Poor Data Quality and Patient Matching

Healthcare’s Dirty Little Secret

The U.S. Healthcare system is in a state of flux. Healthcare data quality has been an issue for years, and it continues to be a challenge for providers trying to deliver quality patient care. One of the biggest problems facing the Healthcare system is poor data quality, leading to inaccurate patient matching, which can seriously affect patients. This article will discuss ways to improve healthcare data quality and mitigate the adverse effects of poor data matching.

How do we define quality data in healthcare?

In healthcare, “quality data” refers to accurate, complete, and timely data. However, achieving quality data can be a challenge due to the complex nature of the healthcare system. Data issues can arise at every stage of the care continuum, from patient registration to billing and claims processing. My favorite stage of poor data is the pre-registration stage, but I digress. In addition, the sheer volume of data generated by the healthcare system can make it challenging to identify and track errors. As a result, quality data is essential for providing quality care. By definition, quality data includes:

• Clinical data: information about patient medical histories, diagnoses, and treatments.