Case Study

Range Finder: AI Driven Predictive Life Care Cost Modeling

Range Finder is an advanced AI-powered solution designed for Physician Life Care Planning™ that transforms complex medical histories into accurate, defensible future cost projections. By matching new cases with highly similar historical medical profiles, the system enables legal and medical professionals to confidently assess long term treatment costs and case viability.

Case Study

Range Finder: AI Driven Predictive Life Care Cost Modeling

Range Finder is an advanced AI-powered solution designed for Physician Life Care Planning™ that transforms complex medical histories into accurate, defensible future cost projections. By matching new cases with highly similar historical medical profiles, the system enables legal and medical professionals to confidently assess long term treatment costs and case viability.

Client

Physician Life Care Planning

73 Strings

www.73 Strings.com

Industry

Healthcare & Life Sciences

Finance/Fintech
Alternative Asset Valuations
Enterprise SaaS

Services

AI Strategy
Development
Implementation

Discovery Research
UX/UI Design
Design System
Front-End Engineering

Project Duration

6 Months

6 months

The Challenge

Legal firms face significant difficulty when evaluating cases that involve long‑term medical care, particularly at the early decision‑making stage. This overwhelming process usually includes:

  • Manual Complexity: Traditional life care planning requires reviewing thousands of pages of medical records, expert reports, and historical settlements.

  • Investment Risk: Without reliable medical cost projections, law firms risk committing substantial time and capital to cases with limited recovery potential.

  • Valuation Uncertainty: Conventional estimation methods are often inconsistent, subjective, and vulnerable to statistical noise.


Without a clear, data-backed projection of long-term medical costs, legal firms are forced to make early case decisions without understanding whether the potential recovery will justify the time, capital, and litigation resources required. This exposes firms to significant Return on Investment (ROI) risk, especially in cases that require years of sustained effort.


As a result, valuation uncertainty remains high. Conventional estimation methods are often subjective, statistically noisy, and difficult to defend, making early‑stage case assessment both financially risky and operationally inefficient.

The Vision

The vision behind Range Finder was to create an AI‑driven system that could transform complex, unstructured medical histories into clear, defensible financial insight.


The goal was to give legal and medical professionals a way to instantly understand whether the potential value of a case justified the time, cost, and resources required to pursue it. Conceptually, this meant building a system that could position every historical medical case within a shared analytical space and use that context to define a realistic cost range for new subjects.


By grounding decisions in real peer outcomes rather than assumptions, Range Finder aimed to bring predictability, transparency, and confidence to Physician Life Care Planning™.


In practical terms, this meant giving firms an early, defensible way to evaluate whether pursuing a case would be financially viable before committing significant litigation resources.

The Approach

The Approach

How It Works

How It Works

How It Works


Range Finder uses AI‑driven medical data similarity modeling to identify historical cases that are most comparable to a new subject.

Each case, whether historical or new, is represented within a high‑dimensional vector space based on diagnostic conditions and clinical characteristics. When a new subject is introduced, the system maps that subject into the same space and identifies the closest peer matches using a KNN‑based similarity model.

To ensure medical relevance and fairness, the model applies weighted guardrails that prioritize:

  • Primary diagnostic conditions

  • Injury classification (such as spinal cord injury or traumatic brain injury)

  • Subject age

This approach ensures that cost projections are based on truly comparable medical profiles rather than surface‑level similarities.


Range Finder uses AI‑driven medical data similarity modeling to identify historical cases that are most comparable to a new subject.

Each case, whether historical or new, is represented within a high‑dimensional vector space based on diagnostic conditions and clinical characteristics. When a new subject is introduced, the system maps that subject into the same space and identifies the closest peer matches using a KNN‑based similarity model.

To ensure medical relevance and fairness, the model applies weighted guardrails that prioritize:

  • Primary diagnostic conditions

  • Injury classification (such as spinal cord injury or traumatic brain injury)

  • Subject age

This approach ensures that cost projections are based on truly comparable medical profiles rather than surface‑level similarities.

End-to-End Pipeline

Key Capabilities

Key Capabilities

Key Capabilities


Once peer matches are identified and validated, Range Finder executes a structured calculation pipeline to arrive at a final projected Nominal Value: The total estimated cost of suggested medical treatment over a subject’s lifetime.

Key stages of this pipeline include:

  • Duration of Care Assessment: Analyzing how long similar patients historically required medical care.

  • Inflation‑Adjusted Costing: Applying Future Medical Recommendations (FMRs) and inflation rates to normalize historical costs to present‑day values.

