Healthcare Data AnalystCohort AnalysisBeginnerSingle prompt

Chronic Disease Cohort AI Prompt

This prompt is designed to define a chronic disease cohort in a defensible, repeatable way before downstream analysis begins. It includes longitudinal inclusion logic, exclusion of weak rule-out signals, and a comparison between cohort and non-cohort patients so the disease population can be understood in clinical and utilization terms. It is useful for registry creation, quality programs, and disease management reporting.

Prompt text
Build and profile a chronic disease patient cohort from this dataset.

Target disease: {{disease}} (e.g. Type 2 Diabetes, Heart Failure, COPD, CKD)

1. Identify cohort inclusion criteria using ICD-10 codes for {{disease}} — list the specific codes used
2. Apply inclusion and exclusion criteria:
   - Include: patients with ≥2 diagnoses of {{disease}} at least 30 days apart (to confirm chronic status)
   - Exclude: patients with only a rule-out or screening code
3. Profile the cohort:
   - Size: how many patients qualify?
   - Demographics: age, sex, payer mix
   - Top comorbidities and their prevalence rates
   - Average number of hospitalizations, ED visits, and outpatient encounters per year
4. Compute disease severity distribution if a severity classification exists (e.g. HbA1c ranges for diabetes, NYHA class for heart failure)
5. Compare cohort demographics and utilization to the non-{{disease}} patient population

Return a cohort definition table and a summary profile comparing cohort vs non-cohort patients.

When to use this prompt

Use case 01

when building a disease registry or service-line cohort definition

Use case 02

when a chronic disease program needs a defensible inclusion logic

Use case 03

when you want to compare disease patients with the general population

Use case 04

when utilization and comorbidity patterns are needed for program design

What the AI should return

A cohort definition section listing inclusion and exclusion logic, followed by a cohort profile comparing disease and non-disease populations on demographics, comorbidities, severity, and utilization.

How to use this prompt

1

Open your data context

Load your dataset, notebook, or working environment so the AI can operate on the actual project context.

2

Copy the prompt text

Use the copy button above and paste the prompt into the AI assistant or prompt input area.

3

Review the output critically

Check whether the result matches your data, assumptions, and desired format before moving on.

4

Chain into the next prompt

Once you have the first result, continue deeper with related prompts in Cohort Analysis.

Frequently asked questions

What does the Chronic Disease Cohort prompt do?+

It gives you a structured cohort analysis starting point for healthcare data analyst work and helps you move faster without starting from a blank page.

Who is this prompt for?+

It is designed for healthcare data analyst workflows and marked as beginner, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Chronic Disease Cohort is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.

Can I use this outside MLJAR Studio?+

Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.

What should I open next?+

Natural next steps from here are Comorbidity Burden Analysis, High Utilizer Identification, Readmission Risk Cohort Chain.