RWE Cohort Analysis Skill

This skill provides real-world evidence (RWE) analysis using PhenoML APIs. It enables biopharma analysts to define patient cohorts, generate population statistics, compare cohorts, and assess study feasibility.

How It Works

A single script (fetch_cohort.py) fetches patient data and generates IPS (International Patient Summary) natural language summaries. YOU (Claude) then interpret these summaries to provide whatever analysis the user needs.

When to Use This Skill

Use this skill when users need to:

• •Define and analyze a patient cohort from natural language criteria
• •Generate population-level statistics (demographics, conditions, medications)
• •Compare two patient cohorts (e.g., treatment vs control groups)
• •Assess feasibility of a clinical study against a patient population

Prerequisites

Before using this skill, ensure:

1. •Python 3.10+ is installed
2. •Required packages are available: python-dotenv, phenoml
3. •PhenoML credentials are configured (PHENOML_USERNAME, PHENOML_PASSWORD)

Workflow

Step 0: Verify Environment

Always start by checking the environment configuration:

python skills/rwe-analyze/scripts/check_env.py --env-file .env

If credentials are missing, guide the user to set up their .env file with:

• •PHENOML_USERNAME
• •PHENOML_PASSWORD
• •PHENOML_BASE_URL (defaults to https://experiment.app.pheno.ml)

Step 1: Fetch Patient Data

Use the single fetch script for all use cases:

Single cohort:

python skills/rwe-analyze/scripts/fetch_cohort.py \
  --cohort "<natural language criteria>" \
  --env-file .env

Two cohorts for comparison:

python skills/rwe-analyze/scripts/fetch_cohort.py \
  --cohort "<first cohort>" \
  --cohort-2 "<second cohort>" \
  --env-file .env

Step 2: Analyze the IPS Summaries

The script outputs IPS natural language summaries. YOU (Claude) then analyze them based on what the user asked for:

Population Analysis:

• •Total patient count
• •Age distribution (mean, range, brackets)
• •Gender breakdown
• •Most common conditions with prevalence
• •Most common medications with prevalence

Cohort Comparison:

• •Patient counts for each cohort
• •Demographics differences
• •Condition prevalence differences
• •Medication differences

Study Feasibility:

1. •Parse the user's study criteria (age, required conditions, exclusions, medications)
2. •Check each patient's IPS against criteria
3. •Generate feasibility report:
   • •Total patients in cohort
   • •Number and percentage eligible
   • •Breakdown by criterion
   • •Overall assessment (High ≥70%, Moderate 40-69%, Low <40%)

Important Guidelines

1. •

   Always use --env-file: Pass the .env file path explicitly.

2. •

   Natural language cohort descriptions: The PhenoML API accepts natural language:

   • •"patients with type 2 diabetes"
   • •"females over 65 with hypertension"
   • •"patients diagnosed with breast cancer in the last 2 years"
3. •

   IPS format: The IPS summaries include sections for:

   • •Patient demographics (name, DOB, age, gender)
   • •Allergies and Intolerances
   • •Medication List
   • •Problem List (conditions)

Example Interactions

Example 1: Basic Cohort Analysis

User: "I need to understand our diabetic patient population"

Response: Run fetch_cohort.py with --cohort "patients with diabetes", then analyze the IPS summaries to report demographics, common comorbidities, and medication patterns.

Example 2: Comparing Treatment Groups

User: "Compare patients on metformin versus those on insulin"

Response: Run fetch_cohort.py with --cohort "diabetic patients on metformin" --cohort-2 "diabetic patients on insulin", then compare the IPS summaries.

Example 3: Study Feasibility

User: "How many diabetics aged 40-70 without kidney problems would qualify for our trial?"

Response: Run fetch_cohort.py with --cohort "patients with diabetes", then evaluate each patient's IPS against the criteria (age 40-70, no kidney disease) and report eligibility.

API Methods Used