Data Normalization in Healthcare: A Comprehensive Guide

The steady growth of healthcare information technology (HIT) has brought us an explosion of data with no end in sight. Today, approximately 30% of the world’s data volume is generated by the healthcare industry, putting a strain on the data management capabilities of every organization.  

However, proper data management also simplifies health information exchange, interoperability, big data, and analytics, promising to bring the industry closer to better care delivery. As organizations strive to ride these waves, data normalization in healthcare becomes the obvious first step. 

But what exactly is data normalization in healthcare, and how does it benefit healthcare providers? Let’s explore these questions and learn the importance of data normalization in healthcare, its methodologies, and its role in shaping patient-centered care’s future.

What is Data Normalization?

Data normalization in healthcare standardizes clinical information from various systems into a unified language. This ensures consistency and reliability, allowing for better patient care, enhanced data analytics, and more effective decision-making. By converting diverse data into common terminology, healthcare providers can integrate and utilize clinical information more efficiently.

In healthcare, data normalization starts with collecting patient records from sources like EHRs, lab systems, billing systems, insurance providers, and pharmacies. These records are standardized and integrated into a centralized data warehouse, creating a unified dataset that supports future queries or analytics needs.

Data normalization enables healthcare organizations to access accurate and comprehensive information, facilitating advanced analysis and informed decision-making.

Why is Normalized Data in Healthcare Important?

Data normalization allows for apples-to-apples comparisons of information from different systems by standardizing local content to terminology standards and semantically translating data to eliminate any ambiguity of meaning. 

Without normalized data, healthcare organizations face limitations in analytics. Clinically, this affects patient outcome analysis, often constrained by fragmented patient registries. Disparate data sources hinder compliance, quality, and financial reports operationally and financially. Normalized data enables comprehensive analytics, supporting initiatives like care consistency, waste reduction, population health management, and predictive analytics at the point of care.

Emergent tech, including several modern AI solutions, excels at handling large datasets swiftly, but reliable insights hinge on high-quality source data. Robust datasets bolster the credibility and relevance of insights derived. Data normalization fosters interoperability across systems, empowering healthcare providers, insurers, and researchers to leverage extensive datasets effectively. Additionally, by incorporating Natural Language Processing (NLP), data normalization supports semantic interoperability and enhances data extraction accuracy, enabling precise retrieval and standardization of clinical information. 

Benefits of Data Normalization in Healthcare

Applying data normalization in healthcare offers significant benefits by unlocking your data capabilities, ensuring data is standardized and integrated effectively through: 

• Improved data quality
• Enhanced data comparability
• Optimized data analysis
• Better decision-making
• Patient-centered care

Industry thought leaders are emphasizing the above. Here’s how:

Improved Data Quality: 

Data normalization in healthcare improves healthcare data quality by removing inconsistencies, duplicates, and errors, resulting in a more accurate and reliable healthcare dataset for analysis. Using data normalization in healthcare also enhances the ability to make informed decisions, ultimately benefiting patient care and operational efficiency within healthcare systems.

Enhanced Data Comparability: 

Data standardization helps ensure consistency and comparability of data from different sources. Leveraging data standardization, healthcare organizations can integrate information from many sources, including electronic health records, insurance claims, and patient survey data, providing more comprehensive and detailed analysis capabilities.

Optimized Data Analysis: 

Normalized data not only simplifies analysis and interpretation but also enhances the efficiency and accuracy of the data analysis process. Through normalization, healthcare organizations can quickly discover hidden trends, patterns, and correlations in raw data. As a result, they can make more informed decisions and improve the quality of patient care while improving operational efficiency and resource management.

Better Decision-Making: 

Optimizing data normalization will provide healthcare organizations with accurate and reliable data, helping them make intelligent decisions about patient care and resource allocation, as well as health policy and development. Data standardization improves the decision-making process and enhances the patient experience, ensuring patients receive the best care possible.

Patient-Centered Care: 

Personalized medicine, which focuses on creating treatment plans tailored to an individual’s unique health profile, is an emerging trend in healthcare. Data normalization plays a vital role in this process by standardizing patient data. By organizing data from various sources like electronic health records (EHRs), medical devices, and patient surveys, healthcare professionals can analyze genetic, clinical, and lifestyle information to develop customized treatment plans. This patient-centric approach enhances care quality and improves health outcomes.

