Staff Product Manager

UCHealth, a Colorado-based nonprofit health system, is partnering with Abridge to deploy AI-powered clinical documentation across its facilities. Following a successful nine-month pilot with 250 providers, nearly one-third of UCHealth's 6,000 doctors, nurse practitioners and physician assistants are now using the technology. Abridge's ambient AI captures patient conversations and generates draft clinical documentation, allowing providers to focus on patients rather than note-taking. During the pilot, providers reported improved patient interactions and reduced administrative burden. Patients receive detailed after-visit summaries in plain language. The collaboration will extend to developing new documentation workflows for inpatient units. Abridge, founded in 2018, is projected to support over 80 million patient-clinician conversations across 250 US health systems this year and was awarded Best in KLAS for Ambient AI in 2025 and 2026.

Innovation at the speed of trust. In December 2025, Abridge rolled out several new specialty-specific model updates, advancing from adaptable note-taking to deeply distinctive, clinician-aligned documentation tailored to highly specialized workflows. These updates are developed and rigorously evaluated to ensure measurable gains in note quality before they are released into the hands of clinicians, which is all part of Abridge's continuous improvement process for clinical AI. When these model updates are ready, they are shipped "quietly," which is to say without build, configuration, or workflow disruption. Health systems are not asked to implement new versions. Clinicians are not prompted to update apps. The process is seamless, enhancing care delivery without interrupting it. Clinicians simply experience better notes, automatically. Specialty selection: data-driven prioritization. "Even small details, like in what circumstances the phrases 'history of' or 'severe' is appropriately used in a note, can have real downstream impact on billing, liability, and the time clinicians spend finalizing a note. This is why every specialty model update starts with a thorough definition phase, where Abridge AI Inc. analyze many real life conversations and encounters to understand the baseline behavior of its models, spend hours speaking to clinicians and their hoped-for improvements, and establish consensus on the meaningful changes to implement. This process of deep analysis of real notes, guided closely by feedback from its clinical partners, is what powers ongoing improvements across all its models so that Abridge today is always better than Abridge yesterday." Katherine Choi Senior Product Manager At Abridge, its documentation models support clinicians across specialties, care settings, and systems at scale. It's that scale that gives Abridge AI Inc. the opportunity to go a million miles deep in specific specialties to make them even better. For example, neurology documentation demands precise chronological symptom mapping, capturing onset, progression, and severity over time to inform diagnostic reasoning and treatment decisions. While surgical documentation requires explicit documentation of risk-benefit discussions and clear medical necessity justification. Each specialty inherently requires specificity, and high-quality documentation must reflect the distinct logic of its respective clinical workflow. Specialty models are tuned to structure and prioritize those findings correctly, often in tighter, more structured note formats with terminology specific to each specialty. Furthermore, these formats also need to be tailored to user preferences, which vary widely within specialties: from comprehensive, to concise, to bulleted across patient narratives, all with clinical judgement represented throughout. And the best approach to aligning note formats with user preferences is simple: let clinicians personalize their notes. Giving clinicians agency over their note structure, refining notes with simple language prompts to adjust for tone or add specificity, is one way Abridge powers efficiency. Next, Abridge will introduce contextual prompts at the point of conversation, surfacing relevant insights in real time during clinical conversations without interrupting workflows. All of this is based on real world insights. By grounding prioritization in clinical demand and feedback, Abridge AI Inc. can identify where a one-size-fits-all model may not capture the realities of a given specialty. When Abridge AI Inc. see friction, that's its signal to investigate and ensure its specialty updates solve challenges clinicians actually experience in their daily practice. Development: the clinician-in-the-loop model. Once a specialty is prioritized, development begins. Its "clinician-in-the-loop" process pairs product and engineering leads with Clinician Science and Clinical Success Directors in cross-functional collaborations we call "NoteGen" teams. Together, Abridge AI Inc. refine the underlying "recipes" that shape how different sections of notes are generated. The goal isn't only accuracy; it's alignment. Abridge AI Inc. build these models to mirror not just how specialists document care but also how they think about care. By developing alongside clinicians, Abridge