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Care is real-time. The data isn’t.

Clinicians decide now, on records scattered across systems that don’t speak. The distance between what is known about a patient and what is available at the point of care is where outcomes, hours and clinicians are lost.

3×
Reduction in clinical documentation time for clinicians using ambient AI scribes for the majority of visits, in a multi-hospital study[1]

A two-year study across five US hospitals, co-led by Mass General Brigham and UCSF, found clinicians who used ambient AI documentation for more than half their visits saw twice the reduction in total EHR time and three times the reduction in documentation time[1]. A separate study found provider burnout in ambulatory clinics fell more than 10% after just 30 days with an ambient AI scribe[2]. Documentation burden is a primary, measurable driver of clinician burnout[3] — and burnout is a workforce and patient-safety cost, not a soft metric.

01
Industry challenges

What actually constrains healthcare technology.

FRAGMENTATION

Records that don’t speak

Patient data is siloed across EMR, lab, imaging and billing — no governed, real-time view at the point of decision.

BURNOUT

Documentation as the driver

Two in five US healthcare workers report their jobs feel unsustainable[4]; more than half of physicians report burnout driven largely by documentation and administrative load[5].

COMPLIANCE

Governance is non-negotiable

Health systems are building formal AI governance and compliance frameworks[6]; a model that cannot be audited and explained cannot be deployed, however accurate.

ADOPTION DEPTH

Breadth without depth

Many organisations use AI somewhere, far fewer have embedded it in core clinical pathways[6] — the gap between pilot and production is the real constraint.

02
Where AI changes the economics

The opportunity map — grounded in deployed systems.

Ambient clinical documentation
AI scribes cut documentation time substantially — up to ~75% charting-time reduction reported[5], with the strongest effect for clinicians who adopt it for most visits[1]. Human-in-the-loop, with an audit trail.
Moves: clinician time · burnout
Governed patient 360
One real-time, access-controlled view across systems — clinicians decide on what is known, not what is reachable.
Moves: decision latency · duplicate work
Care-gap & record intelligence
AI surfacing easy-to-miss details across patient records — 86% of surveyed clinicians were comfortable with AI assisting or fully handling this[7], complementing judgment.
Moves: missed findings · care continuity
Predictive operations
Forecasting capacity, no-shows and risk so operations are planned, not reacted to.
Moves: utilisation · avoidable urgent work
The transformation narrative

From the constraint to the capability.

Where most are
Records scattered. Clinicians do data entry. Two in five say the job is unsustainable[4].
Where we take them
A governed, real-time care backbone — AI-assisted, auditable, measured on time returned to care.
Proof — CodesmoTech engagement

Where the industry data meets our work.

CODESMOTECH ENGAGEMENT · HEALTHCARE

Patient engagement platform

Delivered through PRISM. A patient-engagement platform that unified fragmented records; the headline figure below is reported on CodesmoTech’s primary site for this engagement.

50k+
Daily active users
Governed
Audit-ready by design
PRISM
Full-cycle delivery
Status note: the 50,000+ daily-active-users figure is as reported on codesmotech.com for this engagement. The industry statistics above (markers [1]–[7]) are independently sourced and attributed.
See related work
How we’d engineer it

The capabilities behind this.

Mapped to PRISM — front-loaded into Proof and Roadmap, where the risk to budget and compliance is highest.

Sources

Every industry figure on this page is attributed.

  1. Mass General Brigham & UCSF, multi-hospital 2-year ambient-documentation study — >50% use linked to 2× total-EHR-time and 3× documentation-time reduction (reported Mar 2026). eurekalert.org
  2. Chartis, 2026 Health System Outlook — provider burnout down >10% after 30 days with an ambient AI scribe. chartis.com
  3. Peer-reviewed narrative review (PMC, 2026) — EHR documentation burden as a primary driver of clinician burnout. ncbi.nlm.nih.gov
  4. Indeed, Pulse of Healthcare (reported Dec 2025) — two in five US healthcare workers report their jobs feel unsustainable.
  5. Industry practice surveys 2025–2026 — majority of US physicians report burnout; AI scribes cutting charting time up to ~75%.
  6. Wolters Kluwer / Chief Healthcare Executive expert outlooks 2026 — governance build-out; adoption breadth exceeding depth in core clinical pathways.
  7. Chief Healthcare Executive 2026 leader survey — 86% comfortable with AI assisting (60%) or fully handling (26%) identification of easy-to-miss details across records.
On sourcing: figures are drawn from publicly reported industry research current as of early-to-mid 2026. Before launch, your team should confirm each citation links to the original primary source rather than secondary coverage, and date-stamp them.

What would clinician time returned be worth to you?

That is a Stage P conversation. We model the value with your clinical and finance leads — against your numbers, not industry averages — before proposing a build.

Model my ROI
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