CASE STUDY Heathcare

Lifescan Optimizes Data Architecture to Improve Patient Care with AWS

LifeScan is a US-based medical device company that specializes in diabetic treatment and management.

More than 20 million people around the world depend on its OneTouch® blood glucose monitoring devices to help them manage their condition. For over 35 years, LifeScan has had an unwavering commitment to improving the quality of life for people with diabetes by developing products defined by simplicity, accuracy, and trust.

In support of that mission, LifeScan set out to incorporate highly robust and reusable software coding procedures into its business operations that would save significant time and money, while meeting its mission-critical objectives.

  • SPECIALISTS IN DIABETIC TREATMENT

    SPECIALISTS IN DIABETIC TREATMENT

  • 20 MILLION PEOPLE USE ONETOUCH®

    20 MILLION PEOPLE USE ONETOUCH®

  • +35 YEARS COMMITMENT

    +35 YEARS COMMITMENT

digitize business process for dealer financial services

In 2015, LifeScan partnered with Infostretch to put that into action. Since then, the engagement has expanded to encompass analytics, mobile automation, web testing, front-end development, backend development, and country migration to completely redo their website and platform with a new, digitally enabled architecture, automation, development, and data analytics.

The Customer Journey

2016

2017

2018

2019

2020

2016
  • Established automation for mobile platform
  • Enabled CI/CD for mobile platform

2017
  • Started mobile and web manual testing
  • Mobile 3.0 automation

2018
  • GDPR – web manual & mobile automation
  • NEO – mobile automation
  • Mobile API performance testing
  • POC – BLE simulator

2019
  • 3rd party API AWS migrations
  • 3rd party API automation
  • POC – TDM

2020
  • Admin portal – web manual & API automation
  • MCK analytics & licensing – manual
  • CGM data receiver – performance, API automation

THE CHALLENGE

Building a Data Analytics Platform to Scale with LifeScan’s Business & Technical Requirements

  • One of LifeScan’s most significant challenges was obtaining long-term clinical data accuracy and consistent system performance for blood glucose monitoring of diabetic patients.

    It was critical that the company met and sustained the minimum requirements of the International Organization of Standardization (ISO) and the European harmonized version, demonstrating both product accuracy and consistency. It was also subject to the Health Insurance Portability and Accountability Act (HIPAA), a regulation that mandates security and privacy for Personal Health Information (PHI). Ensuring HIPAA compliance in a SaaS context was a complex and specialized area, encompassing a variety of technical challenges covering intrusion detection, encryption of data, sophisticated auditing capacity, and OS-level security establishments.

Key requirements
Business

Perform massive data processing of glucose meter data to generate various reporting metrics and KPIs and gain real-time insights into patients’ health conditions, percentage of patients with hypoglycemia, hyperglycemia, etc.

Technical
Security design
Security design

Data retention policies
Data retention policies

Provisioning for auditing needs
Provisioning for auditing needs

COST effective
COST effective

Serverless cloud setup to process glucose meter data up to several GB in size.

standardized coding
standardized coding

Design of standardized coding practices for complex ETL logic blocks.

Ability to run concurrent queries
Ability to run concurrent queries

Pay-per-query cost model
Pay-per-query cost model

Supports query load of hundreds of reporting users.

westhill-escalation-management
ACCURATE ANALYTICS

Ability to generate analytics with 100% data accuracy, shorter SLAs and cross-domain reporting capability.

Enable Industry’s best optimizer
Enable Industry’s best optimizer

Parallel query processing, efficient indexes and several intelligent scan techniques to eliminate the limitation on data accessibility as network of patients and physician grows exponentially in the near future.

LifeScan’s end goal was to build and deploy vetted, reusable coding procedures that would save significant time and money while meeting mission-critical objectives.

With the right approach, the company could reduce the cost of on-boarding new diabetic patients by more than half compared to its existing approach.

Our methodology

how
we did it

With this company, Infostretch worked across all stages of the digital lifecycle on multiple projects for two separate business units.

Go Digital
Go Digital

Accelerating the delivery of new digital initiatives with confidence

Be digital
Be digital

Creating the infrastructure and foundation to scale digital initiatives

Evolve Digital
Evolve Digital

Leveraging data and analytics to continuously improve digital delivery processes

The Solution
Amazon Web Services (AWS) & Infostretch

LifeScan needed an ETL tool that could process the glucose monitoring datasets with 100% accuracy, enable in-memory iteration on complex data models at the field level, provide serverless interactive query services, and provide flexibility around query modeling.

Infostretch conducted a two-week assessment of detailed business requirement to understand:

data analytics platform on aws functionality

Key business-critical functions and how each of them contributed to the overall growth of LifeScan taking into account different service offering, patient interaction mechanisms and feedback channels.

aws data analytics architecture

How to best process LifeScan’s large dataset while handling a variety of complex data types and assuring accurate analytics.

cloud services to meet to meet operational efficiency

The right cloud services to meet LifeScan’s cost, performance, security, operational efficiency, and resiliency requirements.

The right choice

AWS Glue was the right choice because of its ability to provide a fully managed ETL tool employing Spark in-memory engine and serverless analytics platform with a dedicated data frame to handle field-level logics.

serverless analytics platform

Its underlying in-memory engine was fully managed Apache Spark, a distributed processing system primarily utilized for big data workloads. Spark enabled in-memory caching and optimized execution for fast performance. It supported general batch processing, streaming analytics, machine learning, graph databases, and ad hoc queries as well as a pay-per-query to save costs for the growing number of reporting users.

Amazon S3 simple storage was used to save on storage costs, and the Amazon Glue crawler populated the data stores of Amazon DynamoDB through its native interface. It also crawled Amazon API Gateway to create, publish, maintain, monitor, and secure APIs.

The engagement also included building a data lake in which Amazon Athena and AWS Glue were employed to meet various corporate reporting needs with high accuracy. By moving to a standard configuration that could be self-provisioned, the team was able to deploy new code changes, enhancements, and updates to the transformation logic. This shift allowed for the in-time data provisioning needed to bring visibility to some of the key business KPIs and scalable systems to support sudden data growth.

our approach

As a part of this project, Infostretch also created compliant infrastructures, error-proof data flow mechanisms, data reconciliation procedures, and reports using an AWS Athena reporting tool.

Testing strategy, planning, and methodology were also critical to ensuring a high level of accuracy in the reports. AWS Enterprise Support also ensured the client’s ability to gain long-term access to the product developers behind the services.

digital platform development approach

ETL modernization, data engineering and analytics

Infostretch was able to provide solutions to each of these challenges and enable the company to transform its data analytics and intelligence platform.

aws
SERVICES USED:
  • Amazon RDS

    Amazon RDS

  • amazon-cognito

    Amazon Glue

  • amazon-dynamodb

    Amazon S3

  • amazon-api-lambda

    Amazon Athena

  • amazon-sns

    Amazon Spark

  • amazon-cloudwatch

    Amazon Enterprise Support

the Results

Key Outcomes

45% reduction
45% reduction

In IT infrastructure costs.

Improved Data Accuracy
Improved Data
Accuracy

Significant improvements in data accuracy and system consistency.

HIPAA Compliance
HIPAA Compliance

Fully compliant solution and improved data processing capacity.