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.
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.
- Natural Language Processing 101: A guide to NLP in clinical documentation | Blog
Need an introduction to NLP? Read this article on how it can help support clinical documentation. - Advancing precision medicine and life sciences: NLP and clinical trials | Blog
Explore how NLP and generative AI can improve clinical trial design and recruitment to benefit investigators and patients. - How to train your LLM: And why semantic interoperability is the key to success | Insight Brief
An understanding of comprehensive clinical terminology is necessary for large language models (LLMs) to offer accurate interpretations, integrate data seamlessly, and provide actionable insights that enhance patient care and decision-making.
INSIGHT BRIEF
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.
INSIGHT BRIEF
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.
- Navigating healthcare’s value set dilemma | Blog
Using value sets for cohorting patients with the same clinical characteristics is essential and burdensome for many healthcare stakeholders. Here’s why. - Demystifying value set management through improved data quality | Blog
In health informatics, accurate value set management is the key to efficiently identifying patients within a target population, simplifying a historically complex process.
INSIGHT BRIEF
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.
