In the complex world of healthcare analytics, particularly concerning Star ratings, the journey of data from its origin to its final calculation is crucial. A critical issue arises when data flows remain unmonitored, significantly impacting Star scores. Often, health plans overlook the intricate pathways their data travels, leaving them vulnerable to errors and inaccuracies that ultimately affect their performance ratings.
The above is very consequential for Drug Measures in particular.
Complex Data Flows in Calculating Drug Measures
A typical data flow begins with various sources, including the Pharmacy Benefit Manager’s (PBM) pharmacy claims, medical claims, and eligibility data. This information is then submitted as both Prescription Drug Events (PDE) and EDPS medical encounter data submissions to the Centers for Medicare & Medicaid Services (CMS). This complex submission flow involves multiple steps, handoffs, and remedial activities, each presenting an opportunity for errors to creep in.
The diagram below illustrates the data flow between the different actors and entities. Below are some highlights of the disconnectedness of the system.
- Medical Claims: Used to identify exclusions and adjustments by CMS, but normally not the PBM
- Enrollment/Eligibility : Used to calculate denominator and sometimes exclusions and adjustments
- PDE: Originate from Pharmacy Claims at the PBM – submitted to CMS, used for calculating the measure compliance by CMS and/or Acumen.
Health plans rarely have agency in building a POV of their own on their own measure compliance or proactively closing gaps. Most health plans are dependent on their PBMs doing a stellar job as well as their own encounter submission process being top-rate. Care gap remediation is often dependent on Acumen reports, which have a major lag, or another PBM process that also has a substantial lag in time.
The Consequences of Unmonitored Data
One of the core issues is that data flows are often not monitored by plans. This lack of oversight means there’s no guarantee that the data used for calculations is correct. Every movement of data – from Plan to CMS, from PBM to CMS, from CMS to plan, from CMS to PBM and from CMS to Acumen – introduces the potential for errors. Unlike HEDIS, where there is more control over data quality and completeness, Drug Measures often suffer from a lack of validation. Plans may not be verifying if all exclusions and adjustments have been applied correctly, leading to inaccurate calculations.
Furthermore, each step in the data movement process can introduce delays and lag, making timely interventions difficult. CMS recommends data validation and correction using Acumen, but this process is not always straightforward. Plans face a tight timeframe for remediating data errors.
Plans have until June of 2025 to remediate for the Measurement Year (MY) 2024. There is still time to affect Star Scores for MY2024 and the window of opportunity is closing fast.
Better Data Visibility and Governance for Better Drug Measure Scores
Drug Measures account for a third of the overall Star rating. Attention to the areas below can help maximize the scores for Drug Measures.
- Data Correction: Is the data used for calculations correct? Without monitoring, there’s no certainty.
- Error Introduction: Every data movement is an opportunity to introduce error.
- Lack of Control: Unlike HEDIS, there’s often a lack of control over the quality and completeness of data.
- Validation Gaps: Is there validation that all exclusions and adjustments have been correctly applied?
- Delays and Lag: Every step of data movement adds delays and lag, making interventions ineffective.
- CMS Recommendations: CMS recommends data validation and correction with Acumen, but it is not always straightforward.
- Remediation Deadlines: Plans have limited time to remediate data errors for specific measurement years.
In conclusion, it’s crucial for health plans to closely monitor their data flows. Unmonitored data can lead to a cascade of errors, affecting the accuracy of Star score calculations and hindering timely interventions, which is acutely important for the Drug Measures. By understanding these challenges and implementing robust data monitoring and validation processes, plans can significantly improve their Star ratings and overall performance.
———————-
If you want to explore how Lilac can help you, check out our Stars Solution page and our Drug Measure Solution page or start a conversation by reaching out to us via the form in this link.
