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Healthcare

AI Clinical Documentation Across 14 Hospitals

Illustrative reference architecture. This is a composite engagement template built to show how Mopshy structures enterprise work. It is not a real Mopshy client, and every figure, KPI, and quotation on this page is illustrative rather than measured client data.

Ambient AI documentation, order-set assistance, and coding integrity deployed across 14 hospitals — HIPAA-aligned, EHR-native, physician-approved.

2.1M

Clinician hours / yr saved

−41%

After-hours charting

+$18M

Annualized capacity value

● The challenge

The bottleneck

Clinicians averaged 2.4 hours/day on after-visit documentation. Coding queries and denials climbed 18% YoY, and physician turnover attributed to documentation burden was a board-level KPI.

● What we built

The system

  • Ambient AI scribe integrated with Epic — draft note in <60 seconds post-visit
  • Specialty-tuned templates for cardiology, orthopedics, and internal medicine
  • Coding integrity layer surfacing CDI queries before note signature
  • Governance: physician steering committee, model change control, and audit log
● Executive KPI board

Outcomes measured, not promised

94%

Physician adoption at 90 days

vs 60% target

−37%

Coding query rework

+11 pts

Physician engagement score

0

PHI incidents post-launch

● ROI model

What it cost, what it returned

Investment

$3.4M program (year one)

Payback

7 months

Annualized value

$18M in reclaimed clinical capacity

Modeled on published wRVU capacity per hour and blended physician cost; validated by finance and CMIO office.

● Reference architecture

Reference architecture

  • Ambient capture on mobile + in-room device (opt-in consent flow)
  • PHI-safe transcription in HIPAA BAA-covered cloud region
  • Specialty-tuned LLM with retrieval over sanctioned clinical templates
  • Epic Haiku / Hyperspace write-back with clinician-in-the-loop review
  • Audit trail, model version pinning, and continuous evaluation harness
Epic (Haiku, Hyperspace, Chronicles)SSO via Azure ADVocera + Rover3M CDI

Representative engagement. This case study describes a composite of Mopshy AI's enterprise engagement patterns, delivery methodology, and observed outcome ranges. Company names, individual quotes, and specific figures are illustrative and used to communicate the enterprise pattern rather than to describe a single identified client.

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