Personalized Patient Care: Predictive Health Analytics

InnovateMed Hospitals, a regional healthcare provider, aimed to improve patient outcomes and reduce operational costs associated with unplanned hospital readmissions. They sought an AI solution to identify at-risk patients proactively.

Through our Launchpad program, AI Booster collaborated with InnovateMed to develop an MVP for a predictive health analytics tool within 90 days.

• High rates of preventable patient readmissions.
• Difficulty in identifying high-risk patients early with existing methods.
• Integrating disparate patient data sources (EHRs, lab results, demographics).
• Ensuring patient privacy and data security in compliance with HIPAA.

The Launchpad team developed a machine learning model using historical patient data to predict the likelihood of 30-day readmission. The solution involved:

1. Data Harmonization: Securely integrating and cleaning data from various hospital systems using FHIR standards.
2. Feature Engineering: Identifying key indicators from patient records that correlate with readmission risk.
3. Model Development & Validation: Building and rigorously testing several predictive models, selecting the one with the highest accuracy and interpretability.
4. Dashboard for Clinicians: A simple interface for care teams to view risk scores and contributing factors, enabling targeted interventions.

The MVP was delivered in 85 days, demonstrating the efficiency of the Launchpad approach.

The predictive model achieved outstanding results in clinical trials:

• 92% accuracy in predicting 30-day patient readmission risk.
• Enabled proactive care planning for high-risk patients, leading to a projected 25% reduction in readmissions.
• Provided clinicians with actionable insights, improving resource allocation.
• Secured follow-on funding for full-scale deployment across the hospital network.