Case Study: Helping Payors Automate Complex Data Workflows Used for Population Health Management
Case Study: Helping Payors Automate Complex Data Workflows Used for Population Health Management
Case Study: Helping Payors Automate Complex Data Workflows Used for Population Health Management
Case Study: Helping Payors Automate Complex Data Workflows Used for Population Health Management
Case Study: Helping Payors Automate Complex Data Workflows Used for Population Health Management
Case Study: Helping Payors Automate Complex Data Workflows Used for Population Health Management
Case Study: Helping Payors Automate Complex Data Workflows Used for Population Health Management
In healthcare, population health management (PHM) is crucial for improving health outcomes, cutting costs, and boosting patient satisfaction. A leading healthcare payor in the US, serving millions of members, aimed to enhance its PHM capabilities to tackle the complex task of identifying high-risk members and providing timely, preventive care to reduce costly health issues. However, managing the intricate data workflows needed for such a solution presented significant challenges. By partnering with Gradient, the team was able to leverage Gradient's AI-powered Data Reasoning Platform to overcome these obstacles, enabling them to build an advanced PHM solution.
Creating an effective PHM solution begins with the data, as it serves as the foundation for predictive models that identify high-risk members. However, the team encountered numerous data-related challenges that limited their ability to harness the full potential of the vast data available. With a limited team of data engineers, it was essential to adopt a solution capable of delivering equal or even superior results without increasing headcount.
Data Fragmentation and Interoperability: PHM relies on integrating diverse data sources like hospitals, pharmacies, and public health records, each with unique formats and standards that often lack compatibility. Data silos within organizations and limited adoption of universal standards (like HL7 and FHIR) further complicate efforts to achieve a unified view of each member’s health.
Unstructured Data: Much of the most valuable data—such as physician notes, patient-reported symptoms, and diagnostic insights—was stored in unstructured formats within EHRs, requiring extensive engineering resources (e.g. data science or ML) to process effectively.
Incomplete and Inaccurate Data: Essential data such as social determinants of health (SDOH) or behavioral factors is critical for assessing member health risk. However this data is often incomplete or missing from traditional medical records.
Data Privacy and Compliance: With stringent HIPAA and other regulatory requirements, sharing data among entities like payors and providers posed compliance risks, particularly without robust data governance and privacy protocols.
These issues collectively limited the payor’s ability to identify high-risk members and offer targeted interventions, making it difficult to manage population health proactively.
The AI-Driven Solution
By partnering with Gradient, the large payor network was able to work together to develop a custom PHM solution leveraging Gradient’s AI-powered Data Reasoning Platform to automate their complex data workflows. Powered by a suite of proprietary large language models (LLMs) and AI tools, Gradient’s Platform eliminated the need for manual data preparation, intermediate processing steps, or a dedicated ML team to maximize the ROI from their data.
Data Ingestion and Integration
Gradient’s Data Extraction Agent enabled seamless ingestion of both structured and unstructured data from diverse sources, including EHRs, SDOH databases, and hospitals. The platform managed data variability, allowing the team to consolidate and interpret data from raw text, clinical notes, behavioral records, and more without manual intervention.
Incorporating Institutional Knowledge
Your institutional knowledge is your most valuable asset. However, transferring that knowledge into an automated process is not easy. Gradient’s Platform enabled the team to not only seamlessly ingest the data, but ensure that the data could be infused with their institutional knowledge to align the model. To do this Gradient’s Control System (GCS) is used to enable real-time human feedback to help tune the system to provide the expected outputs. This improves the accuracy dramatically and ensures a continuous learning process.
Identifying High-Risk Members and Delivering Preventive Care
Gradient’s Data Forge utilized advanced AI techniques to “reason” through integrated data, reshaping and synthesizing it into actionable insights. By using advanced agentic AI techniques to guide the model, Gradient’s Platform utilizes the data to infer necessary insights to identify high risk members and generate customized prevention plans tailored to individual needs, significantly enhancing preventive care delivery.
HIPAA-Compliant Platform
With HIPAA and SOC 2 Type 2 compliance, Gradient’s platform ensured rigorous data privacy and security standards. Additionally, the option for on-premises deployment offered the payor flexibility to maintain high data security, adapting to the unique compliance needs of their business environment.
The Impact
By leveraging Gradient to automate the complex data workflows, the team was able to develop an effective PHM solution that drove substantial improvements across the payor’s operations.
Enhanced High-Risk Identification: The new solution improved the accuracy of identifying high-risk members by 50%, a substantial leap from the previously manual, labor-intensive process. This improvement enabled the payor to proactively manage risk and allocate resources more effectively.
Reduced Emergency Room Visits: By providing timely preventive care and monitoring high-risk members, the payor achieved a 15% reduction in emergency room visits over six months, translating to significant cost savings and improved health outcomes.
Improved Member Satisfaction and Retention: Custom preventive plans tailored to individual members boosted satisfaction scores by 15%, with surveys showing enhanced member engagement and confidence in the payor’s services, helping reduce turnover and enhance overall retention.
Gradient can unlock healthcare’s data potential as demonstrated in this population health management use case. By eliminating data silos, enhancing data accuracy, and personalizing preventive care, Gradient’s Data Reasoning Platform empowered the payor to deliver meaningful health improvements for members while significantly reducing costs. With the help of Gradient, healthcare organizations and companies can simplify complex healthcare workflows, setting a new standard for proactive, data-driven healthcare management.
© 2024 Gradient. All rights reserved.
© 2024 Gradient. All rights reserved.
© 2024 Gradient. All rights reserved.
© 2024 Gradient. All rights reserved.
