: The Nuances of Surveillance and Field Notes

Surveillance and field notes are gold mines of fact patterns—but they’re spread across variable formats and writing styles. A single surveillance report may contain:

• Chronological narratives with time-stamped sightings
• Investigator observations (e.g., carrying groceries, bending, prolonged standing)
• Still-image callouts and captions
• Vehicle movements and stops
• Interaction notes (neighbors, employers, or service locations)

Field investigation correspondence adds even more nuance: employer interviews, job description details, wage and attendance verification, witness statements, social media summaries, and handwritten notes. In Workers Compensation, IME reports and FCE summaries define restrictions and capabilities. In Auto, medical demand letters, CPT/ICD-10 billing, collision dynamics, and prior loss histories paint another layer. The contradictions SIU cares about are rarely in a single paragraph—they’re dispersed across surveillance lines, IME pages, and statement excerpts separated by hundreds of pages and multiple document types.

How the Process Is Handled Manually Today

SIU investigators still spend hours combing through PDFs with inconsistent formatting. Common manual steps include:

1) Skimming surveillance narratives for exertional activities; 2) Locating IME restrictions (e.g., lifting limits, sitting/standing tolerances, ROM constraints); 3) Reconciling field notes about work status with employer wage records; 4) Comparing recorded statements and EUO testimony for date, time, and symptom consistency; 5) Cross-referencing PT/OT frequency with activity logs; 6) Checking prior losses via ISO ClaimSearch and loss run reports; 7) Creating a chronology in spreadsheets; 8) Drafting the SIU referral, attaching citations and exhibits.

This manual review creates critical risks: fatigue, missed details, and inconsistent application of SIU referral criteria. Peak-volume weeks lead to triage compromises—some files get deeper review, others don’t. And when surveillance arrives late, critical contradictions may be discovered after reserves are set or settlement positions are locked.

What Counts as a Red Flag? Patterns Doc Chat Surfaces by Default

To help SIU teams flag activity inconsistent with injury claim, Doc Chat is configured to codify your playbook and highlight common indicators including:

• Activity exceeding medical restrictions: Observed lifting, climbing, prolonged standing, repetitive bending, or overhead work conflicting with IME/FCE limits
• Timeline conflicts: Activities during alleged periods of incapacity, mileage logs that don’t match treatment dates, or surveillance timestamps contradicting alibi statements
• Work status inconsistencies: Surveillance showing work-like tasks while claiming TTD or TPD; field notes indicating side jobs not disclosed
• Contradictory statements: Variances between FNOL, recorded statement, EUO, and medical intake questionnaires
• Behavioral mismatches: Recreational activity (team sports, heavy yardwork, travel with luggage) that contradicts reported pain levels or limitations
• Provider anomalies: Template-like medical narratives, recurring CPT patterns, or pharmacy fills that don’t align with diagnosis and observed function
• Prior loss overlaps: Prior injuries to the same body part or similar mechanisms of injury not disclosed in current statements
• Social presence conflicts: Public posts suggesting travel, athletic events, or physical activities inconsistent with claimed restrictions

These red flags are surfaced with page-level citations, giving SIU teams defensible, auditable evidence for referrals, negotiations, or potential fraud actions.

Doc Chat’s AI Analysis of Surveillance Notes and SIU Materials

Doc Chat performs end-to-end AI analysis across unstructured claims documents and notes. It does more than search; it builds context and resolves contradictions. As described in our piece Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, high-value insurance insight is often implicit, scattered, or unwritten—Doc Chat applies your SIU playbook to infer what matters from what’s buried.

For SIU, Doc Chat ingests and interlinks:

• Surveillance reports and observation logs
• Investigation notes and field correspondence
• IME, FCE, PT/OT notes, disability certificates
• Recorded statements and EUO transcripts
• Police reports, FNOL forms, ISO claim reports
• Medical demands, invoices, CPT/ICD-10 codes, pharmacy records
• Employer wage records, attendance logs, job descriptions

With real-time Q&A, investigators can ask, “Show every observed activity over 10 lbs. lifting and cite IME restriction pages,” or “Summarize contradictions about work status and list source pages.” As documented in our client story Reimagining Insurance Claims Management, the combination of instant answers and page-level citations builds trust and accelerates action.

