Clean, quality data is critical for accurate analytics, but challenging to achieve
Data analytics plays a critical role in today’s quality- and value-based healthcare environment. Yet hospitals and health systems struggle with designing systems to capture information in a way that is complete and accurate. As a result, many are calling into question the integrity of their data and its usefulness as a tool for achieving desired outcomes.
In fact, a Xerox Healthcare Attitudes 2016 survey revealed that 80% of providers “expressed some level of uncertainty about not being able to leverage their patient data for improved outcomes.” Unfortunately, the timeline for value-based initiatives and payment reform is not slowing, and time is of the essence for stakeholders to get their data analytics house in order.
Growing responsibilities associated with assessing and managing population health and accurately predicting financial risk are necessitating that payers and providers alike have access to a full patient picture — including clinical and claims information.
The sophisticated information models required for success within today’s healthcare environment represent uncharted territory for the industry in terms of transparency and high-level information exchange. While strides have certainly been made on the interoperability front, notable terminology management challenges exist that must be overcome to make data useable and to produce accurate reports.
For this reason, health networks are increasingly employing enterprise terminology management strategies to optimize data analytics in support of such programs as accountable care organizations (ACOs) and bundled payment arrangements.
Understanding the Challenge
Today’s health systems manage a growing number of clinical, claims, and administrative systems — all with their own inherent clinical terminology structure. Thus, one of the first steps towards meaningful data analytics is overcoming “language” barriers that keep systems from communicating in a meaningful way.
While the introduction of terminology standards and classification systems such as SNOMED CT, RxNorm, LOINC and ICD-10 are improving the outlook, the complexities of aligning disparate data with industry standards are notable for the average health system. For instance, analyzing a population health cohort for heart failure requires that all representations of the condition be normalized across a multitude of IT systems within an integrated delivery network. These representations must then be mapped to an appropriate industry standard for clean, accurate capture of data to support analytics.
Enterprise terminology management addresses this challenge by establishing a single source of terminology truth at the enterprise level. Supported by an advanced technological infrastructure that works in tandem with well-planned workflows, this strategy provides for clean acquisition of data, centralized content authoring, code group management, integration and distribution of code updates, and policy and governance standards.
Enterprise Terminology Management: Real-life Application
Providers and payers increasingly understand the need for establishing a single source of terminology truth across an enterprise. Here’s how a number of groups are benefitting from enterprise terminology management strategies.
Accountable Care Organization
An ACO recently engaged with a state health information exchange (HIE) to aggregate inpatient and outpatient clinical data for quality measures reporting. Like all ACOs, the organization is required to accurately report whether patients with coronary artery disease receive needed cholesterol medications and angiotensin-related agents. However, medication data flows into the HIE from a number of disparate systems and is represented by a mixture of codes, including proprietary codes from leading clinical information systems.
To support the ACO’s analytics strategies, the HIE is leveraging an enterprise terminology management platform to map all representations of drugs to the RxNorm industry standard. Now, data informing ACO quality measures is comprehensive, interpretable, and reliable. Also, as new drugs come on the market, the infrastructure automatically updates the system to accurately capture all current information.
Like ACOs and other providers, payers are increasingly responsible for reporting electronic quality measures. A number of quality measures depend on laboratory test results — a classic piece of clinical data that is not available in claims information.
Several Healthcare Effectiveness Data and Information Set (HEDIS) measures required for CMS Star Ratings illustrate this. HEDIS diabetes measures include laboratory test results for hemoglobin A1c and urine microalbumin. HEDIS colorectal cancer screening measures include laboratory test results for fecal occult blood.
Recognizing the complexities of accessing and aggregating the needed laboratory data, one payer is leveraging an enterprise terminology management platform that maps billing and claims codes for laboratory studies to the associated results codes in LOINC—the established industry standard.
This framework enables the payer to track down missing laboratory results, vastly improving the accuracy and completeness of quality metrics.
Even in this period of electronic health record consolidation, provider organizations must maintain multiple interlinked clinical information systems. Because terminologies are continually being updated at different periodicities, managing terminology updates can be time-consuming and prone to error.
In response, provider organizations are employing enterprise terminology management as part of a general master data management strategy. This ensures that all terminologies are in sync across every clinical information system, speeding revenue cycle and laying the needed foundation for population health management and quality reporting.
In essence, a stable, manageable flow of clinical and claims information—normalized across varying terminology platforms—is a priority for all stakeholders preparing for new payment models. Department-level approaches to terminology management that could previously be managed through point solution vendors no longer suffice for today’s data analytics needs. Hospitals and health systems that adopt enterprise terminology management strategies are best positioned to achieve the high-level analytics requirements of today’s forward-looking healthcare reform initiatives.