dgx-spark-playbooks/nvidia/station-healthcare-agent/assets/skills/analysis-methods/SKILL.md

name	description	metadata
analysis-methods	Teaches the analyst agent how to write correct, robust Python analysis code for FHIR clinical data using pandas, matplotlib, and scipy.	openclaw requires bins python3 pip

Analysis Code Guidelines

FHIR Helpers Library

Always import the helpers library at the top of every analysis script:

import sys
sys.path.insert(0, '/sandbox/clinical-intelligence/skills/analysis-methods/scripts')
from fhir_helpers import *

Available functions

Function	Use for	HTTP calls
get_patients_with_condition(snomed_code)	Find patients with a condition → list of IDs	1-2
get_latest_labs_batch(loinc_code, patient_ids)	Labs for a cohort → dict: pid → (value, unit, date)	1-2
get_all_medications_batch(patient_ids)	Meds for a cohort → dict: pid → [med names]	1-2
build_cohort_df(patient_ids, loinc, lab_name, drug_check_fn)	Full DataFrame with labs + meds	2-3
get_latest_lab(patient_id, loinc_code)	Lab for ONE patient → (value, unit, date)	1
get_medications(patient_id)	Meds for ONE patient → [names]	1
get_latest_bp(patient_id)	BP for ONE patient → (sys, dia, date)	1-2
check_drug_class(med_list, drug_names)	Check if any med matches drug list → bool	0
fhir_get(path, params)	Raw FHIR GET → parsed JSON	1
get_all_pages(path, params)	Paginated FHIR GET → all entries	1+
save_chart_to_canvas(fig, filename)	Save matplotlib figure to canvas directory	0

Performance rules

• Cohort queries (2+ patients): Use get_latest_labs_batch() and get_all_medications_batch(). These make 1-2 HTTP calls total regardless of patient count.
• Single patient: Use get_latest_lab(), get_medications(), get_latest_bp().
• NEVER loop over patients calling get_latest_lab() per patient. Each HTTP call through the sandbox proxy adds 1-3s. For 48 patients = 48 calls = 2+ minutes. The batch function does it in one call.

Execution Rules

• Run scripts with python (NOT python3)
• Write a SINGLE Python script for the entire task
• Write the script to /tmp/<name>.py, then execute it
• All HTTP inside the sandbox must use subprocess.run(["curl", ...]) — the requests library does NOT work

Mandatory Workflow

STEP 1 - WRITE SCRIPT (import fhir_helpers, write analysis)
STEP 2 - VALIDATE: python /sandbox/clinical-intelligence/scripts/validate_and_run.py --validate-only /tmp/<name>.py
STEP 3 - EXECUTE: python /tmp/<name>.py
STEP 4 - INTERPRET: explain results using clinical-knowledge skill

Code Structure

1. Imports (always start with fhir_helpers import)
2. Data collection (use batch functions)
3. DataFrame construction
4. Analysis (filters, aggregations)
5. Visualization -- use save_chart_to_canvas(fig, filename) (NOT plt.savefig)
6. Summary (print findings)
7. Disclaimer

Care Gap Analysis Pattern

# Example: diabetes care gap
patients = get_patients_with_condition("44054006")  # SNOMED for diabetes
df = build_cohort_df(patients, "4548-4", "HbA1c",
                     lambda meds: check_drug_class(meds, ["metformin", "insulin", "glipizide"]))

gap = df[(df['HbA1c'] > 9) & (~df['on_target_med'])]
denom = len(df[df['HbA1c'].notna()])
pct = f"{len(gap)/denom*100:.1f}%" if denom > 0 else "N/A (no HbA1c data)"
print(f"Care gap: {len(gap)}/{denom} ({pct})")

Visualization

Always use dark theme. Use save_chart_to_canvas() instead of plt.savefig() directly.

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(10, 6))
fig.patch.set_facecolor('#1a1a1a')
ax.set_facecolor('#1a1a1a')

# Histogram with NVIDIA green
ax.hist(values, bins=15, color='#76B900', edgecolor='#1a1a1a', alpha=0.85)
ax.axvline(x=threshold, color='#ff4444', linestyle='--', linewidth=2, label=f'Threshold ({threshold})')
ax.set_title("Title", fontsize=14, fontweight='bold', color='white')
ax.legend()
ax.grid(axis='y', alpha=0.2, color='#444444')
ax.text(0.98, 0.95, f"N = {len(values)}", transform=ax.transAxes, fontsize=11, color='#888888', ha='right', va='top')

# MANDATORY: use save_chart_to_canvas (NOT plt.savefig)
save_chart_to_canvas(fig, "chart.png")
plt.close()

Guardrails

• Never compute statistics on fewer than 5 data points
• Always report sample size: "45.0% (27 out of 60)"
• Flag data quality issues if >30% missing
• Do not fabricate data — report what exists, flag what's missing
• All charts must include N annotation

Output Format

End every script with:

print(f"\nDisclaimer: This analysis is for research and operational purposes.")
print("Clinical decisions should be made by qualified clinicians.")