Workers Compensation: SIU-Specific Nuances Doc Chat Handles

Workers Compensation SIU investigations turn on restrictions, return-to-work status, apportionment, and causation. Doc Chat maps these moving parts into a coherent narrative:

- Restrictions vs. activity: It reconciles IME/FCE restrictions with observed activity in surveillance narratives. If a claimant with a 5-lb lifting restriction is observed unloading cases of water, Doc Chat flags the discrepancy and cites both the observation line and the IME page.

- TTD/TPD alignment: It compares documented off-work status with evidence of paid side work in field notes, business registrations in case file correspondence, or surveillance showing work-like tasks during claimed disability.

- Prior conditions and apportionment: It surfaces mentions of pre-existing injuries or degenerative conditions across medical histories, prior losses, and ISO claim reports that could affect apportionment and causation analysis.

- Treatment utilization: It reviews PT/OT frequency, missed appointments, and pharmacy fills, and compares them with the claimant’s reported pain, function, and observed activity.

- Employer verification: It aligns job descriptions (essential functions) with video narratives and timing to test whether claimed restrictions are consistent with work demands and observed behavior.

Auto: SIU-Specific Nuances Doc Chat Handles

Auto SIU problems often involve mechanism-of-injury plausibility, pre-existing conditions, billing patterns, and inconsistent narratives:

- Mechanism plausibility: Doc Chat juxtaposes police reports, crash diagrams, and medical narratives, surfacing misalignments between alleged injury and vehicle damage or collision dynamics.

- Recorded statement vs. EUO: It highlights discrepancies across statements (times, locations, speed, pain onset) and reconciles them with treatment timelines and surveillance logs.

- Prior loss patterns: It identifies repeat claimants, recurring provider networks, or repeated CPT coding patterns that suggest clinic or billing anomalies.

- Activity vs. demand letters: It flags recreation or travel in surveillance narratives that undermine BI demand representations of impairment and suffering, citing both sides for easy comparison.

The Manual-to-Automated Journey: From Reading to Reasoning

Historically, human reviewers were forced to read everything and then reason about it. As we outline in The End of Medical File Review Bottlenecks, modern AI flips that sequence: machines read everything, consistently, without fatigue; humans apply expert judgment. For SIU in Workers Compensation and Auto, this shift means reliable coverage of enormous document sets and immediate focus on the investigative nuance that truly requires experience.

Step-by-Step SIU Workflow with Doc Chat

1) Intake and triage: Drag-and-drop the entire claim file or set an automated feed. Doc Chat classifies document types (surveillance report, IME, recorded statement, FNOL, police report, field notes) and builds a unified index.

2) Baseline chronology: The system constructs a time-ordered chronology of medical visits, statements, observed activities, work status changes, and correspondence milestones, annotating each entry with citations.

3) Red-flag extraction: Based on your SIU playbook, Doc Chat auto-highlights contradictions and activities inconsistent with restrictions, labeling the severity and listing follow-up questions.

4) Investigator Q&A: Ask targeted questions: “find contradictions from investigations on pain ratings,” “flag activity inconsistent with injury claim involving overhead lifting,” or “compare IME restrictions with surveillance on 4/12 and 4/19.” Answers include page-level links.

5) Draft SIU referral: Generate a structured referral summary including facts, contradictions, supporting citations, and recommended next steps (e.g., additional surveillance, employer re-verification, clinical peer review, EUO).

6) Export and integrate: Push structured fields to SIU case management or claims systems, export CSV/JSON, or copy the referral with linked citations into your template.

Concrete Examples: Doc Chat in Workers Compensation SIU

Example 1: A claimant on TTD with a 10-lb lifting limit is observed loading 40-lb bags of soil into a pickup. Doc Chat extracts the IME restriction, aligns it with the surveillance observation time and narrative, and flags the inconsistency with citations to both documents.

Example 2: Field notes reveal the claimant’s side landscaping business; surveillance captures weekday morning activities aligning with the business address. Doc Chat surfaces the business mention in field correspondence and correlates with surveillance locations and timestamps.

Example 3: Recorded statement indicates the claimant cannot sit longer than 15 minutes; PT attendance logs show full sessions without accommodations; surveillance shows a two-hour recreational fishing trip. The system highlights the triad of contradictions and proposes follow-up questions for the adjuster and counsel.

Example 4: Prior loss surfaced in ISO claim reports shows a back sprain two years earlier. The IME references degenerative changes. Doc Chat flags potential apportionment considerations and lists all references to prior injuries and degenerative findings across the file.

