Availity accelerates data-driven healthcare decision-making with Amazon QuickSight and Amazon Q

This post was written with Doug Sallas and Andy Young from Availity.

Availity is a leading healthcare information technology company that serves as a trusted intermediary between health plans, providers, and technology partners. We’re committed to empowering healthcare stakeholders with the information and insights needed to improve business processes and patient outcomes. Our company facilitates billions of clinical, administrative, and financial transactions annually, connecting over 2 million providers and thousands of healthcare organizations nationwide.

In this post, we explore how we’re using Amazon QuickSight to transform our analytics capabilities and accelerate data-driven decision-making across our extensive healthcare network, an interconnected ecosystem of payers, providers, insurance companies, and individual healthcare practitioners that rely on Availity’s services for seamless data exchange and clinical–administrative workflows.

By implementing QuickSight as our enterprise-wide business intelligence (BI) solution, we’re enabling our vast ecosystem of healthcare organizations to gain real-time insights from billions of transactions using self-service analytics and natural language querying through Amazon Q in QuickSight. The Amazon Q in QuickSight solution maintains strict healthcare compliance requirements while demonstrating how cloud-based BI tools can democratize data access at scale.

The business challenge

Availity’s analytics infrastructure operates at remarkable scale, processing over 2 billion healthcare transactions monthly. Our enterprise AV360+ Dashboard alone draws insights from 500 GB of data comprising 700 million rows. The complete analytics environment includes more than 100 production datasets powered by an Amazon Redshift cluster exceeding 2 petabytes in size. This massive data foundation enables Availity to deliver timely, actionable insights across our extensive healthcare network while maintaining the performance expectations of users.

Despite having access to vast amounts of valuable healthcare data, Availity faced significant challenges in making this data readily accessible and actionable for both internal teams and external customers, which include both healthcare payers and providers. The traditional approach to BI was creating numerous bottlenecks throughout the organization.

Our analytics processes were heavily dependent on specialized BI analysts who were required to create and modify all dashboards, creating a technical skills barrier that limited data accessibility. This dependency resulted in report development cycles that typically took days or weeks to complete, severely impacting the organization’s ability to make timely decisions. The situation was further complicated by a growing backlog of dashboard requests, with teams repeatedly creating one-off reports rather than developing sustainable dashboard solutions that could serve long-term analytical needs.

The varying levels of technical expertise among users across Availity’s ecosystem created additional barriers to data access, with many business stakeholders unable to explore data without technical assistance. Meanwhile, healthcare providers and payers increasingly demanded immediate access to performance metrics and claims data without lengthy IT requests because delays directly impacted their operational efficiency and financial outcomes. The inability to provide self-service access to these critical insights was limiting Availity’s ability to deliver value to its customers.

At Availity, we recognized that to truly unlock the power of our healthcare data ecosystem, we needed a self-service analytics approach that could democratize data access across technical and nontechnical users alike while maintaining appropriate governance and security controls.

The solution

Availity collaborated with Amazon Web Services (AWS) to develop a comprehensive self-service healthcare analytics platform built on QuickSight and powered with Amazon Q in QuickSight. The solution was designed to empower both our internal teams and external healthcare partners to independently explore, analyze, and visualize curated healthcare datasets. A key strength of Availity’s implementation is the strategic use of Amazon QuickSight Embedded capabilities, which we use to seamlessly integrate interactive dashboards directly into our healthcare products, creating a unified experience for customers.

Availity selected Amazon Q in QuickSight to eliminate technical barriers that previously required specialized BI analysts, democratizing data access across the organization. The natural language querying capabilities allow healthcare professionals without technical expertise to ask complex questions about claims and provider performance in plain English. Amazon Q in QuickSight generates instant visual insights, significantly accelerating decision-making processes while maintaining the stringent data governance requirements essential in healthcare environments.

Architecture walkthrough

Availity’s implementation uses multiple AWS services to create an end-to-end analytics platform that addresses the unique challenges of healthcare data processing. The architecture shown in the following diagram illustrates how data flows from various healthcare source systems through a robust processing pipeline to deliver actionable insights to both our internal teams and external customers. This comprehensive solution encompasses data foundation, processing, visualization, and AI-powered analytics capabilities while maintaining healthcare-specific security and compliance requirements throughout.

