By Jacob Warkentien
Today, keeping normalized health data cleaned and organized is integral to the accuracy of analytics and statistics we provide to our clients. Being able to incorporate data points from many different sources (doctors, specialists, surgeons, etc.) while using multiple data file types (837, JSON, .csv, etc.) is no simple task. In addition to patient matching technology, having a data standardization across your many platforms, will create value in finding gaps in care.
When normalizing data elements in disparate systems, inconsistency in data elements for example checkup visits can affect measure outcomes. One way we handle this on our end is by having data specifications that cover our clients’ needs while being easy to adhere to. Having thought-out patterns to be followed and carefully handled in documentation is essential to an overall successful process.
An immense amount of effort and money on all ends can be wasted when these concepts aren’t enforced. As we advance further with technology in the health community, we hope that it becomes easier for the healthcare communities to accomplish this, easing the burden of data quality and allowing us to serve our clients better. The above concepts not only fulfill demographic requirements, but they can be easily used by data scientists, healthcare workers, and patients themselves across multiple platforms.
Direct example of data impact:
-Using unique IDs elements, such as Patient ID, allows for dataset sizes to increase without needing exponential amounts of maintenance as they do so.
-Using specific formats for data points, such as Date of Service or Date of Birth, can reduce the chance of error in data file conversion.