Automating electronic medical records (EMR) testing for customized, interfaced EMR systems requires a scalable and platform-agnostic test automation solution. This solution should extend EMR test coverage beyond functionality to keep up with increasingly complex test requirements such as integration with connected devices, system interoperability, and stability under stress conditions. To adhere to rigorous regulations such as the Health Insurance Portability and Accountability Act (HIPAA), the EMR testing solution also must be non-invasive, eliminating the requirement for source code access or potential for exposure of protected health information.
Test automation for EMR systems leverages artificial intelligence (AI) and machine learning to incorporate real user journeys, fixed regression cases, and past test failures to automatically generate new test cases for effective healthcare automation software testing. This approach broadens coverage beyond basic code compliance and functional test, extending coverage to the overall user experience (UX).
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