Challenges Healthcare Systems Face with Data Normalization

While the advantages of data normalization in healthcare are clear, implementation can be challenging. Healthcare systems may also contend with complex data sources, interoperability issues, and varying stakeholder needs, all of which can complicate and slow the normalization process. Here are some challenges:

• Variability and non-standardized coding
• Data quality and infrastructure
• Data security and compliance
• Need for skilled personnel

Addressing these challenges is a multi-step process:

Variability and Non-Standardized Coding: 

Health data comes from various sources, leading to differences in coding and presentation methods. The difference is especially evident in records and systems created before interoperability standards were developed and adopted. 

Data normalization in healthcare addresses the need for standardized codes and common language across different systems. These differences can lead to duplicate or fragmented information, further complicating the process of achieving comprehensive data standardization in healthcare.

Data Quality and Infrastructure:

Ensuring system effectiveness in healthcare requires stakeholders to consider data quality, storage, and import factors. Data quality significantly impacts extract, transfer, and load (ETL) processes, and overuse can increase expenses. While storing data without a standardized code framework simplifies data import, it complicates data cleansing, processing, and reporting. 

Additionally, implementing a data normalization infrastructure, whether in-house or through a third party, incurs various costs, with challenges related to skilled resources and data processing being among the most significant.

Data Security and Compliance:

Data privacy and security pose significant challenges in the process of standardization and data standardization in healthcare, especially when dealing with sensitive information. Sharing health information across systems can improve patient outcomes but also increases the risk of data hacking. Compliance with solid security measures, encryption techniques, and privacy regulations such as HIPAA remains necessary to protect patient data during data normalization and standardization.

Need for Skilled Personnel: 

Recognized experts are crucial for the effective management of healthcare data. A team of platform-certified software engineers, health analysts, and clinicians is necessary to set up and maintain data processing pipelines. These professionals are essential because they ensure the system’s integrity, facilitate accurate data normalization, and enable sophisticated analytics that improve patient outcomes. 

However, hiring such experts involves significant expenses. They need compensation in line with their specialized skills. Finding individuals with experience handling healthcare data adds another layer of complexity, as this expertise is rare and highly sought after in the industry.

How to Improve Data Normalization in Healthcare

So, the challenges of data normalization primarily stem from the unorganized nature of clinical data. What now? Start by adopting strategies that standardize and harmonize these data sources, which is crucial to overcoming these challenges.  Here are some practical solutions to advance data normalization in healthcare.

Ensure your System Follows ISA Standards

Interoperability regulations bind healthcare providers and payers. These regulations provide a framework, allowing systems some flexibility in coding and standardizing healthcare data. ONC’s Interoperability Standards Advisory (ISA) Reference Edition lists the specifications that define interoperability in healthcare. 

After reviewing the ISA Reference Editions, interoperability and data management personnel can standardize data coding across departments within their health systems, ensuring accuracy and resolving issues early. Adopting ISA standards system-wide simplifies data normalization and promotes meaningful data sharing. 

Develop and apply Data Integrity Strategies

Effective data normalization in healthcare requires implementing coding standards and ensuring data integrity within Electronic Health Records (EHRs). Despite EHRs’ ability to streamline patient record management,  poor data standardization leads to errors in data collection, which harms data normalization efforts by limiting that data’s useability.

Implementing a data completeness tracking system (CTX) has proven effective in enhancing EHR data quality and supporting better data normalization efforts. Strong health data governance practices are essential for maintaining EHR data quality, usability, and exchange—improving clinical efficiency and patient outcomes.

Partnering with HealthTech Experts

Partner with capable experts who understand the intricacies of healthcare data management. Seek out platform-certified experts who specialize in creating robust data normalization frameworks tailored to your organization’s needs.

While establishing an internal data standardization infrastructure can be costly, collaborating with medical technology experts can provide a more cost-effective solution. Furthermore, cooperation with HealthTech suppliers ensures data security, data loss prevention in healthcare and compliance with relevant legal regulations. These vendors can also provide access to advanced tools and technologies that may not be available internally. 

Enhance Your Data Strategy with KMS Healthcare

Data normalization is a pivotal strategy in modern healthcare, enabling organizations to standardize clinical information across systems, ensuring reliability, and enhancing data analytics capabilities. The benefits are achievable despite challenges such as data variability and security concerns.

Harness the full potential of data normalization in healthcare and advanced analytics by partnering with a trusted technology expert like KMS Healthcare. The expert team at KMS provides the right resources and solutions to map, translate, update, and manage the standard of increasing clinical data. We are committed to delivering advanced data analytics solutions that optimize information management, enhance predictive capabilities, and facilitate rapid clinical decision-making.

KMS Healthcare simplifies tackling healthcare data’s toughest challenges. Get in touch with us today to schedule a free project evaluation.