What to consider before investing in a data normalization engine

Integrating a healthcare data normalization engine into any health IT workflow is a big commitment. Check out our checklist of factors to help guide your decision.
Normalization engine

A normalization engine is a software tool that leverages a set of algorithms to transform diverse healthcare data into a consistent, structured, and standardized format. Normalization is critical in healthcare since clinical patient information, diagnoses, medications, procedures, and test results are expressed in different formats, through various terminologies, and with varied levels of accuracy.

Understanding the benefits of healthcare data normalization is quite simple. Through processes like data cleansing, transformation, validation, and mapping, information is streamlined, which facilitates easier sharing and integration with additional data sources. This, in turn, enhances collaboration, communication, and care coordination. But knowing which data normalization engine – and which vendor – is the best fit for your organization may not be so simple.

In The data quality toolkit: Considerations for clean, complete, and usable data, we provide a checklist of factors to weigh when exploring data normalization engines. Understanding your goals and priorities, specifications for deployment and integration, and the potential for scalability are just a few of the details to evaluate before signing on the dotted line.

If your organization is keen to normalize disparate clinical data and transform it into accurate, complete and useful information, scroll to the tool checklist below. If other technologies like value set management and NLP tools are of interest (and spoiler alert…they probably should be), click the button to download the eBook now.

Before you invest: A data normalization engine checklist

  • Define key issues and needs: Identify existing inconsistencies, redundancies, and other obstacles to standardization across different data sources and determine specific objectives for data harmonization.
  • Stakeholder involvement: Engage with key stakeholders from clinical, IT, and administrative departments to ensure alignment, buy-in, and identify benefits across departments.
  • Healthcare data requirements: Decide if healthcare specialization is important compared to generic normalization technologies. If so, the ability to process healthcare-specific terminologies and coding systems should be prioritized.
  • Technical deployment, hosting, and integration: Understand integration requirements (API vs. batch uploads; self-hosted vs. 3rd party hosted) and how the technology does or does not supplement existing HIT infrastructure and workflows.
  • Security and compliance: Ensure the engine adheres to relevant healthcare data privacy and security regulations.
  • Future-proofing and scalability: Consider the evolving healthcare landscape and whether a given solution can adapt to emerging technologies, data standards, and your own organization’s future growth.
  • Prioritize continuous data quality: Know the frequency of the engine’s updates to ensure data reflects the latest clinical terminologies, mappings, and coding standards.

For checklists to help evaluate NLP and value set management solutions, download The data quality toolkit: Considerations for clean, complete, and usable data.

Tired of trying to navigate the complexity of clinical data? We have the tools to help you escape the data maze.

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