Healthcare Data AnalystData Quality and ComplianceBeginnerSingle prompt

Clinical Data Quality Audit AI Prompt

This prompt is a broad clinical data quality audit tailored to common healthcare data failure modes rather than generic spreadsheet issues. It checks missingness, coding validity, temporal logic, and cross-field consistency in ways that directly affect quality measurement, claims logic, and clinical interpretation. It is most useful before reporting, modeling, or submitting data for operational decision-making.

Prompt text
Audit the quality of this clinical dataset and return a structured quality report.

Check each of the following dimensions:

1. Completeness: which required clinical fields are missing?
   - Critical fields (flag if >5% missing): patient_id, admission_date, discharge_date, primary_diagnosis, discharge_disposition
   - Important fields (flag if >15% missing): attending_physician, procedure_codes, payer, age, sex

2. Validity: are clinical values within plausible ranges?
   - Negative LOS (discharge before admission)
   - Age > 120 or < 0
   - Invalid ICD-10 codes (not in official code list)
   - Discharge disposition codes that don't exist in standard NUBC taxonomy

3. Consistency: are related fields logically consistent?
   - Death as discharge disposition but no mortality flag
   - Pediatric patients with adult diagnoses (and vice versa)
   - Procedure dates outside the admission window

4. Timeliness: when was the data last updated? Are there records with suspiciously old last-modified dates?

Return: quality scorecard with pass/fail per dimension, top 10 specific issues, and estimated % of records affected by each issue.

When to use this prompt

Use case 01

when a clinical dataset will be used for reporting, modeling, or executive decisions

Use case 02

when you need a healthcare-specific audit of completeness, validity, and logic

Use case 03

when stakeholders suspect temporal or coding inconsistencies in the source data

Use case 04

when you want a prioritized defect list before downstream use

What the AI should return

A clinical data quality scorecard by dimension, a ranked issue log with affected-record estimates, and a concise summary of the most important defects to fix first.

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 Data Quality and Compliance.

Frequently asked questions

What does the Clinical Data Quality Audit prompt do?+

It gives you a structured data quality and compliance 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?+

Clinical Data Quality Audit 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 Coding Accuracy Analysis, De-identification Verification, POA Flag Validation.