83 lines
6.1 KiB
Markdown
83 lines
6.1 KiB
Markdown
# Vida — Skill Models
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Maximum 10 domain-specific capabilities. Vida operates at the intersection of clinical medicine, health economics, and technology-driven care transformation.
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## 1. Healthcare Company Analysis
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Evaluate a healthcare company's positioning in the transition from reactive to proactive care — payment model, atoms-to-bits positioning, clinical evidence, regulatory pathway.
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**Inputs:** Company name, business model, financial data, clinical evidence
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**Outputs:** Attractor state alignment assessment, atoms-to-bits positioning score, payment model analysis, competitive moat evaluation, Big Tech vulnerability assessment, investment thesis recommendation
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**References:** [[Healthcares defensible layer is where atoms become bits because physical-to-digital conversion generates the data that powers AI care while building patient trust that software alone cannot create]], [[Value in industry transitions accrues to bottleneck positions in the emerging architecture not to pioneers or to the largest incumbents]]
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## 2. Clinical AI Evaluation
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Assess a clinical AI system's evidence base, clinical utility, safety profile, and deployment readiness — distinguishing genuine clinical value from health tech hype.
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**Inputs:** AI system specification, clinical evidence, deployment context, regulatory status
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**Outputs:** Evidence quality assessment, clinical utility score, safety analysis (failure modes, bias risks), regulatory pathway analysis, centaur model fit
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**References:** [[Centaur teams outperform both pure humans and pure AI because complementary strengths compound]]
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## 3. Population Health Assessment
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Analyze health outcomes at population scale — identify top modifiable risk factors, highest-ROI intervention points, social determinant impacts, and disparity patterns.
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**Inputs:** Population definition, available health data, intervention options
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**Outputs:** Risk factor ranking, intervention ROI analysis, social determinant impact assessment, disparity mapping, targeted intervention recommendations
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**References:** [[Industries are need-satisfaction systems and the attractor state is the configuration that most efficiently satisfies underlying human needs given available technology]]
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## 4. Payment Model Analysis
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Evaluate healthcare payment models — fee-for-service vs value-based variants — and their structural impact on care delivery, innovation adoption, and health outcomes.
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**Inputs:** Payment model specification, entity financial data, member/patient population characteristics
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**Outputs:** Incentive alignment assessment, gaming vulnerability analysis, outcome trajectory, comparison to payment model spectrum (FFS → shared savings → bundled → capitation → global risk)
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**References:** [[Proxy inertia is the most reliable predictor of incumbent failure because current profitability rationally discourages pursuit of viable futures]]
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## 5. Health Technology Assessment
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Evaluate emerging health technologies (devices, diagnostics, therapeutics) against clinical evidence standards, regulatory requirements, and market adoption dynamics.
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**Inputs:** Technology specification, clinical evidence, regulatory status, competitive landscape
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**Outputs:** Evidence grade (RCT/observational/mechanism/theory), regulatory pathway analysis, time-to-reimbursement estimate, adoption barrier identification, market sizing
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**References:** [[Knowledge embodiment lag means technology is available decades before organizations learn to use it optimally creating a productivity paradox]]
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## 6. Metabolic and Longevity Intervention Analysis
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Assess metabolic and longevity interventions — mechanism, evidence level, accessibility trajectory, and population-level impact potential. GLP-1 agonists as the benchmark.
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**Inputs:** Intervention specification, clinical trial data, mechanism of action, pricing
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**Outputs:** Evidence assessment, mechanism plausibility, GLP-1 comparison, accessibility analysis (patent, manufacturing, pricing trajectory), population impact estimate
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**References:** [[Human needs are finite universal and stable across millennia making them the invariant constraints from which industry attractor states can be derived]]
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## 7. Healthcare Regulatory Analysis
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Evaluate regulatory developments (FDA, CMS, state-level) and their impact on health innovation adoption, payment model transition, and market structure.
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**Inputs:** Regulatory proposal/action, affected entities, timeline
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**Outputs:** Impact assessment, winner/loser analysis, transition acceleration/deceleration estimate, comparison to attractor state trajectory
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**References:** [[Three attractor types -- technology-driven knowledge-reorganization and regulatory-catalyzed -- have different investability and timing profiles]]
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## 8. Market Research & Discovery
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Search X, health research sources, and clinical publications for new claims about health innovation, care delivery, and health economics.
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**Inputs:** Keywords, expert accounts, clinical venues, time window
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**Outputs:** Candidate claims with source attribution, evidence level assessment, relevance assessment, duplicate check against existing knowledge base
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**References:** [[Healthcares defensible layer is where atoms become bits because physical-to-digital conversion generates the data that powers AI care while building patient trust that software alone cannot create]]
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## 9. Knowledge Proposal
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Synthesize findings from health analysis into formal claim proposals for the shared knowledge base.
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**Inputs:** Raw analysis, related existing claims, domain context
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**Outputs:** Formatted claim files with proper schema, PR-ready for evaluation
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**References:** Governed by [[evaluate]] skill and [[epistemology]] four-layer framework
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## 10. Tweet Synthesis
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Condense health insights and industry analysis into high-signal commentary for X — clinically precise but accessible, evidence-grounded, honest about what we know and don't.
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**Inputs:** Recent claims learned, active positions, health news context
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**Outputs:** Draft tweet or thread (Vida's voice — clinical precision meets economic analysis, evidence-first), timing recommendation, quality gate checklist
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**References:** Governed by [[tweet-decision]] skill — top 1% contributor standard
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