Microsoft Healthcare Canada

PointClickCare

Data Lake / Data Warehouse Modernization · System Migration

Provider disconnects over patient treatment are all too common, and the healthcare industry is experiencing historically low staffing and a wave of new industry clinicians, which puts patients at risk.

Today, more than 27,000 long‐term and post‐acute care providers; over 2,800 hospitals and 2,000 ambulatory clinics; every major US health plan; and over 75 state and government agencies use PointClickCare, actively serving more than 200 million patients.
We wanted to be able to monitor a million tables every 10 seconds," says Datars."Microsoft demonstrated it was a great collaborator in reaching our goals, including building our own data ingester." PointClickCare used Windows Server and SQL Server to identify all existing data sources and blockers, and then synchronized data between source and target databases during the migration process.​ With Azure Kubernetes Service (AKS) , it containerized applications for migration, and now supports new application testing before deployment to ensure quality and compatibility.
With help from Microsoft, PointClickCare migrated 1 million data tables from multiple locations to Azure with minimal disruption and no reported issues by customers.

Reported impact

Business outcome

Today, more than 27,000 long‐term and post‐acute care providers; over 2,800 hospitals and 2,000 ambulatory clinics; every major US health plan; and over 75 state and government agencies use PointClickCare, actively serving more than 200 million patients.

  • Compliance
  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

PointClickCare built its Microsoft solution to improve cloud migration and platform modernization. Provider disconnects over patient treatment are all too common, and the healthcare industry is experiencing historically low staffing and a wave of new industry clinicians, which puts patients at risk. Key components included Azure OpenAI Service ran model inference or heavy processing, and Azure AI Studio coordinated workflows, prompts, or deployment. User prompts and domain content are orchestrated through Azure AI Studio, processed by Azure OpenAI Service, and then delivered through Azure Kubernetes Service so PointClickCare can return context-aware answers or automation in the flow of work. The stack stayed governed and extensible on Microsoft.

Data flow

User prompts and domain content are orchestrated through Azure AI Studio, processed by Azure OpenAI Service, and then delivered through Azure Kubernetes Service so PointClickCare can return context-aware answers or automation in the flow of work.

Solution architecture

5 components · 2 layers
  1. Compute
    • Azure OpenAI Service Runs large language models that generate grounded responses, summaries, and copilots.
    • Azure Machine Learning Builds, trains, and operationalizes predictive models and machine-learning pipelines.
    • Windows Server Supports legacy application and data workloads that are being moved into Azure.
  2. Orchestration
    • Azure AI Studio Provides the build environment for prompt orchestration, safety controls, and model tuning.
    • Azure Kubernetes Service Operates containerized application and AI workloads in a managed Kubernetes environment.

Architecture clues

  • We wanted to be able to monitor a million tables every 10 seconds," says Datars."Microsoft demonstrated it was a great collaborator in reaching our goals, including building our own data ingester." PointClickCare used Windows Server and SQL Server to identify all existing data sources and blockers, and then synchronized data between source and target databases during the migration process.​ With Azure Kubernetes Service (AKS) , it containerized applications for migration, and now supports new application testing before deployment to ensure quality and compatibility.

Evidence from the source

"We needed a single platform to help more people." Access to real-time data Dean Slawson, Vice President of Advanced Technology at PointClickCare, says with historically low staffing and a wave of new industry clinicians, "We've heard concerns from customers about the burden of keeping up with quality documentation and the need for better insights." PointClickCare knew it could remedy both issues by centralizing its data and systems to M icrosoft Azure and using AI tools like Azure OpenAI Service to improve access to real-time data and documentation processes.
Provider disconnects over patient treatment are all too common, and the healthcare industry is experiencing historically low staffing and a wave of new industry clinicians, which puts patients at risk.
Today, more than 27,000 long‐term and post‐acute care providers; over 2,800 hospitals and 2,000 ambulatory clinics; every major US health plan; and over 75 state and government agencies use PointClickCare, actively serving more than 200 million patients.
We wanted to be able to monitor a million tables every 10 seconds," says Datars."Microsoft demonstrated it was a great collaborator in reaching our goals, including building our own data ingester." PointClickCare used Windows Server and SQL Server to identify all existing data sources and blockers, and then synchronized data between source and target databases during the migration process.​ With Azure Kubernetes Service (AKS) , it containerized applications for migration, and now supports new application testing before deployment to ensure quality and compatibility.
With help from Microsoft, PointClickCare migrated 1 million data tables from multiple locations to Azure with minimal disruption and no reported issues by customers.
Transparency Evidence & methodology 95/100 reference value · 89% 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 depth20/25
    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 scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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
    5 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 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. Microsoft

    PointClickCare customer story

    First-party
    https://www.microsoft.com/en/customers/story/19828-pointclickcare-windows-server
    Source type
    Vendor-published customer story
    Published
    Nov 21, 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 Nov 21, 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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