In the wake of the COVID-19 pandemic, the need for greater health information technology interoperability, “digital” measures of healthcare quality and performance, and advanced value-based care systems has grown. In January 2021, the National Committee for Quality Assurance (NCQA) publicly released its vision for healthcare quality measurement to the Biden-Harris Department of Health and Human Services (HHS) transition team. The paper, “The Future of Healthcare Quality,” focuses on four core areas, with three of them being specific to the evolution of a digital quality ecosystem:
- Enabling a Digital Quality System: Implementation of a scalable, sustainable “utility” that would facilitate collection, interpretation, and use of data on healthcare quality and value.
- Moving to Digital Patient Experience Management: In recognition of the need to construct a complete picture of healthcare delivery that takes patient experiences into account, implementation of a system that captures patient experience information more efficiently and uniformly.
- Strengthening Value-Based Programs: Increased adoption of value-based purchasing programs that “drive patient-centered coordination, alignment and accountability across levels of care” (while this section of the paper seemed directed at Medicare, the principles apply to all payers).
As these initiatives become more widely discussed, we often find organizations are not clear on how to tackle the magnitude of these changes. For instance, digital measures are not the same tried and true way that many of us have used to report on healthcare quality. Our old models for the most part still rely on aggregating and making sense of fragmented data which is not captured or codified in a uniform manner, systems that focus on administrative data (claims, encounters), and custom-built data translation and measure generation “engines.” This is punctuated by lengthy audit processes, data lag, and medical record reviews that create significant burden for organizations from February through early May 2021.
Transitioning to a digital measurement model will require major changes in business processes, information technology, and the management and use of information forces us to rethink this model and move in a direction that gives organizations the best results and details of quality scores. Implementing an advanced digital quality measurement system will require advancing the following:
- Data collection methods: The majority of data used for quality measurement reporting today stems from claims systems and, more specifically, adjudicated data. The method for processing this data is lengthy, and it’s not uncommon for health plans to wait two to four weeks just to see the impact that paid claims has against its quality measures. It forces organizations to open data sharing agreements and leverage a mix of information to achieve the best results. It is crucial organizations start planning for this step now by leveraging more standard and non-standard supplemental data which can form the backbone for transitioning to a more robust data processing system.
- Data structure and storage: Most healthcare organizations leverage their own internal data structures and storage systems; this creates an immense amount of variability and requires that every data integration be custom built. An average health plan may need dozens, if not hundreds, of custom integrations just to support its quality reporting architecture, and providers will need to integrate with payors for Electronic Health Record (EMR) data dozens of times over. Digital measures leverage standardized data structures such as QI-Core which creates a base for organizing and documenting clinical data storage and use. This domain is where interoperability and quality measurement widely interact as it’s a principal that essentially creates a standard sharing method for clinical information and measurement design to be built off.
- Measure calculations: Quality measures today depend on lengthy measure coding audits and can lead to a significant amount of variability in build, run-times, and execution. This leads to teams of programmers needing to custom build and certify every new change that happens to be a measure. It is also one of the largest reasons behind why many advanced healthcare organizations have forgone their in-house built quality measurement systems and moved to vendor relationships over the past several years. Digital measures create standard specifications and utilize new language standards such as Clinical Quality Language (CQL) to standardize quality measure logic. This means code can be prebuilt to remove variability and be used in packages to run rates on a standard data set, essentially making the process of custom creating every measure a thing of the past. More importantly, this method has “allowable adjustments” which still let organizations use their measures in ways that best support their clinical programs, but also standardize how the measures are reported.
- Clinical outcomes and value-based care: One of the major limitations of evaluating care delivery and moving to true value-based models is the quality of the data and measures we currently have available to choose from. As the need for more advanced payment models takes shape, new data quality measurement models are needed as well. With digital measures moving us in a direction which unlocks new clinical data elements, creates structure, and changes how we run quality measurement calculations, it opens the potential for more real-time data. At the point of service for a member/patient it creates possibilities of advancing pay-for-performance programs that many state and federal agencies have been driving towards for some time.
With regards to these requirements, HMA has observed that high performing organizations throughout the country have started to make strides; this includes states, health plans, providers, and vendors alike. More importantly, we are seeing many Medicaid programs starting to require the early framework to these models, such as requiring the reporting of Electronic Clinical Data Systems (ECDS) measures or requiring Health Information Exchange (HIE) and/or EMR data sharing arrangements. In its 2020 memo release regarding Quality and Medicare Stars ratings, the Centers for Medicare & Medicaid Services (CMS) further addressed this by indicating that it is well aware of the changes to digital measures and was strongly encouraging plans to start exploring these new measures and processes needed for digital measures.
As healthcare quality management and value-based care programs continue to advance, organizations that are early adopters of digital quality measurement practices and systems and committed to continuous quality improvement will realize improvements in their quality metrics. Healthcare organizations that seek to be high-performing will need to implement more advanced data acquisition strategies, data management structures, and data processing methods which will enable them to leverage both administrative and clinical data more effectively and turn insights derived from that data into quality improvement and better quality scores. These changes are complex and will not happen overnight, however the key to success is to start planning now for what’s to come.
HMA has taken significant time to research, learn, and understand the many facets of this transition and how best practices work. For more information, please reach out to our Quality and IT Advisory Services (ITAS) experts: Anthony Davis firstname.lastname@example.org and Juan Montanez email@example.com.