Specialized QA automation and software testing for healthcare SaaS platforms, EHR systems, and clinical software -- grounded in 7+ years of hands-on healthcare technology experience, including HL7/FHIR interoperability and EHR data-exchange testing.
Discuss your automation challenges, quality goals, and software reliability needs.
The patterns that come up again and again on healthcare software teams, where a testing gap carries more risk than most.
A small change in one module can ripple into patient management, clinical workflows, reporting, or admin functions without dedicated regression coverage.
Integration points between EHR systems, HL7 messaging, and FHIR APIs are exactly where interoperability issues surface -- and where they're easiest to miss without targeted testing.
Every release ships feature work, regulatory updates, and integrations into a system where manual testing alone can't keep pace.
Patient and clinical data moving across modules and integrations needs validation that goes beyond the UI -- business rules, access control, and cross-module consistency all matter.
Concrete, hands-on work scoped to the risks that matter most in a healthcare software platform.
A testing approach and automation framework built around your platform's clinical workflows and release cadence, not a generic template.
Coverage for interconnected modules -- patient management, clinical workflows, reporting, and care coordination -- where a regression in one area can affect several others.
Hands-on validation of HL7 messaging and FHIR-based data exchange between your platform and connected healthcare systems.
Risk-based regression and release validation -- build verification, smoke and regression testing, and go/no-go checks -- built for continuous delivery.
Automated and manual coverage across the surfaces where healthcare software risk actually concentrates.
Functional and regression coverage for electronic health record workflows, from data entry to reporting.
End-to-end coverage across the interconnected workflows care teams and administrators actually use.
Request, response, authorization, and data-consistency validation across your platform's API surface.
Validation of HL7 messaging and FHIR resource exchange between your platform and connected healthcare systems.
Checks that patient and clinical data stays accurate and consistent as it moves across modules and integrations.
Build verification, smoke and regression testing, and go/no-go checks before every deployment, including hotfixes.
The tools and languages this engagement is typically built on -- adapted to what your stack already uses.
Playwright
Selenium
JavaScript
TypeScript
REST APIs
Postman
GitHub Actions
Jenkins
Azure DevOps
7+Years Healthcare SaaS Experience
GlobalSupporting US Healthcare OrganizationsHealthcare has been the primary focus of my quality engineering career -- including hands-on work validating HL7 messaging and FHIR-based data exchange, and supporting an already-live, multi-tenant healthcare SaaS platform through roughly 200 production and hotfix releases.
Six years embedded as Quality Engineer and later Principal Software Quality Assurance Engineer on a cloud healthcare platform -- moving the team from feature-level testing to a scalable, risk-based quality strategy built for continuous delivery.
Common questions from healthcare software teams evaluating a QA engagement.
Healthcare software testing is quality assurance and automation work scoped to the specific risks of clinical and healthcare SaaS systems -- interconnected clinical workflows, patient data integrity, EHR and HL7/FHIR integrations, and the reliability expectations that come with software used in care delivery.
Yes. I have hands-on experience validating HL7 messaging and FHIR-based data exchange between healthcare platforms and connected systems, as part of broader interoperability and integration testing work.
Yes. Much of my healthcare experience comes from testing a multi-tenant healthcare SaaS platform's interconnected clinical workflows -- patient management, care coordination, and reporting -- where a change in one module can affect several others.
The same risk-based approach used for any critical integration: validating requests, responses, authorization, and data consistency at the API layer first, then confirming the integration behaves correctly end-to-end across connected systems -- including HL7/FHIR exchanges where relevant.
Yes. This is where most of my healthcare experience comes from -- supporting an already-live, multi-tenant healthcare SaaS platform through roughly 200 production and hotfix releases, including regression, release validation, and defect management for a system already in active clinical use.
Discuss your automation challenges, quality goals, and software reliability needs.
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