MIPS Raises The Stakes On Clinical Documentation And Quality Reporting

By Dr. Brian Levy, MD, Vice President and Chief Medical Officer with Health Language, part of Wolters Kluwer Health

Is your data ready?

In this two-part series, we will explore the basics of MIPS and its impact on terminology management and quality reporting.

It’s no secret that quality reporting has presented notable challenges to hospitals and physicians. Access to clean, accurate data is necessary for reporting, yet providers continue to struggle with full and consistent capture of data and strategic management of patient information for forward-looking initiatives.

Unfortunately, the timeline of value-based care is not waiting for the industry to catch up. In fact, the recent introduction of the Medicare Access and CHIP Reauthorization Act (MACRA) of 2015 is upping the ante on quality reporting, and providers need to take notice. While all 900 pages of this proposed rule make for good bedtime reading, it’s important that the industry start by focusing on some important practical issues.

MIPS 101

First, providers need to understand that MACRA introduces two options for Medicare Part B reimbursement: Alternative Payment Models (such as an ACO) or the Merit-based Incentive Payment System (MIPS). Essentially, MIPS is the combination of previously separate programs—Physician Quality Reporting System (PQRS), the Value-Based Payment Modifier Program and the Medicare Electronic Health Record Incentive program (Meaningful Use).

Under the MIPS program, clinicians will receive a score based on their performance in four areas, all closely related to the above mentioned programs: quality, resource use, clinical practice improvement and meaningful use of certified EHR technology. Accuracy of data capture will be a critical component to maximizing reimbursement, and the fallout for inadequate methodologies and processes to support proactive and ongoing analysis will be significant. In 2019, providers will receive an adjustment of up to plus or minus 4% based on performance and data reported in 2017. In 2022, the impact will be anywhere between plus or minus 9%. 

Image from Health Affairs at http://healthaffairs.org/blog/2016/04/22/new-health-policy-brief-medicares-new-physician-payment-system/ 

Clean Data Moves Front and Center

MIPS solidifies the importance of collecting accurate and more complete data for reporting, achieving true interoperability of clinical information and enhancing patient engagement.

Hospitals and health systems increasingly must rely on exchanging data with providers and business partners outside of their network to deliver high-quality care and document complete, accurate patient information. Patient information is scattered across provider offices, labs, hospitals, and insurers—and that information is valuable to provider decision-making and quality reporting. Without access to information outside of a network, providers will inevitably order unnecessary duplicate tests, fail to identify diagnoses in some patients and recommend avoidable treatments.

Bringing in out-of-network data requires a data normalization strategy and strong, enterprise-wide information governance. Disparate information systems lack standardization in coding and terminologies, which needs to be addressed from both an interoperability and data warehousing standpoint. Doing this will ensure clinical caregivers have necessary information at the point of care and data is useful for analytics and quality reporting.

For instance, reporting under MIPS will require lab, drug, procedure and diagnosis codes. As local lab results are reported, they may need to be normalized to the right standard (LOINC) required for the reporting.  Problem list codes in SNOMED may need to be converted to ICD-10 codes for reporting and drug codes converted to RxNorm. 

Going forward, providers will need to focus on three critical areas to maximize reimbursement under MIPS. In part 2 of this series, we will discuss how the right terminology strategy enables providers to fully leverage data in support of:

1. Optimizing clinical documentation
2. Interoperability to support accurate data capture
3. Patient engagement