Quadra - Modern Analytics for Patient Flow Management and Adoption of Amazon QuickSight for Better Decision Making

Healthcare

Modern Analytics for Patient Flow Management and Adoption of Amazon QuickSight for Better Decision Making

Customer Overview

A leading healthcare organization, structured as a not-for-profit charitable trust, aims to provide affordable eye care and eliminate curable eye blindness in India. It is one of the largest community eye care providers in the country, operating ten super specialty eye care hospitals nationwide and performing over 150,000 free eye surgeries annually.

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Business Need

Customer is committed to improving patient satisfaction and operational efficiency by reducing waiting time to consult doctors and various service stations, including scan, therapy, pharmacy, appointment, reception, and billing etc. The lack of robust data analytical practices at customer’s HealthCare environment has resulted in significant delays in processing, analysing data and gaining insights. This deficiency has led to substantial challenges in making timely and informed decisions, consequently impacting both patient care and operational efficiency. Without effective data analytics, Customer struggles to manage and interpret their vast amounts of data, compromising their ability to optimize operations and ensure high-quality patient outcomes.

Customer’s Patient management system generated data are stored in NoSQL Database. The nested data structure in NoSQL makes querying and data retrieval cumbersome. Customer started exploring the solution on third party and AWS, requires a solution that streamlines data management and enhances the efficiency of querying and analysing patient data. Such a solution would enable the hospital to derive actionable insights independently, without the constant need for specialized data expertise.

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Quadra at Work and solution design

Understanding customer business needs, Quadra as an AWS Partner took it up, framed a solution with AWS Data Analytics Services.

Initially, the customer team considered utilizing a third-party tool. However, as an AWS partner, we recommended the adoption of Modern Analytics Services. This approach brings the entire solution under a single ecosystem, allowing for streamlined ETL processes and seamless data visualization.
Additionally, current system had considerable licensing costs, scalability challenges, and a steep learning curve for non-technical users.
We configured NoSQL Database as the data source connector for AWS Glue, utilizing S3 Buckets for storing the raw data extracted from NoSQL DB. Subsequent ETL jobs were performed, with the processed data being stored in S3 Buckets.
Utilized Amazon Athena by writing SQL queries in the Athena Query Editor to query the processed data stored in S3 Buckets.
Structured patient service station access details were visualized using Amazon QuickSight. This setup allows stakeholders with author licenses to view and update the data. The readers, provided with reader access, can efficiently view the data. Admins of customer team manages user permission, facilitating interaction with dynamic dashboards and enabling the extraction of valuable insights.

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Quantifiable Improvements

Benefited outcomes of a Leading HealthCare Infrastructure

Customer has been benefited from Pay-as-you-go pricing model resulted in 30% reduced user license cost compared to other visualization tool.
Interactive dashboards assisting 60% faster and better decision-making, Thanks to Amazon QuickSight.
Overall infrastructure and operations costs associated with ETL, and visualization have been eliminated.
50% Exponential Improvement in operational efficiency, care delivery and patient outcomes
With an efficient ETL process, tailored data has been achieved, decreasing human error rates by up to 80% with Amazon QuickSight data visualization.
35% Quicker and Easier to set up, secure, and manage entire data pipeline

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