HIPAA-ready by design, explainable by default.
Clinical, patient-engagement, and operational systems with compliance engineered in from the first commit — not patched on at audit time.
Encryption, least-privilege access, and audit-ready logs are part of the architecture. AI features are grounded and explainable, never a black box over a clinical decision. EHR, FHIR, and HL7 integration where you need it.
Because the cost of a defect is a patient, an audit finding, or a breach — and the system has to be safe, interoperable, and explainable all at once.
The pressure is real: clinicians already spend more time in the record than with patients. So healthcare software lives under three constraints that consumer software never sees at once:
HIPAA-compliant — protected health information handled under encryption, least-privilege access, and a complete audit trail.
Interoperable — speaking FHIR and HL7 to EHRs and labs you don’t control.
Explainable and accountable — where it uses AI, a clinician, a compliance officer, or a regulator has to be able to see why the system produced an output, and a person stays in the loop on anything that touches care.
Most healthcare software development fails on one of these three. Getting all three right, on a fixed timeline, is the job.
This isn’t one product. It’s a set of systems that earn their place in specific clinical and operational workflows. For each: what it does, the benefit it produces, and an illustrative example.
Charting support, clinical decision support, order entry, and care-coordination workflows that fit how clinicians actually work — with every AI suggestion explainable and a clinician in the loop. Benefit — less time in the record, more time on care, without losing the audit trail.
Example: a clinician finishing a visit gets a draft note assembled from the encounter to review and sign — instead of typing it after hours — so documentation stops eating the evening, while the signed note and its edit history stay fully logged.
Scheduling, intake, reminders, secure messaging, and patient portals that reduce no-shows and front-desk load. Benefit — fewer no-shows and lower administrative friction for patients and staff.
Example: a patient completes intake forms and confirms an appointment from their phone the night before, so the front desk isn’t re-keying paperwork at check-in and the slot doesn’t go empty.
Eligibility checks, prior-authorization workflows, coding support, claims, and billing — the administrative machinery that consumes a large share of every healthcare dollar. Benefit — lower cost to collect and fewer denials, with humans reviewing the edge cases.
Example: a claim is checked for the common denial triggers and routed for correction before submission instead of bouncing back from the payer weeks later — shortening the time to payment.
Connects new applications to the EHRs, labs, pharmacies, and devices your organization already runs, through the interoperability standards healthcare requires. Benefit — one source of truth instead of duplicate data entry and reconciliation.
Example: a lab result flows from the EHR into a care-management app automatically, so a nurse isn’t copying values between two screens and risking a transcription error.
Extracts and structures information from referrals, faxes, intake packets, and clinical documents — with a person verifying anything that drives a decision. Benefit — faster intake and less manual data entry, with a verification step on what matters.
Example: a multi-page referral fax is parsed into structured fields for staff to confirm in seconds rather than re-typed line by line.
Re-platforms aging healthcare applications — paying down technical debt without taking the system offline. Benefit — a maintainable, compliant, integration-ready system instead of a brittle legacy one. See our application modernization work.
Example: a decade-old scheduling system is moved to a modern, FHIR-capable stack incrementally, so staff never lose access during the transition.
The scope below is what separates software that passes a security review from software that becomes a liability.
We map the clinical or operational workflow, the data it touches, and the regulatory surface (HIPAA, and where relevant the standards your payers and partners impose) before a line of code — with an honest “this shouldn’t be software yet” call when that’s the right answer.
We build the application itself — clinical and care-team tools, patient-facing access software, or revenue-cycle and operational systems — to fit your workflows rather than forcing your workflows onto a template.
Encryption in transit and at rest, least-privilege and role-based access, complete audit logging, and documented data-handling boundaries — engineered into the architecture from the first commit, so compliance is a property of the system, not a remediation project.
We connect your software to the EHRs, labs, pharmacies, and devices it must exchange data with, through the interoperability standards healthcare requires — so data flows once, correctly, instead of being re-keyed.
Where AI is genuinely the right tool, outputs are grounded and explainable, and human-in-the-loop review is designed into anything that touches a clinical or financial decision. The system shows its reasoning and escalates instead of guessing — see our broader AI development services.
Before go-live, the system is tested against the failure cases that must never ship; after, it runs under a staged rollout with monitoring — and your team is trained to own it.
What you get when you hire us — all assigned to you
The same delivery discipline behind all our work, tuned for a regulated, safety-critical environment — one accountable lead, fixed scope, no handoffs.
Scope the workflow, the data, the integrations, and the compliance surface.
Output: a ranked plan, success metrics & written requirements
Architect security, access control, audit logging, and (where used) the explainability and human-review model up front.
