AI and Analytics Reimagine Workers' Comp Claims Without Notification Fatigue

AI and Data Analytics in Workers' Compensation: From Prevention to Closure

Workers' compensation leaders want fewer injuries, faster closure, and lower friction. Data analytics and AI are giving them the tools to get there without overwhelming claims teams.

Rob Evans, director of claim process technology at Broadspire, recently outlined how data analytics and artificial intelligence are reshaping claim reviews, benchmarking, and decision support. His core message: even strong programs have room to improve, and AI helps surface those opportunities without creating noise.

Why data analytics still drives results

Data analytics has proven value across loss prevention, pre-loss planning, and post-loss performance. Better data visualization makes it easier to spot trend lines, outliers, and systemic issues across books of business.

Benchmarking against industry averages and best-in-class programs keeps strategy grounded. Pair high-level dashboards with drill-down views so you can move from pattern to root cause in a few clicks.

Where AI fits in the claim process

Evans notes that AI, used correctly, can reimagine claim reviews while reducing notification fatigue. Think smart triage, not constant alerts.

His framing is simple: AI provides ingredients you can mix into different "recipes" for quality outcomes. Predictive models and large language models (LLMs) deliver key inputs that claims pros use to pursue closure, return-to-work, and expense control.

"Even the best in class programs we've seen will inevitably have some room for additional improvement. The only constant is change. So even if you've got things optimized, you got to really stay on top of things. And this is where bringing in the AI component is super helpful when it comes to any improvement opportunities."

Practical use cases you can deploy now

• Injury prevention analytics: Identify sites, shifts, and tasks with rising frequency or severity; feed results into safety coaching and ergonomic fixes.
• Intake triage: Predict claim complexity to route early to the right adjuster, nurse case manager, or specialty network.
• Reserve guidance: Use models to propose reserve ranges, paired with explainability so adjusters see the drivers.
• Litigation risk prediction: Flag cases likely to litigate; trigger earlier contact, empathy scripts, and attorney outreach protocols.
• LLM claim summaries: Auto-summarize medical notes, bills, and prior history; free adjusters to make decisions instead of sifting documents.
• Subrogation and SIU signals: Detect potential third-party recovery and fraud indicators without blanket referrals.
• Return-to-work planning: Match restrictions to modified duty and forecast RTW timelines to keep stakeholders aligned.
• Vendor optimization: Recommend the best IME, PT, or pharmacy partner for the case profile and location.

Avoiding alert fatigue

AI should reduce noise, not create it. Set thresholds that match business value, route alerts to the right role, and cap concurrent notifications.

• Precision first: Start with high-precision triggers so teams trust the signals. Expand coverage once adoption is strong.
• Explainability: Show top factors behind each alert (e.g., diagnosis code cluster, provider pattern, lag time).
• Feedback loop: Let adjusters thumbs-up/down alerts to retrain models on what actually helps.
• Digest delivery: Bundle low-urgency items into daily or weekly review lists instead of real-time pings.

Data and visualization that move the needle

The shift isn't just more charts; it's better charts that link to action. Start with portfolio heat maps, then click into location, injury, provider, and adjuster views.

• Loss prevention: Frequency and severity by job class and shift; top 10 incident types with trending.
• Claims operations: Time-to-first-contact, reserve accuracy, cycle time, pendings, and reopen rates.
• Medical management: Network leakage, provider outcomes, utilization patterns, and pharmacy adherence.

Executive lens: AI as ingredients, outcomes as the dish

Evans likens analytics to the menu and AI to ingredients. Executives define the "dish" they want-lower litigation, faster closure, fewer severe claims-and teams combine models and workflows to deliver it.

"If we think of data analytics as a menu, AI lets us think about ways to create the most delicious dish we desire, like finding litigation or closure opportunities that align with achieving the executive's concept of success."

Implementation checklist

• Data readiness: Clean intake fields, consistent ICD/DRG coding, provider normalization, and lag-time accuracy.
• Model governance: Version control, bias checks, monitoring, rollback plans, and access controls.
• Human-in-the-loop: Keep adjusters as decision-makers with clear override paths and audit trails.
• Workflow integration: Deliver insights where work happens (claim system, email digest, or queue), not in a separate portal.
• Change management: Train on "why this alert matters" and measure adoption, not just model AUC.

Metrics that matter

• Indemnity and medical severity trend vs. benchmarks such as NCCI.
• Cycle time: Days to first contact, first payment, and closure by claim type.
• Litigation rate and cost: Pre/post-AI deployment, segmented by jurisdiction and injury class.
• RTW outcomes: Days out of work and durability of modified duty placements.
• NCM and vendor ROI: Impact on duration, readmissions, and total medical paid.
• Subrogation yield: Referral rate, recovery dollars, and net contribution.

90-day starter plan

• Weeks 1-2: Define one outcome target (e.g., reduce litigated claims by 10%). Lock KPIs and a baseline.
• Weeks 3-6: Stand up a pilot: litigation risk model, LLM summaries, and a weekly triage huddle.
• Weeks 7-10: Tune thresholds for precision, add explainability, and capture adjuster feedback.
• Weeks 11-13: Expand to one more use case (subro or RTW) and present impact to leadership.

Governance and trust

Clarity builds adoption. Document data sources, model purpose, and change logs. Keep a simple model card for each use case so legal, compliance, and operations stay aligned.

For broader risk guidance, review the NIST AI Risk Management Framework and adapt it to your carrier or TPA controls.

Upskill your team

Success depends on people who can read the signals and act. Train adjusters, nurses, and supervisors on both analytics literacy and workflow.

If you need a structured path to level up, explore practical programs like the AI Certification for Data Analysis to build the skills required for claims and risk teams.

Bottom line

Analytics sets the menu. AI adds ingredients that help you reach your definition of success-fewer severe claims, lower litigation, faster closure-without flooding teams with alerts. Start small, measure tightly, explain the "why," and scale what works.