---
id: emrvalidator
version: "1.0.2"
license: MIT
license_treatment: permissive
maintenance: aging
---

# emrvalidator — A Data Validation Tool for Healthcare Data

License: permissive · Maintenance: aging · Downloads: 242.6K/mo

## What it is and what it does

EMRValidator is a Python data validation library designed for healthcare and clinical datasets. It provides a fluent API for chaining validation rules, with built-in support for healthcare-specific formats like MRN and ICD codes, alongside standard checks for nulls, ranges, uniqueness, and date formats. The library depends only on pandas and includes data profiling and HTML/JSON report generation.

It positions itself as a lighter alternative to Great Expectations, targeting healthcare ETL pipelines, data warehouses, and clinical analytics workflows. The package offers multiple API styles—fluent chaining, expectation suites, and reusable rule sets—and includes pre-built rule sets for common healthcare scenarios like patient demographics and financial data validation.

Use it for:

- Validate patient demographics, MRN formats, and clinical codes in ETL pipelines before loading to a data warehouse
- Generate data quality reports and profiling summaries for healthcare datasets to identify missing or malformed records
- Define and reuse custom validation rule sets for claims data, encounter records, or revenue cycle management workflows
- Run real-time data quality checks on incoming clinical data in Airflow, dbt, or LLM pipeline contexts
- Validate ICD-9 and ICD-10 diagnosis codes and other healthcare-specific formats in bulk data migrations

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

EMRValidator is a data validation library specialized for healthcare datasets, offering built-in validators for medical record numbers, ICD codes, and other clinical data formats alongside general data quality checks.

Yes, if you are validating healthcare or clinical datasets and want a lightweight, pandas-based alternative to heavier frameworks. The low install friction and healthcare-specific validators make it well-suited for ETL and data warehouse contexts. However, note the aging maintenance status (200 days since last release) and verify Python version support before committing to production use.

## Install

pip install emrvalidator
uv add emrvalidator
poetry add emrvalidator

## Installing emrvalidator

Before you install: Low friction: pure Python wheel with only pandas as a runtime dependency. Maintenance status is aging—last release 200 days ago—so expect slower response to issues or feature requests.

License in practice: MIT license is permissive; you can use, modify, and distribute this package freely in commercial or private projects with minimal restrictions.

Quickstart:

pip install emrvalidator

from emrvalidator import DataValidator
import pandas as pd

df = pd.read_csv('patient_data.csv')
validator = DataValidator("Patient Data Quality")
validator.load_data(df)
validator.expect_column_exists('mrn').expect_mrn_format('mrn')
if validator.is_valid():
    print("✓ Validations passed")

Requires pandas; Python version support is unspecified in the package metadata, so verify compatibility with your environment before use.

Verify before relying:

- Whether numpy is truly optional or a transitive dependency of pandas that users should be aware of
- Actual performance comparison methodology behind the claimed 5-7x speedup over Great Expectations
- Python version support (requires_python is unspecified in the fact sheet)
- Whether the package is actively maintained or in maintenance-only mode given the 200-day gap since last release

## Package facts

- License: MIT (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: aging
- Downloads: 242.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

healthcare data validation, EMR data quality checks, ICD code validation, medical record validation, clinical data profiling, healthcare dataset validation, data quality rules healthcare, healthcare-data, data-validation, etl-pipeline

[View on SkillFed](https://skillfed.io/packages/emrvalidator) · [View on PyPI](https://pypi.org/project/emrvalidator/)