Custom software for health systems, digital health startups, and healthcare operators who need HIPAA-aware platforms built by engineers who know the regulations.
We build patient-facing apps, clinical workflow tools, care coordination platforms, and digital therapeutics that your compliance team can approve.
Patient portals, telehealth apps, and mobile health platforms built for engagement and compliance
Clinical workflow software that fits how your team actually delivers care -- not a generic EMR module
HIPAA-aware architecture, data handling, and audit trail built in from the start
100+ products shipped including healthcare platforms, patient apps, and clinical data tools
Summary
RaftLabs builds custom healthcare software — patient portals, clinical workflow tools, telehealth platforms, care coordination systems, and digital therapeutics — for health systems, digital health startups, and healthcare operators. All platforms are designed with HIPAA requirements in mind from day one. We deliver working healthcare apps in 12–16 weeks at a fixed cost, with source code ownership and post-launch support included.
Healthcare software fails in two ways. Either the engineering team builds something that doesn't pass the compliance review, costing months of rework. Or the compliance requirements are so heavy that the product never gets usable enough for patients or clinicians to actually adopt it.
We build healthcare software where both things are true from the start -- compliant enough for your legal team, usable enough for the people who have to use it every day.
Patient-facing apps for appointment booking, test results, medication reminders, care plan tracking, and provider messaging. HIPAA-compliant data handling, user authentication, and consent management. Designed for the patient who doesn't want to deal with a complicated portal -- intuitive UX that drives adoption rather than frustration.
Video consultation platforms, asynchronous messaging, and remote monitoring integrations. HIPAA-compliant video infrastructure, end-to-end encrypted messaging, and documented data flows. Built for the workflows your clinicians actually follow -- not a generic video call with a health label on it.
Custom clinical tools that fit how your team delivers care -- care plan management, task assignment, handover workflows, escalation alerts, and clinical documentation. Integration with your EMR for data that lives in the system of record. The workflow layer that sits on top of your EMR and handles what it can't.
Software-based therapeutic interventions -- CBT programs, chronic disease management apps, rehabilitation tracking, and behavioural health platforms. Outcome measurement built in. FDA software classification considered in the design. For digital health companies building evidence-based software products.
Clinical data pipelines, population health analytics, outcome dashboards, and regulatory reporting. Data extracted from EMRs, wearables, and IoT devices, aggregated and made usable for clinical and operational decision-making. HIPAA-compliant data architecture with de-identification where research use requires it.
Automating the administrative and operational workflows that don't need a clinician -- prior authorisations, appointment reminders, document routing, insurance verification, and billing data preparation. Freeing your clinical staff from administrative burden so they can focus on patient care.
Before we talk about what we build, it helps to say what we've seen go wrong. These are the four patterns that come up repeatedly in every healthcare project we take on.
Patient data lives in five different systems that don't talk to each other. The EMR has the clinical record. The billing system has the insurance data. The patient portal has appointment history. The wearable integration sits in a spreadsheet. Nothing is connected, so every care decision requires pulling from multiple sources manually.
The practical impact: providers spend more time clicking between screens than on the patient in front of them. And when a patient shows up in a new care setting, the provider is working with incomplete information.
Clinical documentation takes roughly two hours of administrative work for every hour of patient care in US outpatient settings. That ratio gets worse, not better, as EHR requirements expand.
The result is clinicians spending their evenings on documentation, which is both a burnout driver and a quality issue — rushed notes at the end of a shift are less accurate than notes captured in the moment.
Chronic conditions — hypertension, diabetes, heart failure — require continuous monitoring, not a quarterly check-in. But continuous monitoring at scale means someone needs to watch the data. When patients flag abnormal readings themselves, they often do it too late. When providers try to watch population-level data manually, the signal gets lost in volume.
Remote patient monitoring closes this gap, but only when the system can distinguish noise from a genuine deterioration signal.
HIPAA isn't optional and it can't be bolted on afterward. Neither can the audit trail, the encryption architecture, the BAA coverage for every infrastructure provider, or the access control model. When teams treat compliance as a late-stage checkbox, they either miss requirements and face rework, or they over-correct with such rigid constraints that the product never gets to the usability bar patients need.
HIPAA is not a feature you add at the end. If your development team treats it that way, you will pay for it in rework.
We build the technical safeguards HIPAA requires into the architecture at the start:
HIPAA-aware development means building the technical foundation that makes compliance achievable — not certifying compliance ourselves. Only your compliance team can do that. We build the technical safeguards; your team verifies they meet your organization's specific requirements.
