For decades, healthcare payers have understood the power of data and evidence to unlock efficiency and precision in their business and member outreach programs and strategies. But with so much data at their disposal, many payers find themselves wrestling with being “data-rich, insight-poor,” and the path forward can often be characterized by overwhelming complexity.
As health plans look to harness their data stores, extract actionable intelligence, and use it to proactively build strategies, they are using metadata tagging in a variety of ways to dive in and wrangle usable insights.
The data maturity curve and how payers are using metadata tagging
There is a maturity curve to the implementation of metadata tagging: Some payers have been using it to parse their data for decades, while some payers are just getting started with advanced codifications.
Traditionally, in payer organizations, the medical and pharmacy benefits teams remain largely siloed in their day-to-day operations, although it is slowly starting to change. When payers want to look for cost-savings – and they’ve already tackled their most obvious low-hanging fruit – the next step is to look across different claim types and try to extract new insights on where savings or efficiencies might be gained. That could mean not just aligning medical and pharmacy data, but also behavioral and some other claim types, as well as looking to improve outcomes based on social determinants of health (SDOH). In order to do that, organizations need to leverage crosswalks between different and new code sets. They also need to start contextualizing their data to be able to draw insights from it.
Metadata tagging in its simplest form allows claims to be tagged for sorting and grouping by key factors payers may need for general research, population health knowledge, or cost trends. That can be as basic as adult versus pediatric treatments, or more specific, like drug dosing for various body types.
Tagged data can be used by payers for:
- Trend analysis
- Refining processes
- Designing benefits
- Data governance
Analyzing populations and trends
With metadata tagging enabling teams to create new or more detailed groupings, it allows payers to more specifically analyze and assess a population within their membership. This is of particular interest for many pharmacy benefit managers (PBMs) or other entities that have moved more into providing healthcare services fueled by analytics.
Being able to analyze and model data from multiple perspectives to uncover different insights can reveal trends and enhance forecasts for identifying rising risk or intervention opportunities. Ideally, that insight leads to a plan for actionable prevention efforts within a calendar year.
The final step in the analysis is to use metadata tagging to track and measure the results of the intervention: Did it actually work in reducing risk, did the member benefit from the effort, or was it inconclusive? Did it increase member satisfaction in addition to helping? While those results create a whole new set of data in addition to the daunting amount of data that payers and PBMs already have, metadata helps contextualize it for analysis and to determine next steps.
Refining processes and optimizing member outreach
Metadata tagging also helps add a layer of precision and optimization to processes payers and PBMs already have in place.
For example, when pharmacists or physicians are intervening to close gaps in care through medication therapy management or pharmacy care management, payers and PBMs can use the codifications to lead to more cost-effective and successful outcomes. Intervening with the wrong individual at the wrong time can lead to poor clinical outcomes and reduced customer satisfaction. The end result is wasted clinician time that could have been better spent helping a patient or member who would have gained greater benefit from the intervention. Tagging helps identify patient factors more likely to indicate rising need and/ or a need for outreach, maximizing the potential for positive interactions and outcomes.
Designing (and redesigning) benefits packages
Tagging also reveals new potential trends or intervention possibilities that can guide payers to create programs or alter their benefit design packages to be more effective.
By collaborating with provider or pharmacy incentive programs, payers and PBMs armed with data insights can design new programs to better align coverage or that feature member behavior-related incentives.
Indications is a prime example of metadata that serves this function. For example, if a population has obesity but not for diabetes, there is an opportunity to meet these members where they are. Insight from metadata tagging enables targeted messaging focusing on GLP-1 benefits for weight loss rather than for diabetes. This will better resonate with the members, and ideally, encourage them to take action.
The more effectively metadata tagging is used, the higher the rate of member outreach and subsequent actions being taken can be, improving impact on clinically appropriate outcomes, cost savings, and stakeholder decision-making. It will also significantly increase member trust, as they will feel like their health plan knows them and understands their health needs better.
Creating and managing standards for data governance
Establishing data governance is important for healthcare organizations like payers and PBMs to ensure the relevancy and timeliness of their vast stores of information. Ideally, they will set up a data management system with a chief data officer who oversees data governance and with data stewards to make sure that all the data is standardized, is ingested with the same rules behind it, and is maintained in a consistent format. Metadata tagging can be key to categorizing the data, grouping it, and managing it within this system.
Without proper data governance, datasets can be disjointed and scattered across the organization, preventing them from being optimally leveraged, accessed, or managed. That can yield variable or conflicting analyses, results, and/or misinterpretations.
Medi-Span: Helping standardize data with the GPI
As part of the process of setting up data governance and contextualizing data stores, payers and PBMs often rely on Medi-Span® drug database solutions from Wolters Kluwer to help eliminate a lot of waste and inefficiency. With over 50 years’ experience collaborating with standards-setting organizations and regulatory agencies, Medi-Span data management has created many de facto standards in the commercial healthcare industry.
The Medi-Span proprietary Generic Product identifier – or GPI – is a unique therapeutic classification system that allows for flexible and granular tagging related to drug products to help improve the sensitivity of data grouping and sorting. For payers and PBMs, it enables additional groupings and more sophisticated analyses in addition to identifying products by NDC associated within the GPI hierarchy.
Leveraging three aligned solutions delivers a powerful, results-driven approach to optimizing payer data management: By combining the flexibility of the GPI, the interoperability crosswalks and standardized datasets of Medi-Span, and the code set management capabilities from Wolters Kluwer’s Health Language, payers and PBMs can rapidly establish a robust data management foundation or optimize standing data management programs. Together, these tools enable the development of an insight-rich data strategy that drives informed decisions and improved outcomes.
eBook: Navigating complexity with certainty
High-quality data can help payers drive efficiency, improve outcomes, and foster alignment to navigate the complexities of today’s healthcare landscape. Learn more in our eBook, “Navigating complexity with certainty.”