Patient matching is the process of comparing different demographic components from a variety of Health Information Technology (HIT) systems to determine if they are referring to the same patient. Patient matching is crucial because providers must be able to properly match patients to their medical records in order to provide them with proper care.
According to Health Data Management, “a provision in the Fiscal Year 2018 funding bill put forth in the Labor, Health and Human Services and Education funding bill, now under consideration in the House Appropriations Committee, could jumpstart efforts to develop a patient matching strategy for Medicare beneficiaries to improve identification of patients, with the intent of ensuring the safety of their care.”
But why is patient matching so difficult in the first place?
Lack of Quality Data
Healthcare providers are expected to maintain accurate medical records; however, throughout a patient’s life, the only piece of information that is guaranteed to stay the same is the patient’s date of birth. This can have a major impact on the quality of a provider’s data about their patients.
Poor quality data makes it nearly impossible to match patients to all of their records, and when they aren’t matched correctly, some major problems can occur. Lack of patient matching can result in a patient receiving duplicate procedures or even a patient receiving the wrong prescription. This poses a significant risk to patient safety. So how should healthcare providers ensure data quality in order to successfully match patients to their health records?
How to Ensure Data Quality
In order to solve data quality issues for patient matching, healthcare providers need a solution that bridges the gap between the ingestion and the consumption of big data. The solution should validate every piece of data from all HIT’s, seamlessly integrate the data-to-insights process, and empower healthcare providers to operationalize the insights generated from analyzing big data.
The solution should offer intuitive features that allow providers to easily prepare data, pinpoint data issues, conduct multi-dimensional data quality checks, perform in-depth analysis and put the results to work immediately. The solution should be designed specifically to process high volumes of data and give healthcare providers the confidence in their data they need to successfully treat their patients.
To learn more about how an all-inclusive data intelligence platform can help ensure data quality, download the data sheet below.
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