Challenges in Data Integrity in Value-Based Care Arrangements

This is part one of a three-part blog series exploring from both a provider and payer perspective a topic that impacts the entire healthcare industry—data integrity—including the various challenges it poses, the negative implications of those challenges, and how best to address them.

Part 1: Challenges in Data Integrity in Value-Based Care Arrangements

Data is the fuel that powers change in healthcare. Without clean and relevant data, no actionable insights can be generated to model value-based care (VBC) contracts successfully, let alone address population health, care management, or any such initiatives that improve quality, reduce costs, and drive value-based care. The adage “garbage in, garbage out” is very apt when applied to bad data and the information it produces. The dirty secret in delivering analytics is that 85% or more of the effort involved can be offset through good data cleansing, harmonization, and optimization.

Both payers and providers have unique needs and challenges. Payers have good adjudicated claims data. They do not have clinical data, but need clinical data such as ADTs (admit, discharge, transfer) to deliver effective care management and care coordination. They also need EMR data (supplemental data sets) to facilitate quality improvement and cost reduction.

On the other hand, providers—such as health systems, large provider practices, and ACOs—have clinical data, but do not have clinical data from outside their four walls. They also do not have adjudicated claims data, or claims from other providers along with the costs that their patients in value-based care arrangements may have incurred as out of network fees. Thus, they do not have any knowledge of the patient that went outside their network to have knee surgery, open-heart surgery, or a kidney transplant.

A large health system may have anywhere between ten and fifty or more contracts with a dozen or more health plans. This data is provided in formats unique to each health plan, and will generally require cleansing and normalization efforts by the provider group. The challenges of missing data, redacted data, or suppressed data (for sensitive subjects like a patient’s substance abuse history or HIV status) are very common and can present roadblocks to data-driven patient care.

The impact of data gaps, inconsistencies, and overall poor data integrity can be catastrophic for an organization, whether a payer or a provider group. Consequences that may emerge include, but are not limited to, an inability to model contracts successfully, miscalculation of a patient’s risk profile, improper quality scores, and a lack of identification of costly patients and providers along with an inability to benchmark provider care delivery. All of this will result in financial losses and deter the provider groups from taking on greater risk.

Conclusion

Payers and providers need each other since they have complementary data sets. In order to succeed in value-based care arrangements and deliver significant value to each other’s efforts, payers and providers need to collaborate to create a holistic data set comprised of both claims and clinical data. Additional data sets, including behavioral health data and social determinants of health data, will aid greatly in identifying and addressing health equity issues. When multiple data sets are merged together, they also need unique identifiers to ensure the accuracy of patient (or member) information.

In the next two posts in this three-part series, I will discuss different approaches deployed to address data integrity issues by both providers and payers. Please feel free to email me at raj.lakhanpal@spectramedix.com with any questions or thoughts.