In today’s world of big data, the ability to collect, store, analyze, and move data from one place to another has become essential to business operations. Unlimited variables exist within the creation of data and it gets more complicated by storing and merging.

No surprise to anyone, having access to quality, accurate, consistent, and secure data is critical to healthcare. With more and more systems, interfaces, and tools, it’s getting increasingly complicated to share data in the name of delivering better patient care.

The starting point of being able to consume and share data depends on it being “normalized” or standardized into specific, expected, and transportable units of information. Normalized data allows for interoperability, the capability of computer and software systems to exchange and share data from a range of vital sources, including laboratories, clinics, pharmacies, hospitals, and medical practices.

Wait, What’s Data Normalization? 

Normalizing data prepares it to be loaded into a structured database called a data warehouse. It stores massive amounts of data in a structured format for ease of lookup. The database is made up of predefined tables and columns that are determined by specific business needs. 

Normalization consists of multiple processes that scrub, reorganize, and reformat the data during the data load from different sources. These processes are designed to eliminate duplicates, redundancies, inconsistencies, and anomalies while resolving data conflicts and maximizing data integrity

A use-case of data normalization is finding exceptions or conflicts that are easily identified for correction such as inconsistencies and missing data. 

  • Inconsistency - Name: Bill Jones / Name: William R. Jones
  • Missing - Name:                 

Another example would be when rules are applied during processing to automatically convert non-conforming data into uniform output. 

  • 1-999-999-999 converts to 1(999)999-999 
  • Texas converts to TX, Arizona converts to AZ 
  • 01/01/1999 converts to January 1, 1999

Is Normalized Data Really That Important?

Very enthusiastically, yes. 

The ability to quickly access, view, query, and analyze consistent data can be transformative if you have a clear strategy. The power to modify and update data on the fly is enhanced by the presentation of clean data without duplicates, redundancies, and errors.

If your database has standard API requests for data retrieval like Healthjump’s Platform, Business Intelligence (BI) tools can easily bolt-on to provide SQL queries and produce drag and drop dashboards for direct access. This access can enable you to make decisions with confidence with a real-time look at what’s happening.

If you don’t have a team that can help with that, you can partner with companies like Clarify Health Solutions. They empower providers, health plans, and life sciences companies to deliver better care through insights derived from your own data.

Data consistency brings even more benefits you might not be thinking about:

  • The addition of easy to understand descriptive and standardized naming conventions. 
  • Referential integrity (data in one field being dependent upon the value in another) consistently provides validation and control of data accuracy.
  • Security is enhanced by the ability to lock-down and control access to sensitive data elements. 
  • Optimal performance and space utilization is simply inherent in a well-normalized database. 

Without data normalization, raw data is a jumble of unusable and inaccessible elements. It’s the normalization process that brings the order necessary for effective data management. This lays the groundwork for machine learning.

Where does machine learning fit into data normalization?

Big data and machine learning are already part of everyday life:

  • Voice recognition to compose a text message or get directions
  • Hear when you ask Alexa to change your music
  • Personalize services based on previous buying history
  • Identity recognition by touch, iris or face
  • Email filtering to automatically mark as trash or spam
  • Blocking unwanted telephone calls
  • Fraud detection in banking
  • Language translation

Data normalization is the first step in preparing training data for machine learning algorithms. The power of machine learning is that it includes algorithms that further generate algorithms.

Machines learn via computing power, algorithmic innovation, and data availability. They require data availability as in: clean data, accurate data, dependable data. 

What’s next for data normalization?

The ultimate vision of your data normalization strategy is to have clean data, consistently formatted. The data elements read exactly the same throughout all records in the entire data warehouse structure, no matter where they came originally.

Connecting to multiple systems or interfaces and normalizing each set of data can get complicated fast. Each time an additional system or interface is added, it adds more processes that have to account for complexities and customizations. Once you start expanding, it becomes harder and harder to scale.

And we didn’t even mention the pain in maintaining those connections which can easily change and cause a domino effect in your data.

Do you need data like patient demographic and social history, in addition to appointment, encounter, allergy, diagnosis, immunization, lab, vitals, and procedure records. Is it coming from more connections than you can handle right now? 

Healthjump was built to solve the issues with collecting, storing, and normalizing healthcare data. Check out our video here featuring our Chief Technology Officer Cliff Cavanaugh talking about the state of interoperability in healthcare and how normalized data fits into the mix.

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