Solution
Public health coding for sensitive, high-stakes survey data
For patient feedback, service evaluation, and population health studies, qualcode.ai codes each verbatim in its own isolated API call with two independent LLMs, so no response leaks context into another's classification. Reconciliation outcomes feed back as training data, so the system gets sharper with every coding cycle — and full records show exactly how each decision was made.
Patient and citizen feedback
Classify patient and citizen feedback with per-response isolation — each comment processed independently by two LLMs, so no response context leaks into another's classification.
Designed for careful reporting
Dual-rater coding produces auditable agreement metrics and a complete reconciliation trail — the kind of documentation governance boards and ethics committees expect. Each reconciliation outcome becomes a training example, so repeat studies produce sharper results.
Works with small or large studies
Start with a few dozen responses or scale to much larger survey waves without changing the core workflow, including the three-AI codebook suggestion: two LLMs independently propose categories, a third merges and deduplicates them into a starting framework.
Where public health teams use it
| Use case | Example source | Why it matters |
|---|---|---|
| Service evaluation | Patient experience surveys and feedback forms. | Each response coded in isolation by two independent raters. Summarize patterns without losing the underlying verbatim context. |
| Program monitoring | Open-ended responses from public programs or campaigns. | Reusable guides plus self-learning from reconciliation make each wave sharper. Compare themes across cohorts or time periods with consistent methodology. |
| Topic discovery | Comments that need a starting codebook. | Two independent LLMs suggest categories, then a third merges them — so your starting codebook reflects multiple analytical perspectives rather than a single model's bias. |
| Sensitive reporting | Internal analysis for health authorities or NGOs. | Per-response isolation, dual-rater agreement, and full reconciliation records — a transparent workflow that governance boards can audit. |
Trust and transparency matter here
Public health projects often require careful governance, data minimization, and a clear explanation of third-party processing. qualcode.ai's trust center, DPA, and transfer impact assessment give procurement teams the documentation they need.
What to put in the workflow
- Use clear category names that policy, clinical, or program teams can read without interpretation games.
- Keep the same guide across waves so trend reporting stays comparable.
- Use the reconciliation step to document why categories changed when the data changed — resolved disagreements automatically become training data that sharpens the next run.