Python for Predictive Healthcare Analytics

About the Client

NDA Signed, a UK-based healthcare provider, aimed to improve patient outcomes by predicting hospital readmissions and managing high-risk cases more effectively. With thousands of patient records generated daily, they needed a data-driven system to analyze medical histories, prescriptions, and test results to highlight potential risks before they escalated.

The Solution

Dotsquares developed a powerful analytics platform leveraging Python’s Machine Learning and Data Science libraries. The solution used Pandas and NumPy for structured data processing, and scikit-learn for training predictive models. By integrating Natural Language Processing (NLP) modules, the system could also analyze doctors’ notes and unstructured patient data.

• Integration with existing Electronic Health Record (EHR) systems for secure data exchange
• Predictive modeling for patient readmission risks
• NLP-based processing of unstructured notes and lab reports
• Automated alerts for high-risk patients to trigger timely interventions

Results

• 85% accuracy in predicting patient readmissions within 30 days
• Improved allocation of medical staff by identifying critical cases early
• Reduction in avoidable hospital stays, lowering operational costs
• Enhanced patient satisfaction through proactive treatment strategies