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Data Integrity Checks, Data Validation & Quality Assurance

Data Integrity Checks, Data Validation & Quality Assurance

1. Introduction

In modern data-driven systems, data quality is critical to application reliability, analytics accuracy, regulatory compliance, and overall business decision-making. Ensuring data is accurate, consistent, complete, and trustworthy requires a combination of data integrity checks, data validation practices, and robust quality assurance (QA) workflows, often supported by automated testing frameworks.
This document outlines best practices for implementing data integrity and validation processes, and provides guidance on how automated testing—particularly using Playwright—can be used to verify data quality across APIs, UI components, and backend systems in openmrs.

2. Data Integrity Overview

2.1 What is Data Integrity?

Data integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle—from creation and storage to transmission and retrieval.

Good data integrity ensures that:

  • Data is free of corruption

  • Data is consistent across systems

  • Data remains unchanged unless modified through controlled mechanisms

  • Unauthorized or accidental changes are prevented

2.2 Types of Data Integrity

a. Physical Integrity

Protection against hardware failures, data loss, storage corruption.

b. Logical Integrity

Ensuring data follows business rules, constraints, and relationships:

  • Entity integrity (unique keys, primary keys)

  • Referential integrity (foreign keys, relationships)

  • Domain integrity (valid ranges, formats, enumerations)

3. Data Validation

3.1 What is Data Validation?

Data validation ensures that input or processed data adheres to defined rules before being accepted into a system.

3.2 Common Data Validation Techniques

  • Format validation (email, phone numbers, UUIDs)

  • Range and boundary checks (ages, dates, numeric thresholds)

  • Type validation (integer, string, boolean)

  • Uniqueness checks

  • Cross-field validation

  • Reference validation (matching foreign keys)

  • Business rule validation (domain-specific logic)

3.3 When Should Validation Happen?

  • Client-side (UI)

  • API gateway / backend

  • Database layer

  • ETL pipelines

  • Data ingestion and transformation processes

4. Data Quality Assurance (DQA)

4.1 Goals of DQA

  • Ensure trustworthy analytics and reports

  • Prevent data corruption or loss

  • Maintain compliance

  • Maintain reliable system behavior

4.2 Components of a Strong QA Strategy

a. Data Profiling

Understanding data distributions, anomalies, missing values.

b. Data Quality Metrics

  • Completeness

  • Accuracy

  • Timeliness

  • Uniqueness

  • Consistency

  • Validity

c. Automated Quality Checks

  • ETL validation tests

  • Schema validation tests

  • API response validation

  • Front-end UI validation

d. Governance & Monitoring

  • Versioning of schemas

  • Data freshness checks

  • Alerting for data drifts and anomalies

  1. Automated Testing in Data Quality Workflows

Automated testing ensures that data integrity rules and validation logic work consistently across environments.

5.1 What Should Be Automated?

a. Data-Related Tests

  • Schema validation (JSON schema, DB schema)

  • Referential integrity (FK relationships)

  • Data type consistency checks

  • Business rule validation

  • Data transformation correctness

  • API response accuracy

b. System-Related Tests

  • Form and input validation in UIs

  • Data flows across systems (end-to-end)

  • Regression checks after deployments

6. Using Playwright for Data Validation & Quality Assurance in OpenMRS

Playwright is a powerful end-to-end automation tool ideal for verifying data accuracy across the OpenMRS UI, REST API, and clinical workflows.

6.1 Why Playwright for OpenMRS?

  • Works seamlessly with OpenMRS 3.x (O3) frontend

  • Supports both UI workflows and REST API calls

  • Validates patient registration, program enrollments, encounters, and observations

  • Auto-waits for network calls used heavily in O3 React components

  • Multi-browser support for ensuring OpenMRS runs well across environments

  • Easy to integrate into OpenMRS CI/CD pipelines or QA workflows

6.2 Common Data-Focused Use Cases in OpenMRS with Playwright

1. UI Data Validation (OpenMRS 3.x Patient Registration & Forms)

Ensures OpenMRS UI components display correct and validated data.

Examples

Required field validation

await page.click('button:has-text("Submit")'); await expect(page.locator('[name="givenName"] ~ .error'))   .toHaveText("Given name is required"); Phone number or identifier format validation await page.fill('[name="phoneNumber"]', '123'); await page.click('button:has-text("Submit")'); await expect(page.locator('.error-message'))   .toHaveText("Phone number must be at least 10 digits");

Reference data validation

await expect(page.locator('#location-select')).toContainText("Outpatient Clinic");

2. API Schema & Response Validation (OpenMRS REST API)

Validate patient response

const response = await request.get('/ws/rest/v1/patient/uuid-of-test-patient'); expect(response.status()).toBe(200); const data = await response.json(); expect(data).toMatchObject({   uuid: expect.any(String),   person: {     gender: expect.stringMatching(/M|F/),     birthdate: expect.any(String)   } });

Validate encounter & obs datatypes

const response = await request.get('/ws/rest/v1/encounter/enc-uuid');

const encounter = await response.json();

const weightObs = encounter.obs.find(o => o.concept.uuid === WEIGHT_UUID);

expect(typeof weightObs.value).toBe("number");

3. Cross-System Data Integrity (UI → REST API)

Verifies that data submitted through the OpenMRS UI matches what is stored server-side.

Example:

await page.fill('[name="givenName"]', 'John'); await page.fill('[name="familyName"]', 'Doe'); await page.click('button:has-text("Save")'); const apiRes = await request.get('/ws/rest/v1/patient?q=John Doe'); const patient = await apiRes.json(); expect(patient.results[0].person.display).toBe("John Doe");

 

4. Indirect Database Validation

Playwright does not connect directly to the OpenMRS database but can verify backend behavior by:

  • Creating encounters from the UI

  • Fetching them via REST API

  • Ensuring obs, orders, and metadata match expected values

  • Confirming concepts and datatypes aren’t corrupted

7. Integrating Automated Data Quality Checks into CI/CD

Best Practices

  • Run schema validation tests on every pull request

  • Automate nightly ETL quality tests

  • Trigger Playwright end-to-end tests on every deploy

  • Use containerized test environments

  • Integrate test reporting (Allure, Playwright HTML Reporter)

  • Fail builds on data anomaly detection

Example CI/CD Flow

  1. Developer pushes changes

  2. Static validation (lint, schema validation)

  3. API & business rule automated tests

  4. Playwright end-to-end tests (UI + API)

  5. DQA checks on ETL/warehouse

  6. Deploy to staging

  7. Deploy to production

8. Best Practices and Recommendations

For Data Validation

  • Validate at multiple layers (UI → API → DB)

  • Use centralized validation rules where possible

  • Version your schemas

For Automated Testing

  • Keep tests deterministic

  • Mock external systems only when necessary

  • Use data factories for test data

  • Prioritize E2E tests for mission-critical flows

For Data Quality Assurance

  • Monitor data in production

  • Track key data quality KPIs

  • Build feedback loops with engineering & analytics teams

9. Conclusion

Combining strong data integrity checks, data validation systems, and robust automated testing techniques—especially using Playwright—creates a reliable, scalable data quality framework.

This helps ensure:

  • Clean and trustworthy data

  • Reduced manual QA overhead

  • Faster development cycles

  • Improved system stability

  • Better business analytics and decision-making