AI Inc. ensure specialty updates not only reflect real workflows but also feel familiar. It's a collaborative process Abridge AI Inc. continue to strengthen, with "recipes" Abridge AI Inc. continue to refine. Testing: the multi-layered validation stack. When model updates are in development, Abridge AI Inc. pressure-test quality, accuracy, and clinical flow through a rigorous three-layer validation process that includes hands-on clinician review, third-party audits, and automated model evaluation. To start, Abridge AI Inc. apply an automated LLM evaluation, what Abridge AI Inc. call the "Judge" layer. These "judges" score the new model notes on a number of different variables, including: * Non-Inferiority: Strict safeguards that ensure new specialty models match or exceed the generic baseline performance * Misattribution Rates: Monitoring against "hallucinations" or misattributed patient data Then its Clinical Success Directors evaluate real-world examples against a predefined, data-driven baseline to determine if the update meaningfully improves performance. Finally, Abridge AI Inc. bring in external, third-party auditors who review hundreds of additional examples to add an impartial and objective layer of quality assurance. Only when a model clears every layer do Abridge AI Inc. consider deploying. This multi-layer validation ensures that specialty model updates are not just different, but demonstrably better. Intentional rollout: the "early wave" Strategy. At Abridge, its rollout process has four phases, each with defined audiences, expanding scope, and clear goals. The table below shows how specialty model updates move through this strategy: progressing from Alpha to Beta, into staged General Availability with randomized clinician cohorts, and finally to 100% GA across its most complex partner environments. Scaling to GA: silent excellence. "General Availability isn't just a launch moment for Abridge AI Inc.. It's a performance threshold. When a specialty model update is released, it's because it has already proven itself through multiple layers of quality and clinician review. When Abridge AI Inc. release an update to its models, its partners can trust that they have gone through rigorous review and validation to deliver improvement clinicians can experience. They will feel the difference because their notes will be all that much more aligned with their specialities." Reba Schenk SVP, Partner Experience The progression to GA is governed by continuous measurement, not milestones. Every specialty model can be observed in its live A/B testing dashboard where Abridge AI Inc. monitor real-time star ratings alongside effort-reduction signals: how much clinicians edit, rewrite, or restructure notes. When a specialty model consistently outperforms the baseline across these metrics, it earns its way into GA. This dashboard-driven approach lets Abridge AI Inc. validate improvements in the wild, in real-time, across diverse workflows, without relying on anecdotes or assumptions. When models do move to GA, they do so seamlessly, deployed in the background without pop-ups, retraining, or workflow changes. From the clinician's perspective, notes simply read better, require fewer edits, and feel more aligned with their thinking, literally overnight. Did Abridge get an update? Notes were good but now are 100% better. Kudos! At the same time, lightweight feedback loops remain active. One-click surveys and simple thumbs up/down signals help Abridge AI Inc. capture feedback early, whether that's an Orthopedic surgeon preferring a "more concise" HPI or an Emergency Medicine clinician requesting a more tightly structured, problem-first assessment. When sharing more nuanced or detailed feedback, clinicians leave comments directly in the app. At Abridge, feedback is foundational to the world Abridge AI Inc. do. Feedback is its oxygen. Without it, continuous advancement would not be possible. Conclusion: the future of iterative intelligence. By the end of the December 2025 sprint, Abridge moved several new specialty model updates into General Availability, including Hematology-Oncology, Gastroenterology, and the full surgical suite. But more important than any number of updates is the framework behind it: a repeatable process to deliver deeply specialized intelligence without disrupting care. This is what allows Abridge AI Inc. to scale across every corner of medicine while holding quality to an enterprise-grade standard. And the impact is measurable. In some specialties, redundant language like "history of" in the HPI dropped from 46.8% to 2.5%, reflecting higher quality, "cleaner" notes. These improvements are grounded in real-world validation from its Champion Network of hundreds of clinicians across specialties who provide the clinical, on-the-ground insights that help guide its work. Paired with focused Specialty sprints, this iteration framework compresses months of development and validation efforts into just weeks. The result is not just faster development, but rather a durable, reliable system built to improve its models. Delivering updates with measurable improvements into the hands of clinicians who know their care won't be disrupted? That's what it means to innovate at the speed of trust.

