Types of Clinical Data
Know your data: Clinical data types and their varying sources
For payers, good clinical data is becoming an increasingly important strategic asset.
In an era of value-based care, health insurers must ensure that their members get timely and appropriate care. They also need to spend resources to focus on quality and compliance measures (Star ratings, HEDIS®1, etc.) all while meeting revenue and profitability goals. CMS has added to the urgency with the requirement that all payers provide members access to their data using the Fast Healthcare Interoperability Resources (FHIR) standard by July 1, 2021.
As it turns out, however, accessing direct and actionable clinical data is easier said than done thanks to issues with siloed approaches, poor data quality, and various political and bureaucratic roadblocks. Payers are challenged by finding the right types of clinical data and gaining access to them. Furthermore, many payers lack the expertise needed to transform raw data into insights they can use to fuel performance and influence patient outcomes.
In this, the first of a four-part blog series on The Value of Managing Clinical Data from a Strategic Enterprise Approach, we’ll outline the different types of clinical data.
Clinical and administrative data tend to be intertwined as part of nearly every encounter between a patient and their physician, with personal identity, benefits eligibility, and ability to pay mixed together with physician notes, diagnostic and procedure codes, and treatment activities. For payers, options for how you can access the data are dictated by several factors:
- How the data elements are stored
- The accuracy and timeliness of the information
- The level of interoperability at the data store
To evaluate those options, it’s helpful to define the differences in the types of clinical and administrative data captured from an access point. Because clinical and administrative data tend to be co-mingled, definitions of both can be blurry and may vary by operation.
What’s more, the value and utility of the data can vary depending on how it’s formatted to be shared and consumed for future analysis.
For the purposes of this blog post, clinical data consists of a range of information, including measures of health and health status, documentation of care delivery and outcomes, and determinants of health. Providers mostly store these data in electronic medical records (EMR) systems and patient registry data stores and use them for a variety of purposes. Payers also capture data through member support and care management systems.
Clinical data are captured in, and stored using, a range of formats and sources that include:
- Paper records
- Chart abstracts
- EMR logins
- Various levels of interoperable data gateways using HL7 version standards
Each format has distinct technical and cost challenges that affect their potential value to payers.
With the primary goal of supporting payment operations, administrative data consists of information such as a member’s demographics and enrollment benefits, eligibility and payment terms, and provider and network affiliations. Like clinical data, administrative data is captured for a range of purposes and stored in a variety of transactional systems across payer and provider operations.
While the capturing and sharing of clinical data are still being refined in the market, the standards for doing so with administrative data have scaled over the years to power more mature claims administration processes.
A more coordinated approach
With the starting point of enabling member access, payers can seek ways to align their data and analytics operations to a more coordinated, centralized approach. Creating more robust member profiles with more timely and relevant clinical data yields strategic gains in higher part fulfillment, improved HEDIS® and Stars scores, stronger population health analytics, more powerful insight and responsiveness in member engagements, and more accurate risk adjustment.
But that’s challenging in a fragmented landscape for accessing clinical data where there are wide discrepancies along factors including levels of adoption, timeliness of the clinical data, cost and ease of access, and levels of interoperability.
In our next installment, we’ll examine the pros and cons of chart chasing and other common methods payers use to access clinical data.
1HEDIS® is a registered trademark of the National Committee for Quality Assurance (NCQA).