Healthcare
Extending the capabilities of Healthcare with AI and Sparkflows
Sparkflows accelerates healthcare delivery by improving patient outcomes, reducing costs, and enhancing the overall quality of care through a Best-In-Class AI-powered Business-driven
Self-Service Data & AI Solution building Platform.
Healthcare Business Opportunity
Healthcare Business needs to address Data challenges and adopt AI-driven culture
Business Challenges
IT Challenges
Negative Impact
Incomplete view of patient data
Lack of scalable Integration with Cloud Clusters and Data Lakes
Inability to handle growing no of use cases
Low ROI
Fraudulent Claims
Incomplete Security and Governance Models
Lack of Multi-persona Collaboration
Delay in Time-to-market
Disproportionate Member Costs
Unmanageable Cost of Data Movement and Process Execution
Rising costs of skilled resources
Missed Business
Opportunities
Inability to detect adverse effects
Performance and scalability Issues for migration , integration and modernization
Data outpacing legacy systems
Declining Revenue
Multi-Persona Self-Service
Data Science Platform
Leverage the Self-Service Push-Down Data-Centric AI Platform on Data Lakes to build Business-driven Point-n-Click Apps, Pipelines, and Workflows for quickly turning large amounts of data into actionable insights through minimal Touchpoints
Healthcare Data Products
Build solutions using pre-built templates
CASE STUDY
Sparkflows at a large Healthcare Company
Company Objective
A very large healthcare insurance company operating in all states of US had a requirement to speed up business case development and boost overall productivity and engineering efficiencies.
The data engineers and analysts in each region need to be empowered with self-serve advanced analytics and deliver top quality results quickly.
Business Use Cases
Customer had identified several data engineering and Big Data analytics science use cases including :
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EHR data analysis
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Patient Admission record analysis
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Claims data analysis
Challenges
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Complex & Time-Consuming Engineering process
The current data engineering processes are somewhat complex and time consuming. They involve a lots of local coding and little automation. Oftentimes lacks regulated process. Cloud automation and Compute Push-down are not easy to implement.
Hard for Data Analysts, Scientists, Engineers and Product to Collaborate
In the absence of a powerful Self-Service Co-development Platform, it is impossible for the users to collaborate.
Getting Value Out of Data
Not enough Data Analytics and ML applications are currently in production. Hence, they need to be quickly built.
Inability to scale out use cases
Need to scale from local machine to cloud. Need to boost productivity and reduce time-to-market while accelerating the solutions.
Solution
Fast development and deployment on top of AWS Datalake and EMR. Quick delivery of Business use cases and fast Time-to-Market
Sparkflows was installed in the secure air-gapped cloud environment.
Admin quickly configured the secure connections with AWS and RDS.
Data Engineers could quickly connect to Data Lakes and perform scalable ETL, automatically generate distributed code.
Users could create the visual point & click workflows in minutes using existing templates.
They could read data from S3, DynamoDB, RDS etc., transform them and perform extensive Data Quality and generate Reports at scale.
The jobs would run distributed on EMR and automatically scheduled
through Airflow and hence could scale easily.
Using the 450+ functions in Sparkflows and 80+ ML algorithms, the
users were quickly able to build accurate ML models.
Multiple teams quickly collaborated with each other by sharing projects
and workflows in secure manner and by pushing workflows to Github.
Domain knowledge could be easily persisted and reused.
Business teams quickly built Analytical Apps and powerful reports in
the same project.
Benefits
15X
Increase in User Adoption
35X
Reduction in Time to Market
20X
Increased Collaboration
15X
Higher Accuracy of Models
20X
Higher Sales of SKUs which are
difficult to sell otherwise