Output: a HIPAA-ready architecture & a validation plan
Develop in your own cloud tenant, inside your access controls, with integration and logging in place from the start.
Output: a working system behind your security boundary
Validate against the must-never-ship failure cases, ship behind a staged rollout, monitor, and train your team to operate it.
Output: a production system with audit-ready logs
Most engagements reach steady state in 4–8 weeks, with full work-for-hire IP assignment signed at kickoff and payment tied to the ROI we agree on.
Here is the honest version — the delivery discipline a regulated, multi-location operation demands, which is the discipline healthcare software lives or dies on.
We are a Stanford-rooted Responsible AI lab, founded in 2011, with offices in Los Angeles and Palo Alto, run by founder Kelvin Tran — 20+ years of production engineering, delivery of multimillion-dollar systems for one of the world’s largest manufacturers, and personal accountability for every engagement we take.
The clinical and efficiency figures on this page are third-party research, attributed as such; we don’t claim a healthcare outcome we haven’t delivered, and we won’t.
What we can show is the delivery discipline a regulated operation demands. For BJ’s Restaurants, a 200+ location enterprise where software runs the business, we restructured how work ships and held the cadence at twice-a-week releases with zero critical defects across four years — not by moving fast and breaking things, but with pre-release quality gates, staged rollout, and continuous production monitoring.
That is the same discipline — evals before launch, change control, audit-able process, monitoring after — that a clinical or revenue-cycle system requires. It’s an operational-reliability analogy, not a clinical claim: different domain, same engineering rigor under defect-intolerant conditions.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. When a workflow shouldn’t be software yet, we’ll tell you — which a vendor paid to build won’t.
Compliance and explainability are the design, not the cleanup. HIPAA-readiness, audit logging, and explainable AI are architected from the first commit. Retrofitting them after a security review is how healthcare projects blow their timeline — and their budget.
Responsible AI is the founding charter. In a domain where an unexplained output can affect a patient, human-in-the-loop review and “show your reasoning” aren’t features we add — they’re how we build.
Founder-led, one accountable lead. No account managers, no offshore handoffs. The person who scopes your system answers for it, and we’ll tell you plainly when something shouldn’t be software yet — which a vendor paid to build won’t.
Built to transfer. The system, the integration layer, the validation artifacts, and the code are assigned to you, and your team is trained to run and extend it when we step back.
What teams want to know before they commission healthcare software development.
Yes — and we build it HIPAA-ready from the first commit rather than patching for compliance before an audit. That means encryption in transit and at rest, least-privilege and role-based access, complete audit logging, and documented data-handling boundaries engineered into the architecture.
We treat HIPAA-readiness as a property of the system, established in the design phase, not a remediation project at the end. Compliance posture is something your security and legal teams verify against the documented data paths, not something they take on faith.
A Business Associate Agreement is a standard part of any engagement where we would handle protected health information, and we expect to execute one.
Conservatively and with a person in the loop. Where AI is the right tool, outputs are grounded in approved data and explainable — the system can show why it produced a result — and anything that touches a clinical or financial decision routes through human-in-the-loop review rather than acting on its own.
We don’t put a black box over a clinical decision. This is also why we’ll tell you when AI is not the right answer for a given workflow.
Yes — through the interoperability standards healthcare uses, including FHIR and HL7, for EHRs, labs, pharmacies, and devices. We scope the specific systems and data flows during discovery so integration is designed in, not discovered late.
The goal is that data flows once and correctly, instead of staff re-keying it between screens and risking transcription error.
You do — completely. The system, the integration layer, the validation artifacts, and the code transfer under full work-for-hire IP assignment signed at kickoff, and your data stays in your own cloud tenant under your controls throughout.
Your team is trained to operate and extend the system; keep us on a reduced retainer or take the keys.
Most engagements reach steady state in 4–8 weeks under a fixed-scope arrangement with one accountable lead and payment tied to the ROI we agree on.
Healthcare scope varies widely with the compliance and integration surface, so we price against the specific build after discovery rather than quoting a generic rate — and we’ll tell you honestly if the realistic timeline is longer than four to eight weeks for your case.
Because one accountable person scopes and answers for the work, the engagement is fixed-scope with payment tied to ROI, and everything is assigned to you — so you’re not locked into a vendor to keep your own system running.
We also frame our proof honestly: the clinical and cost figures here are cited third-party research, and our directly comparable track record is operational reliability under defect-intolerant conditions (BJ’s Restaurants, four years of twice-a-week releases with zero critical defects), not an invented healthcare case study.
Thirty minutes · No pitch deck
Bring the workflow or the system — we’ll tell you honestly what it takes to build it HIPAA-ready, how we’d handle the AI and the integrations, and what it costs to run.