Math Class: Reasoning Models for Real-World Decisions in Workers’ Compensation

The Trained A-Eye

Welcome back to class, friends! Sharpen those pencils and grab your calculators. Today we are heading into Math Class. In workers’ compensation, so much of our world is built on logic, planning, and pattern recognition. From setting reserves to structuring return-to-work plans, success comes down to how we connect data and human insight. This week, we are talking about reasoning models, the next evolution in AI tools designed to “think” before they speak. These models do more than identify patterns. They pause, process, and provide thoughtful, step-by-step conclusions, the kind of disciplined thinking we love in our industry!

This project continues to be inspired by my professor friends Chris Snider and Christopher Porter, the Innovation Profs, who created the AI Summer School series. Their exploration of reasoning models immediately caught my attention because it speaks to how we make decisions in workers’ compensation every day. Our industry depends on critical thinking, accuracy, and accountability. Translating the Innovation Profs work into our world gives us an opportunity to explore how reasoning models can help us strengthen both the analytical and the human sides of our work. This is where logic meets empathy. Plus, a little sparkle never hurts! 

What Are Reasoning Models?

Reasoning models are a newer generation of large language models that go beyond predicting the next word. Traditional models focus on the speed of getting to the “answer” as fast as possible. Reasoning models, on the other hand, slow down and plan before they respond. They generate an internal chain of thought, check their logic, and then deliver a concise, accurate response. Reasoning models act like a thoughtful colleague who sketches ideas on the whiteboard, talks through the logic, and then summarizes the best answer for everyone to follow.

Reasoning models such as OpenAI’s o-series, Anthropic’s Claude 4, and Google’s Gemini Pro represent the most advanced versions available right now. These models are designed to handle tasks that require structured reasoning such as math, law, financial forecasting, and complex decision-making. For workers’ compensation, this means reasoning models can analyze claims data, forecast cost exposure, or help structure return-to-work timelines in ways that are both detailed and digestible. Think of it like having an analytical partner who does not get overwhelmed by the paperwork.

Why Reasoning Models Matter in Workers’ Compensation

Our work in workers’ compensation is rarely simple. Decisions require layers of logic as this space operates with policy language, jurisdictional nuances, medical details, human factors, and budget implications. Reasoning models thrive in these multi-step environments like the world of workers’ compensation. These models can follow the full path from data to decision, considering each factor instead of jumping to conclusions by organizing the chaos. By offloading the mental heavy lifting, reasoning models give us more space to focus on empathy, strategy, and the human connections that drive recovery.

How Reasoning Models Work

When we ask reasoning models a question, an internal “chain of thought” is created. You might see a note like “thinking for 40 seconds” before the answer appears. This is the model mapping out its logic. The result is an explanation of how the model got there in addition to the answer. That transparency helps us check the reasoning, spot assumptions, and refine prompts for even better results.

For example, if you would ask, “Based on these restrictions and wage details, estimate temporary total disability exposure for the next six months,” a reasoning model will not guess. The model will average recovery durations, modified-duty options, wage replacement calculations, and potential medical interventions. The reasoning model will then summarize the logic, showing how each factor contributed to the estimate. This step-by-step approach mirrors the way seasoned claims adjusters think, only faster.

Real-World Applications in Workers’ Comp

Think about reserves. Setting reserves is where reasoning models can make a real difference. If you input injury details, jurisdiction, and historical claim data, the model can produce projected medical, indemnity, and expense reserves with justification for each category. It can compare optimistic, expected, and conservative outcomes and flag the conditions that might trigger a change. This helps new adjusters learn faster and ensures consistency across teams.

In return-to-work planning, reasoning models can develop phased schedules that align medical restrictions with safe job tasks. How many times have you heard an employer or leader say they have no modified duty options available? Turn to the reasoning model. These models can generate scripts for supervisors, communication plans for employees, and checklists to track progress. These models can also integrate ADA considerations and highlight milestones for reevaluation. When someone is juggling multiple cases, having a structured, logic-based assistant keeps everyone aligned.

Even in policy interpretation or compliance work, reasoning models shine. You can paste statute excerpts or policy language into the model and ask for a plain-language summary of what is required versus what is recommended. You can then ask for a version tailored to supervisors, employees, or executives, and even ask the model to break it down like you are a fifth grader. The model can even generate documentation checklists, turning abstract rules into actionable steps. Action is power!

Tips for Getting Better Results

Using reasoning models effectively requires intentional prompting. Start by asking the model to “walk through its thinking step by step before deciding.” This type of prompting encourages reasoning that separates strong results from generic ones. As the model to “compare two or three possible approaches” before recommending one, which can help uncover creative options that may have not been considered.  You can also ask the reasoning model to cite its sources, which if you are like me, I want to know where the information is coming from. 

Ideally, you want to educate the reasoning models to respond in a manner that is accurate and manageable. If you are brainstorming solutions, a prompt such as “Give me three very different options” expands the conversation. When you need real-world context, specify it: “Use examples from workers’ compensation data” or “Frame this for a self-insured employer.” The more context you provide, the more usable the output becomes.

Keeping the Human in the Loop

As powerful as reasoning models are, remember that they are tools. These tools are not replacements for professional judgment. As the human behind the tool, we must still verify outputs, maintain data privacy, and use discernment before acting on recommendations. These models do not hold claims adjusting licenses or carry empathy. Reasoning models are her to enhance and support what humans already bring to the table. The goal is to amplify your expertise. These models handle the structure so we can handle the soul. When we combine AI’s logic with human empathy, programs become smarter, faster, and more compassionate.

Class Takeaway

The disciplined thinkers of the AI classroom? Meet our reasoning models. These models take time to plan, analyze, and explain. For workers’ compensation, they represent a natural evolution toward precision and trust. Whether you are using them for reserve analysis, return-to-work design, or compliance interpretation, reasoning models can help you make better decisions faster, with greater transparency and consistency.

Your homework: test a reasoning model this week on one claim scenario. Ask it to “show its work,” evaluate the steps, and then compare its reasoning to your own. The more you experiment, the better you will understand where these tools shine, and where your human insight still leads the way.

Class dismissed. ✨

Next week: History Class – Deep Research in Claims & Medical Management.

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