Overview

In healthcare, the lack of standardized and normalized data creates significant challenges for clinicians, data engineers, and analysts. Clinical records, especially those derived from structured, semi-structured, and unstructured CCDA data, often lack consistency in coding and terminology. This inconsistency complicates efforts to extract meaningful insights and hampers efficient patient care and data analytics.

What does normalization do?

Data normalization ensures that customers receive a standardized and universally understood set of clinical concepts derived from the coded and descriptive data in a patient's electronic medical record (EMR).

With the vast amount and complexity of clinical records, normalization plays a crucial role in delivering high-quality data that enables meaningful insights at both patient and population levels. This is essential for direct patient care, data analytics, quality management, and various other use cases.

By curating standardized data outputs, data normalization enhances the usability and value of the data Particle provides, empowering customers with more actionable and reliable data products.

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What data domains are being normalized?

Labs, Medications, Problems, and Procedures.

Value

Data normalization and standardization serves as the foundation for building advanced data concepts, including neural networks and machine learning (ML) applications. These future advancements in data products rely on the accuracy and consistency provided by normalization.

Data normalization doesn’t just enhance the quality of today’s data; it lays the groundwork for transformative healthcare innovations that will drive better outcomes, improved patient care, and operational efficiency.

Speed to Insights:

Early Detection of Diseases:

ML can identify patterns and early warning signs, enabling clinicians to intervene before diseases progress. For instance, detecting rising glucose levels could prevent the onset of diabetes and associated complications like chronic kidney disease (CKD).

Prediction of Health Outcomes:

ML can forecast health risks, allowing clinicians to develop personalized care plans to prevent emergency department visits or hospital readmissions.

Risk Assessment:

Algorithms can proactively identify patients at risk for conditions like heart disease or cancer, empowering targeted preventive interventions.

Precision Medicine:

With future access to genomic data, ML could personalize treatments by analyzing genetic profiles, leading to more effective therapies with fewer side effects.

Comparability:

Normalized data creates a common framework for comparing information across diverse sources. This enhances the ability to analyze trends, uncover patterns, and make informed decisions with confidence.

Efficiency:

With a standardized structure, normalized data is easier to access, search, and retrieve. This streamlines workflows, reduces errors, and improves the overall efficiency of data management.

Accuracy:

By eliminating inconsistencies and duplications, normalized data ensures correctness and clarity, empowering healthcare providers to make better clinical decisions.

Compliance:

Normalization aligns data with industry standards, supporting compliance requirements. This improves patient safety, ensures high-quality care, and reduces risks of fraud or abuse.

Comprehensiveness:

Normalized data minimizes information loss by capturing both textual and coded diagnoses across multiple systems. This ensures clinicians have access to complete and actionable information.