Healthcare

AI-Powered Healthcare Operations Knowledge Assistant

Healthcare knowledge assistant reduces document search time by up to 35% while improving access to approved operational procedures.

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healthcare
0%
Source-Grounded Responses
0%
Approved Knowledge Coverage
RAG
Knowledge Retrieval

Client Profile

An Australian healthcare organization that wanted to modernize how hospital staff and care coordinators access operational knowledge. The organization had healthcare procedures, appointment guidelines, follow-up processes, and escalation information distributed across multiple documents and repositories.

Use Cases
AI Agents, Healthcare Knowledge Management, Intelligent Search, Operations
Industry
Healthcare
Project Type
Healthcare Knowledge Management
Scale of Operation
Healthcare operations involving hospital staff and care coordinators accessing operational procedures and patient-care information.
End Users
Hospital Staff & Care Coordinators
Service Provided
Healthcare AI SolutionsKnowledge ManagementRAG DevelopmentIntelligent Document SearchGenerative AI Development
The Client Challenge

Business Process Challenges

  1. Operational knowledge was distributed across multiple documents and repositories.
  2. Appointment procedures, patient follow-up guidelines, escalation processes, and patient-care information were not centralized.
  3. Hospital staff and care coordinators spent significant time manually searching for relevant information.
  4. Staff faced difficulty identifying the latest approved procedures.
  5. Manual document searches slowed responses to operational questions.
  6. There was a risk of inconsistent information being referenced.
  7. The organization needed reliable access to approved healthcare documentation with source traceability.
Our Approach

Our Strategic Approach

  1. Developed an AI-powered Healthcare Operations Knowledge Assistant using RAG on Azure.
  2. Built a conversational interface using React with a Python FastAPI backend.
  3. Centralized healthcare documents using Azure Blob Storage.
  4. Processed documents through extraction, cleaning, chunking, embedding, and indexing.
  5. Used Azure AI Search to retrieve relevant document sections based on user questions.
  6. Used Azure OpenAI to generate grounded responses based on retrieved healthcare documentation.
  7. Added source and section references so staff could trace responses back to the original documentation.
Our Solution Architecture

How we delivered it.

AO
Azure OpenAI
Integrated Azure OpenAI layer in the solution architecture.
AAS
Azure AI Search
Integrated Azure AI Search layer in the solution architecture.
RG(
Retrieval-Augmented Generation (RAG)
Integrated Retrieval-Augmented Generation (RAG) layer in the solution architecture.
AOE
Azure OpenAI Embeddings
Integrated Azure OpenAI Embeddings layer in the solution architecture.
ABS
Azure Blob Storage
Integrated Azure Blob Storage layer in the solution architecture.
R1
React 19
Integrated React 19 layer in the solution architecture.
T
TypeScript
Integrated TypeScript layer in the solution architecture.
V
Vite
Integrated Vite layer in the solution architecture.
TC
Tailwind CSS
Integrated Tailwind CSS layer in the solution architecture.
P
Python
Integrated Python layer in the solution architecture.
F
FastAPI
Integrated FastAPI layer in the solution architecture.
MEI
Microsoft Entra ID.
Integrated Microsoft Entra ID. layer in the solution architecture.
Visual Proof

Explore the Solution Through visuals

Featured Screenshot
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The Outcome

What changed for the client.

  1. Faster access to healthcare operational information.
  2. Reduced reliance on manual document searching.
  3. Quicker responses to routine operational questions.
  4. Improved access to standardized and approved healthcare procedures.
  5. Better traceability through document and section references.
  6. Centralized healthcare knowledge in a conversational platform.
  7. More efficient support for care coordinators and hospital staff.
  8. Scalable foundation for future healthcare applications, including appointment support, patient follow-up assistance, escalation guidance, and workflow automation.
Results & Business Impact

The numbers behind the rollout.

01
Source-Grounded Responses
90%
02
Approved Knowledge Coverage
100%
03
Knowledge Retrieval
RAG
Reference Tech Stack

The full integration layer.

Azure OpenAI
Azure AI Search
Retrieval-Augmented Generation (RAG)
Azure OpenAI Embeddings
Azure Blob Storage
React 19
TypeScript
Vite
Tailwind CSS
Python
FastAPI
Microsoft Entra ID.
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FAQ

Frequently asked questions.

Softree delivers custom solutions across AI and automation, Power Platform, SharePoint customization, full-stack web and SaaS engineering, and data analytics.
We combine modern software engineering standards, secure cloud configurations, pre-built accelerators, and agile delivery methodologies to produce governed, scalable applications.
Our agile delivery model typically produces scoped initial MVPs in 4 to 8 weeks, with comprehensive enterprise deployments completed in 10 to 12 weeks.
Yes. We design and build secure custom API gateways, REST connectors, and database bridges to ensure our custom solutions integrate seamlessly with your existing legacy infrastructure.

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