Case Study

Ideas2IT | AWS Case Studies

How We Helped Element5 Unlock Self-Service Insights with TAG-based Chat DB for Data-Driven Decision-Making

About the Client:

Element5 is a leading provider of agentic workflow automation solutions for post-acute healthcare. By leveraging AI-driven automation, they enhance operational efficiency and enable healthcare professionals to focus on delivering quality patient care. Their intelligent, autonomous systems streamline complex administrative processes, allowing healthcare teams to operate more efficiently and improve patient outcomes

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Industry

HealthTech

Location

Headquartered in San Jose, California

Challenge

Limited data literacy among post-acute care professionals hinders data-driven decision-making, leading to inefficiencies and suboptimal patient care.

Featured Services

(Amazon) Bedrock, SageMaker, EC2, RDS, OpenSearch Service, Redshift; AWS Lambda, AWS Glue

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Challenge

Healthcare professionals and management often face challenges in accessing and analyzing critical data due to the complexity of SQL queries and the limitations of traditional BI dashboards. These technical barriers slow down decision-making and hinder operational agility.

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Element5 needed a solution that democratized data access to enable instant self-service insights without requiring technical expertise.

Amazon Web Services leveraged:

  • Amazon Bedrock: Powers AI-driven natural language processing to translate user queries into SQL.
  • Amazon SageMaker: Used to do the preprocess with the redshift data and transfer to s3
  • AWS Lambda: Handles real-time processing of natural language queries in a cost-efficient, serverless environment.
  • Amazon RDS: Stores and processes structured healthcare data with high performance and reliability.
  • AWS Glue: Automates ETL pipelines, transforming raw healthcare data into structured formats for analysis.
  • Amazon Redshift: Provides a scalable data warehouse for storing and processing healthcare data at high speed.

Solution Architecture

Element5 adopted TAG-based Chat DB, an AI-powered query engine that democratizes data access for healthcare professionals and management. Instead of navigating complex BI tools or writing SQL queries, users can simply ask questions in plain English.‍

The system automatically translates these queries into SQL, retrieves relevant data, and delivers precise, natural language summaries. Interactive charts and visual insights are auto-generated, helping users quickly identify patterns and trends. Advanced auto-complete suggestions and query refinement features guide users toward the most relevant insights, even without technical expertise.

We built the solution with robust privacy safeguards to ensure data protection and compliance, while intelligent query history management minimizes redundant processing, optimizing both performance and cost. This AI-driven approach empowers healthcare professionals to make faster, data-informed decisions, improving operational efficiency and patient outcomes.

High-level architecture diagram:

Data Literacy Chatbot latest
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Business Impact:

  • Faster Decision-Making – Data retrieval time reduced from hours to seconds, enabling quicker, more informed actions.
  • Democratized Data Access – Non-technical staff can independently access insights without relying on data analysts.
  • Operational Efficiency – Streamlined workflows improved decision-making speed by 40%, boosting overall productivity.
  • Cost Optimization – AI-powered query history management eliminated redundant processing, reducing cloud expenses.
  • Data-Driven Culture – Widespread adoption of real-time insights empowered staff to make evidence-based decisions for better patient care and strategic planning.

Results:

  • 90% reduction in time spent retrieving and analyzing data
  • 30% increase in operational efficiency across healthcare teams
  • Significant boost in data-driven decision-making adoption
  • Higher accuracy and insights for patient treatment plans
  • Cost savings through AI-driven query optimization and cloud efficiency

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