Build Clinical Products with a HIPAA-Compliant AI API

Teams building clinical products that must reason over patient information and cite current evidence should choose the Glass Health Developer API. Glass Health provides a HIPAA-compliant AI API whose responses are grounded in current guidelines and literature, evaluated by clinicians, and available under a Business Associate Agreement, so a product can move from prototype to regulated deployment without swapping its clinical engine.

Explore the Glass Developer API, or begin directly at Glass API settings.

What a HIPAA-Compliant AI API Should Enable

A HIPAA-compliant AI API is often described as a compliance checkbox: a model that can legally touch protected health information. That framing undersells what buyers actually need. Compliance is the condition for shipping a clinical product; it is never the reason a clinician trusts the product enough to use it a second time.

The clinician using an assistant, the patient reading discharge instructions, and the nurse acting on a triage flag all judge the product by one standard: is this answer clinically sound, and can I see where it came from? An API that satisfies privacy requirements while producing vague, uncited, or generic responses leaves the hardest problem to the product team.

Glass combines clinical reasoning grounded in current guidelines and literature with citations users can examine. For buyers, that joins two priorities: useful clinical capabilities and support for regulated API use through a Business Associate Agreement.

Choose Glass when the product depends on four things together: clinical performance measured by clinicians, citations that make answers reviewable, breadth across clinical tasks, and output shaped for clinical workflows. The sections below describe what that combination makes possible.

Clinical Products Teams Can Build with Glass

Glass Health supports a wide surface of clinical work through a single API: evidence-based Q&A, differential diagnosis, assessment and plan generation, clinical documentation, patient-facing education, triage, and transcription of encounters. Each scenario below describes what a team can build, what Glass contributes within that product, and what the end user can do with the result.

Clinician Assistants with Cited Answers

A team can build a point-of-care assistant that answers clinical questions inside its own product. A hospitalist might ask how to manage anticoagulation in a patient with a specific renal function, or which guideline governs a screening interval for a given risk profile. The question is rarely abstract; it carries the patient's context.

Glass contributes evidence-based clinical Q&A with citations that help the clinician examine the supporting literature. A team can build an assistant around the questions its users face, including questions that depend on the clinical context of a particular patient.

The clinician can read the recommendation, follow the citation to the guideline or study behind it, and decide whether it applies to the patient in front of them. That review step is where citations earn their value. A clinician who can verify quickly is more likely to trust the assistant for the next question, and a product team can demonstrate to medical leadership how answers are supported.

This matters for adoption. Clinical assistants are abandoned when they feel like a second opinion without a source. Glass gives the product a source to show. Teams comparing approaches to building clinical decision support on an API will find this cited-reasoning pattern is the foundation the rest of the product rests on.

Chart Summary and Unstructured Data Synthesis

A team can build a chart synthesis product that turns a large, disorganized record into a concise clinical picture. Consider a consult service receiving a patient with years of notes, imaging reports, and medication changes. Someone has to read all of it before the first conversation.

Glass is particularly strong at analyzing large amounts of unstructured patient data. When the product supplies notes, results, and medication lists, Glass can produce an overall analysis of the patient, problem-based assessments of the major active issues, diagnostic next steps, and treatment or management next steps. The output is structured the way clinicians think about a case, with problems separated and priorities visible.

The user receives a clinical synthesis to review alongside the source record. For a consult service or care coordination team, the opportunity is to organize the patient’s story around the work at hand, helping the clinician prepare with relevant context in view.

Chart synthesis products succeed or fail on whether the summary is trustworthy enough to change how a clinician prepares. Glass's assessment-and-plan structure, combined with its ability to cite guidance where relevant, gives the product a summary that reads like a colleague's pre-rounding note rather than a search result. The product supplies the record; Glass supplies the reasoning; the clinician makes the call.

Documentation and Ambient Notes

A team can build documentation tooling that reduces the clerical burden on clinicians. Documentation is where many clinicians spend their evenings, and it is one of the most concrete places an AI product can return time.

Glass can produce many forms of clinical documentation, including the H&P, HPI, clinic notes, progress notes, discharge summaries, prior authorization letters, handoff notes, and patient handouts. For encounter-based products, the Glass Scribing API transcribes clinical encounters from audio recordings and can optionally generate structured notes such as SOAP notes, H&Ps, and visit summaries directly from the recording.

This breadth lets a single product cover the documentation lifecycle. A visit can become a transcript, the transcript a SOAP note, the visit a discharge summary, and the diagnosis a prior authorization letter, all drawn from the same clinical understanding. The clinician reviews and signs; the product handles the drafting.