Concrete Examples: Doc Chat in Auto SIU

Example 1: Demand letter claims severe cervical limitations; surveillance notes show the claimant playing in a weekend softball league and carrying equipment. Doc Chat auto-compares demand statements to observations and IME findings.

Example 2: EUO testimony places pain onset immediately at scene; triage nurse call records two days later state “pain started yesterday after lifting at home.” The tool flags the timeline conflict and gathers all references to pain onset across documents.

Example 3: Billing patterns: recurring CPT codes for modalities not supported by exam notes; pharmacy fills inconsistent with reported pain control; Doc Chat assembles the pattern and cites each occurrence.

Example 4: EDR and police report suggest low-speed impact unlikely to cause claimed injuries; Doc Chat surfaces conservative mechanism analyses from provider notes, contrasts with demand language, and lists defense-oriented inferences for SIU and defense counsel review.

Generative AI Built for Insurance Investigations

Generic summarization tools miss the nuance SIU requires. Doc Chat is tailored for insurance and claims, and—critically—trained on your playbooks, documents, and standards. Its purpose-built agents deliver consistency across claim files, remove triage bottlenecks, and scale instantly to surge volumes without extra headcount. We detail this further in Reimagining Claims Processing Through AI Transformation.

Business Impact: Speed, Cost, Accuracy, and Defensibility

SIU investigators operate in a high-stakes environment where speed and evidence quality determine outcomes. Doc Chat delivers:

• Speed: Turn multi-thousand-page reviews from days into minutes; instant answers with page-level citations
• Cost relief: Reduce manual touchpoints and overtime; let investigators focus on interviews, strategy, and coordination with counsel
• Accuracy at scale: Machine consistency removes human fatigue; comprehensive review eliminates blind spots
• Leakage reduction: Surface contradictions early, enabling faster, more favorable resolutions or denials when appropriate
• Audit-ready: Page-level, document-level traceability supports compliance, reinsurer requests, and regulatory scrutiny

Carriers like Great American Insurance Group report order-of-magnitude improvements when AI provides instant answers and citations across massive files, as described in this case study. In medical-heavy matters, consider the gains outlined in The End of Medical File Review Bottlenecks: weeks of reading compressed to minutes while improving the depth of insight.

Why Nomad Data Is the Best Partner for SIU

Nomad Data’s Doc Chat is not a one-size-fits-all widget. It is an enterprise-grade solution with the volume, complexity handling, and white-glove approach SIU teams need.

- The Nomad Process: We capture your SIU referral criteria, investigative heuristics, and documentation standards. Your unwritten rules become repeatable instructions the system follows every time.

- White glove, rapid implementation: Typical go-live is 1–2 weeks, including configuration for your document types, output templates, and workflows. No in-house data science required.

- End-to-end document intelligence: From intake and triage to contradictions and structured referrals, Doc Chat automates the heavy reading and standardizes best practices across investigators.

- Security and governance: Built for regulated industries with robust controls and audit trails. Page-level citations ensure every insight is traceable and defensible.

- A strategic partner: As your SIU needs evolve, we co-create new red-flag triggers and investigative prompts. You are not just buying software—you are gaining a partner that learns alongside your team.

Handling Volume and Variability

SIU workloads spike after major events, marketing campaigns, or counsel-driven demands. Manual teams can’t elastically scale. Doc Chat ingests entire claim files—thousands of pages at a time—without adding headcount. It reads every line with consistent rigor, surfaces every mention of restrictions, activity, and contradiction, and lets your investigators jump straight to analysis. As we note in AI’s Untapped Goldmine: Automating Data Entry, the biggest wins often come from automating repetitive document work so experts can apply judgment where it matters.

Defensible Investigations: Citations, Consistency, and Compliance

SIU findings must withstand internal audits, reinsurer reviews, and courtroom scrutiny. Doc Chat’s answers include page-level citations back to the source—surveillance observation line, IME page, recorded statement transcript excerpt. That transparency speeds up internal approvals, supports counsel, and builds trust with compliance. It also ensures that “AI analysis of surveillance notes insurance” doesn’t become a black box—your team always sees the why behind the finding.

From Red Flags to Action: Integrating with SIU and Claims Systems

Doc Chat exports structured contradictions, timelines, and referral summaries in your format. Many teams push results into SIU case management or claim-handling platforms via modern APIs, or simply download CSV/JSON to join with ISO ClaimSearch and internal data. Because implementation is measured in weeks—not quarters—you can realize value quickly without a core-system replacement or long IT projects.

