Mohammed Salim Sayed - Databricks

Mohammed Salim Sayed

Principal Data Engineer, Optum

I have been developing software since 2001,at present I am working for Optum Inc on a medical claim processing system. The healthcare industry being old is full of monoliths. I have been helping to revamp these applications to achieve simplicity, integrability, scalability and performance so that development and maintenance of the software is painless and running cost is proportionate to its earning.On the personal front I care a lot about environment, equal opportunities and human rights.I wish to build a compassionate word through software. My Stack Overflow profile – https://stackoverflow.com/users/3746637/salim

UPCOMING SESSIONS

Healthcare Claim Reimbursement using Apache SparkSummit 2020

Optum Inc helps hospitals accurately calculate the claim reimbursement, detect underpayment from the Insurance company. Optum receives millions of claims per day which needs to be evaluated in less than 8 hours and the results need to be sent back to the hospitals for revenue recovery purposes. There is a very low tolerance for delay and error in the claim processing pipeline because it saves millions of dollars in revenue for the hospitals. Most of the US hospitals run at a profit margin of 2%, without accurate claim processing, many hospitals won't be able to survive. In the past 3 years, we have been replacing an Oracle-based system by one written in Apache Spark, Scala, and Java.

In this presentation, we will discuss

  1. The conversion of claim ingestion process from PLSQL on Oracle to Apache Spark which improved the performance by 50% and reduced the cost significantly.
  2. The design and POC have been done to replace the 'claim reimbursement' module from PLSQL to Spark.
    1. How using Spark opens up our solution space to accommodate both batch and streaming which was not viable with Oracle. Oracle is good for only batch processing with tons of trouble.
    2. How Spark is helping us write code once and run in a variety of ways which saves us development cost without compromising performance and scalability.
    3. How Spark helps us write better code which can be easily unit tested, integration tested on our IDE without needing any special infrastructure.
    4. How using Spark and the public cloud helps us reduce the cost of operation.
    5. How it enables us to enhance the claim processing pipeline with the application of machine learning which was pretty much impossible before with Oracle.

PAST SESSIONS