  • Annualized Cost Calculation: Deriving an average annual cost based on treatment frequency and unit pricing.

  • Adjusted Life Expectancy Modeling: Incorporating subject‑specific and cohort‑based data to estimate the likely duration of care.

  • Final Nominal Value Synthesis: Combining annual costs and adjusted duration into a single, transparent lifetime cost projection.


Once peer matches are identified and validated, Range Finder executes a structured calculation pipeline to arrive at a final projected Nominal Value: The total estimated cost of suggested medical treatment over a subject’s lifetime.

Key stages of this pipeline include:

  • Duration of Care Assessment: Analyzing how long similar patients historically required medical care.

  • Inflation‑Adjusted Costing: Applying Future Medical Recommendations (FMRs) and inflation rates to normalize historical costs to present‑day values.

  • Annualized Cost Calculation: Deriving an average annual cost based on treatment frequency and unit pricing.

  • Adjusted Life Expectancy Modeling: Incorporating subject‑specific and cohort‑based data to estimate the likely duration of care.

  • Final Nominal Value Synthesis: Combining annual costs and adjusted duration into a single, transparent lifetime cost projection.

The Range FInder

Evolution of Concept

Evolution of Concept

Evolution of Concept


The Range Finder concept evolved through multiple iterations to improve clarity, accuracy, and stakeholder trust in the underlying data.

Early versions focused on establishing a reliable baseline and introduced interactive elements, such as life expectancy sliders, to demonstrate how changes in assumptions affected total cost in real time. Later iterations incorporated statistical distributions to anchor projections with similarity confidence indicators.

The current version prioritizes precision and transparency by excluding rare outliers and presenting a scatter plot of comparable historical cases. This allows users to visually confirm that projections are grounded in real data rather than abstract averages.

The added transparency reduces the financial risk of misjudged case selection and helps firms allocate litigation resources toward cases with the strongest economic justification.


The Range Finder concept evolved through multiple iterations to improve clarity, accuracy, and stakeholder trust in the underlying data.

Early versions focused on establishing a reliable baseline and introduced interactive elements, such as life expectancy sliders, to demonstrate how changes in assumptions affected total cost in real time. Later iterations incorporated statistical distributions to anchor projections with similarity confidence indicators.

The current version prioritizes precision and transparency by excluding rare outliers and presenting a scatter plot of comparable historical cases. This allows users to visually confirm that projections are grounded in real data rather than abstract averages.

The added transparency reduces the financial risk of misjudged case selection and helps firms allocate litigation resources toward cases with the strongest economic justification.

Customer Outcomes

Results

Results

Results


Range Finder product delivers tangible business value as a predictive intelligence tool for legal and medical professionals.

For legal firms, Range Finder functions as a business intelligence system for early-stage case selection, providing a clear projected cost range that supports informed ROI-driven decisions.

By understanding the likely Nominal Value upfront, firms can assess whether the potential recovery justifies the financial, operational, and time commitments required to litigate the case through resolution.

The transparency of the peer‑based modeling, particularly the visual presentation of comparable cases, strengthens trust and defensibility. Instead of relying on estimates or intuition, decision‑makers can point directly to historical data that supports the valuation.

Ultimately, Range Finder helps customers reduce financial risk, allocate resources more effectively, and pursue cases with greater confidence in their long‑term outcomes.


Range Finder product delivers tangible business value as a predictive intelligence tool for legal and medical professionals.

For legal firms, Range Finder functions as a business intelligence system for early-stage case selection, providing a clear projected cost range that supports informed ROI-driven decisions.

By understanding the likely Nominal Value upfront, firms can assess whether the potential recovery justifies the financial, operational, and time commitments required to litigate the case through resolution.

The transparency of the peer‑based modeling, particularly the visual presentation of comparable cases, strengthens trust and defensibility. Instead of relying on estimates or intuition, decision‑makers can point directly to historical data that supports the valuation.

Ultimately, Range Finder helps customers reduce financial risk, allocate resources more effectively, and pursue cases with greater confidence in their long‑term outcomes.

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Copyright © 2026 Roko Labs Inc.

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aria-label="Home"

Copyright © 2026 Roko Labs Inc.

All rights reserved.

1250 Broadway, 36th Floor, New York, NY, 10001

aria-label="Home"

Copyright © 2026 Roko Labs Inc.

All rights reserved.

1250 Broadway, 36th Floor, New York, NY, 10001

aria-label="Home"

Copyright © 2026 Roko Labs Inc.

All rights reserved.

1250 Broadway, 36th Floor, New York, NY, 10001