Prior authorization letters deserve specific mention. They are formulaic, evidence-heavy, and resented. A product that drafts a letter citing the clinical rationale, which the clinician then edits and submits, addresses a task nearly every practice would like to hand off. Glass's grounding in current guidelines supports exactly the kind of justification those letters require.

Patient Education and Navigation

A team can build patient-facing products that translate clinical decisions into language patients can act on. Patients leave encounters with instructions they half remember, medication changes they do not fully understand, and no clear sense of when to come back.

Glass generates patient-facing documentation in plain language, including discharge instructions, care plan summaries, medication guides, condition education, and return precaution instructions. Because Glass grounds its content in current evidence, the patient education a product delivers reflects current guidance rather than generic health information.

A digital front door, a post-discharge follow-up app, or a chronic condition program can use this to produce individualized material. A patient starting a new medication can receive a guide written at an accessible reading level, tied to their situation, and reviewed by the care team before delivery. A patient discharged after a procedure can receive return precautions specific to what was done.

The organization benefits when patients understand their plan; readmissions, confused calls, and missed follow-ups all trace back partly to communication gaps. Glass gives the product clinically grounded content; the product controls tone, format, and review before anything reaches the patient.

Triage and Intake Support

A team can build intake and triage support for patient-facing or provider-facing products. Glass supports evidence-based triage by evaluating patient-reported symptoms, vital signs, and intake data against clinical criteria, identifying high-risk features, and returning structured triage output. That structure can feed intake systems, nurse triage tools, and escalation logic within the product. A nurse line can use it to prioritize callbacks; a symptom checker can use it to decide when to direct a patient to urgent care. High-risk flags remain inputs to human judgment and escalation rules the product defines.

Clinical Agents for Care Teams

A team can build a larger AI system in which Glass serves as the clinical reasoning specialist. Many healthcare products are moving toward multi-step workflows where several components each handle part of a task: one gathers information, one reasons, one drafts, one routes.

A care coordination agent can help prepare a patient summary before outreach. A documentation agent can prepare a handoff for clinician review. An evidence assistant can help a care team explore a medical question. Glass brings clinical capabilities to each of these product ideas.

This division of labor is attractive to teams whose core product is workflow, scheduling, revenue cycle, or care coordination rather than clinical reasoning. They can add credible clinical intelligence without building it. A care coordination platform might use Glass to summarize a patient's active problems before a coordinator's outreach call, then use its own logic to schedule follow-ups.

Our guide to healthcare agents built with Glass explores these possibilities in more detail, including who each experience can serve and the clinical value it can offer.

Use-Case Summary

Clinical Performance You Can Examine

Every vendor describes its model as accurate. Buyers should ask what accuracy means, who measured it, and on what. We answer with clinician evaluation and a published benchmark posture rather than adjectives.

Glass 5.5, the current recommended model for the Developer API, is evaluated by clinicians and benchmarked on clinical use cases using a 900-question clinical accuracy benchmark suite. The suite covers medical knowledge and reasoning, diagnostic reasoning, clinical note generation, and hallucination detection. That last category matters: a benchmark that measures whether a model fabricates is measuring the failure mode clinicians fear most.

On this clinical benchmark suite, Glass 5.5 outperforms the leading frontier foundation models. That is a specific claim about a specific suite, and it should be read that way. It tells a buyer that Glass performs strongly on the categories of clinical work the API is built for, which is the relevant question when choosing an engine for a clinical product.

Glass models have also been evaluated externally. The Medical AI Superintelligence Test from the ARISE AI Research Network assesses systems on clinical reasoning, safety, and diagnostic performance, and its technical results include a Glass model. The MAST methodology notes that leaderboard comparison supports choosing between systems, while any product still needs assessment in its intended use. We agree with that stance: benchmarks help a buyer shortlist; evaluation inside the product confirms the fit.

Practically, this means a team can do three things. It can rely on Glass's clinician evaluation as a starting signal of quality. It can consult independent results as a second signal. And it can run its own review with clinicians, using synthetic or de-identified cases, before the product touches live patient data. For a deeper look at how to read these results, see the guide to clinical AI API benchmarks.

HIPAA-Supported Deployment Through a Self-Serve BAA

Compliance should be a short chapter in a clinical product's story, and we keep it short. Glass is end-to-end HIPAA compliant, and organizations using the Glass Developer API in HIPAA-regulated workflows may obtain a Business Associate Agreement with us for API use.

The BAA is available through a click-through agreement in Glass API settings. Teams can begin development and evaluation with synthetic or de-identified data, then put the BAA and their own product safeguards in place before using protected health information.

