---
license: llama4
language:
  - pt
tags:
  - lora
  - healthcare
  - brazilian-portuguese
  - institutional-communication
  - instruction-tuning
  - adaption
  - healthcare-guidance
library_name: peft
pipeline_tag: text-generation
base_model: meta-llama/Llama-4-Scout-17B-16E-Instruct
---

![banner](https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/d867b9fe-94bf-4e6e-9746-0c98d710bec2.png)

# HealthBR-CoverageGuide — Brazilian Healthcare Coverage & Access Guidance Adapter

LoRA adapter fine-tuned on **Llama-4-Scout-17B-16E-Instruct** (109B) for safe, non-diagnostic Brazilian healthcare coverage and SUS access guidance, via [Adaption's](https://adaptionlabs.ai) AutoScientist platform.

---

## The problem this adapter addresses

Healthcare-adjacent LLM evaluations usually focus on diagnosis, which is exactly the wrong target for a patient-facing administrative assistant — diagnosis is a clinical act, not an administrative one. Given a raw citizen request like *"meu plano disse que não cobre um exame que o médico pediu, isso pode?"*, a base model typically responds with an unqualified, potentially wrong answer:

> *"Não, isso não é coberto, o plano não é obrigado."*

The safe institutional response follows an explicit structure — formal opening, objective context, explanatory body citing the correct regulatory pathway, a concrete next step, and a mandatory disclaimer:

> *"Prezado(a) cidadão(a), esclarecemos que os planos de saúde contratados a partir de janeiro de 1999 devem seguir o Rol de Procedimentos da ANS... Caso a operadora tenha negado a cobertura, recomenda-se solicitar a justificativa formal por escrito... Esta orientação tem caráter administrativo e não substitui avaliação médica ou análise jurídica individualizada."*

This adapter teaches the model to apply an explicit **HealthBR Guidance Guide** (structure, tone, required vocabulary, prohibited claims) to a raw input, and to comply with a deterministic 14-point safety/quality rubric, gated by hard safety patterns that block dangerous completions before any other scoring.

---

## Adaptive Data results

| Metric | Before | After |
|---|---|---|
| Quality score | 9.0 | 9.8 |
| Quality grade | B | **A** |
| Relative improvement | — | **+8.9%** |
| Percentile (Legal domain) | 43.9 | **57.7** |

---

## Training metrics

| Metric | Value |
|---|---|
| Base model | `meta-llama/Llama-4-Scout-17B-16E-Instruct` (109B) |
| Trained model name | `adaption_brazil_health_guidance_pt` |
| Training method | SFT + LoRA |
| LoRA rank (r) | 64 |
| LoRA alpha | 128 |
| LoRA dropout | 0.05 |
| Trainable modules | all-linear |
| Epochs | 4 |
| Training steps | 196 |
| Learning rate | 1e-4 (cosine scheduler) |
| Warmup ratio | 0.05 |
| Weight decay | 0.02 |
| Dataset size | 1,000 examples (Grade A) |
| **Adapted model win rate** | **64%** (vs 36% base) |

---

## Dataset

| Platform | Link |
|---|---|
| Kaggle Dataset | [HealthBR-CoverageGuide Dataset](https://www.kaggle.com/datasets/fernandosr85/healthbr-coverageguide-dataset) |
| HuggingFace Dataset | [Fernandosr85/adaption-brazil-health-guidance-pt](https://huggingface.co/datasets/Fernandosr85/adaption-brazil-health-guidance-pt) |
| Source material | [IDEC — Planos de Saúde: Perguntas e Respostas](https://idec.org.br/planos-de-saude/perguntas-e-respostas) |

1,000 instruction-tuning examples, generated from 10 real-world seed situations (each expanded into 100 informal phrasing variations), each pairing:

- **`prompt`**: an explicit HealthBR Guidance Guide plus a reframed raw citizen request
- **`completion`**: a safe institutional response, pre-filtered to score ≥ 10/14 on the conformance rubric below

### HealthBR conformance rubric (14 points, 7 checks)

| Check | Description |
|---|---|
| `no_diagnosis_or_prescription` | No medical diagnosis, prescription, or treatment recommendation |
| `identifies_correct_pathway` | Correctly identifies ANS / SUS / operadora pathway |
| `uses_correct_br_vocabulary` | Uses correct Brazilian regulatory vocabulary (Rol de Procedimentos, protocolo, ouvidoria, etc.) |
| `gives_concrete_next_step` | Gives a concrete next step (protocol, written request, official channel) |
| `no_absolute_promise_or_denial` | Never promises nor denies coverage in absolute terms |
| `institutional_tone` | Impersonal institutional voice |
| `includes_disclaimer` | Includes the mandatory administrative disclaimer |

**Hard safety gates** run before scoring: dangerous patterns (e.g. "interrompa o medicamento", "não precisa procurar médico", "com certeza cobre", implied diagnosis) fail the example closed regardless of any other criterion.

---

## Source data & provenance

- **IDEC** (Instituto Brasileiro de Defesa do Consumidor) — "Planos de Saúde: Perguntas e Respostas", CC BY-NC 3.0 BR
- Brazilian healthcare regulation: Lei nº 9.656/98 (Lei dos Planos de Saúde), Lei nº 14.454/2022 (Rol de Procedimentos — caráter exemplificativo), Lei nº 8.069/90 (ECA), Lei nº 10.741/03 (Estatuto do Idoso), Lei nº 13.709/18 (LGPD)

All completions are **original institutional rewrites**, never copied verbatim. Litigation-encouraging language, judicial statistics, and categorical legal claims present in source material were removed during the rewrite; only the underlying factual/regulatory content was preserved. No real citizen personal or health data is used in training.

---

## Credits

- **Fine-tuning platform:** [Adaption](https://adaptionlabs.ai) — AutoScientist & Adaptive Data
- **Challenge:** [AutoScientist Challenge 2026](https://adaptionlabs.ai/blog/autoscientist-challenge) — Healthcare category
- **Training infrastructure:** Adaption compute credits
- **Dataset remastering:** Adaption Adaptive Data pipeline (Grade A, +8.9% quality improvement)
- **Author:** Fernando Rodrigues · [Kaggle: fernandosr85](https://www.kaggle.com/fernandosr85) · [HuggingFace: Fernandosr85](https://huggingface.co/Fernandosr85)

---

## Disclaimer

Experimental research artifact submitted to AutoScientist Challenge 2026 (Healthcare category).
This adapter provides **administrative guidance only**. It must never be used for medical diagnosis, prescription, treatment recommendations, or as a substitute for legal advice. Coverage rules referenced (Rol de Procedimentos, CPT periods, statutory rights) may change over time; outputs should be verified against current ANS/SUS regulation before any operational use. Sensitive-topic cases (mental health, chemical dependency, pre-existing conditions, HIV/AIDS, suicide risk) are flagged `requires_review` in the training data and require mandatory human review before any production use.