QA and software testing leaders across healthcare providers, insurers and adjacent firms are facing mounting pressure to modernise delivery pipelines without compromising compliance, governance or patient and policyholder trust.
As digital transformation accelerates, from online claims and telehealth platforms to AI-driven underwriting and diagnostics, the underlying test and DevOps foundations are being pushed to their limits.
A new white paper from OpenText, based on discussions at the QA Financial Healthcare & Insurance Forum in London, sets out “7 QA challenges you can’t ignore, and how to solve them”, outlining structural weaknesses across the software development lifecycle (SDLC) that continue to slow delivery, fragment visibility and complicate audit readiness in highly regulated environments.
At the top of the list is inconsistency. Many organisations “struggle with inconsistent DevOps approaches across multiple teams, leading to duplicated work, siloed operations, and missed opportunities for automation due to the lack of standardisation.”
For healthcare and insurance firms operating across multiple product lines, geographies and legacy estates, this lack of uniformity can directly undermine quality control and resilience.
Complexity is compounding the issue. The paper points to “highly complex environments with shadow IT, multiple pipelines, and legacy systems” that “create interoperability issues and make simplification and integration extremely challenging.”
In sectors where core policy administration or electronic health record systems often sit alongside newer digital front ends, fragmented tooling can restrict end-to-end traceability and make defect analysis slower and more error-prone.
Governance and visibility gaps remain persistent pain points. According to the paper, “siloed data and disconnected tools limit end-to-end traceability, slow risk identification, and complicate audit and compliance readiness.”
For QA leaders navigating strict data protection, clinical safety and financial conduct rules, incomplete traceability across planning, testing and release cycles increases operational and reputational exposure.
Test management
Test management itself is also under strain. Teams “face difficulties completing the testing loop, achieving adequate test coverage, and maintaining stable test suites while trying to shift testing earlier in the development cycle.”
As organisations push for earlier validation and faster, more frequent releases, maintaining resilient automation frameworks and meaningful coverage metrics has become more complex.
Automation adoption is proving uneven. The paper notes that “automation adoption is slow due to resource constraints, difficulties converting manual tests, and challenges in describing tests in natural language for business pipelines.”
In healthcare and insurance, where compliance-heavy manual test cases are common, scaling automation while preserving audit trails remains a significant hurdle.
Pipeline performance is another concern. “Outdated information and structural inefficiencies result in slow pipelines, requiring optimization and modernization to accelerate delivery,” the report states.
For digital health platforms and insurers investing in customer-facing innovation, slow feedback loops and bottlenecks can directly affect time to market and service quality.
Finally, AI is emerging as both opportunity and risk.
The white paper highlights “growing interest in using AI for validation and competitive differentiation but concerns about risk assessment and stakeholder communication hinder effective implementation.”
While generative AI promises smarter prioritisation, defect detection and release readiness insights, healthcare and insurance firms must balance innovation with governance, explainability and risk management expectations.
For QA and software testing teams in these sectors, the message is clear: without consistent DevOps practices, end-to-end lifecycle visibility and resilient automation foundations, digital transformation efforts risk being constrained by structural weaknesses in testing and governance.
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