Background
A large Medicaid health plan serving over 1.5 million members noticed stagnation in its quality measure improvements, despite implementing care management and coordination programs. The organization suspected that data gaps in the quality measures were undermining their efforts. To address this, they aimed to identify and understand these gaps to uncover the root causes and improve performance.
Challenge
Operating within Medicaid presented unique challenges:
- The health plan supported a vast member base, making data analysis complex.
- Limited availability of skilled quality-focused workers made it difficult to analyze the data and its relationship to quality measures.
- The task needed to be completed promptly to meet deadlines for HEDIS (Healthcare Effectiveness Data and Information Set) reporting and state reporting requirements.
Solution
The health plan partnered with Hexplora, a HEDIS-certified organization with expertise in healthcare data analysis, particularly from a quality perspective. Hexplora proposed an agile approach to quickly identify problem areas without running their full quality engine.
Key Steps:
- Focus on Denominators: Start with the denominators already captured by the quality vendor in the most recent monthly run.
- Prioritize External Data Dependencies: Identify measures reliant on external data sources, such as lab results or registry data, instead of claims or eligibility data.
- Targeted Analysis: Avoid running a full end-to-end analysis by focusing on specific problem areas, which allowed for quicker identification of gaps.
This streamlined approach enabled the health plan to efficiently pinpoint issues affecting their quality measures.
Implementation
The initiative was primarily a data analysis project led by Hexplora’s core quality team. Their expertise in healthcare data and quality measures played a pivotal role in identifying gaps and providing actionable insights. The collaboration yielded significant improvements for the health plan.
Results:
The targeted approach led to measurable improvements across several key quality metrics:
| Measure | Improvement (%) |
| CCS – Cervical Cancer Screening | 2.93% |
| HBD – Hemoglobin A1c Control | 2.06% |
| CBP – Controlling High Blood Pressure | 2.44% |
| IMA – Immunizations for Adolescents | 0.87% |
| CIS – Childhood Immunization Status | 0.47% |
| PPC – Postpartum Care | 0.51% |
| PPC – Prenatal Care | 6.63% |
These improvements not only enhanced compliance with HEDIS reporting but also contributed to better patient outcomes.
Conclusion:
The case study demonstrates the significant impact of addressing quality data gaps through targeted analysis and strategic partnerships. By leveraging Hexplora’s expertise, the Medicaid health plan successfully overcame resource constraints and operational challenges, achieving measurable gains in key quality measures. This success underscores the importance of data-driven decision-making in navigating complex healthcare environments and meeting regulatory requirements effectively.
Next Steps:
To further enhance the quality measures and obtain a more comprehensive view of each member, the health plan should consider the following steps:
- Integrate HL7 Data: Incorporate Health Level Seven (HL7) data into the analysis to enhance interoperability and streamline data exchange across different healthcare systems
- Incorporate CCDA Information: Utilize Consolidated Clinical Document Architecture (CCDA) data to access standardized summaries of patient information, ensuring a more accurate and complete medical history for each member
- Leverage AI and Data Analytics: Explore the use of artificial intelligence and advanced data analytics to automate compliance tasks, improve data security, and facilitate real-time monitoring of quality measures.
- Enhance EHR Integration: Focus on seamless integration of Electronic Health Records (EHRs) using HL7 standards to improve patient care coordination and outcomes.
- Continuous Monitoring and Improvement: Implement ongoing assessment and refinement of data collection and analysis processes to identify additional opportunities for quality measure improvements.
By implementing these next steps, the health plan can work towards achieving a more holistic view of its members and potentially uncover further improvements in quality measures, ultimately leading to better patient care and outcomes.
