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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →AI is already part of some clinical workflows, but “deployed” can mean anything from software made available to staff to a tool used in patient care and evaluated over time. The sources available here support 12 distinct health-system or project examples—not a verified, canonical list of exactly 18. They show AI being used or evaluated for clinical documentation, sepsis alerts, heart-failure detection support and other workflow tasks, with very different levels of evidence behind each use.
What counts as a real clinical AI deployment?
A deployment claim is most useful when it says what actually happened. NHS England distinguishes first prospective deployment, multisite deployment and real-world evaluation; those are different stages, not synonyms for proven routine care. Its 2024 account of the AI in Health and Care Award describes 13 technologies independently evaluated for multisite deployment and real-world evidence, and more than £100 million allocated by the programme over 2020–2024. Neither figure is a count of successful routine deployments or proof of effectiveness. NHS England’s evaluation account identifies safety, accuracy, effectiveness, value, fit with sites, implementation, scaling and sustainability as relevant evaluation domains.
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The examples below are a dated, curated set of 12 site or project instances supported by the cited sources. They are not an exhaustive inventory. Availability, access, active use, evaluation and reported outcomes are marked separately because they answer different questions.
Where health systems are using or evaluating AI
| Health system or project | Clinical task and status | What the cited source reports |
|---|---|---|
| Kaiser Permanente / Abridge | Ambient clinical documentation; availability announced in August 2024. | Available at 40 hospitals and more than 600 medical offices. Patients consented and clinicians reviewed the generated notes. The announcement establishes availability, not use by every clinician or improved patient outcomes. Kaiser Permanente’s announcement. |
| Vanderbilt University Medical Center | Ambient scribing; enterprise access for ambulatory and emergency-department clinicians beginning January 15, 2025. | More than 2,400 clinicians had access; 1,223 had used the system by March 31, 2025. In the final study week, it was used in 20.1% of visit notes. That note share is not a measure of all eligible visits or of the proportion of clinicians using it. Vanderbilt’s deployment report. |
| Cleveland Clinic / Ambience Healthcare | Ambient documentation; rollout began March 10, 2025. | An implementation report says the rollout reached more than 4,000 ambulatory clinicians within four months. Scale describes rollout, not clinical benefit. Cleveland Clinic’s implementation report. |
| National University Health System (NUHS), Singapore / MediVoice | In-house multilingual ambient scribe; deployed in September 2024. | NUHS’s account says local data requirements influenced its decision to build the system in-house. That is a description of this institution’s context, not a general rule for other health systems. NUHS deployment source. |
| Cleveland Clinic hospitals / TREWS | Sepsis prediction; the Stanford AI Index describes deployment of a system developed at Johns Hopkins and commercialized by Bayesian Health across 13 hospitals. | The Index reports a relative reduction in sepsis mortality of 18.7% among the cited deployment results. This is a result attributed to that system and report, not an expected effect at other hospitals. Stanford AI Index 2026. |
| UC San Diego Health / COMPOSER | Sepsis prediction; the Stanford AI Index describes a deep-learning model monitoring more than 150 variables per patient. | The Index summarizes results across 6,217 admissions: a reported 17% relative reduction in mortality, equivalent to 1.9% in absolute terms, and an estimated number of lives saved. These figures belong to the cited study and population. Stanford AI Index 2026. |
| Sharp HealthCare | Ambient documentation. | The Stanford AI Index reports an 83% reduction in note-writing effort and a 3.5%–6% increase in work relative value units (work RVUs) per encounter. These are source-specific reported measures, not a guarantee of time saved or productivity gains elsewhere. Stanford AI Index 2026. |
| University of Chicago Medicine | Ambient documentation. | The Stanford AI Index reports a 47% reduction in cognitive load and a 58% increase in undivided patient attention. These measures concern clinician experience and attention, not clinical outcomes. Stanford AI Index 2026. |
| MaineHealth | Ambient documentation. | The Stanford AI Index reports 23% less time spent on clinical notes and use in 70.3% of encounters. The use figure depends on the study’s encounter definition and observation period. Stanford AI Index 2026. |
| Northwestern Medicine | Ambient documentation; the report highlights physicians using the tool in more than half of their encounters. | For that subgroup, the Stanford AI Index reports 11.3 additional patients monthly, a 24% reduction in documentation time and a reported 112% return on investment. The subgroup results and return calculation should not be generalized to every clinician or institution. Stanford AI Index 2026. |