What we don't do: recommend you skip the legal and compliance review. Every healthcare software deployment needs it. What we do: hand your compliance team an architecture that doesn't require them to redesign it.
Beyond encryption and access control, there are implementation-level decisions that matter for HIPAA that teams without healthcare experience get wrong.
Mobile apps: certificate pinning for API calls, secure local storage that wipes PHI on session end, app store compliance for health-related app categories. Third-party integrations: every API call that could carry PHI is evaluated for BAA coverage before we use the service. Logging: structured audit logs that capture the fields your compliance team needs, not just application errors.
AI in healthcare is easier to talk about than to ship correctly. The constraint that distinguishes production healthcare AI from proof-of-concept work is that the AI has to operate within a HIPAA-compliant data environment — which rules out using general-purpose AI APIs that log or train on user input.
We've deployed AI in healthcare on AWS Bedrock with Claude 3 Sonnet as the model layer. Bedrock provides a HIPAA-eligible environment where PHI can be processed without leaving the AWS boundary and without being used for model training. This is the technical requirement most healthcare AI pilots skip — and then have to rebuild.
Patient risk scoring based on real-time vital sign streams and historical health data. The model categorizes patients into risk tiers and triggers alerts to the right provider based on the risk level — not a generic alert to everyone.
In the RPM system we deployed, the AI layer reduced clinical decision-making time by 20% by automating the triage of incoming patient data. Providers spent time acting on flagged patients rather than manually reviewing all incoming data.
Natural language processing applied to clinical notes, discharge summaries, and prior authorization documents. Extracting structured data from free text, generating note drafts from voice input, and summarizing patient histories from unstructured records.
The practical application: 30+ minutes per clinician per day in documentation time that can be recovered when the system drafts notes from ambient clinical input rather than requiring manual typing after the encounter.
Continuous monitoring of vital sign streams for patterns that deviate from a patient's individual baseline — not just population norms. Hypertension patients with normal readings might still show an anomalous pattern relative to their own history that predicts a deterioration event.
AI abnormality detection on continuous streams is what makes RPM more than a data collection exercise. Without it, you have a data pipeline. With it, you have a clinical tool.
PDC is a remote patient monitoring platform for chronic care management — patients with hypertension, heart failure, and diabetes, monitored through wearable devices including continuous glucose monitors and blood pressure monitors.
Our client had built the core PDC platform and wanted to add AI capabilities to differentiate in a crowded market. The goal was to automate patient data analysis so providers could move from reviewing all incoming data to acting on AI-flagged priority cases.
We built the AI layer on AWS Bedrock using Claude 3 Sonnet, with AWS SQS for reliable and scalable data processing. The architecture kept all PHI within an AWS HIPAA-eligible environment throughout.
What we built:
Results:
Read the AI in remote patient monitoring case study for the full project breakdown.
Healthcare software development requires a compliance scoping step that most software development processes skip. We've standardized our process to include it.
Two to three weeks. We work with your clinical, legal, and compliance team to map:
This step often surfaces requirements that would have caused rework if discovered mid-build.
Engineering sprint cycles with compliance requirements built into the definition of done. Features don't ship until the security review for that feature is complete. EMR integrations are prototyped early — they're the highest-risk dependency and need the most lead time.
For AI components: model selection, data handling architecture, and PHI anonymization before any AI call are scoped before the first line of AI code is written.
A practical example of how this works in practice: on our RPM project, we selected AWS Bedrock before writing the first AI feature. The reason was straightforward — Bedrock supports a HIPAA Business Associate Agreement, meaning PHI processed by the AI model stays within the AWS compliance boundary. Deciding this at the architecture stage cost one day. Discovering it after three months of development would have cost a rebuild.
Security testing runs in two phases. Internal code review and security testing runs during the build — not as a final gate. External penetration testing runs before launch against the production environment.
The documentation package for your compliance team includes: data flow diagrams showing where PHI enters, moves, and rests; infrastructure architecture diagrams with BAA coverage mapped; access control matrix documenting which roles can access which PHI; and audit log format specifications.
Staged deployment with monitoring on all PHI access patterns in production catches anything the security review missed. Production monitoring is permanent — audit log review is an ongoing operational responsibility, not a one-time launch activity.
We work with your compliance team, not around them. That means the handoff at launch is a technical package with documentation, not a black box handed over and forgotten.
