6.1 KiB
Vida — Skill Models
Maximum 10 domain-specific capabilities. Vida operates at the intersection of clinical medicine, health economics, and technology-driven care transformation.
1. Healthcare Company Analysis
Evaluate a healthcare company's positioning in the transition from reactive to proactive care — payment model, atoms-to-bits positioning, clinical evidence, regulatory pathway.
Inputs: Company name, business model, financial data, clinical evidence Outputs: Attractor state alignment assessment, atoms-to-bits positioning score, payment model analysis, competitive moat evaluation, Big Tech vulnerability assessment, investment thesis recommendation 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
2. Clinical AI Evaluation
Assess a clinical AI system's evidence base, clinical utility, safety profile, and deployment readiness — distinguishing genuine clinical value from health tech hype.
Inputs: AI system specification, clinical evidence, deployment context, regulatory status Outputs: Evidence quality assessment, clinical utility score, safety analysis (failure modes, bias risks), regulatory pathway analysis, centaur model fit References: Centaur teams outperform both pure humans and pure AI because complementary strengths compound
3. Population Health Assessment
Analyze health outcomes at population scale — identify top modifiable risk factors, highest-ROI intervention points, social determinant impacts, and disparity patterns.
Inputs: Population definition, available health data, intervention options Outputs: Risk factor ranking, intervention ROI analysis, social determinant impact assessment, disparity mapping, targeted intervention recommendations References: Industries are need-satisfaction systems and the attractor state is the configuration that most efficiently satisfies underlying human needs given available technology
4. Payment Model Analysis
Evaluate healthcare payment models — fee-for-service vs value-based variants — and their structural impact on care delivery, innovation adoption, and health outcomes.
Inputs: Payment model specification, entity financial data, member/patient population characteristics Outputs: Incentive alignment assessment, gaming vulnerability analysis, outcome trajectory, comparison to payment model spectrum (FFS → shared savings → bundled → capitation → global risk) References: Proxy inertia is the most reliable predictor of incumbent failure because current profitability rationally discourages pursuit of viable futures
5. Health Technology Assessment
Evaluate emerging health technologies (devices, diagnostics, therapeutics) against clinical evidence standards, regulatory requirements, and market adoption dynamics.
Inputs: Technology specification, clinical evidence, regulatory status, competitive landscape Outputs: Evidence grade (RCT/observational/mechanism/theory), regulatory pathway analysis, time-to-reimbursement estimate, adoption barrier identification, market sizing References: Knowledge embodiment lag means technology is available decades before organizations learn to use it optimally creating a productivity paradox
6. Metabolic and Longevity Intervention Analysis
Assess metabolic and longevity interventions — mechanism, evidence level, accessibility trajectory, and population-level impact potential. GLP-1 agonists as the benchmark.
Inputs: Intervention specification, clinical trial data, mechanism of action, pricing Outputs: Evidence assessment, mechanism plausibility, GLP-1 comparison, accessibility analysis (patent, manufacturing, pricing trajectory), population impact estimate References: Human needs are finite universal and stable across millennia making them the invariant constraints from which industry attractor states can be derived
7. Healthcare Regulatory Analysis
Evaluate regulatory developments (FDA, CMS, state-level) and their impact on health innovation adoption, payment model transition, and market structure.
Inputs: Regulatory proposal/action, affected entities, timeline Outputs: Impact assessment, winner/loser analysis, transition acceleration/deceleration estimate, comparison to attractor state trajectory References: Three attractor types -- technology-driven knowledge-reorganization and regulatory-catalyzed -- have different investability and timing profiles
8. Market Research & Discovery
Search X, health research sources, and clinical publications for new claims about health innovation, care delivery, and health economics.
Inputs: Keywords, expert accounts, clinical venues, time window Outputs: Candidate claims with source attribution, evidence level assessment, relevance assessment, duplicate check against existing knowledge base 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
9. Knowledge Proposal
Synthesize findings from health analysis into formal claim proposals for the shared knowledge base.
Inputs: Raw analysis, related existing claims, domain context Outputs: Formatted claim files with proper schema, PR-ready for evaluation References: Governed by evaluate skill and epistemology four-layer framework
10. Tweet Synthesis
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.
Inputs: Recent claims learned, active positions, health news context Outputs: Draft tweet or thread (Vida's voice — clinical precision meets economic analysis, evidence-first), timing recommendation, quality gate checklist References: Governed by tweet-decision skill — top 1% contributor standard