Healthcare AI development company
We design, build and integrate healthcare AI for providers, payers and health tech companies: ambient clinical documentation, coding assistance, prior authorization, imaging workflow and predictive risk models. Each one reads from and writes back to the EHR over FHIR or HL7, is evaluated on your own de-identified data before go-live, and ships with the monitoring, audit trail and transparency documentation your governance committee will ask for.
Discuss your healthcare AI use caseFor CMIOs, CTOs, digital health product leaders and payer operations teams taking AI from pilot to production. Updated September 26, 2026.
FHIR R4, HL7 v2, DICOMweb and document feeds, scoped to the minimum data the use case needs
LLM drafting, extraction or a trained risk model, grounded in the patient record
Clinician or reviewer accepts, edits or rejects, with sources shown for every suggestion
Signed output returned to the EHR; accuracy, drift and override rates tracked
Illustrative workflow · scope tailored to your environment
The short answer
Healthcare AI solutions are software systems that use machine learning or large language models to do a defined piece of clinical or operational work: drafting a visit note, suggesting ICD-10-CM and CPT codes, assembling a prior authorization request, triaging an imaging worklist or flagging patients at risk of readmission or sepsis. The model is one component. A production solution also needs EHR integration, a human review step, an evaluation on local data, monitoring for drift and the transparency documentation that ONC, FDA and state rules now expect. That full system is what we build.
Read our guide to testing LLM outputs in healthcareStart with the real problem
A model that summarizes a synthetic record in a demo is easy to build. Production is harder: the EHR connection needs vendor approval, notes must match each specialty's format, every output needs a reviewer and an audit trail, performance has to be measured on your patients rather than a benchmark, and the governance committee wants to know what happens when the model is wrong. We scope those requirements in the first weeks, so the pilot is built to go live.
What we build
Each solution type carries a different data, integration and regulatory load. We name it before the estimate.
Visit and session audio turned into a draft note (SOAP, H&P, progress note or nursing assessment) that the clinician reviews, edits and signs inside the EHR.
Clinical documentation AISuggested ICD-10-CM, CPT, HCPCS and HCC codes with the supporting sentence highlighted, plus queries when documentation does not support a code.
Medical coding AI guideAI that reads the chart and the payer policy, fills the documentation template and assembles the request, with a person approving before submission.
Prior authorization automationWorklist prioritization, report drafting and follow-up tracking around FDA-cleared imaging algorithms and your PACS and RIS.
Diagnostic imaging AIReadmission, deterioration, no-show, sepsis and care-gap models trained and validated on your population, surfaced where clinicians already work.
Healthcare data engineeringAnswers grounded only in the patient's record and approved content, with escalation to the care team and a clear AI disclosure.
AI agents for healthcare workflowsYour starting point
A model works in a notebook or a vendor demo, but it is not in the clinical workflow. We add the EHR integration, review step, evaluation and monitoring that production needs.
Your customers are asking for ambient notes, coding help or summaries. We build the feature into your product with the transparency documentation hospital buyers request in security and AI reviews.
Prior authorization, coding, document intake or denials consume staff time. We automate the repetitive steps, keep a reviewer on every decision that affects a patient or a claim, and measure the result.
The Engineering Engagement
Workstreams are selected around your priorities. Each comes with an output your team can inspect, test and own.
Rank candidate use cases by value, data readiness and risk. Classify each against the FDA device software and CDS criteria, HTI-1 decision support rules and state AI laws before any build.
Map the minimum data each use case needs from FHIR R4, HL7 v2, claims, DICOM and documents. Build de-identified evaluation sets under HIPAA Safe Harbor or expert determination.
Choose between hosted LLMs under a BAA, open-weight models deployed in your cloud and classical ML. Ground generation in the patient record and approved sources with retrieval, not model memory.
Launch inside Epic, Oracle Health or athenahealth with SMART on FHIR, surface suggestions through CDS Hooks, and write signed output back as FHIR resources or HL7 messages.
Measure accuracy, omission and hallucination rates, calibration and subgroup performance on your data, with clinician review of a stratified sample. Set the thresholds that gate release.
