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Microsoft Healthcare Brazil

CASSI

Data Lake / Data Warehouse Modernization · IT Operations Automation

The goal was to optimize the procedure authorization workflow to streamline the reviews and allow our technical team to focus on highly complex cases.

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CASSI reduced review times for authorizations, increased efficiency by automating 10% of medical authorizations, saved R$4 million per year by reducing operational costs and improved the allocation of the technical team to focus on more complex cases.
Caixa de Assistência dos Funcionários do Banco do Brasil (CASSI) faced significant challenges to optimize its authorization processes for medical procedures.The need to reduce review times and free the technical team to focus on highly complex cases prompted the organization to seek innovative technological solutions.
Conclusion CASSI's partnership with Microsoft demonstrates how the adoption of advanced technologies can transform traditional processes, resulting in greater efficiency, cost reduction, and improved quality of care.
Cost reduction: There has been estimated savings of R$4 million a year, mainly due to the reduction of medical labor cost.

Reported impact

Business outcome

CASSI reduced review times for authorizations, increased efficiency by automating 10% of medical authorizations, saved R$4 million per year by reducing operational costs and improved the allocation of the technical team to focus on more complex cases.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

CASSI, a health care provider for Banco do Brasil employees, aimed to optimize medical procedure authorizations by reducing review times. The goal was to optimize the procedure authorization workflow to streamline the reviews and allow our technical team to focus on highly complex cases. CASSI used Microsoft Azure Databricks, Data Factory, and Data Lake to integrate systems and automate medical authorizations with AI. Arcilia Machado, Technical Regulation Division Manager, CASSI Challenges and objectives Before the implementation of Microsoft Azure , CASSI was handling authorization processes manually, which took a long time to review and respond to applicants. CASSI reduced review times for authorizations, increased efficiency by automating 10% of medical authorizations, saved R$4 million per year by reducing operational costs and improved the allocation of the technical team to focus on more complex cases.

Solution architecture

5 components · 3 layers
  1. Ingest
    • Azure Data Factory Ingests and orchestrates data movement into the analytics platform.
  2. Compute
    • Azure Databricks Processes and analyzes large datasets for the solution.
    • Azure Provides the secure, scalable cloud foundation for the solution.
  3. Storage
    • SQL Server Stores structured operational and analytical data used by the solution.
    • Azure Data Lake Storage Stores source and curated data used by analytics and AI workloads.

Architecture clues

  • Arcilia Machado, Technical Regulation Division Manager, CASSI Challenges and objectives Before the implementation of Microsoft Azure , CASSI was handling authorization processes manually, which took a long time to review and respond to applicants.
  • As reported by Arcilia Machado, CASSI's Technical Regulation Manager, “the goal was to optimize the procedure authorization workflow to streamline the reviews and allow our technical team to focus on highly complex cases.” A three-pronged transformation Overcoming these challenges would require a three-pronged action plan: Authorization automation: The need to automate manual reviews for authorizing medical procedures to streamline that process and free up the technical team for more complex cases.
  • CASSI used Microsoft Azure Databricks, Data Factory, and Data Lake to integrate systems and automate medical authorizations with AI.
  • Endrigo Castro, Analytics Manager, CASSI Exploring Microsoft Azure data services To meet these challenges, CASSI chose to implement Microsoft tools, which were crucial to the organization's digital transformation.
  • Systems integration: Integrate different systems and platforms to create a continuous flow of data between the review system and the post-review processing system.
  • The goal was to optimize the procedure authorization workflow to streamline the reviews and allow our technical team to focus on highly complex cases.
  • The solutions chosen were: Microsoft Azure Databricks , used for processing and analyzing large volumes of data; Microsoft Azure Data Factory , for orchestrating and moving data between different systems; in addition to Microsoft Azure Data Lake and Microsoft SQL Server for storage and transaction of historical data.
  • They faced challenges in system integration and data automation.

Evidence from the source

Arcilia Machado, Technical Regulation Division Manager, CASSI Challenges and objectives Before the implementation of Microsoft Azure , CASSI was handling authorization processes manually, which took a long time to review and respond to applicants.
CASSI reduced review times for authorizations, increased efficiency by automating 10% of medical authorizations, saved R$4 million per year by reducing operational costs and improved the allocation of the technical team to focus on more complex cases.
CASSI used Microsoft Azure Databricks, Data Factory, and Data Lake to integrate systems and automate medical authorizations with AI.
The goal was to optimize the procedure authorization workflow to streamline the reviews and allow our technical team to focus on highly complex cases.
Transparency Evidence & methodology 90/100 reference value · 87% 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 value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    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 confidence87%
  • 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 evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    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. Microsoft

    CASSI customer story

    First-party
    https://www.microsoft.com/en/customers/story/19007-cassi-azure-data-factory
    Source type
    Vendor-published customer story
    Published
    Oct 14, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Oct 14, 2024 · Microsoft · First-party
    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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