KLAS names Abridge #1 market leader for Ambient AI in Revenue Cycle Management for second year in a row. Recognition driven by independent feedback from the nation's largest and most complex health systems SAN FRANCISCO, CA - February 4, 2026 - Abridge, the leading enterprise-grade AI platform for clinical conversations, was named #1 Best in KLAS for Ambient AI in the Revenue Cycle Management category for the second year in a row. The 2026 Best in KLAS Software and Services designation is based on direct feedback from healthcare organizations, reflecting both customer satisfaction and real-world impact. Customers highlighted the ability to drive tangible outcomes and deliver long-term value, as just two of the ways Abridge stood apart. Abridge earned the highest overall score based on independent customer feedback interviews from the nation's largest and most complex health systems conducted by KLAS Research. This included A+ ratings across the customer experience pillars for Culture, Loyalty, Relationship, and Value. "The Best in KLAS awards recognize the vendors who consistently deliver excellence through partnership with healthcare organizations," said Adam Gale, CEO of KLAS Research. "Winning this award means customers trust you to help them succeed in our rapidly changing healthcare landscape. This means helping them to improve patient care, achieve better outcomes, and find true ROI. We're honored to amplify the voice of providers and payers at KLAS, and to celebrate those vendors who turn feedback into action." This award is validation of the enterprise-grade AI platform Abridge has built in deep collaboration with more than 250 health system partners powered by a purpose-built system of intelligence for healthcare. Abridge is projected to support 80 million clinician-patient conversations this year, helping reduce administrative burden for clinicians and revenue cycle teams across every step of the patient care journey. "What makes this recognition meaningful is the trust Abridge has earned at scale from the health systems we're privileged to serve," said Dr. Shiv Rao, CEO and Co-Founder of Abridge. "Enterprise-grade isn't just about technology, it's about delivering consistently high performance across a broad and growing customer base. This award reflects how our team and our partners combine to produce outcomes that are greater than any one of us alone, even in the most complex environments in healthcare." Even as the ambient AI landscape has changed over the past year, Abridge's approach has remained consistent: partner deeply, innovate with purpose, and build trust over time. In a crowded and shifting category, this recognition underscores the durability of Abridge's platform and the loyalty it continues to inspire. ABOUT ABRIDGE Founded in 2018, Abridge is the leading enterprise-grade AI platform for clinical conversations. Powered by a purpose-built system of intelligence for healthcare, the company is building real-time bridges between patients, providers, and payers. This year, Abridge is projected to support more than 80 million patient-clinician conversations across 250 of the largest and most complex health systems in the U.S. With deep EHR integration, support for 28+ languages, and 50+ specialties, Abridge is used across the entire care journey, including outpatient, emergency department, inpatient, and nursing workflows. Abridge is setting the industry standard for the responsible deployment of AI across health systems. Features like Linked Evidence map AI-generated clinical documentation to source data, helping clinicians quickly trust and verify the output. Abridge was awarded Best in KLAS for Ambient AI in 2025 and 2026, in addition to other accolades, including TIME Best Inventions of 2024 and 2025, CNBC Disruptor 2025, and Fast Company's Most Innovative Companies 2025. ABOUT KLAS KLAS helps healthcare providers make informed technology decisions by offering accurate, honest, and impartial vendor performance information. KLAS monitors vendor performance through interviewing thousands of healthcare providers representing healthcare organizations throughout the US and here and there across the globe. KLAS uses a simple methodology to ensure all data and ratings are accurate, honest and impartial to help create market moving moments.