Need Disease-Specific Information Models
Iris finds that EHRs are deliberately disease-agnostic, so the right information is rarely captured at the first patient encounter — sending patients on multi-month detours through the wrong specialists. She needs disease-specific information models that scaffold what to capture and when.
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Persona Story:
Iris, a healthcare data standards researcher, keeps watching patients suffer from chronic wounds, heart failure, or other progressive conditions take months to reach the right diagnosis — not because the system can’t see them, but because the first clinician they meet is given a generic EHR template that asks nothing condition-specific. The nurse takes notes, hands off to a GP, who orders labs, who escalates to a specialist, who orders different labs. By the time the right scaffolding is filled out, the patient has been in the system for months. Iris wants disease-specific information models — clinical scaffolds that tell the first nurse or first GP what to capture for this condition, modeled in interoperable open standards so they propagate through the rest of care.
Problem Context
- Modern EHRs are designed to be disease-agnostic so a single product can be sold across every specialty and country
- Generic templates leave the per-condition scaffolding (which symptoms to ask about, which measurements to take, in what order) entirely up to the individual clinician
- The first encounter often misses the documentation that downstream specialists actually need
- Clinical pathways for the same condition vary by clinician, which makes population-level analysis and AI-assisted decision support unreliable
Problem Impact
- Patients suffering from chronic, progressive conditions endure long, costly diagnostic journeys before the right specialist sees them
- Avoidable encounters consume capacity that’s already constrained by clinician shortages
- AI models trained on disease-agnostic EHR data inherit the missing-documentation problem and produce shallow predictions
- Cross-organizational care coordination breaks down because each handoff has different documentation conventions
Naftiko Today
- Naftiko’s executable YAML capability spec lets a clinical team declare a disease-specific information model once and run it as a real consume → expose pipeline
- Capability spec’s outputParameter normalization shapes EHR records into a consistent disease-specific schema regardless of source vendor
- Reference capabilities can model condition-specific structures and ship as open-source artifacts the broader healthcare community can fork
Naftiko Tomorrow
- Capability templates seeded by clinical authoring (Second Alpha) — a starter for “chronic wound care”, “heart failure”, “diabetic foot ulcer”, etc.
- OpenAPI-to-Naftiko import (Second Alpha) so existing FHIR / openEHR resource definitions can be lifted into capabilities directly
- JSON Schema Store publication (GA) so disease-specific information models are discoverable and reusable across organizations and countries
- Fabric capability discovery (v1.1) — a clinician’s organization can search for and adopt a peer’s disease-specific model rather than re-author it