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Automated Testing for Reliable Data Analytics in the Healthcare Industry
Ensuring The Highest Data Analytics Accuracy In Healthcare
Healthcare organizations are often required to submit detailed reports to regulatory bodies, covering everything from patient outcomes and clinical trial data to operational metrics and compliance audits. These reports must be accurate, timely, and adhere to strict regulatory standards such as HIPAA, GDPR, or FDA guidelines. BI & Analytics platforms empower healthcare organizations to build dynamic dashboards and reports by integrating data from electronic health records (EHRs), lab systems, and other sources. However, the challenge lies in ensuring that the BI content is error-free, clinically valid, and fully compliant with healthcare regulations.
Below are some common issues that arise in healthcare reporting and analytics, and how these can be addressed at scale through automated testing.
Check for Up-to-Date Refreshed Data
Healthcare organisations handle highly sensitive, tightly regulated, and mission-critical data every day, ranging from patient records and clinical trial results to operational and compliance metrics. For decisions to be accurate, safe, and compliant, this data must always be current and accurate. With Wiiisdom, setting up automated tests and validations to verify the freshness of your data is simple and efficient.
For example, imagine a column named “admission_date” in a hospital admissions dataset. You can configure a test to ensure that the most recent (maximum) date in that column matches expectations, whether it’s a static date or dynamically checking that today’s date is present. This helps ensure that dashboards and reports reflect the latest patient activity or clinical updates.
TIP: Schedule this test to run after your database tables have been refreshed, and when the data is expected to be available in your dataset.
Example of setting up a test to check for the presence of the most up-to-date refreshed data in Power BI.
Detect Any Unexpected Values In Critical Healthcare Reports
In healthcare reporting, the sudden appearance of unexpected values can confuse users, trigger unnecessary escalations to report developers, and lead to serious compliance risks during audits. You can eliminate these surprises by setting up automated tests on the most critical columns in your reports.
For example, in a hospital performance dashboard, you might want to monitor the treatment_type column to ensure only approved categories like inpatient care, outpatient care, or emergency services are present.
In the example below, we’ve configured a test to ensure that no unexpected values appear in the treatment_type column. This helps prevent inaccurate figures from being displayed in reports, which could otherwise mislead clinicians or administrators and compromise regulatory compliance.
A pass example of testing for unexpected values in a specific column of a Power BI semantic model.
A test could also be set up to check for the values, and only the values, that would be expected in a given column.
Easily Identify Business Rule Violations
Healthcare reporting and analytics often include key measures such as patient wait times, medication dosages, readmission rates, or treatment durations. Automated testing can be implemented to ensure that these metrics consistently comply with clinical or operational business rules. For instance, a hospital might have a rule that the average emergency room wait time should not exceed a certain threshold. If it does, it could indicate a breach of service standards or a data integrity issue, either of which would require immediate attention from the report owner.
This can be achieved by setting up a test, like the one below, to verify that values in a specific column (e.g., wait_time_minutes) do not exceed the defined limit. Similar tests can be applied to other critical healthcare KPIs to ensure compliance with internal policies, clinical guidelines, or regulatory standards.
Testing to ensure business rules haven’t been breached on a specific column of a Power BI semantic model.
Ensure Role-Based Data Access with Row-Level Security
Healthcare reporting often relies on row-level security (RLS) to ensure that sensitive data is only visible to authorised users. This is especially important when dealing with patient records, departmental performance, or clinical outcomes. For example, a department head may only have access to data from their unit, while a hospital administrator might see aggregated data across multiple departments or facilities.
Wiiisdom enables you to test and validate that the correct data is being shown to the right users. One powerful feature allows you to run a testing pipeline as a specific user, based on their security group assignment. This ensures that RLS is functioning as intended and that no unauthorised access to sensitive healthcare data occurs.
Option to run a testing pipeline as a given user.
Schedule Tests & Set Up Alerts To Run Automatically
In healthcare environments, data refreshes often occur at specific times. With Wiiisdom, you can schedule automated tests to run shortly after these refreshes. For example, if your datasets update at 6:00 AM, you might schedule tests to run at 6:15 AM.
Schedule pipeline execution feature.
Imagine the advantage of being alerted to data issues before your clinical or administrative teams even start their day. With Wiiisdom, alerts can be delivered not only via email but also through collaboration tools like Microsoft Teams, Slack, Google Meet, or even integrated directly into ticketing systems like Jira or ServiceNow, helping you resolve issues before they impact care delivery or compliance.
Email and communication channels alerts setup.
Mitigate Risks from Changes to Your Data Sources
In healthcare analytics, switching data sources can introduce unexpected visual or data-level changes in reports. It’s critical to ensure that these changes don’t compromise the accuracy or integrity of dashboards used for patient care, compliance, or operational decisions.
With Wiiisdom, you can take a snapshot of all your reports before the data source change, and another snapshot after, using automated regression testing. This allows you to quickly identify:
- Which reports have changed.
- What the specific changes are (clearly highlighted in red, as shown in the example below).
This approach is also invaluable during system migrations or upgrades. Think of it as an instant quality check and a safeguard against unexpected disruptions in your reporting environment.
Example of a regression test on a Power BI report before and after a data source change.
Ensure Continuous Trusted Data Analytics in the Healthcare industry
Having accurate and reliable healthcare reports is essential for meeting regulatory requirements and ensuring high-quality patient care. With automated testing solutions, you can easily build and run comprehensive tests, document the results, and certify (or decertify) your BI content, empowering your teams to make trusted, data-driven decisions.
If you work in the healthcare industry and would like to learn more about automated testing for your BI & Analytics platform, get in touch with us today.
