Health Language Blog

An Overview of How to Get Started with a Data Normalization Solution

Posted on 03/18/15


A harsh reality of today’s healthcare environment is that providers and payers use a multitude of systems -- each with their own way of representing data. 

A health system may be integrating a hospital bringing a new electronic health record (EHR) system into the network, while maintaining legacy EHR applications. Health systems often grapple with how to accommodate practices using a mix of EHRs.  A health plan, meanwhile, may employ multiple claims systems. In the past, those systems often operated in isolation, using conflicting terminologies to represent clinical data.

To support tomorrow’s analytics initiatives, this fragmentation makes it difficult for healthcare organizations to leverage clinical data to trigger decision support rules, support disease stratification efforts, and accurately compute quality measures. This terminology barrier must be overcome if the industry is to reach national goals around increased interoperability, transparency and collaboration.

What’s needed is a way to unify multiple, incompatible terminologies. Data normalization is the process that accomplishes this task, reconciling disparate terminologies into a shared vocabulary. Data coming from different sources -- and encoded in different reference terminologies and classifications systems -- can be normalized into standard terminologies.

For example, a healthcare organization may find itself dealing with numerous labels for a Hemoglobin A1C test: HbA1C, A1C, HA1C, A1C Hemoglobin, HbA1c (%), and HEMOGLOBIN A1c for instance. But data encoded in differing terminologies can be normalized to the LOINC® lab standard. In a more prosaic example, a health system might seek out a standard way to identify each of its sites: ACME Hospital 1, ACME Hospital 2, etc. In both cases, data normalization lets the healthcare organization more easily aggregate data from trending and analysis. The ability to aggregate and share data among the different players in the healthcare ecosystem rank among the top benefits of normalization.

Getting Started

A business analyst or IT manager may intuitively grasp the need for normalization, but may be mapping terms manually using spreadsheets and the size and scope of the analytics project has reached a tipping point where help is needed.  Or in some cases, they may have no idea how to launch a project. For the next several weeks, I'll be providing insights on how to take your project to the next level or help you get started, supplying project leaders with a step-by-step guide for initiating, managing and sustaining a normalization project.  Each post also contain some “real world” examples but will change the names of individuals to protect the innocent.  This will hopefully give you additional guidance to avoid the common pitfalls that can occur when you are implementing a data normalization project.

Here are the topics that we plan on covering in the series::

  • How to Get Executive Buy-in on Your Data Normalization Solution: This post will cover the importance of ongoing executive support.  A normalization project will often involve a number of stakeholders and project team members who will be asked to devote their time to the effort. Having the executive team on your side is critical for securing that level of commitment --  along with the funding necessary to support the project.
  • Identify Constraints that May Impact Your Data Normalization Plan: Once a project team has taken stock of its data, the next step is to determine what could go wrong during the course of a normalization initiative. This post will describe the constraints a project team will likely face: time, money, resources and technical barriers.
  • Mastering the Data Normalization Cycle: This post will summarize the data normalization cycle and underscore the importance of following through each step until the project enters the maintenance phase.

Taken together, this blog series provide a walk-through that will help you size up and execute your first normalization project. 

Next up: How to Get Executive Buy-in on Your Data Normalization Solution

data normalization

Topics: data normalization

About the Author

Brian Laberge joined Health Language in September 2010 and has served in multiple capacities including Technical Consultant, Project Manager, and Director Of Client Implementations. He brings over 18 years of implementation and software deployment experience to Health Language and has managed several Data Normalization and ICD-9 to ICD-10 projects for Payers and Providers during his tenure here.