In our latest data normalization blog series, we introduced the importance of semantic interoperability and how healthcare organizations can achieve a framework of data normalization by mapping disparate data to industry standards. The first blog in the series explored the value of terminology mapping for lab data.
In this second installment, we review the complexities of normalizing drug terminologies and why mapping these variations to a common standard language is critical for driving accurate clinical decisions support and quality patient care. Mapping to a common standard can also help improve data accuracy for care and disease management initiatives, optimize quality measures reporting, and achieve reliable analytics.
The importance of data normalization in healthcare
In the age of electronic patient records, medication standards are critical for advancing health information exchange to optimize patient care and support high-level analytics initiatives. Accurate and complete data aggregation provides value for research, population health, and medication best practices for care and disease management.
The multitude of medication standards used within the industry—both standard and proprietary—creates challenges in reconciling all available data in a meaningful way. Most hospitals use at least 10 disparate IT systems, all relying on various terminologies with multiple pharmacy access points, making the consolidation and normalization of data a significant obstacle. Additionally, some medication standards are updated daily, requiring time-intensive ongoing maintenance. Accurate and complete data aggregation doesn’t happen by accident.
What does it mean to normalize medication data?
Without a data infrastructure in place to support data normalization, healthcare organizations risk experiencing negative downstream impacts resulting in revenue loss, misidentified gaps in patient care, inaccurate analytics, or incorrect clinical decision support.
As with lab data, the business case for leveraging an infrastructure that automates medication mapping to RxNorm is an easy one to make due to the sheer volume of data that exists across a health system. I am lucky to be using the Health Language Data Normalization solution to automate the mapping that I do on a daily basis. Without this added efficiency I would never be able to complete mapping hundreds of thousands of medication concepts. It combines the efficiency of machine learning with the deep clinical knowledge of the Health Language experts to help organizations address the burdensome, error-prone processes traditionally managed across numerous spreadsheets and departments. Most organizations simply do not have the staff to tackle this problem manually, and even if they did that clinical staff should be working in their areas of expertise and analyzing the data rather than normalizing it.
Specifically, the web-based Map Manager software application allows healthcare organizations to collaboratively map, search, and distribute data throughout the enterprise. Clinical auto-mapping powered by domain-specific algorithms ensures the highest map rates and accuracy, minimizing the need for manual review. Flexible workflows allow cross-departmental collaboration, with management dashboards to alert teams of project progress. In addition, audit trails provide the traceability needed to follow the lifecycle of a code for HEDIS® measures or just for effective governance.
How data mapping makes a difference beyond EHR systems
Health Language clients use the Map Manager application to normalize data and provide the level of governance needed to support an organization’s semantic interoperability requirements for data.
One biopharmaceutical company is using the Map Manager application to power a learning health system (LHS) to improve disease outcomes. The LHS leverages the solution to harmonize vast amounts of medication data from over 10,000 patient records to create a single source of truth. By mapping proprietary codes to industry standards for improved analytics, the LHS is curating real-world data to improve research.
In another example, a vendor organization uses the Health Language Data Normalization solution to power a real-time clinical surveillance solution. Through this partnership, the surveillance solution can break down data silos that exist across hospital systems by normalizing medication EHR feeds from over 500 different hospitals. This allows them to close patient information gaps and overcome roadblocks that hinder real-time analysis of a patient’s condition.
To learn more about how the Health Language Data Normalization solution can help your organization, speak to a Health Language expert today.
Read on to the third installment of this data normalization blog series, in which we explore the importance of allergy mapping in clinical data.
HEDIS® is a registered trademark of the National Committee for Quality Assurance (NCQA)