Most healthcare app development companies claim HIPAA expertise. Few can demonstrate it with a production system that passed a compliance review and has real patients on it.
When you're evaluating partners, here are the questions worth asking:
Do they understand the difference between HIPAA-aware and HIPAA-certified? No software company can certify your HIPAA compliance — only your compliance team can assess that. A company that claims to "deliver HIPAA compliance" is either misstating what they do or doesn't understand how compliance works. The correct answer is: we build the technical safeguards and provide documentation for your compliance review.
Have they actually integrated with your EMR? Epic FHIR R4 integration looks very different from Cerner Millennium integration looks very different from a legacy HL7 feed. Companies without real EMR integration experience often discover this complexity mid-project. Ask for specifics: which version of FHIR, which endpoints, what the authentication model looks like.
Can they show you a deployed healthcare product with measurable outcomes? Not a case study slide with logos. A real product, with real patients, where you can ask specific questions about what was built and how compliance was handled.
Do they have experience with your regulatory context? A patient portal has different regulatory exposure than a digital therapeutic that might require FDA software as a medical device (SaMD) review. A company that can't distinguish these contexts can't help you navigate them.
We've deployed production healthcare software including an AI-enabled remote patient monitoring system with 150+ active patients, 80+ adopting clinics, and a compliance architecture that passed review. That's the baseline we bring to a new project.
Healthcare software costs more to build than equivalent consumer apps because of the compliance layer — and because the cost of getting it wrong is higher. A consumer app that ships with a bug gets a one-star review. A healthcare app that mishandles PHI gets a HIPAA violation notice.
The compliance overhead — discovery scoping, security reviews, BAA management, documentation packages — adds roughly 20–30% to what you'd pay for equivalent non-healthcare software. This is not overhead you can skip; it's overhead that protects you.
Here are realistic ranges based on what we've shipped:
| App type | Typical range | Timeline |
|---|---|---|
| Patient portal (basic: scheduling, results, messaging) | $30,000–$80,000 | 12–20 weeks |
| Telehealth platform (video, async messaging, scheduling) | $50,000–$120,000 | 16–28 weeks |
| RPM system (wearable integration, provider dashboard) | $80,000–$160,000 | 20–32 weeks |
| RPM with AI layer (risk stratification, alerts, analytics) | $120,000–$220,000 | 24–40 weeks |
| EHR integration layer (FHIR/HL7, bidirectional sync) | $40,000–$100,000 | 12–24 weeks |
| Clinical documentation AI tool | $60,000–$150,000 | 16–28 weeks |
These are ranges, not quotes. The actual number depends on the complexity of your EMR integration, the number of device types, the AI capabilities required, and the regulatory classification of the software (FDA SaMD classification adds scope). We scope fixed-cost delivery after discovery.
This isn't a trends section for its own sake. These are the technical directions that are affecting what we scope and build right now.
AI and ambient clinical intelligence. The biggest shift in healthcare software in the past two years is AI moving from the analytics layer into the clinical encounter itself. Ambient listening tools that draft clinical notes, AI that processes wearable data streams in real time, NLP-based prior authorization automation — these are production deployments, not research projects. The constraint is always the HIPAA-eligible data environment: you can't use general-purpose AI APIs with PHI unless they have a BAA in place.
RPM reimbursement expansion. CMS billing codes for remote patient monitoring (CPT codes 99453, 99454, 99457, 99458) have matured over the past several years, making RPM economically viable for practices that previously had no business model for it. This is driving demand for RPM platforms that handle not just the monitoring but the billing compliance tracking — which is why the PDC system we built includes AI-driven billing adherence prediction.
FHIR API maturity. The ONC's interoperability rules have pushed major EMR vendors to expose FHIR R4 APIs, which has reduced integration complexity significantly compared to HL7 v2 or proprietary API work. FHIR integrations that used to take months now take weeks. This is making EHR integration scopes more predictable and the cost of building on top of EMR data lower.
Wearable device ecosystem expansion. CGMs, blood pressure monitors, and pulse oximeters were the first wave. We're now building integrations for ECG patches, sleep monitors, and activity trackers with clinical-grade accuracy requirements. The challenge is that each device has its own protocol and data format — the normalization layer in an RPM system is where the real engineering work lives.
