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Looking Glass Made Sense for Alice: Its Modern AI Version Can Help a Bewildered Citizen Patient Take Informed Treatment Decisions

Being Human for a Better Tomorrow in the Age of AI

  • November 15, 2024
  • Part 3 of 4 part
  • 23rd edition and 12th in Vol 2
Looking Glass Made Sense for Alice 11zon

Context and Recap

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 three AI-driven ideas: Florence AI, an intelligent health assistant; Looking Glass, an open health data platform; and Rawls Maximin AI Health Exchange , a platform to support equitable healthcare access.
In this edition we “step through the looking glass” to address our second challenge of incomplete information and mistrust with Looking Glass AI companion.of external entities and agendas as primary reasons.

If US, the most advanced nation in the world has this challenge, then we can imagine how much bigger this problem is for the Transparency and Trust Challenge: Key Questions, Variability and Mistrust

The Transparency and Trust Challenge: Key Questions, Variability and Mistrust

Faced The with an illness or a medical decision, we are as bewildered as Alice in Lewis Carroll’s epic ‘Through the Looking Glass’ when she poked the mirror and stepped into a room. We struggle to find credible, unbiased, trusted doctors to reach to. Even when we do find them, their advice may be anecdotal, incomplete without knowing all costs and outcomes options.

The problem is further compounded by extreme variability in disease information, outcomes and costs.

Variability in Disease and Treatment Information

Consider the variability in information in finding answers to key questions when faced with medical situations: What exactly is my condition? What are my treatment options?

Multiple studies (sample below) have shown that medical information on the Internet has wide variability in quality, readability and trustworthiness:

  • Gynecologic Cancer: An assessment of 35 top-ranked web pages for approval and readability found only 7% had approval. "The online information about gynecologic cancer provided by the most frequently visited Web pages is of variable quality and in general difficult to read and understand."
  • Septic Arthritis: A study to assess the content, readability, and quality of online resources on septic arthritis, a crucial orthopedic condition necessitating immediate diagnosis and treatment to avert serious complications, found the quality of websites giving information was variable and not optimal.
  • Pain Management: A study published in Nature of quality of information of 300 websites from search engines for five conditions (pain, chronic pain, back pain, arthritis, and fibromyalgia) showed only 36% had health approval and abysmal 11% readability.
  • Alternative Medicine: Even with credible sources (like Cochrane Review, NCCAM, Natural Standard), they are not patient-centric. The nature of treatment, which involves personalized formulations and dosage, needs different evidence methods and reporting.
Variability in Outcomes and Costs
An analysis of data from Medicare’s Hospital Compare Dataset (2023) showed variability in hospital reporting and outcomes:
  • Only 42% of hospitals reported all required quality metrics
  • Readmission rates for the same procedures varied by as much as 300%
  • Mortality rates for similar procedures ranged from 1% to 5%, indicating significant differences in care quality across hospitals
The RAND Corporation’s Hospital Price Transparency Study (2023) revealed substantial price variability for identical medical procedures within the same geographic regions:
  • Employers and private insurers paid about 2.5 times what Medicare would have paid for the same services in the same hospital in 2022
  • Median prices for hospital services vary substantially by state, from below 200% of Medicare prices to above 300%
  • Prices for common outpatient services performed in ASCs averaged more than 1.7 times Medicare prices (170 percent)
  • Commercial insurance prices for select administered drugs received in a hospital setting averaged 278 percent of average sales price (ASP)

Looming Trust Deficit Due to Conflict of Interest

A survey led by a team from Massachusetts General Hospital (MGH) and Harvard Medical School showed that the proportion of US adults reporting “a lot of trust” in physicians and hospitals decreased from 71.5% in April 2020 to a low of 40.1% in January 2024.

Analysis of responses to open-ended questions showed the lowest levels of trust pointed to physicians and hospitals favoring financial motives over patient care, poor quality of care and negligence, and influence of external entities and agendas as primary reasons.

