Healthcare Provider & Payer — ValueBased Care (VBC)

Executive Overview

OPSTIFY™ designed, engineered, and operationalized an end-to-end Value-Based Care (VBC) Analytics Engine proof of concept (POC) to solve the healthcare sector's fragmented quality-measurement and shared-savings tracking challenges. Modernizing unstandardized clinical and claims registries into a high-velocity Databricks Lakehouse architecture, this proprietary OPSTIFY™ accelerator demonstrates how healthcare organizations can automate risk stratification, eliminate contract performance lag, and maximize financial returns under alternative payment models.

Challenge

"High Risk, Now What?" Operational Gap: Most healthcare organizations can generate a basic patient risk score using standard models (e.g., identifying that a patient has a 72% risk profile). However, care managers are traditionally left to decide next steps blindly based purely on personal experience, lacking an automated data system that

"High Risk, Now What?" Operational Gap: Most healthcare organizations can generate a basic patient risk score using standard models (e.g., identifying that a patient has a 72% risk profile). However, care managers are traditionally left to decide next steps blindly based purely on personal experience, lacking an automated data system that maps a patient's risk profile to specific, high-probability clinical interventions.

Resource Capacity Bottleneck: In an attributed population tier (e.g., 20,000 members with 3,000 high-risk and 5,000 rising-risk individuals), a small team of 15 care managers faces an immense resource bottleneck. Without programmatic, data-driven prioritization, it is impossible to efficiently allocate care management hours to the highest-opportunity patients where intervention will yield the greatest clinical and financial return.
Multi-Year Analytical Maturity Wall: Building a full "Next Best Action" engine is a multi-year evolutionary roadmap requiring massive historical data tracking (Patient 360, Intervention Tracking, Outcomes Analytics, and ROI modeling). Organizations need to prove immediate technical viability and business ROI through a tightly scoped, high-impact pilot before deploying a full multi-year data pipeline.

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Solution

Challenge

Scoped Avoidable Readmission Engine: Built a precise optimization prototype focused strictly on Avoidable Readmission Reduction, analyzing a baseline cohort of 1,000 attributed Medicare Advantage members with history of hospitalizations over a rolling 12-month window.

Targeted Multi-Condition Mapping: Concentrated the semantic and analyti

Scoped Avoidable Readmission Engine: Built a precise optimization prototype focused strictly on Avoidable Readmission Reduction, analyzing a baseline cohort of 1,000 attributed Medicare Advantage members with history of hospitalizations over a rolling 12-month window.

Targeted Multi-Condition Mapping: Concentrated the semantic and analytical layers on the highest-stakes chronic profiles: Diabetes and Congestive Heart Failure (CHF); enforcing strict data definitions to isolate high-opportunity patient profiles.

5-Tier Structured Intervention Logic: Programmed a 5-tier intervention prioritization framework (mapping specific, repeatable actions like scheduling podiatry visits, arranging medical transportation, and triggering diabetes educator follow-ups) into a centralized Databricks Lakehouse.

Natural Language Discovery Interface: Deployed a natural-language access layer via Databricks Genie, allowing care coordination leaders to use plain-English queries to instantly inspect the underlying prioritization data tables without engineering delays

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Result

Challenge

Result

Turnkey Care Manager Prioritization: Successfully proved how data and optimization can programmatically isolate the highest-opportunity patients, delivering a structured "Next Best Action" output that care managers can immediately execute.

Validated Resource Optimization: Demonstrated how a small team of 3 care managers can use automated d

Turnkey Care Manager Prioritization: Successfully proved how data and optimization can programmatically isolate the highest-opportunity patients, delivering a structured "Next Best Action" output that care managers can immediately execute.

Validated Resource Optimization: Demonstrated how a small team of 3 care managers can use automated data scoring to confidently prioritize their daily patient lists, focusing their clinical energy where it will drive the sharpest drop in avoidable readmissions.

De-Risked Multi-Year AI Roadmap: Provided a clear, low-overhead blueprint that demonstrates immediate business value, giving hospital leadership the precise data validation required to confidently fund Year 1 and Year 2 foundational pipelines (Patient 360, Attribution, and ROI Models).

100% Compliant Data Testing Shelf: Established a secure, sandboxed Databricks environment wrapped in Unity Catalog data privacy controls, ensuring sensitive patient hospitalization histories are protected.

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Which specific patients?

Identifies exact attributed members at a high-velocity level (e.g., Mary, John, Susan).

Why these individuals?

Which specific patients?

Programmatically balances Risk + Opportunity metrics to isolate hidden care gaps.

What should we do?

Which specific patients?

What should we do?

Outputs specific, actionable interventions (e.g., Schedule podiatry visit, arrange transport ).

Who should do it?

Expected financial savings?

What should we do?

Automatically assigns tasks based on capacity (e.g., Care Manager Susan).

Why that intervention?

Expected financial savings?

Maps choices back to proven historical effectiveness models for similar patient profiles.

Expected financial savings?

Calculates precise risk-reduction savings thresholds (e.g., $4,200.00 per avoided readmission).

Healthcare Provider & Payer — ValueBased Care (VBC)

Executive Overview

Challenge

Challenge

Challenge

Solution

Challenge

Challenge

Result

Challenge

Result

Value Based Care AI Assistant with OPSTIFY™ and Databricks

Which specific patients?

Which specific patients?

Which specific patients?

Why these individuals?

Which specific patients?

Which specific patients?

What should we do?

Which specific patients?

What should we do?

Who should do it?

Expected financial savings?

What should we do?

Why that intervention?

Expected financial savings?

Expected financial savings?

Expected financial savings?

Expected financial savings?

Expected financial savings?

Databricks and OPSTIFY™ Partnership

Healthcare Provider & Payer — ValueBased Care (VBC)

Executive Overview

Challenge

Challenge

Challenge

Solution

Challenge

Challenge

Result

Challenge

Result

Value Based Care AI Assistant with OPSTIFY™ and Databricks

Which specific patients?

Which specific patients?

Which specific patients?

Why these individuals?

Which specific patients?

Which specific patients?

What should we do?

Which specific patients?

What should we do?

Who should do it?

Expected financial savings?

What should we do?

Why that intervention?

Expected financial savings?

Expected financial savings?

Expected financial savings?

Expected financial savings?

Expected financial savings?

Databricks and OPSTIFY™ Partnership

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