Frequently asked questions
HIPAA-aware development means building the technical safeguards that HIPAA requires into the software architecture from the start -- not adding them after. This includes data encryption at rest and in transit, role-based access controls with minimum necessary access, audit logging of all access to protected health information, business associate agreement (BAA) coverage for all infrastructure providers, and documented data flows for your compliance review. We don't offer HIPAA compliance certification -- only your compliance team can assess that. We build the technical foundations that make compliance achievable.
Yes. EMR integration is one of the most common requirements for healthcare software. We've integrated with Epic (FHIR R4 APIs), Cerner (Millennium and PowerChart APIs), Athenahealth, and smaller regional EMR systems. The integration approach depends on what the EMR exposes -- FHIR APIs where available, HL7 feeds for older systems, and flat-file exchange as a last resort. We scope the EMR integration during discovery because it's usually the most complex and the highest-risk part of the project.
Mobile health apps require the same compliance foundations as web platforms -- plus considerations specific to mobile, like secure local storage, certificate pinning for API calls, and app store compliance for health-related apps. We build iOS (Swift) and Android (Kotlin) native apps and React Native cross-platform apps depending on your requirements. For apps that handle PHI, we design the data handling architecture so PHI is never stored locally on the device longer than necessary.
We've built patient-facing apps, clinical workflow tools, healthcare data platforms, and digital health products. Our clients include digital health startups, private healthcare operators, and health tech companies building B2B software for the healthcare market. Every healthcare project we take on involves HIPAA-aware architecture as a baseline requirement -- it's not an add-on for us.
HIPAA compliance in app development means building the technical safeguards required under the HIPAA Security Rule into the software from the start. This includes data encryption at rest and in transit, role-based access controls with minimum necessary access, audit logging of all access to protected health information, and business associate agreements with every infrastructure provider that handles PHI. A HIPAA-aware build gives your compliance team the technical foundation they need for their review — it doesn't replace the legal and compliance review.
Healthcare app development typically ranges from $30,000 for a basic patient portal to $220,000+ for an AI-enabled remote patient monitoring system. The compliance layer adds 20–30% to the cost of equivalent non-healthcare software. EHR integration complexity is the largest cost variable — FHIR API integrations are straightforward; legacy HL7 systems require more custom work. We provide fixed-cost delivery after a discovery phase that scopes the full compliance and integration requirements.
Timeline depends on scope and EMR integration complexity. A basic patient portal takes 12–20 weeks. A telehealth platform takes 16–28 weeks. An AI-enabled RPM system — our most complex healthcare project type — takes 24–40 weeks. The discovery and compliance scoping phase (2–3 weeks) runs before the build and significantly reduces rework risk. EMR integrations are the most common cause of timeline delays, which is why we prototype them in the first sprint.
Telehealth connects patients with providers for real-time or asynchronous clinical encounters — video consultations, secure messaging, e-prescriptions. The interaction is provider-initiated and appointment-based.
Remote patient monitoring (RPM) collects patient health data continuously between clinical encounters, from wearable devices, and streams it to a provider dashboard. The monitoring is passive and continuous. Providers review data or respond to AI-generated alerts — they're not scheduling an encounter each time.
Many health systems use both: telehealth for the encounter, RPM to monitor the patient between encounters. They are complementary, not interchangeable.
patient portals, clinical workflow tools, EHR integration, HIPAA-aware
video consultation, scheduling, asynchronous telehealth, EMR integration
mHealth, digital therapeutics, clinical workflow apps
chronic disease management, mental wellness, wearable integration, medication adherence
RPM platforms, device integration, CPT billing
appointment booking, test results, secure messaging, care plans
custom EHR, FHIR R4, clinical documentation, e-prescribing, billing
prior auth automation, clinical documentation, care gap outreach, scheduling
patient triage, symptom checking, appointment booking, HIPAA-aware
prior auth, scheduling, document routing, insurance verification
technical safeguards, encryption, audit logging by design
eligibility verification, claims scrubbing, denial management, payment posting, patient billing
bed management, staff scheduling, pharmacy, lab integration, billing, operations dashboards
SaMD classification, companion apps, telemetry platforms, FHIR integration, FDA cybersecurity
clinical documentation, prior auth automation, predictive analytics, patient triage, AI medical coding
teletherapy, HIPAA EHR, outcome tools, group sessions
SOAP notes, exercise programmes, outcome tracking, telehealth
scheduling, charting, insurance billing, recall, loyalty
appointment booking, medical records, pharmacy, wellness plans
appointment booking, loyalty/membership, HIPAA records, before/after photos
Tell us what you're building, who the users are, and your compliance requirements. We'll tell you how we'd approach it.