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Rawls Maximin AI: A New Health Exchange to Unburden Medical Debt Without Additional Taxation and Public Spending

Being Human for a Better Tomorrow in the Age of AI

  • November 22, 2024
  • Part 4a of 4
  • 24th edition
Rawls Maximin AI

Introduction

When health becomes a challenge, life can feel cruel. A heart attack, cancer, or chronic illness brings not only emotional and physical burdens but often financial ruin. Millions worldwide face a harsh reality: access to care depends on wealth, not need.

Rawls Maximin AI offers a novel solution inspired by philosopher John Rawls’s Theory of Justice. It’s a sustainable, AI-driven health exchange model designed to eliminate medical debt and achieve universal health—without raising taxes or increasing government spending.

The Global Medical Debt Crisis

1.United States ( KFF 2024):

  • Total Medical Debt: $220 billion.
  • Adults Affected: 20 million (1 in 12)
  • Vulnerable Groups:
  • African Americans are 2.6 times more likely to face medical debt.
  • Parents are disproportionately affected (58% vs. 35% for non-parents).
  • Consequences
  • 500% higher odds of care avoidance (e.g., skipping tests or prescriptions)
  • One in seven denied care due to unpaid bills
  • 42% have used up all or most savings due to healthcare debt

2. Global Perspective (World Bank, 2023):

  • 1 billion people (14% of global population) face catastrophic health spending (>10% of household budget)
  • 1.3 billion people (17% of global population) pushed or further pushed into poverty by health costs
  • 96 out of 138 countries are off-track in either service coverage, financial protection,

Limitations of Traditional Healthcare Funding

Existing systems—whether government-funded, insurance-based, or philanthropic—are stretched thin:
  • Growing healthcare costs straining federal and state budgets; there is no room or appetite for increasing national debt
  • Studies show additional taxation for the ultra rich will only result in offshore flight of money and wealthy
  • Insurance coverage is expensive and inadequate for even out of pocket expense
  • A third of the US households can’t readily fund an unplanned $1000 medical bill.
  • Another NIH study across 34 low income countries found poor 5 times more vulnerable when healthcare costs exceeds 10% of income.
  • Philanthropic funding is only 18% of current funding ; It is also unpredictable

Rawls MaxiMin AI driven Health Exchange:

Based on 21st century philosopher John Rawls’s Theory of Justice and using modern AI technology as can be seen below:

Philosophical Foundation

  • Rawlsian Justice Healthcare Extension by Norman Daniels*
  • Justice as fairness requires principles chosen behind a "veil of ignorance"
  • Difference principle: inequalities must benefit least advantaged
  • Maximin rule: maximize minimum level of well-being

Healthcare Funding Model Principles

Core Component

  1. Tax-exempt endowment structure of income and capital gains tax in perpetuity

  2. Anonymous direct benefit transfers-benefactor and beneficiary do not know each other

  3. Securitization of medical debtin tranches starting first with on high-value interventions and highest gap between medical debt and income and then covering smaller deficits and gaps

Key Innovations from Traditional Model

  • Medical debt is fully funded and forgiven for a clean restart for the beneficiary for the needy
  • Preserves generational wealth while making it productive as funding is new money from asset monetisation and exempt in perpetuity from income and capital gains
  • Direct connection between wealth and need for impact and motivation
  • Maintains dignity for the beneficiary and benefactor through mutual anonymity
  • Efficient bulk purchasing of medical debt ((cents to the dollar) from securitisation from insurers and hospitals stretching the funding impact
  • No impact to government taxes and finances

Model Implementation Framework

Endowment Structure

  • Tax exemption on creation and deployment or any capital gains made for endowment creation in perpetuity
  • Investment returns fund healthcare needs
  • Keeps capital within domestic economy
  • Sustainable funding mechanism

Benefit Distribution

  • Pools medical debt into distress-based tranches
  • Prioritizes highest debt-to-income ratios
  • Enables bulk purchasing efficiency
  • Anonymous benefactor-beneficiary relationship

Stakeholder Benefits

  1. Wealthy Contributors: Tax benefits, asset monetization without losing control

  2. Government: No deficit impact, keeps wealth productive

  3. Recipients: Debt relief based on need, dignity preservation

  4. Healthcare Providers: Guaranteed payment for otherwise uncollectible debt

Advantages Over Current Systems

Financial Efficiency

  1. Reduces administrative overhead with direct funding

  2. Creates economies of scale in debt resolution

  3. More efficient use of resources through bulk debt purchase

  4. Sustainable funding through investment returns

Social Impact

  1. Implements Rawls’s maximin principle practically

  2. Preserves dignity through anonymity

  3. Creates systematic rather than charitable support

  4. Addresses both immediate needs and long-term sustainability

System Benefits

  1. Keeps wealth productive within domestic economy

  2. Reduces strain on public healthcare systems

  3. Creates sustainable funding mechanism

  4. Addresses healthcare inequities systematically

System Implementation Framework:

