# Ehr Governance Packet > Produce the normalized JSON deliverable for an EHR quality-governance task — duplicate-merge readiness packets, specialty referral coordination packets, care-transition handoff packets, duplicate-review + ServiceRequest QA, and referral-batch audits. Use when a prompt gives clinical/governance object IDs (patients, duplicate candidates, referrals, ServiceRequests, batches) plus an `input/payloads/answer_template.json`, and asks for normalized JSON that reconciles a read-only EHR/referral environment against that template. Read this before gathering data or writing the answer. - Skill: `prism-shadow/ehr-governance-packet-8` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add prism-shadow/ehr-governance-packet-8` - Raw SKILL.md: https://api.skillmd.com/api/skills/prism-shadow/ehr-governance-packet-8/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: prism-shadow (https://skillmd.com/u/prism-shadow) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/prism-shadow/ehr-governance-packet-8 --- # EHR quality-governance packet / audit tasks These tasks share one shape: a prompt names some governance objects (patient IDs, a duplicate candidate, a referral, a ServiceRequest, a batch) and points to an `input/payloads/answer_template.json`. A read-only EHR/referral environment is described in the task's own access file. Your job is to gather evidence from that environment and emit **JSON that conforms exactly to the answer template** — right keys, right enum tokens, right ordering, no prose. The winning approach is evidence-driven and template-driven, not narrative. The template is the spec; the environment is the source of truth; your output is a faithful reconciliation of the two. ## Workflow 1. **Read the template first, in full.** `answer_template.json` defines every required key, the allowed enum values, which arrays are "sets", and the ordering rules. Build your output object to mirror its structure key-for-key. Note every `enum:` list — your value MUST be one of those tokens verbatim. Note every ordering note ("sorted alphabetically", "sort by code", "newest to oldest", "set semantics") and obey it. 2. **List the entities you must resolve.** Every ID in the prompt, plus every ID the template asks you to output (providers, documents, encounters, codes…). 3. **Discover the environment from the task's own access description** (the file the task provides listing the available read-only resource families — patient detail, active clinical lists, encounters, immunizations, disclosures, documents, service-requests, audit logs, duplicate candidates, referrals, ICD-10 directory, provider directory, service-code directory, etc.). Query only what you need for the entities above. Do not hardcode or assume specific URLs — read them from the provided access description each run. 4. **Gather evidence** for every template field. Prefer the primary record endpoints (a patient's own active lists) over any pre-computed "preview" or summary the environment offers — the primary records are authoritative. 5. **Apply the decision rules** in `references/decision-rules.md` to turn raw records into the template's normalized fields. 6. **Normalize and self-check** (below), then output JSON only. ## Core principles (transfer across all of these tasks) - **Active-list unions preserve everything active.** When a field asks for a union of active condition / medication / allergy keys, take the `normalized_key` of every record whose `status == "active"` across all in-scope patients, and include *all* of them — even generic-looking filler keys. Dropping an active key (a med, an allergy) is a clinical-safety error and loses points. Records with any non-active status (`inactive`, `entered-in-error`) are the "excluded distractors", never part of the union. - **Primary records beat previews.** A duplicate candidate's `merge_preview` (or any summary) can be incomplete or null. Reconcile against the patients' own active-list endpoints; where a "reconciliation / added-from-active-endpoints" field exists, it captures exactly what the preview missed. - **Separate the classification from the action.** Identity/duplicate status (confirmed / needs-review / not-duplicate) is a *different* decision from the action (merge / hold-for-review / do-not-merge). Strong identity matches (same DOB + insurance + address) can mean a *confirmed* duplicate that is still *held for review* because conflict signals exist. A benign conflict signal → review-with-conflict, not do-not-merge. Only record a concrete merge target/source when a merge is actually proceeding; if the environment's preferred target/source is null and the action is a hold, leave them null. Merge target = the active/canonical record; source = the record flagged duplicate/possible_duplicate. - **Readiness follows real data completeness, not reminders.** A soft coordination note ("confirm the allergy before sending") does not make a fully populated active record incomplete. Base readiness/blocking status on whether the required structured data and documents actually exist. - **Relevance beats recency for evidence selection.** Pick the encounter / document / audit row that is *topically tied to the packet's purpose* and has a final/signed status — not merely the newest row. Exclude generic exports (chart summaries / EHR exports), stale/out-of-window records, and rows about other patients or other candidates. Where the template wants both selected and excluded IDs, list every reviewed-but-rejected ID in the exclusion field. - **Referral code sets are narrow.** The codes that "belong in the letter" are the referral's primary diagnosis plus the narrative's symptom code — not every cardiovascular/comorbid condition on the chart. Mark only those `referral_relevant`. Validate each code against the ICD directory: compare its chapter to the service line and its `expected_terms` to the narrative. - **Emit every evidenced flag; don't prune.** For risk-flag / signal fields, output every value that has chart evidence (a condition, a med+condition pair, an allergy, a care-plan note), including chronic conditions. Provide the per-flag evidence the template asks for. - **Providers come from the directory verbatim.** Resolve the relevant provider_id (receiving / performer / recipient, or the provider tied to an external continuity document) in the provider directory and copy name/role/facility/phone/fax exactly. See `references/decision-rules.md` for the field-by-field playbook, including the batch-audit rules (single canonical expected chapter, in-chapter laterality/narrative mismatches, same-patient duplicates, shared-insurance anomalies, data-driven follow-up queues, and issue-based tiering). ## Normalization conventions - **Enums:** copy the exact token from the template's `enum:` list. Never invent a variant or add a suffix. For controlled labels/reason-codes that mirror environment signals, reuse the environment's own token strings. - **Ordering:** obey each field's ordering note. Default for "set" arrays is ascending/alphabetical; sort even set-semantics arrays for determinism. Object arrays: sort by the key the template names (e.g. `referral_id`, `code`). - **Verbatim values:** copy IDs, names, phone/fax, dates, `normalized_key`, and code strings exactly as returned. Dates are `YYYY-MM-DD`. - **Nulls:** use `null` (not "", not omitted) where the template types a field as nullable and the evidence is genuinely absent/undetermined. - **Output:** a single JSON object with the template's keys only. No commentary, no markdown, no fields the template did not define. ## Self-check before finishing - Every required top-level key present; no extra keys. - Every enum value is one of the template's allowed tokens. - Every array obeys its ordering rule; set arrays sorted. - Every emitted ID / value is traceable to a fetched record. - Active unions include all active keys; distractors (inactive / unrelated / stale / generic-export) are excluded (and listed where the template asks). - Counts (if any) equal the lengths of the sets you actually produced. - The output parses as JSON and matches the template's shape exactly.