Claude-Driven Intelligent Test Automation: A Unified API and UI Framework for Enterprise and Healthcare Compliance

Abstract

Enterprise and healthcare software systems demand rigorous, compliance-aware testing across both API and user interface layers. Conventional test automation toolchains treat API and UI testing as separate disciplines, forcing organizations to maintain parallel frameworks, duplicate test logic, and reconcile compliance evidence from disparate sources. This fragmentation imposes compounding overhead in regulated environments governed by HIPAA, HL7 FHIR R4, and FDA 21 CFR Part 11. The cost of maintaining disjoint toolchains is particularly acute in healthcare information technology, where test failures may carry patient safety implications and every audit trail gap constitutes a regulatory risk. This paper introduces ClinQA, a large language model orchestrated framework that unifies API and UI test generation through a single compliance-aware pipeline. ClinQA uses a structured prompt schema with five components, which it sends to Claude, an instruction-following language model. It then routes the generated test artifacts to platform-specific adapter modules for Playwright-based UI execution and consumer-driven contract testing for API validation. A four-layer architecture separates intent capture, test generation, execution, and compliance reporting into independently maintainable components. The framework embeds PHI masking, FHIR conformance evidence capture, and 21 CFR Part 11 audit trail emission at the infrastructure level rather than delegating compliance responsibility to individual test authors. Preliminary observations from 12 scenarios spanning Salesforce CRM workflows and FHIR R4 conformance testing indicate a Cross-Layer Coverage Index of 87%, a Test Authoring Effort Reduction of 68% compared to manual authoring, and a Compliance Coverage Score of 94% against active compliance profiles. ClinQA demonstrates that unified LLM-orchestrated test generation is a feasible approach for regulated enterprise and healthcare environments, and it establishes a foundation for controlled empirical validation at production scale.