Oracle Healthcare United States

Billings Clinic - Logan Health

Document Processing · IT Operations Automation

Billings Clinic - Logan Health needed to reduce clinician burnout caused by manual, interrupt-driven note taking and EHR documentation so physicians could spend more time with patients and less time completing charts after hours.

1,200 physicians across 80 specialties
16.69% reduction in average adjusted time spent in Oracle Health Foundation EHR per patient
18.36% increase in the number of patients seen
37.59% reduction in average adjusted documentation time

Reported impact

Business outcome

Billings Clinic - Logan Health reduced documentation burden, increased patient throughput, and improved clinician work-life balance by deploying Oracle Health AI Agents at scale across its multisite rural health system.

  • Customer Experience
  • Productivity Improvement

Solution

Billings Clinic - Logan Health deployed Oracle Health AI Agents as a documentation layer integrated directly with Oracle Health Foundation EHR. The system captures patient-physician conversations, drafts relevant clinical notes, and routes them into existing physician workflows without the complex interfaces common in stand-alone voice tools. Because the AI-generated note stream is embedded in the same EHR environment clinicians already use, the health system can improve documentation speed while keeping clinical information available to other departments sooner. The deployment was evaluated against competing voice-to-note platforms, but Oracle's tight workflow integration and product roadmap made it the preferred option for scaling across a large multisite organization. At production scale, the platform supports thousands of employees and 1,200 physicians across 80 specialties, helping move clinician time back to patient care while improving timeliness and consistency of charting.

Data flow

Patient-physician conversations are captured by Oracle Health AI Agents, turned into draft clinical notes, and inserted into Oracle Health Foundation EHR workflows so physicians can finalize charts faster and downstream departments can access more timely clinical information.

Solution architecture

2 components · 2 layers
  1. Storage
    • Oracle Health Foundation EHR Receives the AI-generated notes inside existing physician workflows and makes them available across care teams.
  2. Serving
    • Oracle Health AI Agents Captures conversations and drafts clinical notes to reduce physician documentation workload.

Architecture clues

  • AI notes flow into Oracle Health Foundation EHR without complex interfaces
  • Deployment was scaled across a multisite healthcare environment
  • Documentation is generated directly from patient-physician conversations

Evidence from the source

Collectively, these gains contributed to an 18.36% increase in the number of patients seen.
Since go-live, Billings Clinic – Logan Health has significantly improved efficiency, demonstrating a 37.59% reduction in average adjusted documentation time.
The solution’s seamless integration with Oracle Health Foundation EHR was also a major differentiator.
Transparency Evidence & methodology 100/100 reference value · 74% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence74%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence15/20
    3 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Oracle

    Billings Clinic - Logan Health customer story

    First-party
    https://www.oracle.com/customers/billings-clinic/
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived May 14, 2025 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.