Lost in the healthcare data maze: Finding your way to quality 

Ready to make complex clinical data usable? Learn how NLP technology, data normalization engines, and value set management tools can help.
data quality in healthcare

Healthcare has no shortage of clinical data. In fact, the healthcare industry alone generates more than 30% of the world’s data volume. However, while plentiful, provider and non-provider organizations share the challenge of ensuring that the wealth of information is actually usable.

Trying to transform data into actionable insights is complex and often difficult to navigate, almost mazelike. This is partly because inconsistencies in data capture and standardization, and a lack of interoperability, create twists and turns that prevent organizations from effectively using this information and can cause frustration for the teams that rely on it.

So, what’s the solution? For many, it’s investing in data quality management tools that help ensure data is clean, complete, and consistent. Tools such as:

  • Natural language processing (NLP) technology
  • Data normalization engines
  • Value set management tools

Unsure how to begin evaluating these technologies? Our Data Quality Toolkit provides essential considerations and checklists for assessing each one. Click the button below to download or continue reading for more resources on these powerful solutions.


Data quality toolkit: Considerations for clean, complete, and usable data

NLP in healthcare

As healthcare organizations look to leverage artificial intelligence (AI) to reduce the cost and time needed to gain clinical insights from unstructured text, NLP technology is increasingly sought after as a solution.

Check out the resources below to learn more and understand why clinical terminology is crucial to effectively using this tech in healthcare.


The future of healthcare with generative AI: Hopes and hesitations

Healthcare data standardization

Standardizing inconsistent and incomplete clinical data can lead to drains on IT, analytics, and clinical resources, as staff must spend time manually filling in the gaps. This is where a healthcare data normalization solution grounded in clinical terminology can help:

  • Figure it out or fail: Extracting the value from unstructured data | Blog
    With nearly 80% of clinical data being unstructured, organizations need the ability to extract and standardize inconsistently structured and unstructured diagnoses, procedures, medications, and lab data from diverse systems. This process involves transforming these elements into clinically validated terminology with comprehensive mappings to standard industry codes – but doing so in-house is costly.
  • CyncHealth and IMO Health partner to improve clinical data quality | Case study
    Learn how one organization enhanced its healthcare data quality and standardization, leading to better patient care and efficient data management.


Moving beyond data cleansing: How data scientists are reclaiming their time

Value set management

How do you find the right people? With the right clinical codes.

Unfortunately, clinical and code set complexity drains resources and contribute to inaccurate value sets. Manual processes, frequent code set updates, and inconsistent governance often result in the use of generic clinical descriptors and inaccurate reference codes that don’t fully define a concept or reflect a group of patients.

Discover how value set creation and data maintenance workflows can be streamlined to identify patient populations for quality reporting, reimbursement, and research.


Accurate value sets: The basis for clinical initiatives and targeted analysis

Ready to escape the data maze?

Request a demo from our experts to learn how IMO Health’s solutions in NLP, data normalization, and value set management can help accelerate your data readiness and maximize use. 

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