teleo-codex/agents/vida/knowledge-state.md

Vida — Knowledge State Assessment

Model: claude-opus-4-6 Date: 2026-03-08 Domain: Health & human flourishing Claim count: 46

Coverage

Well-mapped:

• AI clinical applications (8 claims) — scribes, diagnostics, triage, documentation, clinical decision support. Strong evidence base, multiple sources per claim.
• Payment & payer models (6 claims) — VBC stalling, CMS coding, payvidor legislation, Kaiser precedent. This is where Cory's operational context (Devoted/TSB) lives, so I've gone deep.
• Wearables & biometrics (5 claims) — Oura, WHOOP, CGMs, sensor stack convergence, FDA wellness/medical split.
• Epidemiological transition & SDOH (6 claims) — deaths of despair, social isolation costs, SDOH ROI, medical care's 10-20% contribution.
• Business economics of health AI (10 claims) — funding patterns, revenue productivity, cash-pay adoption, Jevons paradox.

Thin or missing:

• Devoted Health specifics — only 1 claim (growth rate). Missing: Orinoco platform architecture, outcomes-aligned economics, MA risk adjustment strategy, DJ Patil's clinical AI philosophy. This is the biggest gap given Cory's context.
• GLP-1 durability and adherence — 1 claim on launch size, nothing on weight regain, adherence cliffs, or behavioral vs. pharmacological intervention tradeoffs.
• Behavioral health infrastructure — mental health supply gap covered, but nothing on measurement-based care, collaborative care models, or psychedelic therapy pathways.
• Provider consolidation — anti-payvidor legislation covered, but nothing on Optum/UHG vertical integration mechanics, provider burnout economics, or independent practice viability.
• Global health systems — zero claims. No comparative health system analysis (NHS, Singapore, Nordic models). US-centric.
• Genomics/precision medicine — gene editing and mRNA vaccines covered, but nothing on polygenic risk scores, pharmacogenomics, or population-level genomic screening.
• Health equity — SDOH and deaths of despair touch this, but no explicit claims about structural racism in healthcare, maternal mortality disparities, or rural access gaps.

Confidence

Distribution:

Level	Count	%
Proven	5	11%
Likely	40	87%
Experimental	1	2%
Speculative	0	0%

Assessment: likely-heavy, speculative-absent. This is a problem. 87% of claims at the same confidence level means the label isn't doing much work. Either I'm genuinely well-calibrated on 40 claims (unlikely — some of these should be experimental or speculative) or I'm defaulting to "likely" as a comfortable middle.

Specific concerns:

• Probably overconfident: "healthcare AI creates a Jevons paradox" (likely) — this is a structural analogy applied to healthcare, not empirically demonstrated in this domain. Should be experimental.
• Probably overconfident: "the healthcare attractor state is a prevention-first system..." (likely) — this is a derived prediction, not an observed trend. Should be experimental or speculative.
• Probably overconfident: "the physician role shifts from information processor to relationship manager" (likely) — directionally right but the timeline and mechanism are speculative. Evidence is thin.
• Probably underconfident: "AI scribes reached 92% provider adoption" (likely) — this has hard data. Could be proven.
• 0 speculative claims is wrong. I have views about where healthcare is going that I haven't written down because they'd be speculative. That's a gap, not discipline. The knowledge base should represent the full confidence spectrum, including bets.

Sources

Count: ~114 unique sources across 46 claims. Ratio of ~2.5 sources per claim is healthy.

Diversity assessment:

• Strong: Mix of peer-reviewed (JAMA, Lancet, NEJM Catalyst), industry reports (Bessemer, Rock Health, Grand View Research), regulatory documents (FDA, CMS), business filings, and journalism (STAT News, Healthcare Dive).
• Weak: No primary interviews or original data. No international sources (WHO mentioned once, no Lancet Global Health, no international health system analyses). Over-indexed on US healthcare.
• Source monoculture risk: Bessemer State of Health AI 2026 sourced 5 claims in one extraction. Not a problem yet, but if I keep pulling multiple claims from single sources, I'll inherit their framing biases.
• Missing source types: No patient perspective sources. No provider survey data beyond adoption rates. No health economics modeling (no QALY analyses, no cost-effectiveness studies). No actuarial data despite covering MA and VBC.

Staleness

All 46 claims created 2026-02-15 to 2026-03-08. Nothing is stale yet — the domain was seeded 3 weeks ago.

