Challenges

Differentiation in a Competitive Market

Established competitors in the Healthcare Risk Adjustment space create a strong need for product differentiation. Many products in the market focus on utilizing alternate data sources and implementing rules for suspecting and targeted analysis.

Elaboration Challenges

As a startup, the customer faced difficulties in elaborating their ideas and were caught in a continuous cycle of ideation.

Balancing Parallel Demo and Product Roadmap

There were conflicting requirements to align with the product roadmap that required the creation of parallel demo versions for potential customers and investors.

Accuracy in Suspect and Target Analysis

Ensuring accurate suspect and target analysis posed a challenge to provide multi-tenant capabilities while maintaining data isolation, security, and customization options for different tenants.

Simultaneous Product Launch across LOB’s

Contrary to the initial plan of a phased go-to-market strategy, targeting different Line of Businesses (LOBs) at different times, the expectation was parallel launches for both Medicare and Commercial LOBs.

Our Solution

To address these challenges the approach was as follows:

Differentiation in a Competitive Market

Ejyle enhanced the accuracy of the analytics by using a combination of ML and rule-based algorithms to build a single platform that could be used across Medicare, Medicaid, and ACA Commercial LOBs. This made the client’s product more accurate and versatile, which helped them to differentiate themselves from the competition.

Elaboration Challenges

Ejyle’s team took a consultative approach and helped the client to zero in on the specific solutions and modules needed. We also provided complete requirement elaboration and design, as well as rapid incremental builds. This has helped the client to elaborate their product idea to meet their needs.

Balancing Parallel Demo and Product Roadmap

Ejyle’s platform development approach allowed parallel demo builds and core product development. This resulted in quick and easy demos that showcased key features of the product, feedback from potential customers and the product ready for the market.

Accuracy in Suspect and Target Analysis

Ejyle used a combination of rule-based and ML algorithms to enhance the accuracy of the suspect and target analysis by leveraging multiple data sources. This helped to identify and target high-risk patients accurately and to improve the health of their population.

Simultaneous Product Launch across LOBs

Ejyle incorporated parallel builds of the risk adjustment algorithms across LOBs to ensure product readiness across multiple LOB’s. This has allowed the client to launch their product quickly and to reach a wider audience.

Key Outcomes

Improved Accuracy of Suspecting

The combination of ML and rule-based algorithms improves the accuracy of suspecting, which allows the client to target high-risk patients more effectively.

Accurate Financial Impact and Revenue Projections

Accurate financial impact and revenue projections, which helps the client to make informed decisions about how to allocate their resources.

Chase Recommendations

Chase recommendations, which helps the client to close risk gaps more quickly.

Rich Population Risk Analytics

Rich population risk analytics, which helps the client to identify populations that are at risk for certain health conditions.

Prediction of Future Health Conditions

ML to predict future health conditions, which helps the client to target patients with preventive interventions before they develop health problems.

Improved Care for Patients

Help providers to improve the care they provide to their patients by providing them with insights into their patients’ health status.