Health Language Blog

At this point in the integration of your data normalization solution, the project team has a strong executive sponsor and has assessed the healthcare organization’s data normalization needs. So far so good. Now it’s time to identify the constraints that might hinder the normalization initiative. Those constraints will determine the pace of the project and highlight areas in which additional planning will be required to overcome obstacles.

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In a previous blog post, I discussed the importance of data normalization and the need for executive-level support as the first step in the implementation process.

This post focuses on the next step: taking an inventory of your data normalization needs. A healthcare system will be hard-pressed to reach its desired data destination if it has no idea of where it stands today. Describing the current environment—where does the key data or terminology exist and who owns it—is the objective of the inventory phase of a data normalization project.

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The EHR Revolution

Electronic Health Records (EHRs) originated as an attempt to digitize traditional paper medical records (hence the early moniker “Electronic Medical Record” (EMR), implying the system is an electronic representation of a paper medical record). There were certainly drivers for this shift from paper to electronic records, such as enhanced storage, retrieval, update, availability of patient data, and reduction of paper waste, but the capabilities of first-generation EMRs were not designed to extend the use of patient records significantly beyond similar uses to those met by their paper forebears.

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Data normalization is finding an expanding role in a range of healthcare settings.

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Welcome back to the Data Normalization blog!  In the last post, I outlined the key steps to take your project to the next level or get started at a high level. This post focuses on the first step toward implementation: obtaining executive buy-in.

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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.

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Population health management seeks to identify at-risk populations, tailor interventions for individuals and measure the clinical impact.

It’s a complex task that relies on the ability to collect and aggregate patient data and use that data to measure the quality of care. The job also calls for health systems to generate reports on those quality measures for internal use and external regulators.

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Communication among healthcare IT systems has reached a critical stage as providers and payers rollout more applications to improve patient outcomes and boost operational efficiencies.

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The task of achieving semantic interoperability ranks among the toughest problems in health IT.

The rapid adoption of IT systems has digitized masses of clinical data, but at a cost. Different health IT (HIT) systems tend to represent data in different ways, which creates numerous interoperability issues. Accordingly, the healthcare community faces significant hurdles when it comes to efficiently sharing and using data across clinical care processes, business functions and systems.

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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.

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