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Patient Risk Score Prediction

Implemented a solution to identify a patient's risk score based on their profile

Client Background

The client is a technology company which provides cloud-based platforms to the healthcare industry.

Challenges

  • The client wanted to implement a solution to identify a patient’s risk score based on their profile – a certain demographic, health condition, etc.
  • The aim was to identify the set of individuals prone to diseases who would benefit from proactive care or lifestyle changes. For example, those patients at risk of developing diabetes, who would benefit from preventive care.

Solutions

  • Collected patient profiles from various ETL Data pipelines and fed them into the database.
  • Used factors such as demographics, disease conditions and HCC model to arrive at a total individual risk factor.
  • Used clustering algorithms to identify the classes of ailments patients are suffering from based on the claims data.

 

Tools & Technologies

R, Python, Greenplum, Hortonworks, tableau

Key benefits

  • The ultimate goal of the risk model is improving the quality of care while reducing costs.
  • It improves the quality of care for those with chronic and expensive-to-treat diseases by using IT solutions that track and manage their care.
  • This model improves clinical care & outcomes while reducing costs, and proves to be a win-win situation for both patients and providers.

 

Key Benefits - Patient Risk Score Prediction

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