Canada
Puerto Rico
USA
Belgium
France
UK
India
Client Background
The client is one of the leading healthcare providers for patients with chronic and acute renal disease.
Client Need
The client sought to transition from reactive to proactive patient care by leveraging predictive analytics to prevent the hospitalization/re-hospitalization of patients’ post-dialysis.
Lacked the ability to accurately predict the possibility of hospitalization for high-risk dialysis patients
Inefficiency in resource allocation and patient scheduling led to suboptimal utilization of dialysis slots
Solution
We addressed the client’s challenges through:
Data-Driven Patient Risk Assessment: Analyzed comprehensive patient visit data, providing a foundation for accurate and insightful patient risk assessment
Predictive Insights: Developed classification ML models using advanced ML and DL algorithms, enabling the prediction of patient hospitalization rates
Feature Engineering: Analyzed important features and derived new ones to augment the model’s performance
Targeted Interventions: Interpreted the reasons behind predicted hospitalizations using model interpretability techniques for improved patient outcomes
Realized Benefits
The implemented solutions resulted in significant improvements:
Achieved a 72% ROC-AUC score in predicting the likelihood of patient readmission
70% increase in dialysis slot utilization
Improved client’s rating by monitoring the hospitalization of CKD patients
Enabled efficient utilization of resources by improving care to high-risk patients
Achieved substantial medical cost savings through preventive care
Tools & Technologies
Python
Anaconda
Spyder
Scikit Learn
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