Abridge began with clinical documentation because notes consume about two hours of clinicians' time each day and affect both billing and future care.
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Healthcare AI has to meet high standards for quality, latency, and cost because errors can affect patients and live conversations require timely responses.
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Abridge evaluates clinical decision support with physician-written rubrics and separate judges for quality, safety, adversarial boundaries, tone, and style.
Summary
Chaitanya Asawa describes Abridge's move from ambient clinical documentation toward clinical intelligence. The company started by reducing the time clinicians spend writing notes after visits. That product reached 300 of the largest US health systems because documentation affects billing, provider satisfaction, and the context passed to the next clinician. Asawa's broader view is that healthcare administration grew around the doctor-patient conversation, so systems should act on that conversation directly. He discusses voice interactions, clinical trial matching, order placement, and access to EHR data, live dialogue, guidelines, and medical literature. The hardest engineering problem is evaluation. Clinical answers can have many valid forms, while a verifier strong enough to judge them may be as difficult to build as the generator. Abridge uses physician-created rubrics, adjudication, quality assurance, and separate judges for safety and boundaries. At high volume, it decomposes notes into sections and post-trains smaller models to control cost and latency.
Healthcare has technical problems with unusually high stakes
Asawa wants engineers to treat healthcare as a serious technical domain, even though parts of it still run on fax machines. Abridge's work spans clinical documentation, clinical decision support, and agentic experiences. He describes healthcare as a field where errors can affect patients and destroy trust. In other products, an incorrect answer might be inconvenient. In healthcare, an incorrect answer can have consequences. The engineering bar is therefore high across quality, latency, and cost. Latency matters because an answer must arrive at the right time during a visit. Cost matters because the system operates across a large volume of medical conversations.
Documentation was the practical entry point into healthcare
After every patient visit, a clinician has to create a note, often in a SOAP format with sections such as the chief complaint, assessment, and plan. Asawa says clinicians commonly spend around two hours a day writing notes after work, a period known as pajama time. The notes are high stakes because they support billing and give the next clinician context about the patient's longitudinal record. Abridge started with documentation because this was a long-standing pain point and newer technology could produce high-quality notes personalized to each clinician. Better documentation could improve provider satisfaction, let clinicians see more patients, and support billing audits.
The doctor-patient conversation contains the context for downstream work
Asawa's central product thesis is that healthcare activities downstream of the visit depend on the conversation between doctor and patient. Billing, clinical trial matching, and clinical decision support can all use information from that exchange. Abridge's voice interface lets a physician ask whether a patient qualifies for a trial, receive an answer based on the available criteria, and order a needed echocardiogram. The system can also prepare charts for the next day. Asawa describes a broader visit workflow that includes suggested discussion topics before the visit, a patient summary and clinical note afterward, and orders that are prepared for signing.
Clinical intelligence combines several sources of patient context
Abridge's system can read EHR context such as previous laboratory results and prior notes. It also listens to the live conversation, which Asawa calls the place where the current patient situation becomes clear. Medical literature and clinical guidelines provide additional sources for grounding the system's work. This combination supports questions whose context is not fully stated, such as whether a patient meets criteria for a condition. The system must retrieve relevant patient history, use what was said in the current visit, and combine those details with medical references before producing an answer for the provider.
Evaluation is treated as the operating system for product work
Abridge begins product development with internal benchmarks and offline evaluation for products such as clinical trial matching, notes, decision support, and coding. It then moves through staged deployment, starting with a trusted alpha group, followed by beta and broader testing. After release, the system continues to be monitored because offline cases cannot capture everything that happens in practice. Clinicians are embedded across the company, and their judgment is encoded into language-model judges so non-clinicians can work against the same product standards. Online signals include edits to notes, thumbs-up and thumbs-down feedback, star ratings, and free-form comments.
Clinical answers need several judges because correctness has multiple dimensions
Asawa focuses on contextual clinical decision support, where a provider's question may leave much of the relevant context unstated. He says the gap between generating and verifying an answer is small in this setting. Sudoku is difficult to solve but easy to verify, while a clinical verifier that was good enough might itself be the generator. Abridge therefore uses several signals rather than one reference answer. These include a clinical quality judge, a boundary and adversarial judge, a clinical safety judge, and judges for product qualities such as tone and style. Each evaluates a different part of the behavior the product needs.
Physician rubrics make evaluation possible when many answers can be right
Abridge does not create one golden response for each clinical case because different responses can all be correct. Instead, independent physicians review real clinical cases and write rubrics describing the elements a good answer should contain. A separate physician adjudicates the independent rubrics into a final rubric, and a fourth clinician performs quality assurance. An LLM judge then compares an agent's response with the rubric elements using semantic matching. This gives teams a reference for improving agent architecture, model choice, and search ranking without pretending that every case has one exact wording or one exact answer.
Smaller models and targeted triggers control cost at scale
Abridge runs on a projected volume of 100 million medical conversations a year, so cost and latency shape the system design. For clinical notes, the company decomposes the work into sections such as history of present illness, past medical history, and assessment and plan. It post-trains smaller models for narrower tasks instead of using a foundation model for every section. Asawa says Abridge has a large, distinctive dataset of medical conversations, which may give it an advantage on focused problems. The same pattern appears in order capture. Cheaper gates detect likely order events before heavier models perform order matching and prepare the item for signing in the EHR.
"You can't just create a human golden response because there is a lot of variability in the potential responses."16:27
Who should watch
You are building an AI product where mistakes can affect patient care and need an evaluation process before deployment.
Your system must respond during a live interaction, so latency and operating cost matter alongside answer quality.
You want to turn expert judgment into repeatable model evaluation when there is no single correct answer.