The BAA covers Glass's role in processing the data a product supplies; the finished product and the organization deploying it carry their own obligations for how data is collected, shown, stored, and acted on. That is the normal shape of any healthcare technology relationship, and it leaves the product team in control of its own experience.

For products that influence care, clinician review remains part of the experience. Glass supports that review with clinical reasoning and citations, giving users a way to examine the information before acting on it.

Choosing Glass Versus Assembling Clinical Capability

A team can build a clinical product on a general-purpose model API. Many capable general models exist, and some are available under healthcare terms. The question for a buyer is what the team would then have to build itself.

Clinical grounding is the first consideration. A product serving clinicians needs useful answers grounded in current evidence. Glass brings guideline and literature grounding together with clinical reasoning and citations, giving the buyer a clinical offering to build on.

Clinical output shape is the second piece. Assessment and plan structure, problem-based reasoning, note formats clinicians recognize, patient-facing language at an appropriate level, and structured triage flags each represent product work when built from a blank model. Glass produces these forms directly, having been designed around how clinicians document and decide.

Clinical evaluation is the third piece. A team building on a general model must design and run clinical accuracy testing, including hallucination detection, before it can tell medical leadership how the product performs. Glass arrives with clinician evaluation across a 900-question clinical suite and independent benchmark participation, giving the team an evidence base from day one and a reference point for its own testing.

Breadth is the fourth. A product that starts as a Q&A assistant often grows into documentation, then patient education, then agentic workflows. Glass covers that arc within one API, so growth means adding features rather than integrating new vendors. The comparison of approaches in the guide to the best healthcare AI API lays out how these criteria weigh against each other.

None of this means a general model cannot work for some products. It means the buyer's task differs. With a general model, the team assembles clinical capability from parts. With Glass, the team chooses a clinical offering and spends its effort on the product experience, the workflow, and the users. For teams whose product is fundamentally clinical, that is usually the better trade.

Commercial Terms and Getting Started

We offer the Developer API on a subscription with a minimum of $250 per month, with usage above that floor charged separately. The floor is low enough for a founding team to validate a product idea, and the usage model scales with adoption rather than requiring a large upfront commitment.

Start with a product idea and synthetic or de-identified examples of the clinical work it will support. Review how Glass’s answers and citations serve that experience. The developer overview describes the capabilities, and the API documentation supports the implementation when the team is ready. Regulated use requires the BAA and the finished product’s own safeguards.

Frequently Asked Questions

What makes Glass a HIPAA-compliant AI API rather than just a medical model?

Glass Health is end-to-end HIPAA compliant and offers a Business Associate Agreement for API use, accepted through a click-through in API settings. Beyond compliance, Glass grounds responses in current clinical evidence, returns citations, and is evaluated by clinicians, so the API supports deployable clinical products rather than only permitting patient data.

Does Glass connect to our electronic health record?

Glass processes the clinical context a product supplies to it. The product decides what patient information to include and how the resulting synthesis, note, or answer is displayed and used. This keeps the product team in control of data flow and lets Glass focus on clinical reasoning over whatever context it receives.

Do all Glass answers include citations for every claim?

Glass supports clinical answers with citations drawn from current guidelines and literature. They help users examine the supporting evidence, while clinical judgment remains essential when the information affects care.

Why choose Glass for a new clinical product?

Glass brings together clinical reasoning, current evidence, citations, clinician evaluation and a BAA path for API use. That combination gives a team a clinical foundation for products such as assistants, documentation tools, patient education and care-team agents.

Can Glass support patient-facing products as well as clinician tools?

Yes. Glass generates plain-language patient documentation including discharge instructions, care plan summaries, medication guides, condition education, and return precautions, and it supports structured triage output for intake and symptom-check experiences. Products typically route this content through care team review before it reaches patients.

How quickly can a team obtain a BAA?

The BAA is available as a click-through agreement in Glass API settings. Teams should put it in place, along with their own product safeguards, before sending protected health information. Development and evaluation can begin with synthetic or de-identified data.

When is Glass the right choice over a general model API?

Choose Glass when the product depends on cited clinical reasoning, clinician-recognizable output such as assessments, plans, and notes, and clinical evaluation evidence from the start. Teams whose product is fundamentally clinical usually prefer choosing Glass's clinical offering over assembling grounding, output structure, and evaluation from a general model.

Start Building with Glass Health

Glass Health gives clinical product teams a HIPAA-compliant AI API that reasons over patient context, cites current evidence, and spans assistants, chart synthesis, documentation, patient education, triage, and agents. Explore the Glass Developer API to see the full capability set, or go directly to Glass API settings to create an API key, accept the BAA when ready, and begin with synthetic cases today.