Case Study: Predicting Patient Readmission Rates

Healthcare

Predicting Patient Readmission Rates

The Challenge: Improving Outcomes and Reducing Penalties

A multi-hospital healthcare system was facing significant challenges related to unplanned patient readmissions within 30 days of discharge. High readmission rates not only indicated potential gaps in care coordination and discharge planning but also resulted in substantial financial penalties under value-based care programs like the Hospital Readmissions Reduction Program (HRRP). Identifying which patients were at the highest risk of readmission using traditional methods proved difficult, often relying on limited criteria or clinician intuition, leading to missed opportunities for targeted intervention. The hospital system needed a more accurate, data-driven approach to proactively identify high-risk patients and implement preventative measures.

Our Solution: Predictive Analytics for Proactive Care Coordination

Lydatum developed and deployed a robust predictive analytics solution leveraging the Microsoft Azure cloud platform to identify patients at high risk of 30-day readmission. The solution integrated seamlessly with the hospital's existing data infrastructure:

• Data Integration and Preparation: We established secure data pipelines (potentially using Azure Data Factory) to extract and consolidate relevant patient data from the hospital's Electronic Medical Record (EMR) system into Azure SQL Database. This included demographics, diagnoses (ICD codes), procedures, medications, length of stay, prior admission history, lab results, and discharge summaries. Significant feature engineering was performed to create meaningful predictors for the machine learning models.
• Machine Learning Model Development (Azure ML): Using Azure Machine Learning, we trained, evaluated, and deployed several predictive models (e.g., logistic regression, gradient boosting machines) to predict the likelihood of readmission. The models were trained on historical patient data, identifying complex patterns and risk factors that were not easily discernible through manual review. The final deployed model provided a risk score for each discharged patient.
• Risk Stratification and Visualization (Power BI): The readmission risk scores generated by the Azure ML model were fed into interactive Power BI dashboards. These dashboards allowed care coordinators, discharge planners, and clinicians to easily visualize patient risk levels, understand the key contributing factors for high-risk patients, and prioritize follow-up interventions. Dashboards provided views at the patient, unit, and hospital level.
• Workflow Integration: The risk scores and alerts for high-risk patients were integrated into the EMR and care management workflows, ensuring that clinicians received timely notifications and could initiate appropriate post-discharge support, such as follow-up calls, home health visits, or medication reconciliation checks.

The Impact: Lower Readmissions, Better Coordination, Reduced Costs

The predictive analytics solution empowered the hospital system to transition from reactive to proactive patient care management:

By accurately identifying high-risk patients before discharge, the care teams could implement targeted interventions tailored to individual patient needs, leading to a significant reduction in unplanned readmissions. The Power BI dashboards facilitated better communication and coordination among different care providers involved in the patient's post-discharge journey. This improvement in care continuity not only enhanced patient outcomes and satisfaction but also directly reduced the substantial costs associated with readmissions and the financial penalties imposed by payers. The system provided a clear ROI while fundamentally improving the quality of care transition.

Technologies Used: Azure Machine Learning (Azure ML), Azure SQL Database, Microsoft Power BI, Azure Data Factory (implied), Azure