Success Story

AWS SaaS portal for patient experience

  • Data engineering
  • Healthcare

We developed a new real-time analytics portal which processes patient visit data and survey results for further analysis and reporting. This product was designed so that:

  • frontline employees can improve their service of nurses, physicians, etc.; 
  • experience managers can track and manage the institution performance; 
  • hospital leadership can monitor patient satisfaction level, increase patient experience, and timely react to incidents through feedback analytics and alerts. 

Project numbers

3
runs

took us to successfully complete the delivery

10K
users

can be accommodated on the new portal

>8
months

time-to-market for our real-time platform

Project details

Industry:

Healthcare

Country:

United States of America 

Team composition:

  • Front-End Developer
  • Back-End Developer
  • Data Engineer
  • QA Engineer
  • DevOps
  • Project Manager
  • Business Analyst
  • Data Analyst 

Service provided:

  • Data Engineering 
  • Solution Architecture
  • Quality Assurance
  • Cloud & DevOps
  • Cloud Migration 

Project background

Our client is a US-based company that helps healthcare providers make a difference in patient experience capabilities. They had an on-premises data center, so Engineering had to work closely with the IT department to secure new resources. They planned to create a unified experience platform, enabling organizations with instant knowledge on what matters most to each person they serve. 

 

However, at that point, their technology infrastructure was obsolete, which hindered them from implementing modern features and innovations like AI. Along with that, their data storage was slow to build prompt analytics. Here, we came up with a solution in the form of cloud migration and a new platform development to streamline patient surveying process and optimize costs. 

Challenges

  • figuring out the business domain, available data structure and sources; 
  • quickly switching from IT dependent resource provisioning to self-service; 
  • migrating all the key customers to the new environment and setting automated deployments; 
  • maintaining a balance between infrastructure governance and freedom to provision resources for engineers; 
  • supporting the rapidly growing platform and improving major non-functional requirements. 

Tech stack

AWS EMR
AWS ECS
AWS
Apache Spark
Tableau
Vertica

Solution delivered

We launched a self-service real-time platform with sophisticated security and subscriptions capabilities for a healthcare IT solution provider. We scaled the system from 0 to 10K users that allowed for quick onboarding during rapid customer growth.

Phase 1: Initial design and implementation 

  • Data pipeline was built for ingesting data and storing it in data warehouse; 
  • Dataset preparation steps were taken to set the right data workflow; 
  • We chose to go forward with AWS to facilitate a self-service paradigm for infrastructure management; 
  • The existing infrastructure was migrated to support the new platform into AWS and BI stack was implemented in the cloud. 

Phase 2: Rapid growth. Transition to serverless approach  

  • Critical services to AWS Managed Service were refactored instead of EC2 instances; 
  • A scalable Data Lake pipeline was implemented to support BI, Serverless Subscriptions module;
  • User-facing BI Portal was migrated to serverless managed services in AWS. 

At this milestone, the main deliverable was a serverless web application with the following features:

  • dashboards displaying relevant data; 
  • admin panel enabling users to set security filters, subscriptions, make data extracts and exports to PDF, CSV. 

Phase 3: High-availability and quick recovery  

The next step included: 

  • reinforcing further the new platform availability requirements by rebuilding a single VPC account AWS infrastructure into a multi account setup; 
  • switching all the business-critical processes to the new setting with automated deployment of the platform components; 

At this stage, a complex security mechanism was developed for setting permissions for each user by location-based, role-based, and patient health information security.

Benefits

No idle periods

Self-service infrastructure made the Engineering team much more efficient (no idle periods caused by waiting for the IT department to provision resources) 

Cost savings

Cost optimization was significant: lower implementation cost compared to on-premises infrastructure and cheaper infrastructure cost compared to EC2  

Total support

Product supports network segmentation between environments and products & resource access segregation, that is relevant due to sensitive data nature 

HIPAA compliance

The switch made it easier to pass audits with HIPAA certified AWS Managed service 

Ongoing concern

No interruption in the work when switching from the old infrastructure to a new one 

Autoscaling

The system adapts to increasing load without compromising the system performance 

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