Case Study: Interpreting Medical Data

The Problem

A major insurance provider wanted to mitigate their risk by greater use of a government-funded support program for Medicare and Medicaid patients. The state administrator of Medicare and Medicaid did not have a systematic way to identify patients who qualify for support services. They needed help interpreting their medical data.

The Goal

Interpreting medical data, create a risk stratification pool based on demographics, behavior, and past health history. This pool will provide the list for referral to additional government assistance. Insurance company will now know who they can refer.

How We Solved it

The RoboSource team developed an algorithm to determine risk pools, using weighted criteria. The criteria are based on demographics, past hospitalizations, current medications, and ICD10 diagnosis codes. We also built the algorithm to account for duplicate records since it runs once per month and may pull in the same insured multiple times. The database was developed using Hadoop, Apache Spark, SQL queries, and Kylo. It automates the process of getting files from insurance providers into the database to determine the risk stratification score.

The Result: Interpreting Medical Data

There is now systematic criteria based on data analysis to determine qualified patients for government assistance. Anyone over a certain risk stratification score will be eligible for a referral, and duplicates are filtered out of the report.

The RoboSource solution has increased referrals from the major insurance company to the government program by approximately 500%.

There are implied cost savings to the major insurance provider. Patients that use government services in this program have improved outcomes. And soon, those outcomes will become quantifiable.

Read more about RoboSource solutions here.