Quality Engineering for Digital Health
A defect in your product isn't a bad user experience.
It's a clinical event, and clinical events get reported.
So you test carefully, which means slowly. Meanwhile your enterprise customers are asking for a release cadence you can't hit and evidence you don't have time to produce.
Segment
- Digital health / HealthTech
- Funded scaleups
The data problem nobody solves properly
How do you test healthcare software without patient data?
- Your test data
You can't test with real patient data. So your test data is a handful of tidy fictional patients — and tidy fictional patients don't have three comorbidities, a fifteen-year record with two name changes, and a medication list that contradicts itself.
Which means the defects that matter only appear in production, where the data is real and the stakes are highest.
- Your vendor
The second half of the problem is your vendor. Anyone who touches PHI, including in a test environment, becomes a Business Associate — and now your counsel needs a signed BAA, your security team needs a questionnaire returned, and a six-week engagement has a four-week legal preamble.
We solve both by staying outside your PHI scope by default.
Synthetic patient data shaped to real clinical distributions — the comorbidities, the messy longitudinal records, the demographic edge cases your model handles badly. Generated, not sampled.
The default engagement
- No production access
- No PHI
- No BAA
That's not a compliance workaround. It's better testing, because synthetic data can be built to contain the cases your real data set happens not to include.
What actually breaks
What makes testing digital health products difficult?
Clinical logic is undertested. Dosage calculations, risk scores, and care pathway branching get exercised on typical patients. The atypical ones are found by clinicians.
You integrate with systems you don't control. HL7 and FHIR interfaces against EHRs that each interpret the standard slightly differently, with no test instance you can break.
Requirements are ambiguous and the ambiguity is dangerous. In most software an unclear requirement produces a rework ticket. In clinical software it produces a defect that behaves correctly according to the spec and incorrectly according to medicine.
Traceability is manual. You need requirement-to-test-to-result evidence for enterprise customers and eventually for regulators, and someone is maintaining it in a spreadsheet.
Consent and access paths get less attention than clinical features. Role-based access, consent withdrawal, and audit logging are where breaches actually originate.
Where HIPAA sits in this
HIPAA today is stable and well understood: safeguards around ePHI, access control, audit logging, breach notification. What matters for testing is scope — who touches PHI, and under what agreement.
A significant Security Rule overhaul has been proposed — encryption and MFA becoming required rather than addressable, 72-hour incident reporting, annual technical testing. As of now it is not final; the timeline has slipped and finalisation is not expected imminently. Once it does land, entities get roughly 240 days.
We're not going to tell you that's an emergency. It isn't yet. It does mean that a testing programme built to produce evidence continuously will absorb the change without a scramble, and one built to produce evidence on demand won't.
What we do
- Synthetic clinical data.Patient records built to real distributions, including the cases your production data doesn't contain. Provisioned per test run, torn down after.
- Interface testing against unstable partners.Contract tests for HL7 and FHIR integrations so an EHR's interpretation change surfaces in your pipeline rather than at a customer site.
- Requirement analysis before test design.Ambiguity found at the requirement stage costs a conversation. Found after release, it costs an incident report. This is where most of our value lands in clinical software.
- Traceability generated automatically.Requirement to test to result, produced by the pipeline. The evidence pack for an enterprise customer's due diligence becomes a report you run.
Delivered through AI-Driven Validation, Test Architecture, or Intelligent Quality Operations depending on what the constraint turns out to be.
Proof
AI Requirement Analysis
A WeAreQA engagement
A healthcare solutions provider.
Business requirements needed extensive manual review before anything could be tested, and ambiguity was reaching implementation. We put requirement analysis on models that flag ambiguity, contradiction, and missing acceptance criteria before test design starts.
- 50%Faster requirement analysis
- 45%Fewer requirement defects
- 100%Traceability coverage
The middle number is the clinical one. A requirement defect in health software produces a system that works exactly as specified and wrongly as medicine. Catching 45% more of those before code is written is worth more here than in any other sector we work in.
The business case
For the conversation with your CEO or board.
Cost of the current state
- Clinical riskA defect reaching patients is reportable, investigable, and public
- Sales frictionEnterprise health customers run quality and security due diligence. Weak evidence slows deals or loses them
- Release dragCareful-therefore-slow means competitors ship what your customers asked you for
- Regulatory readinessContinuous evidence absorbs rule changes. On-demand evidence turns each one into a project
The second row is the one that shows up in the revenue number rather than the risk register, and it's usually the one that gets this funded.
Where we're not the right fit
Providers, payers, and hospital IT. Different buyer, different procurement, different problem. We work with digital health companies.
Medical device software and SaMD. FDA-regulated device software under IEC 62304 needs a specialist regulatory partner.
Engagements that need us inside your PHI scope. We work with synthetic data and no production access by default, which is what lets us start in days. If yours genuinely requires a Business Associate, that is a different conversation and a slower start — not a no.
That default is deliberate. It's what lets us start in days rather than after a legal review.