Case Study: Enhancing Non-Profit Efficiency with Advanced Analytics

At Causalytics, we draw on deep expertise in healthcare analytics to empower non-profits with innovative tools that drive equitable service delivery and resource optimization. This case study highlights how a high-needs model significantly improved the identification of underserved populations, enabling a non-profit to maximize its impact.

1 Overview

Non-profits serving vulnerable communities often face challenges in identifying individuals with the highest needs. Traditional models tend to prioritize cost-based metrics, which can overlook critical social determinants and other non-clinical factors.

1.1 Challenges Addressed:

Accurately identifying individuals with high needs in underserved populations.
Allocating limited resources effectively to achieve the greatest impact.
Addressing social determinants of health alongside clinical factors.

2 Our Approach

2.1 High-Needs Model

Leveraging claims data and social determinants of health, our founder developed a composite model that provides a weighted scoring mechanism to identify individuals most in need of support. Key components of the model include:

Claims Data Analysis: Utilized historical healthcare utilization patterns and clinical conditions to establish a baseline.
Social Determinants Integration: Incorporated factors such as housing stability, income, and community access to healthcare.
Weighted Scoring Mechanism: Combined clinical and social factors into a unified, equitable scoring model.

2.2 Implementation Strategy

The model was implemented to address the specific goals of the non-profit:

Data Integration: Merged clinical and non-clinical data sources for a holistic view of individual needs.
Equity-Focused Scoring: Prioritized high-need individuals based on a composite score that balanced medical and social criteria.
Resource Optimization: Enabled the organization to focus its interventions on the most underserved populations.

3 Results

The high-needs model delivered transformative results:

50-Fold Improvement in Identification: Increased the ability to identify high-need individuals compared to traditional cost-focused models.
Optimized Resource Allocation: Allowed the organization to direct resources to individuals who needed them most, enhancing efficiency and impact.
Equitable Service Delivery: Addressed social determinants alongside clinical factors, creating a more inclusive approach to care.

4 Why It Matters

This case study demonstrates the power of combining clinical data with social determinants to create a holistic model for identifying high-need individuals. By adopting such advanced analytics, non-profits can:

Deliver more equitable and impactful services.
Ensure resources are allocated where they are needed most.
Improve outcomes for underserved populations.

At Causalytics, we specialize in building and implementing models that drive measurable impact for organizations dedicated to social good.

5 Learn More

Find out how Causalytics can help your organization achieve similar outcomes with advanced analytics and customized solutions.