data standardization

4 Keys to Accurate and Standardized Healthcare Data


Accurate and standardized data. It sounds a bit aspirational, doesn’t it? The struggle of interoperability and standardized healthcare data is an ongoing battle. With well-documented barriers, it isn’t clear when healthcare data will become more accessible and standardized. According to some industry experts, the lack of healthcare data standardization is a key barrier to interoperability in the healthcare industry.

While barriers are often high, the advantages of standardized data are significant. Benefits can range from taking less time for statistical analysis to requiring less oversight and resource allocation. Regardless of the application, from clinical trials to supply chain management, data is king when it comes to saving money.

While an individual or an entire organization doesn’t have the bandwidth to tackle all data standardization and interoperability challenges within healthcare, these issues are still worth addressing, even if on a smaller scale. By developing a model for standardizing your data for a small subset, you can create a blueprint for success.

Four keys to developing accurate and standardized data

1. Capture data from disparate sources

For your database to contain all of the most relevant, accurate and up-to-date data, the database must capture that information from various sources. For different use cases, sources can range from patient intake forms to electronic health records (EHRs) to CRM systems. Across applications, the ability to capture this data through integration is crucial to data standardization.

2. Reconcile data

If different sources have different values for a record, two things need to happen. First, no duplication should occur. Second, the most accurate or up-to-date record should become the ‘master record’. For example, if the patient Sally Ride changes her last name to Morris and has records that contain both her current and previous name, then the following must happen: you must have a process to determine which record is up-to-date; the accurate record must become the ‘master’ record; and thirdly, any duplicates containing the outdated name must be deleted.

3. Data mapping and normalization

When standardizing data, it’s important to determine what will be normalized. This approach will determine which key pieces of data can be used across applications. To aid this effort, we recommend that you develop a normalization matrix. Additionally, you should create or refine your data mapping, which describes the relationships between different entities in your database. This allows you to map your “dirty data” to new, standardized fields and values. By creating an accurate data mapping, you can avoid repeating old mistakes in the future. Click here to learn more about creating a data model (also known as an entity relationship diagram).

4. Consistency is key

Data mappings, data normalization and data cleaning result in consistency. Once data is standardized and clean, it can be easily used across a myriad of applications. Some common applications in healthcare include clinical data used for digital health apps and intake protocols for provider schedules. Applications are most effective when powered and empowered by accurate data, which translates to improved patient outcomes and significant cost savings for health systems. 

These four steps won’t solve all of the healthcare industry’s data issues. However, they can help you start to solve your own organization’s data issues. With standardized, clean data, you can make better business decisions across departments. Additionally, you can save significant time for reporting, and can better explain and back up the value that your organization brings its clients. By tidying up your own data, you are doing your part in making healthcare work better for all.