Health Language Blog

What to Look for in a Data Normalization Platform

Posted on 10/15/14


Data normalization solutions aim to overcome a difficult problem: the explosive growth of disparate healthcare IT systems and the resulting fragmentation of data.

The systems rapidly proliferating across the healthcare ecosystem each have their own ways of representing clinical terms. The range and variety of terminologies -- from highly localized codes to international standards -- complicate attempts to share and aggregate data. Healthcare organizations must overcome this terminology obstacle if they are to realize the national vision of increased interoperability, transparency and collaboration within the healthcare field.

A data normalization platform maps data coming from different sources -- and encoded in different reference terminologies and classification systems --  into standard terminologies. Such solutions provide a foundation for achieving semantic interoperability, and this foundation, in turn, helps healthcare companies meet their compliance and business objectives. Data normalization, and the improved interoperability it provides, supports the terminology standards required by the federal Meaningful Use initiative and also contributes to emerging healthcare delivery models such as Accountable Care Organizations.

Needless to say, a data normalization platform represents an important purchasing decision. Here are a few things to look for when evaluating vendor options:

Product Breadth

Look for a comprehensive technology platform that lets you single-source your content and centralize your normalization activities. A platform should provide the software, content, and consulting solutions to effectively map, translate, update, and manage standard and enhanced clinical terminologies on an enterprise scale. This integrated approach takes the guesswork and minimizes redundancies in a very complex process.

Automated Mapping

The primary task of a normalization platform is terminology mapping. A solution should be able to match the terminology from local healthcare data sources to broader standards. This mapping should occur automatically. Today’s healthcare IT mapping needs are immense, and although a normalization platform must have web-based support for manual mapping workflows, the solution should be able to automate the majority of enterprise mapping needs. Healthcare organizations will be exchanging growing volumes of data in the new collaborative environment. Automated terminology mapping makes that exchange practical. Organizations should look for automated mapping algorithms that can be seamlessly integrated into enterprise systems via real-time web service calls. Those mapping algorithms can normalize full catalogs or distinct elements from cryptic and poorly maintained source data into standard terminologies.

Roles-Based Collaboration Workflows

While a normalization platform should seek to automate mapping as much as possible, it should also support roles-based collaboration workflows. Those workflows support local content modeling and the subsequent mapping of that content to standards. The inevitable need to manage custom content makes roles-based workflows an important feature. There’s more than one way to go about mapping data; it all depends on a healthcare organization’s objectives for a given data exchange. These workflows should be tightly integrated into the aforementioned automated matching algorithms. In cases where an automated match is impossible, a user-facing interface should present a task list of “fallouts” that can be manually mapped.

Content Databases

Data normalization platform buyers should also consider the quality of the solution’s content databases. A platform’s mapping algorithms and user-facing tools tap into those databases to accomplish their goals. The database at the core of a normalization solution must contain:

  • All relevant standard terminologies such as ICD-9, ICD-10, SNOMED CT®, RxNorm, LOINC®, and CPT®.

  • Multilateral maps between various terminology code sets - ICD-9/ICD-10 to SNOMED CT and ICD-9/ICD-10 to CPT, for example.

  • The ability to manage code groups that can be used to represent a single clinical concept. One example: ICD-9-CM, ICD-10-CM, and SNOMED CT codes defining patients who have a history of myocardial infarction.

  • A synonym library of provider friendly (e.g., “ank fx”) and consumer friendly (e.g., “nosebleed”) terminologies and their mappings to standards.

  • Management interfaces that let users manage terminology updates as well as model their own local content as needed.

In the Market?

A data normalization platform can help advance your data sharing and aggregation needs. Are you evaluating or planning to evaluate a platform? What concerns do you have? Leave your comments below.

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Topics: data normalization

About the Author

Sarah Bryan is Director of Product Management with Health Language, part of Wolters Kluwer Health.