Healthcare data is gathered from numerous, disparate sources and is highly variable, leaving it prone to gaps. This large volume of data isn’t just a headache for hospitals — it affects everything from patient care to research.
Luckily, there are tools designed to clean up this chaos.
In our recent webinar, Navigating the data maze: AI, NLP, and value set solutions for clinical data challenges, health tech experts dove into the challenges preventing usable data, the innovative tools that help extract, standardize, and enrich data, and considerations for implementing new solutions.
IMO Health offers three pivotal tools designed to tackle these challenges head-on: a data normalization engine for standardizing clinical data, natural language processing (NLP) technology for extracting insights from textual data, and a value set management tool for ensuring clinical consistency.
Click below to watch the entire webinar.
Pressed for time? Continue scrolling for a short recap of this session.
Data normalization
Data normalization is a core component of a data quality management solution. Algorithms wield their transformative power, converting diverse inputs – like diagnoses, medications, and test results – and turn them into a unified format.
At the heart of normalization lies sophisticated software that cleans, transforms, validates, and maps inputted data. These steps ensure that errors, inconsistencies, and missing values are identified and fixed, resulting in a data set that is standardized and primed for other applications such as predictive analytics and regulatory compliance.
Once clean, data can now improve interoperability and facilitate seamless data sharing, power insightful analytics, and drive evidence-based decision-making.
NLP in healthcare
NLP is a subfield of artificial intelligence (AI) that allows a computer to understand and process human language. In healthcare, NLP focuses on extracting meaningful insights from free text, such as clinical notes or patient records.
While rich in insights, this data often exists in formats that are difficult to navigate and prone to inaccuracies. Manual review of said data consumes valuable clinician time; time that could be better spent on patient care.
Converting clinical free text into structured data streamlines retrieval, analysis, and sharing — in turn improving operational efficiencies and clinical outcomes.
Beyond data organization, NLP uncovers hidden patterns and relationships within the data which can help organizations understand patient needs, inform research strategies, and facilitate interoperability.
Value set management
Value sets are a collection of related terms or clinical code sets (ICD-10, SNOMED CT®, etc.) that define a clinical concept. These sets are curated to identify specific patient populations or clinical scenarios.
Historically, the creation and upkeep of value sets have been entirely manual and are not standardized across healthcare organizations. Data analysts often work in silos, independently creating value sets in response to immediate needs. This fragmented approach leads to duplicated efforts, outdated sets, and inefficiencies in healthcare workflows.
In response to these challenges, a value set management tool centralizes creating, maintaining, and utilizing value sets through a centralized repository that allows for intuitive creation and editing with version control and comparison.
Value set management tools represent a critical advancement in modern healthcare data management, addressing longstanding challenges and paving the way for enhanced clinical outcomes and operational efficiencies.
The challenges of disparate and variable healthcare data are formidable but not impossible to overcome. IMO Health’s suite of solutions is at the forefront of innovation in addressing these complexities.
To learn more, watch the on-demand webinar Navigating the data maze: AI, NLP, and value set solutions for clinical data challenges.
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