Claims Flow Process

Hospital Initiation

  1. Hospital identifies and raises eligible cases where medical debt is greater than 10% of income

  2. Regular billing process completed with addition of Maximin Exchange option

  3. Initial screening for eligibility (debt-to-income ratio, high value and vulnerability criteria)

  4. Patient chat and consent for anonymous processing

Exchange Processing

  1. Claims received into secure Maximin platform

  2. AI verification of claim accuracy and completeness

  3. Assessment of distress level based on established criteria

  4. Assignment to appropriate securitization tranche

  5. Complete anonymization of patient data

Securitization Process

  1. Pooling of verified claims into tranches based on value of debt and distress levels

  2. Priority ranking based on maximin principle

  3. Bulk processing for efficiency (Cents to the dollar)

  4. Matching with available endowment funds

Settlement Flow

  1. Direct settlement to hospital from endowment pool

  2. Confirmation of debt clearance

  3. Anonymous notification to beneficiary

  4. Capture program feedback and benefit impact

  5. Impact tracking and reporting (maintaining anonymity)

AI driven technology

Matching System

  • AI algorithms can match donors to medical cases while maintaining anonymity
  • Machine learning can prioritize cases based on debt-to-income ratios and urgency
  • Predictive analytics can optimize fund allocation across debt tranches[1]

Claims Processing

  • AI can analyze medical claims to verify legitimacy and completeness
  • Automated systems can process claims faster and more accurately
  • Reduces administrative overhead and errors in benefit distribution[1]

Fraud Prevention

  • AI systems can detect irregular patterns or suspicious activities
  • Cross-reference multiple data sources while maintaining anonymity
  • Ensure funds reach genuine cases[1]

User Experience

  • AI-powered interfaces for both donors and recipients
  • Virtual assistants to guide users through processes
  • Automated documentation and verification systems

Impact Tracking

  • AI analytics to measure program effectiveness
  • Real-time monitoring of fund utilization
  • Predictive modeling for future needs and outcomes at city, regional, national level

Data Security & Ethics

Using John Rawl’s veil of ignorance and Prof H V Jagdish code for data ethics and previous Mindvista articles on Five Fold Path for Data Ethics (18th edition) and AI Security using Sun Tzu Art of War principles (17th edition) Florence data security principles and ethical values are fundamental as seen below:

1. No personal information is required for chat

2. Minimal data collection is used solely for identification purposes—no cross-linking with non-health data

3. All claims data is localized by city/state to minimize large-scale vulnerabilities

4. Aggregate data is used only for public health planning—no personally identifiable information is shared without explicit consent

Will this work? Early evidence is positive

Undue Medical Debt is an US nonprofit whose purpose is to strengthen communities by erasing financially burdensome medical debt. Founded in 2014 by former debt collection executives, Undue Medical Debt is one of the leading charitable organizations that help pay medical bills.
They use donations (which gets a income tax exemption) to buy medical debt in large bundled portfolios at a steep discount ($1 donation to $100 medical debt). The beneficiary hets a letter that they no longer owe any medical debt. With no penalties, strings, they can make a fresh start. They have delivered $15B in debt and supported 9.3 million people in the US,

Rawls Maximin HealthExchange AI builds upon the success of initiatives like Undue Medical Debt by introducing a philosophically grounded, sustainable, and incentive-driven approach to addressing medical debt:

  • Philosophical Foundation: Grounds the model in John Rawls' Theory of Justice, appealing to the moral and ethical sensibilities of potential contributors.
  • New Funding Mechanism: Creates a tax-exempt endowment structure that incentivizes and motivates the wealthy to contribute, providing a more stable and long-term source of funding compared to relying solely on philanthropic donations.
  • Public Finance Viability: Ensures that the funding does not impact government income taxes, making it a more politically viable and sustainable solution.
  • Innovative Approach: Combines the best aspects of existing models with innovative philosophical and financial frameworks, representing a significant step forward in addressing the urgent issue of medical debt and healthcare inequity.

Conclusion and Call to Action

In 2015, UN launched the 2030 Agenda for Sustainable Development, with health as a fundamental human right and a central promise to leave no one behind. Despite laudable efforts from academia, governments, international organizations, public health services, and philanthropic initiatives, we find ourselves nine years later in 2024 with widening health inequity gaps.
Call to Action
  • Like, comment, and share this article with your network to spread awareness.
  • Tag experts, policymakers, and technologists who might contribute to advancing this vision.

In 21st edition of Mindvista, “From Utopia to Reality: Three Citizen-Facing AI-Driven Innovations for Health for All and Future Generations,” we identified three major challenges in public health: staffing and resource shortages, rising mistrust, and inequity in access to care. We also introduced new AI-driven ideas: Florence AI, an intelligent health assistant; Looking Glass, an open health data platform and now Rawls MaxiMin HealthExchange AI to address health inequity.
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Let’s make health a universal right—together

I remain passionate and committed to helping anyone interested in exploring these ideas further.

To good health for all and for generations.

Best wishes

Select Quotes

“Social and economic inequalities are to be arranged so that they are to the greatest benefit of the least advantaged.”
— John Rawls
“From each according to his ability, to each according to his needs.”
— Karl Marx
“Nothing is more powerful than an idea whose time has come.”
— Victor Hugo

II. Questioning / Asking

Good conversations flow from well‑sequenced questions—topical, simple, coherent, cohesive.
LLM Conversation Example 1
Q: What are empirical judgments?

A: Empirical judgments are based on observation, experience, or experimentation.
Q: What are moral judgments?

A: Moral judgments are based on ethical principles and values.
“AI is a language. Treat it like one: practice, iterate, and mind your grammar prompts, assumptions, and verification.”
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