| Stanford Health Care | Ambient documentation; prospective study involving 48 physicians. | The Stanford AI Index summarizes median time savings of 20 minutes per half-day clinic and statistically significant reductions in task load and burnout in that study. Its sample and study setting matter when interpreting the result. Stanford AI Index 2026. |
| NHS TRICORDER / Eko-DUO | Evaluation of an AI-enabled stethoscope to support heart-failure detection in primary care; this was a project, not evidence of a nationwide routine rollout. | NHS Digital reported more than 200 participating GP practices by February 2024 and a project timeline from January 2023 to August 2025. The case study’s £2,400 per-patient and £100 million nationwide savings figures are projections, not realized savings. NHS Digital’s TRICORDER case study. |
What the reported benefits do—and do not—show
Documentation studies measure different things
Ambient documentation appears in several of the examples because it can be integrated into clinical visits without making the AI the decision-maker. The reports measure different outcomes: time spent writing notes, clinician cognitive load, patient attention, encounter use and productivity measures. A percentage reduction in documentation effort is not interchangeable with a change in mortality, and a tool appearing in a share of notes does not establish that it improved the visit.
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Adoption figures also have different denominators. Kaiser Permanente’s figure counts locations where the tool was available. Vanderbilt’s report separates clinicians enabled from clinicians who had used the system and gives a share of notes in a specific final study week. Cleveland Clinic’s figure describes clinicians reached during rollout. None of those numbers, on its own, tells a reader how consistently each eligible clinician used the tool or what happened to patient outcomes.
Sepsis results are specific to their systems and study settings
The Stanford AI Index summarizes reported sepsis results for TREWS at Cleveland Clinic and COMPOSER at UC San Diego Health. Their mortality figures describe different systems and study populations, so they should not be ranked against one another or treated as a forecast for a new hospital. Establishing whether an alert system helps in a particular service also requires looking at how it fits into clinicians’ work and what happens after an alert—not just the model’s predictions.
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Projected savings are not observed savings
The TRICORDER case study gives potential savings figures, but labels them as projections. They are not a record of money saved through the project. Likewise, a programme’s funding, the number of sites reached or the number of technologies evaluated shows investment or activity, not whether a technology is safe, cost-effective or beneficial in routine care.
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How to judge a hospital AI deployment
When reading a deployment announcement or study, check the measure and context rather than relying on the word “deployed.” These questions help distinguish access from routine use and promising findings from established outcomes:
- What stage is it? Is the tool available, actively used, being prospectively evaluated, or integrated into routine care?
- Who used it, and how often? Separate eligible staff or patients from active users, and check the period and denominator behind any usage rate.
- Where does it fit in care? Look for the setting, task, handoff and clinician oversight. For Kaiser Permanente’s described documentation workflow, patients consented and clinicians reviewed notes.
- What outcome was measured? Note-writing time, patient attention, workflow adoption, mortality and financial return answer different questions.
- How strong is the evidence? An institutional announcement can document availability or rollout; an implementation report describes how adoption proceeded; an outcome study is needed to assess effects in its specified population and period.
- Can the result hold up at other sites? NHS England includes fit with sites, implementation, scaling, sustainability, safety, accuracy, effectiveness and value among its evaluation domains. A successful result at one institution does not establish the same result elsewhere.
Why the exact count of 18 is not established here
The sources support a useful cross-section of real activity, but not a canonical set of exactly 18 equivalent deployments. They combine health-system announcements, implementation reports, NHS project and programme evidence, and a Stanford synthesis of underlying studies. Some document availability or rollout; others describe evaluation or outcomes in a defined setting. Counting programme funding, evaluation projects or workflow research alongside active site deployments would change what “deployment” means.
For that reason, the 12 examples above are limited to distinct health-system or project instances identified in the cited material. The NHS AI Award’s 13 independently evaluated technologies and its programme funding are relevant context, but they are not added to the count as 13 additional routine deployments. The evidence shows where AI has entered clinical work; it does not justify presenting a precise list of 18 as if every entry had the same status or proof of benefit.
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