Hospital Readmission Prediction Model for a Fortune 500 Healthtech Startup

  • Published: March 29, 2024

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Discover how Reveal used predictive modeling to determine whether a patient was likely to need skilled nursing care or home care after hospital discharge.

 

Minimizing Patient Readmissions

Readmitting a patient just after they have been discharged from the hospital is not an ideal scenario. It bears a cost implication for the patient as well as the provider. Our client wanted to leverage predictive modeling to determine the likely risk of patient readmission.

 

Crafting the Solution

Reveal crafted a tailor-made machine learning model to forecast patients’ need for post-discharge care and their risk of readmission. The model analyzed over 10 TBs of hospital discharge and referral data that was gathered over seven years. A HIPAA-compliant, AWS-based data science platform was built that ensured the confidentiality and integrity of patient data while enabling seamless data processing and analysis.

 

Streamlined Healthcare Efficiency

The implementation of Reveal’s solution demonstrated how predictive modeling could generate impactful insights. By proactively addressing patient needs, the risk of readmission could be reduced. The model was also able to drive optimization of resource allocation and workflows leading to improved patient outcomes.

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