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Oracle Healthcare Brazil

Unimed Grande Florianopolis

Fraud Detection · Real-time Analytics

Unimed Grande Florianopolis needed to replace fragmented spreadsheets and departmental systems so it could standardize healthcare data, run predictive analysis, monitor operations in real time, and reduce fraud and manual reporting work.

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Client base exceeding 200,000
More than 1,700 affiliated doctors
More than 270 clinics and hospitals
Over 200 hours of manual report preparation saved

Reported impact

Business outcome

Unimed Grande Florianopolis reduced healthcare costs, saved manual reporting time, improved fraud detection, and gained a more reliable analytics culture for patient management and operational decision-making.

  • Cost Reduction
  • Productivity Improvement
  • Risk Reduction

Solution

Unimed Grande Florianopolis adopted Oracle Analytics Cloud as its enterprise analytics layer after evaluating architecture, integration, and cost options through a proof of concept. Oracle Analytics Cloud consolidates operational and hospital data that had been spread across spreadsheets and disconnected systems, then delivers dashboards for mobile access and digital displays. The platform's AI and machine learning features are used to support predictive analytics, anomaly detection, and healthcare-specific analysis across preventive medicine, bed provisioning, operational indicators, and fraud detection. Because the analytics environment is fed from standardized data instead of manual spreadsheet work, the health cooperative can track patient flows and treatment-management needs more reliably while reducing time spent preparing monthly reports. The result is a single analytics environment that supports cost savings, compliance, and data-driven patient and operations management.

Data flow

Hospital and business data from previously disconnected spreadsheets and departmental systems is consolidated into Oracle Analytics Cloud, where dashboards, predictive analytics, anomaly detection, and regulatory reporting are used by managers and care teams for operational and patient-management decisions.

Solution architecture

1 components · 1 layer
  1. Serving
    • Oracle Analytics Cloud Centralizes healthcare data analysis, predictive models, dashboards, and anomaly detection for operations and patient management.

Architecture clues

  • AI and machine learning features are used for predictive and anomaly analysis
  • Analytics are consumed through mobile and digital-display dashboards
  • Platform supports initiatives from preventive medicine management to bed provisioning

Evidence from the source

Oracle Analytics Cloud helped the organization identify areas and processes for improvement, contributing to savings of R$1.4 million in healthcare costs.
Oracle Analytics helped detect fraud, leading to savings of over 1 million reais.
Using automated dashboards in Oracle Analytics Cloud, Unimed Grande Florianópolis saved approximately 200 hours of manual work that was previously spent preparing monthly reports.
Transparency Evidence & methodology 95/100 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 value95/100
  • 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
    2 metrics describe measurable impact.
  • Deployment scale5/10
    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

    Unimed Grande Florianopolis customer story

    First-party
    https://www.oracle.com/customers/unimed-grande-florianopolis/
    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 Aug 13, 2024 · 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.

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