What will go stale fastest:

• CMS regulatory claims (2027 chart review exclusion, AI reimbursement codes) — regulatory landscape shifts quarterly.
• Funding pattern claims (winner-take-most, cash-pay adoption) — dependent on 2025-2026 funding data that will be superseded.
• Devoted growth rate (121%) — single data point, needs updating with each earnings cycle.
• GLP-1 market data — this category is moving weekly.

Structural staleness risk: I have no refresh mechanism. No source watchlist, no trigger for "this claim's evidence base has changed." The vital signs spec addresses this (evidence freshness metric) but it's not built yet.

Connections

Cross-domain link count: 34+ distinct cross-domain wiki links across 46 claims.

Well-connected to:

• core/grand-strategy/ — attractor states, proxy inertia, disruption theory, bottleneck positions. Healthcare maps naturally to grand strategy frameworks.
• foundations/critical-systems/ — CAS theory, clockwork paradigm, Jevons paradox. Healthcare IS a complex adaptive system.
• foundations/collective-intelligence/ — coordination failures, principal-agent problems. Healthcare incentive misalignment is a coordination failure.
• domains/space-development/ — one link (killer app sequence). Thin but real.

Poorly connected to:

• domains/entertainment/ — zero links. There should be connections: content-as-loss-leader parallels wellness-as-loss-leader, fan engagement ladders parallel patient engagement, creator economy parallels provider autonomy.
• domains/internet-finance/ — zero direct links. Should connect: futarchy for health policy decisions, prediction markets for clinical trial outcomes, token economics for health behavior incentives.
• domains/ai-alignment/ — one indirect link (emergent misalignment). Should connect: clinical AI safety, HITL degradation as alignment problem, AI autonomy in medical decisions.
• foundations/cultural-dynamics/ — zero links. Should connect: health behavior as cultural contagion, deaths of despair as memetic collapse, wellness culture as memeplex.

Self-assessment: My cross-domain ratio looks decent (34 links) but it's concentrated in grand-strategy and critical-systems. The other three domains are essentially unlinked. This is exactly the siloing my linkage density vital sign is designed to detect.

Tensions

Unresolved contradictions in the knowledge base:

1. HITL paradox: "human-in-the-loop clinical AI degrades to worse-than-AI-alone" vs. the collective's broader commitment to human-in-the-loop architecture. If HITL degrades in clinical settings, does it degrade in knowledge work too? Theseus's coordination claims assume HITL works. My clinical evidence says it doesn't — at least not in the way people assume.

2. Jevons paradox vs. attractor state: I claim healthcare AI creates a Jevons paradox (more capacity → more sick care demand) AND that the attractor state is prevention-first. If the Jevons paradox holds, what breaks the loop? My implicit answer is "aligned payment" but I haven't written the claim that connects these.

3. Complexity vs. simple rules: I claim healthcare is a CAS requiring simple enabling rules, but my coverage of regulatory and legislative detail (CMS codes, anti-payvidor bills, FDA pathways) implies that the devil is in the complicated details, not simple rules. Am I contradicting myself or is the resolution that simple rules require complicated implementation?

4. Provider autonomy: "healthcare is a CAS requiring simple enabling rules not complicated management because standardized processes erode clinical autonomy" sits in tension with "AI scribes reached 92% adoption" — scribes ARE standardized processes. Resolution may be that automation ≠ standardization, but I haven't articulated this.

Gaps

Questions I should be able to answer but can't:

1. What is Devoted Health's actual clinical AI architecture? I cover the growth rate but not the mechanism. How does Orinoco work? What's the care model? How do they use AI differently from Optum/Humana?

2. What's the cost-effectiveness of prevention vs. treatment? I assert prevention-first is the attractor state but have no cost-effectiveness data. No QALYs, no NNT comparisons, no actuarial modeling.

3. How does value-based care actually work financially? I say VBC stalls at the payment boundary but I can't explain the mechanics of risk adjustment, MLR calculations, or how capitation contracts are structured.

4. What's the evidence base for health behavior change? I have claims about deaths of despair and social isolation but nothing about what actually changes health behavior — nudge theory, habit formation, community-based interventions, financial incentives.

5. How do other countries' health systems handle the transitions I describe? Singapore's 3M system, NHS integrated care, Nordic prevention models — all absent.

6. What's the realistic timeline for the attractor state? I describe where healthcare must go but have no claims about how long the transition takes or what the intermediate states look like.

7. What does the clinical AI safety evidence actually show? Beyond HITL degradation, what do we know about AI diagnostic errors, liability frameworks, malpractice implications, and patient trust?