Hire developers who design for PHI, not around it
Our engineers build patient data handling into the schema, the audit trail into the feature list, and the human sign-off into the workflow. You interview every one before they touch your repo.
What you are getting
4 things that decide this
- 01Senior engineers who have shipped a HIPAA-aligned platform end to end, not developers reading the requirement for the first time on your project.
- 02PHI de-identification and field-level access control designed into the data model from day one, not patched on before an audit.
- 03A human stays in the loop wherever a clinical or coverage decision is made, and the interface makes that boundary visible.
- 04You interview every engineer before they start, and every line they write is yours from the first commit.
What this role actually does here
Most of the job is not the AI model. It is deciding what a nurse sees, what an insurer sees, and what gets logged either way.
On TrialTriage, our engineers built a platform that ranks oncology trials against NCCN guidelines and drug data, with a nurse reviewing and finalizing every match. Patient records are de-identified on entry, using age ranges, ZIP prefixes and pseudo-patient IDs instead of raw identifiers. Every action a user takes, across 23 tracked action types, writes to an audit trail. Login runs through TOTP multi-factor authentication, and access is scoped by role across four user types: admins, nurses, patients and insurers.
That is the pattern a healthcare build needs repeated. De-identify before storage. Decide who can see what before you write the query. Put the accountable person back in the loop before the system acts.
What they take off your roadmap
PHI masking in the schema
Age ranges instead of birthdates, ZIP prefixes instead of addresses, pseudo-patient IDs instead of names. Decided before the first migration, not bolted on after a security review.
An audit trail that is a feature
Every access and every change logged with who, what and when, built as a product requirement rather than a log file nobody reads until an incident.
Human-in-the-loop, coded as a boundary
The AI ranks or drafts; a clinician or reviewer signs off. The interface makes that step impossible to skip, not just a policy in a document.
Role-based access across user types
Patients, clinicians, admins and payers each see a different slice of the same record. Access is scoped per role, not filtered in the UI after the query runs.
- EntryPatient data captured
- De-identifyAge range, ZIP prefix, pseudo-ID
- ModelRanks against guidelines and data
- Human reviewClinician approves or adjusts
- Audit logAction recorded, role-scoped
The masking happens before storage. Everything downstream, including the model, only ever sees the de-identified version.
A HIPAA-aligned platform, built and running
How hiring works
- 01
Tell us what you are building
A free call about the workflow, the data you hold, and where a clinician or reviewer needs to stay in the loop.
- 02
Meet the engineers
We shortlist people who have designed PHI handling and audit trails into a live product, and you interview them directly.
- 03
They embed
Your repo, your standups, your ticket system. One engineer is named as accountable for the parts touching patient data.
- 04
They hand over
The data model, the access rules and the audit trail, documented, plus someone on your team trained to run them. The first 2 months of support and maintenance are free, with every build.
Stack
Backend and data
AI and integration
Security and access
Tell us what your platform needs to prove
Bring the workflow and the data you are responsible for. The scoping call is free, and you will leave it knowing where the human sign-off belongs.
01Do your developers hold a HIPAA certification?
HIPAA has no certification body for individual developers to hold. What matters is whether the engineer has designed to its requirements before. That means de-identification at the point of entry, access scoped by role, and an audit trail that records who touched what. Our engineers have done that on a live platform, and we can show you the build.
02How is this different from hiring a generalist through a job board?
A generalist can write the CRUD and still miss where PHI needs masking before it reaches a log or a model. Our engineers have already made those calls on a shipped product. If the fit is wrong, we replace them rather than leaving you to manage a hiring mistake alone.
03How much overlap do we get with our working hours?
A daily overlap fixed before anyone starts, so standups and reviews happen while both sides are awake. Healthcare data decisions need a conversation, not a ticket answered a day later. We agree the window during scoping.
04Who signs off on a clinical or coverage decision, the AI or the engineer?
Neither. That decision stays with the clinician or reviewer on your team. Our engineers build the interface so the model ranks or drafts, and a named person approves before anything reaches a patient or a payer.
05What drives the cost of a build like this?
How many data sources you are merging and how many roles need different views of the same record, more than anything else. Scoping calls are free. Where we need to review an existing codebase before quoting, a paid two-week diagnostic produces a fixed price instead of a guess.
Read next
- the healthcare software constraints we build to →The vertical, and what a compliant build actually requires.
- how we build HIPAA-compliant AI →De-identification, audit trails and human review, explained.
- HIPAA-compliant development in depth →The regulation, mapped to engineering decisions.
- what a BAA does not cover →The gap a signed agreement leaves for engineering to close.
