Optimizing data strategies through reference data management
Clinical documentation drives revenue in the new world of fee-for-value care delivery. Fully extracting “value” for the bottom line requires that healthcare organizations have systems in place to ensure all appropriate clinical data is captured and used for analytics and quality metrics reporting. Yet, the reality is that many organizations are simply leaving money on the table.
Patient information is stored in a variety of locations—clinical notes, insurance claims, and problem lists, for example. All of this data plays an important role in optimizing revenue within a fluid regulatory environment designed to reward healthcare organizations for quality outcomes performance. As such, it is increasingly critical for hospitals and health systems to deploy strategies and infrastructures that support complete aggregation of information from disparate sources and effective oversight of all data assets to ensure clean and accurate analytics and reporting.
Reference data management (RDM) has emerged in recent years as a best-practice for managing growing volumes of clinical and claims data. A function of Master Data Management, RDM provides the framework for organizing the industry’s wide array of disparate terminologies and codes around a single source of truth. Without a centralized RDM strategy, critical patient information is often missing from quality metrics reporting and population health initiatives.
RDM: A Deeper Look
Reference data can take the form of external standards or private internal data including public standards such as SNOMED, ICD-10, CPT, LOINC and RxNorm or private data such as legal entities, employees and chart of accounts.
A comprehensive approach to RDM encompasses managing local and standard content, then mapping and grouping that content appropriately. Increasingly, industry stakeholders are leveraging the creation of code groups as a function of RDM to effectively address analytics and reporting needs. These custom groupings of codes help answer specific use cases unique to an organization’s goals. For instance, a provider may need to establish and update value sets for Clinical Quality Measures to support the Merit-Based Incentive Payment System (MIP)S. On a more system-specific level, a healthcare vendor may leverage specific code groupings to drive application user interfaces. A payer may utilize these groupings to address preauthorization for procedures. A clinical system may utilize these groupings to address issues of utilization, such as those pertaining to imaging or antimicrobials.
Patient cohorts often form the basis of code group development. Essentially defined as groups of patients sharing specific characteristics, patient cohorts have a variety of uses including quality measures reporting, disease management and population health initiatives and submitting patient information to disease registries. Heart failure, a focal point of industry quality initiatives is a good example. The following patient characteristics may be used to define a patient cohort for heart failure: ejection fraction values, lab tests such as B-type natriuretic peptide or problem list entries.
While these strategies can substantially improve the outlook on analytics initiatives, the success of code group management rests with a healthcare organization’s ability to accurately and completely identify all pre-defined attributes of a patient cohort. Otherwise, strategies run the risk of falling short due to missing or inaccurate data.
That’s where an effective RDM strategy comes into play. Unfortunately, many healthcare organizations find that sustainable RDM strategies exhaust and consume resources due to manual processes. Yet, without central management of data assets, critical patient information is missed, producing negative downstream impacts to the bottom-line that could include: coding that does not capture the highest level of severity; skewed quality metrics or risk adjustment scores; or negative impacts to clinical decision at the point of care.
Consider the free text challenge as an example. Many data governance strategies lack an effective way of extracting unstructured patient data. One study found that EHR-derived quality measures—where only structured data is analyzed—can undercount practice performance when compared to a manual review of electronic charts. For example, MIPS measure 005 (NQF 0081) looks at the use of angiotension converting enzyme (ACE) inhibitor or angiotension receptor blocking (ARB) therapy for patients with documented ejection fractions of less than 40 percent. The quantified ejection fraction is rarely documented in a structured form, and the inability to find this data will skew the measure reporting.
Similarly, key indicators of well-documented diagnoses such as diabetes can get lost in free text. Eye and feet exams, for example, are important identifiers of the severity of diabetes, although documentation of these exam orders does not always show up in structured EHR text. According to one survey, the scale of the problem increases with certain complex conditions. While findings reveal accurate documentation of hypertension and diabetes more than 80 percent of the time, rates for dyslipidemia and ischemic cardiovascular disease were substantially lower.
Improving RDM Strategies for a Better Bottom Line
Hospitals and health systems can lay the groundwork for a sustainable RDM strategy by first identifying all reference data and the determine the most important elements. Key questions to ask include:
• Where is reference data managed, and how is it updated?
• How is versioning addressed?
• What questions do we need to answer with reference data – quality measures, patient cohorts?
• How can we manage reference data? (Internal, RDM platform, or third-party?)
Technology is an important consideration, and the right RDM platform can address both structured and unstructured patient data, ensuring patients are not excluded from patient cohort analytics. Advanced solutions exist that automate and streamline the complexities of RDM by addressing the following:
• Content—establish a single source of truth for all terminology-related maps, value sets, and code sets
• Applications—enable interoperability and increase the quality of analytics with mapping support, custom content modeling and code group management
• Web-based APIs—integrate reference data into existing data warehouses or analytics platforms by utilizing a suite of cloud-based APIs
Healthcare organizations must master data governance to position for such initiatives as the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) and 21st Century Cures Act. Optimal reimbursement depends on the ability to acquire more complete and accurate patient data and perform advanced analytics. RDM infrastructures can lay the needed foundation for helping hospitals and health systems improve data quality and optimize reimbursement.
Barbara Antuna, MD, FACEP, ABPM-CI, is a Medical Informatics Specialist at Wolters Kluwer, Health Language.