• Data foundation – We established a curated data layer with properly modeled datasets specific to different healthcare use cases.
• Amazon Redshift integration – Healthcare transaction data from various source accounts is consolidated into Amazon Redshift, creating a centralized data warehouse with over 2 PB of data. This serves as the primary data repository for Availity’s analytics workloads.
• QuickSight Super-fast, Parallel, In-memory Calculation Engine (SPICE) implementation – Data is periodically refreshed from Amazon Redshift into SPICE, the in-memory engine QuickSight uses to deliver consistently low-latency dashboard performance, even when analyzing massive datasets with hundreds of millions of rows.
• QuickSight enterprise deployment – Building on the optimized data foundation, our team deployed QuickSight across the organization with custom-designed dashboards and analytics experiences tailored to different healthcare scenarios. This cloud-based implementation eliminated the maintenance overhead of Availity’s previous on-premises BI platform while providing greater scalability to support our growing user base.
• Security and governance – Row-level security and data access controls were implemented so that users could only access information relevant to their role and organization.
• Amazon Q integration – Amazon Q in QuickSight was configured to understand healthcare-specific terminology and metrics, enabling natural language querying against the datasets.

Implementation strategy

We designed the platform with templates and guided analytics experiences, enabling users to customize visualizations without requiring technical expertise, a true self-service dashboard creation environment. Before committing to full deployment, Availity conducted a controlled pilot with 300 customers to validate the approach and gather essential feedback on usability and functionality. This methodical testing phase proved invaluable for refining the solution.

Following the successful pilot and incorporating user feedback, the solution was systematically scaled to serve our entire customer base of over 100,000 healthcare organizations. This phased implementation approach across 6 months ensured that challenges could be addressed incrementally while maintaining service quality and user satisfaction throughout the enterprise rollout.

Dashboards

We have dashboards that align into three main groups that include activity, operations, or customer engagement. Below, we provide examples of an activity dashboard and customer engagement dashboard. These dashboards are empowering our internal and external customers to quickly solve difficult problems without having to be data experts.

The AV360+ dashboard enhances our legacy summary dashboard, providing users with new visualizations and insights into the health and trends of the healthcare network. This dashboard enables both customers and internal users to customize their views according to specific needs, allowing a single dashboard to serve a variety of business requirements. By providing this flexibility, AV360+ has successfully addressed the challenge of delivering comprehensive network health monitoring while accommodating diverse analytical perspectives.

A key enhancement to AV360+ is the integration of Amazon Q in QuickSight, which transforms how users interact with their data. Availity uses Amazon Q natural language capabilities to give users the ability to ask questions about our AV360+ dashboards in plain English, helping them dig deeper into anomalies in transaction volume without writing complex queries. For example, users can type questions such as, “Show me rejected claims by provider last quarter” or “How many unique submitters did we have in the last 7 days?” and immediately receive visualized insights. This capability has been particularly valuable for identifying trends and potential issues before they impact healthcare operations. Amazon Q has empowered business users to uncover insights that would have previously required assistance from data analysts.

The implementation was completed efficiently; we migrated from the legacy version while learning QuickSight in only 1 month. Today, over 600 internal users actively use this dashboard, demonstrating its value as an essential tool for monitoring critical healthcare network performance metrics. The following screenshot shows the Availity 360+ dashboard powered by Amazon Q in QuickSight.

The Provider Engagement dashboard delivers on-demand visibility for health insurance companies to monitor how their providers utilize tools purchased from Availity. This transparency means payers can make data-driven decisions to improve their Availity toolsets so that providers consistently receive a top-tier experience. The dashboard serves as a critical feedback mechanism in the healthcare ecosystem. Payers can identify adoption trends, usage patterns, and potential areas for enhancement. This implementation marked an important milestone in our analytics evolution as the first dashboard where we enabled Highcharts functionality. Users can explore detailed breakdowns of provider activity with unprecedented depth and interactivity. By making these insights readily accessible, payers can optimize their provider tools and strengthen their healthcare network relationships. The following screenshot shows the Provider Engagement dashboard.