Build reviewer tooling that shows sources, captures edits and overrides, and logs who accepted what. Produce the source-attribute and model documentation hospitals ask for under HTI-1 and Section 1557.
Track input drift, output quality, override rates and latency in production, with alerts, rollback and a schedule for re-evaluation when the EHR, model or population changes.
Compare your options
Buying is often right. Building is right when the workflow, the data or the product is yours. This is how we advise buyers to choose.
| Custom healthcare AI development | Off-the-shelf AI product | EHR vendor AI features | |
|---|---|---|---|
| Best for | Workflows, specialties or products the market serves poorly, or AI you sell | Common use cases such as general ambient scribing | Organizations standardized on one EHR that want native features |
| Workflow fit | Built to your templates, roles and review steps | Configured within the product's options | Tied to the EHR vendor's roadmap and release schedule |
| Data and models | Your choice of model, hosting and data residency | Vendor's model and hosting under their BAA | Vendor's models inside the EHR environment |
| Evaluation on your data | Built in and repeatable | Depends on the vendor's reporting | Depends on the vendor's reporting |
| Multi-EHR support | Designed for every EHR you connect to | Varies by product | One EHR |
| Cost shape | Build cost, then run and inference cost | Per-user or per-encounter subscription | License add-on to the EHR contract |
Many teams combine them: buy a general-purpose scribe, then build the specialty templates, coding layer or analytics around it.
Expertise is in the decisions
FDA's revised Clinical Decision Support guidance of 6 January 2026 keeps most clinician-facing CDS outside device regulation when a clinician can independently review the basis for a recommendation. Software that analyzes images or signals, or that directs treatment without that review, is a device function. We design the output, the explanation and the intended use statement to fit the path you choose.
Certified health IT must support source attributes for predictive decision support interventions under 45 CFR 170.315(b)(11), and 45 CFR 92.210 requires covered entities to identify and mitigate discrimination risk in patient care decision support tools. HTI-5, proposed in December 2025, would narrow the ONC requirement, yet hospital AI committees keep asking for the same facts. We produce them as part of every build.
California AB 3030 requires a disclaimer and a route to a human when generative AI writes patient clinical communications that a licensed provider has not reviewed. Texas, Utah and Illinois have added disclosure and use limits, and Colorado has rewritten its AI act for 2027. We build disclosure, provider review and logging as configurable controls per state.
Hosted models from major cloud providers are available under a BAA, and open-weight models can run inside your own cloud account. The choice sets data residency, latency, cost per note and how quickly you can switch models later, so we make it explicit and reversible.
Our solutions draft, suggest, prioritize and predict. A clinician or authorized reviewer accepts every output that enters the record or affects a patient, and the audit log shows who did.
When a function is a medical device, we build to the design controls, documentation and validation evidence it needs and support your regulatory lead through the FDA pathway.
Prompts, templates, thresholds and escalation rules are specified and approved by your clinicians. We implement, test and version what they approve.
Timeline
Data access and EHR approvals set the pace more than model work. Typical ranges after discovery:
Two to four weeks: use case ranking, data inventory, regulatory classification and success metrics.
Four to eight weeks: model selection, grounding, reviewer UI and a first evaluation against your thresholds.
Eight to sixteen weeks: EHR integration, security review, clinician pilot in one department and monitoring.
Ongoing: more departments or customers, re-evaluation on schedule and model updates.
Delivered work
Client names are withheld; each is a real engagement running in production.
Session-to-note drafting for progress notes, assessments and plans, with every section linked to its source and accepted or edited by a licensed clinician before signing.
Nurses speak a bedside assessment and receive structured chart fields to confirm, built for a long-term care operator where charting time drove burnout.
A voice and chat assistant that answers only from the patient's own record and cited guidelines, and hands off to the care team instead of improvising.
The FHIR prior authorization stack that AI-assisted prior auth plugs into, tested with the Inferno test kits and published on GitHub.
From discussion to delivery
Use case, data, workflow and regulatory path agreed in writing.
Working prototype measured on de-identified local data against agreed thresholds.
EHR integration, reviewer tooling and a monitored pilot with named clinical owners.
Monitoring, re-evaluation and model updates under an agreed support model.
