Clinic Protocol Cds

Clinic Protocol Decision-Support

These tasks give you a case id (of the form CASE-...), a clinic runtime environment (access details are provided separately for each run), and an answer template (input/payloads/answer_template.json). Your job is to return exactly one JSON object that conforms to the template, derived by applying the matching protocol to the case data.

Core procedure

1. Read the prompt and template first. Identify the target case_id, the required top-level keys, every field's type/enum/allowed values, nullability, precision, and any ordering rules. The template is the contract — every enum value, key name, and precision note is enforced.
2. Fetch the case record from the runtime environment: the case detail endpoint returns the case, patient, findings, observations, imaging, medications, allergies, problems, sdoh, and care_registry together. Do not call piecemeal endpoints when the bundled case view exists.
3. Fetch the matching protocol. The protocols list shows protocol ids; pick the one whose scope matches the case type (respiratory/CAP, pediatric head injury, potassium repletion, care-management routing, observation-window). The protocol body holds the authoritative rules and controlled codes.
4. Derive each field from the protocol + case data, not from generic clinical memory. The protocol is the source of truth for thresholds, codes, dose formulas, follow-up timing, and gates.
5. Emit exactly one JSON object — no markdown, no comments, no extra top-level keys, no prose. Use null only where the template explicitly permits it.

How to read the case record

• findings is the ground-truth clinical narrative. Each finding has a finding_key, finding_value, and source_id. The source_ids are your evidence identifiers — collect the relevant ones for evidence_ids.
• current_time finding = the clinical review timestamp. Use it verbatim (ISO-8601 UTC with trailing Z) wherever a review time is required.
• observations each carry code, status, effective_time, value_number, interpretation, patient_id, observation_id. Status and code matter enormously — see below.
• Distractor observations are deliberately embedded (wrong date, wrong code, wrong patient, non-final status). Filter rigorously.

Observation filtering (the most common source of error)

Protocols state authoritative/excluded statuses and controlled codes. Apply them strictly:

• Status: only status = "final" satisfies protocol gates. Exclude preliminary, entered-in-error, canceled (and anything non-final) from matched sets and from "latest" selection — even if a non-final result is newer or more abnormal.
• Code: match the protocol's controlled code exactly. K (serum/plasma potassium) is not the same as 6298-4 (potassium in whole blood / point-of-care). A whole-blood or point-of-care result with a different LOINC does not satisfy a serum-potassium gate.
• Patient: the observation's patient_id must equal the target patient. (Note: a wrong-patient observation is excluded from matched, but see the exclusion-list rule below.)
• Window: inclusive from, exclusive to, both ISO-8601 UTC. An otherwise-valid result outside the window does not qualify.
• Latest final: among observations that pass status+code+patient+window, pick the one with the greatest effective_time (then observation_id) as the "latest final."

matched_observation_ids vs excluded_observation_ids

• matched = all final, target-code, target-patient observations inside the window. Sort by effective_time ascending, then observation_id ascending.
• excluded = relevant distractor observations that do not qualify. Read the template's wording for the exclusion reasons carefully — it lists specific failure categories (typically date, code, or status). Only list distractors that fail for a listed reason. A wrong-patient observation is excluded from matched, but if "patient" is not among the template's listed exclusion reasons, do not put it in excluded_observation_ids. When in doubt, follow the template's enumerated reasons literally rather than "common sense."
• Sort excluded by effective_time ascending (then observation_id ascending) when the template asks for it.

Protocol gates and downstream results

Map the latest final target value to the gate enum using the protocol's thresholds (e.g. normal vs. low-repletion vs. critical/urgent vs. no-final-lab-in-window). The gate then drives the repeat-lab / disposition / plan:

• A normal latest final that satisfies the gate ⇒ repeat-lab typically not recommended (recommended: false, scheduled_time: null).
• A low latest final ⇒ repletion plan + a scheduled repeat.
• No final lab in window ⇒ recommend obtaining one.
• Use the protocol's exact gate labels; do not invent intermediate states.

Medication and allergy safety

• Pull active allergies only (ignore status: inactive). Map allergens to the template's allergen enum and populate avoid_allergens.
• Choose the antibiotic/medication strategy from the allowed enum such that it avoids the patient's active allergen classes (e.g. a penicillin- and sulfa-allergic patient cannot use a beta-lactam-based outpatient regimen — select the non-cross-reactive alternative the enum offers).
• Safety-check booleans assert you did not make unsupported claims: e.g. no penicillin/sulfa in the plan, no claim of a normal CXR when imaging is abnormal, no claim of "clear lungs," no false LOC/vomiting/photophobia. Set these true only when your answer genuinely avoids the false claim.
• Stabilization actions are reserved for cases meeting escalation thresholds (e.g. SpO2 < 90, respiratory rate ≥ 30, systolic BP < 90, or other protocol red flags). For an outpatient case above those thresholds, use an empty list — do not add supplemental oxygen for borderline-but-non-escalating vitals.

Recommended tests

List the diagnostic tests that are actually performed/documented in the case (the protocol's controlled codes that appear as observations/imaging). Do not pad the list with controlled codes that have no corresponding result in the case record — an unperformed test is not a "recommended test" for scoring purposes unless the protocol explicitly requires ordering it.

Numeric derivations

Apply the protocol's dose/numeric rules exactly as written — formula, target, and rounding:

• Example pattern: dose = (target − current) / 0.1 × mEq_per_0.1_below_target, then round to the protocol's nearest-mEq increment. Use the latest final value as current, not a preliminary or wrong-code value.
• Respect precision in the template (one decimal place for lab values, two for probabilities, integer hours/days/counts). Format blood pressure as systolic/diastolic (e.g. 140/90).
• Timestamps: ISO-8601 UTC with trailing Z. "Next morning" follow-up = the next calendar day at 08:00:00Z. The review current_time comes from the case finding verbatim.

Set-valued fields

• Evaluators normalize most lists as sets (order-independent) unless the template specifies an ordering. Sorting correctly never hurts; over-inclusion does hurt — each false-positive element lowers the score. Select only codes with direct evidence in the case/protocol.
• Map case facts to the enum codes precisely (e.g. diastolic HF + a volume-overload admission ⇒ the specific HFpEF/volume-overload code; SDOH "choosing between renal diet foods and utility bill" ⇒ a food financial barrier, not a medication barrier).
• For source-provenance grouping, distinguish chart facts (registry/lab/vital/imaging data) from member-disclosed facts (SDOH and member-reported barriers/preferences). Include a chart fact only if it actually appears in the record (e.g. do not list egfr if no eGFR observation exists for the case).

Final checks before emitting

• Every required top-level key present; no extra top-level keys.
• Every enum value is spelled exactly as in the template.
• null used only where permitted; integers/numbers at the specified precision.
• evidence_ids populated with stable identifiers (case id first when the template says so, then clinical source ids).
• Single JSON object only — no surrounding text, no markdown fences.