Custom AI models, EHR integration, and clinical decision support for health systems serious about reducing preventable readmissions.
From startup to established health systems, we help organizations prevent readmissions at scale.
MyDischargeAssist is trusted by 3,900+ clinicians across 76+ countries. 99% retention. Real traction. Real adoption. We know what works in clinical workflows.
Sukumar Rajasekhar consults with CMS on digital health platforms. Deep knowledge of Medicare policies, value-based care models, and regulatory requirements.
Not a consulting firm forcing generic AI tools into healthcare. We speak the language of clinical workflows, readmission prevention, and patient outcomes.
No expensive licensing models. No lock-in. Work with us on readmission reduction, then deploy independently. We align incentives with your outcomes.
Tailored solutions for your health system's readmission challenges.
Custom ML models trained on your data. We analyze your EHR, claims data, and clinical notes to build institution-specific readmission prediction models.
MyDischargeAssist tailored to your health system. White-label deployment with EHR integration, single sign-on, and custom workflows.
Real-time insights for clinicians. Risk scores, recommended interventions, and post-discharge action plans integrated into clinical workflows.
Track readmission reduction and ROI. Custom dashboards measuring readmission rates, cost savings, and patient outcomes over time.
Predictable, phased approach to readmission reduction.
Data audit, workflow analysis, gap identification. 4 weeks.
Train custom ML models on your data. Validation and testing. 8 weeks.
Pilot with select departments. Clinician training. Monitoring. 4 weeks.
Full implementation: 4-6 months. Typical time-to-impact: 90 days of deployment.
Enterprise solutions for hospitals and health systems of all sizes.
200–500 beds. Building readmission prevention programs. Looking for proven, cost-effective solutions.
Multi-hospital systems. Complex workflows. Need centralized readmission analytics and intervention coordination.
Accountable care organizations, Medicare Advantage plans, clinically-integrated networks investing in value-based care.
How a mid-sized hospital system reduced readmissions by implementing predictive analytics.
A 400-bed health system was facing Medicare penalties under HRRP for elevated 30-day readmission rates. Care coordination was manual and reactive. No predictive capability.
VLab developed a custom readmission prediction model trained on 2 years of institutional data. Integrated with their EHR. Deployed to discharge planning and case management teams.
Results based on pilot implementation. Actual outcomes depend on institutional factors, data quality, and intervention adherence. Case study details anonymized.
Let's discuss how VLab can help your health system reduce preventable readmissions, improve outcomes, and lower costs.
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