How Doc Chat Automates the SIU Process End to End

Doc Chat automates the investigative reading while leaving the decision-making to your experts:

- Intake: Automatically classifies documents and creates a navigable table of contents across the file.

- Extraction: Pulls key facts—restrictions, observed activities, dates of service, pain scales, job duties, wage entries.

- Cross-check: Compares statements vs. surveillance vs. IME/medical across time to find contradictions from investigations.

- Red flag scoring: Applies your SIU rules to rank and group issues by severity and impact on liability and damages.

- Q&A: Real-time questions across the entire file (“List all mentions of overhead lifting,” “Show all contradictions regarding ability to drive”).

- Output: Generates a structured, citation-rich SIU referral draft ready for review.

Measuring Impact: What SIU Leaders Should Expect

SIU and Claims Managers ask for tangible, near-term improvements. Teams using Doc Chat typically see:

• 40–80% reduction in time spent reading and assembling contradictions
• Fewer missed red flags, leading to measurable leakage reduction
• Faster, more consistent referrals that speed determinations and negotiations
• Higher investigator satisfaction and lower burnout by eliminating drudge work
• Scalable surge capacity without incremental headcount

Your exact ROI depends on mix of Workers Comp and Auto claims, red-flag prevalence, and litigation intensity. But the throughline is consistent: machines do the reading; your team does the thinking.

Trust Building: Hands-On Validation with Your Own Files

We encourage SIU teams to load live or recently closed files and test Doc Chat against known answers. As documented in the Great American Insurance Group story, side-by-side comparisons build confidence quickly when the system returns accurate, cited answers in seconds. This approach also calibrates expectations—Doc Chat is an expert assistant, not a decision-maker. Humans remain in the loop to confirm context, intent, and next steps.

FAQ for SIU Investigators

Can Doc Chat really do AI analysis of surveillance notes insurance investigators receive daily?

Yes. Doc Chat reads entire surveillance reports, maps activities to restrictions, and links facts across IME pages, statements, and field notes. It returns answers with citations so you can verify and act with confidence.

How does it find contradictions from investigations without reviewing every page manually?

Doc Chat reads every page automatically and applies your SIU rules to cross-check facts across documents. It highlights contradictions and evidence conflicts, ranked by severity, and supports each with source links.

Will it reliably flag activity inconsistent with injury claim narratives?

Yes. It specifically compares observed activities against medical restrictions, pain/function reports, and work status declarations. The system surfaces inconsistencies with citations for immediate follow-up.

Security, Privacy, and Explainability

Doc Chat is built for sensitive claim files. Its page-level explainability and strong governance controls help SIU and Compliance teams maintain confidence. Outputs can be exported for audits, reinsurer requests, and litigation support. Our approach keeps human decision-making at the center: Doc Chat recommends; your experts decide.

Why Now: The Competitive Edge for SIU

The gap between manual and AI-augmented investigations is widening. As competitors automate reading and contradiction detection, they move faster, reduce leakage, and negotiate from stronger positions. The cost of inaction is mounting—lost time, missed red flags, and more difficult litigations. Doc Chat gives your SIU team an immediate edge.

Implementation: White Glove, 1–2 Weeks to Value

We configure Doc Chat around your document types, SIU triggers, and output formats. In most cases, SIU and Claims leaders see production value in 1–2 weeks, starting with drag-and-drop usage and expanding to API integrations later. Our white-glove team partners with you to encode best practices, iterate on red-flag logic, and ensure rapid adoption.

Getting Started

Target your biggest bottlenecks first: claims with heavy surveillance, multiple statements, and contested medical issues. Provide sample files and your SIU criteria. We’ll stand up a pilot that demonstrates value in days, not months. When you’re ready to scale, we integrate with your claim platforms and SIU case management to make contradiction detection a standard part of every investigation.

Conclusion: Turn Every Page into Action

In Workers Compensation and Auto, the truth hides across pages—surveillance lines, IME footnotes, statement transcripts, and field notes. Doc Chat reads it all, connects the dots, and puts contradictions at your fingertips with citations you can defend. That’s how SIU stops drowning in documents and starts winning with speed, accuracy, and consistency. Explore what’s possible at Doc Chat for Insurance.