If US, the most advanced nation in the world has this challenge, then we can imagine how much bigger this problem is for the world.

Every country for the sake of health of its citizens needs a single trusted transparent source of truth on disease, treatment, outcomes and costs for informed decision making for better outcomes and lower costs.

Looking Glass AI: An AI Companion for Citizens on Diseases and Options for Treatment, Outcome and Cost

Last week, we explored how ‘Florence AI’ could address the resource challenge through prevention, focusing on five diseases that cause 80% of hospitalizations globally: diabetes, osteoarthritis, chronic respiratory diseases, cardiovascular diseases, and cancer.

Imagine an AI-driven application meant for every citizen patient for the five diseases listed above that is:

Comprehensive Country and Region-Specific Information:

  • Disease and treatment options (western, alternate and complementing treatment)
  • Outcomes and Costs
  • Absolute costs and relative to public hospitals
  • Reported by insurers and participating hospitals
  • Patient feedback system
  • Standardized outcome metrics
Easy to Access:
  • Universal Free Public Access with Natural Language Interface
  • Conversational, empathetic interaction
  • Layered information delivery based on patient needs
  • Visual representation of choices (radar diagrams)
Trustworthy:
  • Government operated platform (no commercial interests)
  • Data sourced from authoritative institutions in each country
  • Information that complies with global practices in reporting and medical standards of accuracy
  • Cost data mandated from providers/insurers and participating hospitals
  • Quality metrics from government health agencies
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

The Looking Glass for Alice and the Citizen Patient

In Carroll’s tale, Alice steps through a mirror into a world where confusion transforms into clarity. Like Alice’s looking glass, our platform serves as a portal where complex medical terminology becomes plain understanding, hidden costs turn into clear pricing, and opaque quality metrics transform into comprehensible outcomes.

Just as the White Knight guides Alice through her journey, explaining the rules and helping her navigate the chess-board world, the AI companion serves as a trusted guide. It helps citizens navigate the healthcare landscape, explaining options in simple terms while preserving their autonomy in decision-making.

Most importantly, like Alice’s looking glass book where reversed words become readable only when held up to the mirror, the platform reflects back medical information, costs, and choices in ways that citizens can truly understand.

What was backwards – a system controlled by providers and insurers – becomes clear when viewed from the citizen’s perspective. What was hidden becomes visible; what was complex becomes simple.

Looking Glass helped a bewildered Alice make sense of her world. Its AI companion can do the same for every citizen patient facing healthcare decisions.

Implementation: A Healthcare Moonshot

While initiatives like Colorado’s Center for Improving Value in Healthcare (CIVHC) have demonstrated the value of healthcare transparency at a state level, and Rand Corporation provides insights on costs from claims data, they are localized and limited but still offer valuable learning ground.

Looking Glass as a comprehensive country-specific platform that’s as transformative and ambitious as putting a person on the moon was in 1969. Just as NASA’s moonshot required integrating multiple technologies and stakeholders toward an ambitious goal.

It requires each country governments for coordinating healthcare providers, insurers, and government agencies to create unprecedented transparency for citizens. International organisations like WHO, philanthropic foundations for public good can provide templates, standards and practices for accelerated adoption.

With this inspiration and modern technology as our toolset, we have an opportunity to reshape healthcare into something more sustainable and equitable—for all citizens and future generations.

Let’s explore solutions together. If you like these explorations, please subscribe to newsletter, like, share, comment and connect.

To good health for all and for generations.

Best wishes

Select Quotes from Looking Glass by Lewis Carroll

"When I use a word," Humpty Dumpty said... "it means just what I choose it to mean - neither more nor less."
— On Clarity & Understanding:
"Why, sometimes I've believed as many as six impossible things before breakfast."
— On Empowerment Through Knowledge:
"Would you tell me, please, which way I ought to go from here?"
"That depends a good deal on where you want to get to," said the Cat.
— On Navigation & Guidance:
"It's a poor sort of memory that only works backwards."
— On Perspective

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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