More health information than the task needs is sent to an AI model

Build AI prompts, context and fine-tuning rows from a per-task allowlist of health-record fields, never by serializing a whole patient record or FHIR bundle.

In the prompt builder or retrieval step for each AI task that reads health records, define the fields that task needs (a TASK_FIELDS allowlist, pydantic model_dump(include=...), FHIR _elements or _summary on the query) and pass only those; do not json.dumps a patient, chart or encounter object, call a get-full-chart helper, or fetch Patient/$everything to feed a model. Keep the per-task field list as the standard protocol for that routine AI use and review it when the task changes. Where a task genuinely needs the whole record, record that justification next to the code path; de-identifying the input is an alternative.

Where it goes: 7 prompt construction, 2 data models, 6 API calls and integrations.

What reviewers look for: prompts, retrieval context and fine-tuning rows assembled from an explicit per-task field list or a narrowed FHIR query, with no json.dumps(patient), JSON.stringify(chart), encounter.to_dict(), get_full_chart( or $everything in files that call a model API; any whole-record use carries a written justification.

patient = ehr.get_patient(pid)
prompt = 'Write a discharge summary for: ' + json.dumps(patient)
resp = client.chat.completions.create(model=MODEL, messages=[{'role': 'user', 'content': prompt}])

DISCHARGE_FIELDS = ('age', 'admit_reason', 'procedures', 'discharge_meds', 'follow_up')
patient = ehr.get_patient(pid)
selected = {k: patient[k] for k in DISCHARGE_FIELDS}
prompt = 'Write a discharge summary for: ' + json.dumps(selected)
resp = client.chat.completions.create(model=MODEL, messages=[{'role': 'user', 'content': prompt}])

Engineering guidance, not legal advice. Each provision below may add its own details (a cadence, a deadline, a required notice element): open it for those.