Benefits and results

The implementation of QuickSight has transformed Availity internal users and how our customers interact with healthcare data:

• Accelerated insight generation – Dashboard creation time reduced from days to minutes, enabling rapid decision-making
• Democratized data access – Users with varying levels of technical expertise can now independently generate meaningful insights without extensive training
• Reduced development backlog – The self-service approach has significantly decreased the backlog of dashboard requests that previously overwhelmed the BI team
• Enhanced user adoption – The intuitive, natural language interface provided by Amazon Q has driven exceptional adoption rates across the organization
• Scalable solution – The platform seamlessly scaled from 300 pilot users to over 100,000 customers ahead of schedule
• Operational efficiency – Healthcare providers can now access critical business insights on demand, supporting improved operational decision-making
• Managed and compliant solution – As a fully managed service, QuickSight eliminates infrastructure management while maintaining Health Insurance Portability and Accountability Act (HIPAA) compliance, which means our teams can focus on analytics rather than platform maintenance

Lessons learned

Throughout this implementation, we gained valuable insights that can benefit other organizations pursuing similar initiatives:

• Start with well-defined use cases – Beginning with specific, high-value use cases helped demonstrate immediate value and build momentum
• Invest in data quality – The effectiveness of self-service analytics depends heavily on properly modeled, accurate data foundations
• User training is essential – Even with intuitive interfaces, providing targeted training sessions significantly accelerated adoption
• Iterative improvement – The pilot program provided critical feedback that shaped the final solution before enterprise-wide deployment

Looking to the future

Amazon QuickSight and Amazon Q are transforming data access in the complex healthcare industry. By implementing this self-service analytics platform, we’ve successfully broken down traditional barriers to data insights, empowering thousands of healthcare stakeholders to make more informed decisions without specialized technical expertise. Looking ahead, we plan to expand our QuickSight implementation by scaling to tens of thousands more users, enhancing self-service capabilities through API-driven dashboard embedding and using Amazon Q to further democratize data access.

Our journey with Amazon QuickSight and Amazon Q represents only the beginning of how we’re reimagining healthcare analytics at Availity. Our partnership with AWS has been instrumental in that we’ve become early adopters of Amazon Q in QuickSight, developing valuable expertise in implementing generative AI–powered analytics in highly regulated healthcare environments. We’re committed to continuing this innovation journey, developing new ways to help healthcare organizations unlock the full potential of their data while maintaining the highest standards of compliance and security.

To learn more about Availity’s healthcare solutions and how we’re transforming healthcare data, visit Availity. To learn more about how AWS can help your healthcare enterprise meet its goals, visit AWS for Healthcare & Life Sciences.

For additional information about Amazon Q’s integration with QuickSight, please refer to the documentation on Amazon Q in QuickSight:

• Getting started with Amazon QuickSight Q
• Answering business questions with Amazon QuickSight Q
• Using Generative BI with Amazon Q in QuickSight

About the Authors

Doug Sallas is a healthcare industry leader with over 20 years of experience in product development, engineering, and analytics. As Director of Data Products at Availity, he specializes in implementing data-driven solutions to enhance healthcare operations and improve patient outcomes. With a unique blend of technical expertise and strategic vision, Doug translates complex data into actionable insights, driving innovation across the industry. He is passionate about the evolution of data products and their role in transforming healthcare through smarter, more efficient solutions.

Andy Young is the Senior Director of Data Engineering, Assets and Products at Availity, overseeing the company’s technology infrastructure and ensuring operational efficiency across the enterprise. Andy brings decades of experience in healthcare IT systems and has been instrumental in Availity’s digital transformation journey.

Praveen Allam is an Account Solutions Architect at Amazon Web Services (AWS), specializing in helping organizations harness cloud technologies to solve complex business challenges. With a focus on data-driven transformation, his expertise in analytics and generative AI technologies leads customers to accelerate AI adoption and create more intelligent, responsive systems across industries.

Danilo Liberato serves as a Customer Success Manager at Amazon Web Services (AWS), partnering with organizations to ensure they achieve their desired outcomes with AWS services. Danilo focuses on helping customers accelerate their cloud adoption journey through best practices and continuous optimization.