We agree the scope, dependencies, acceptance criteria and commercial model before implementation. Your existing team can stay involved throughout.
Find the right starting point ↗Healthcare AI solutions are software systems that apply machine learning or large language models to a defined clinical or operational task, such as drafting notes, suggesting codes, preparing prior authorization requests, prioritizing imaging worklists or predicting readmission risk. A production solution includes EHR integration, human review, evaluation on local data and monitoring, not only a model.
Our healthcare AI development services cover use case selection and regulatory classification, data access and de-identification, model selection and grounding, EHR integration with SMART on FHIR, CDS Hooks and HL7, evaluation before go-live, reviewer tooling with audit trails, transparency documentation and production monitoring.
The most common production uses are ambient clinical documentation, coding and documentation integrity, prior authorization preparation, patient message drafting, imaging worklist prioritization and predictive models for deterioration, readmission and no-shows. Operational uses such as denial management and document intake are growing fastest because the risk is lower and the benefit is easy to measure.
It depends on the function. Under FDA's revised Clinical Decision Support guidance of 6 January 2026, software that supports a clinician who can independently review the basis for its recommendation is generally not a device. Software that analyzes medical images or physiological signals, or drives treatment without that review, is a device function and needs the appropriate FDA pathway. We classify each use case during discovery.
The ONC HTI-1 rule added the decision support interventions criterion, 45 CFR 170.315(b)(11). Certified health IT must let users see source attributes for predictive decision support, such as intended use, training data, validation and fairness information, and developers must apply risk management practices. HTI-5, proposed in December 2025, would scale this back; until a final rule changes it, the criterion applies to certified EHRs and hospitals still expect the same information from AI vendors.
California AB 3030 requires a disclaimer and instructions for reaching a human when generative AI writes patient clinical communications without provider review. California SB 1120 keeps medical necessity decisions in utilization review with licensed clinicians. Texas, Utah and Illinois have AI disclosure or use limits, and Colorado has replaced its 2024 AI act with a new framework effective in 2027. We build disclosure and review controls that can be configured per state.
We build a de-identified test set from your own data, measure accuracy, omission and hallucination rates, calibration and performance across age, sex, race, ethnicity, language and payer groups, and have clinicians review a stratified sample. Release is gated on thresholds agreed with your clinical owners, and the same test set is rerun whenever the model, prompt or EHR changes.
Yes. We launch AI applications inside the EHR with SMART on FHIR, surface suggestions through CDS Hooks where the EHR supports them, read data through FHIR R4 and HL7 v2 feeds, and write signed output back as FHIR resources or HL7 messages. Vendor app review and customer approvals are planned from the first week.
Using an LLM with protected health information is possible when the model is provided under a Business Associate Agreement or runs in infrastructure you control, access is limited to the minimum necessary data, prompts and outputs are logged and retained under your policies, and the use is covered by your risk analysis. Consumer chat products without a BAA must not receive PHI. We sign BAAs and design the full data path.
We ground generation in the patient record and approved sources through retrieval, require citations for every statement, block outputs that cite nothing, constrain formats with templates and structured output, and route low-confidence or high-risk outputs to a reviewer. Hallucination and omission rates are measured in evaluation and monitored in production.
The main cost drivers are the number of EHRs and data sources, the availability of de-identified evaluation data, whether the function is a medical device, the depth of reviewer tooling, the hosting model, inference volume and the monitoring and re-validation you need after go-live. Discovery produces an estimate with these assumptions written down.
A healthcare AI solution can be a single model inside one step, such as a coding suggestion or a risk score. An AI agent plans and carries out a multi-step task, such as gathering documents and submitting a prior authorization, using tools under defined permissions. We build both; our AI agents for healthcare workflows are described on a separate page.
A useful first conversation
Tell us what exists today, who uses it and where the workflow breaks. We’ll discuss the scope, access dependencies and the next practical step.
A product overview and a de-identified workflow are enough to start. No patient records or credentials are needed.
These are independent reference sources, not endorsements. Applicability, platform access and current requirements are confirmed for your project.
From our YouTube channel
Explore the workflows and decisions behind this service. Demonstrations and recorded discussions have the scope described in each video.
A recorded discussion of AI deployment in regulated environments.
