Simplifying Data Unification in Healthcare

These 3 Steps Will Help Your Data Unification Process

Julie SkeenOctober 24, 2017

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Unifying data from a few data sources containing only a few records doesn’t sound like such an arduous task.  Multiply those records and data sources by 100, and it becomes increasingly difficult, but still doable—as long as you have the time and expertise required. But if you consider your business, a few hundred data sources still doesn’t seem realistic, does it? As the world takes on big data, the data sources and records multiply by factors of many, so managing that data accurately is a regular burden on many organizations. Same can be said for healthcare organizations. With likely hundreds of thousands, if not millions, of records in each data source, they’re not exempt from this challenge.

This is the challenge many IT professionals in healthcare are currently facing. According to Health Data Management, “They [healthcare organizations] need to combine tens or hundreds of separate data sources with perhaps millions of records each.” This may seem like a daunting task, but there are tools that healthcare organizations can implement to help them simplify data unification on such a large scale.

Simplifying Data Unification 

According to the same Health Data Management article, “Data unification is the process of ingesting, transforming, mapping, deduplicating, and exporting data from multiple sources.” Not only do healthcare organizations have to perform data unification on such a massive scale, they must also deal with duplicate records in data sources. To help simplify data unification and ensure there are no duplicate records, healthcare organizations need to implement a three-step process.

Validate for Quality: Quality data is crucial in healthcare for many reasons, including financial reporting, regulatory compliance, risk management, system migrations, and fraud prevention. In this case, however, data quality is critical to ensure duplicate files are eliminated before data unification.

Duplicate files are a common occurrence in healthcare because files are received daily from many applications and sources. Sometimes, the previous day’s file has been sent in error. Prior to processing the file, healthcare organizations need to execute data quality business rules to ensure only the correct day’s file will be processed. In the case of an incorrect file being received, an exception report should be immediately flagged and the business rules should prevent the incorrect file from processing, ensuring there are no duplicate files for data unification.

Implement a Data Governance Framework: Data governance can ensure healthcare organizations establish and maintain data processes, policies and procedures, helping simplify data unification. A data governance framework will allow healthcare organizations to easily define, track and manage all aspects of their data assets. This includes all facets of the data—from the data available, and its owner/steward, lineage and usage, to its associated definitions, synonyms and business attributes—making it much easier to track and locate data for unification.

Analyze the Data: Data changes over time, especially in healthcare. Patients move, and their overall health will change as well, from acute episodes to chronic conditions. With predictive analytics, healthcare organizations can successfully predict how their data may change in the future. In addition, machine learning can augment this process. This ensures that only the most up-to-date records are unified and after it is unified, the records stay up to date.

To implement this three-step process, healthcare organizations will need an all-inclusive big data platform.

An All-Inclusive Big Data Solution

Healthcare organizations need an all-inclusive big data platform designed to handle not one, but rather multiple steps from data ingestion and preparation to data analysis and operationalization. The platform should identify, validate and resolve data issues as close to the source as possible, enabling them to unify data from multiple data sources, platforms and applications much quicker and easier.

To learn more about data unification and an all-inclusive big data platform, download the data sheet below.

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