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Quality Data: Simplify Data Governance, Advance Interoperability, and Improve Analytics
September 25, 2019

Quality Data: Simplify Data Governance, Advance Interoperability, and Improve Analytics


Brian Diaz is the senior director of strategy with Wolters Kluwer Clinical Surveillance, Compliance and Data Solutions and is responsible for directing and managing the Health Language suite of content and service offerings. He has over 15 years of experience in healthcare information technology, including providing leadership in the areas of product management, marketing, and software development, and has a Bachelor of Science in Computer Engineering from the University of Minnesota.
Building a Framework for Data Quality
A multifaceted strategy that engages technology, expertise, and the right processes is essential to achieve data quality. Comprehensive strategies must address terminology management and data governance from three vantage points.
 
1. Establish a Single Source of Truth Through Reference Data Management
Effective management of reference data is foundational to any data quality strategy. Comprised of industry health information technology standards—such as ICD-10, RxNorm, Logical Observation Identifiers Names and Codes, and Systematized Nomenclature of Medicine—Clinical Terms—and other proprietary content, reference data provides the building blocks for analytics efforts by establishing of framework of interoperability to support the free flow of information between systems. An optimal Reference Data Management (RDM) strategy encompasses oversight and ongoing maintenance of these enterprise assets to ensure that all stakeholders are drawing from the most up-to-date terminology standards and a single source of truth for accurate analytics and reporting.
 
Advanced solutions exist that automate these functions and help healthcare organizations optimize 4 key tenants of an RDM strategy including: (1) data acquisition of all code sets used across on enterprise; (2) list management that represent clinical concepts comprised of different terminologies; (3) integration and distribution of data into existing enterprise infrastructures; and (4) data governance that aligns people, processes, and technology.
 
2. Normalize Clinical Data to Standards
Data normalization solutions can automatically map nonstandard clinical data, such as local labs or drugs, to standard terminologies that are maintained as part of an RDM strategy. This process bridges the gap between disparate systems by establishing semantic interoperability of data across the healthcare enterprise. Due to the volume of disparate data that exists, many executives find that the business case for leveraging automation is an easy one to make. It eliminates burdensome, error-prone, manual processes and ensures nothing is missed.

3. Unlock Unstructured Data with Clinical Natural Language Processing
To fully ensure data quality, healthcare organizations must address challenges with data captured in free text fields. Unstructured data currently accounts for as much of 80% of clinical documentation, thereby essentially locking up clinically relevant patient data, making it unusable for downstream initiatives. Clinical Natural Language Processing solutions provide a foundation of comprehensive clinical data and provider-specific synonyms and acronyms to extract valuable data such as problems, diagnoses, labs, medications, and immunizations from unstructured text fields, so that it can be used to improve health outcomes and increase quality of care.

When payers implement practices to achieve high data quality, they can more effectively extract value from their own data assets using emerging technologies such as machine learning and AI. Clinical and financial leaders are wise to identify high-value areas first that could benefit most from improved data quality as a starting point and then progressively design more complex initiatives.

With the right investments targeted at improving the quality of data by establishing a single source of truth, normalizing structured data to standards, and unlocking unstructured data—the potential to draw deeper insights is sizeable and can support health plan’s goal to be more successful in this competitive marketplace.

 
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