Poor data quality in healthcare is the leading problem that maligns patient outcomes. Hospitals and health information exchanges (HIEs) still struggle with patient matching issues, with most citing data quality problems and poor algorithms as top barriers to patient matching. Correctly linking patient data across organizations is a key element of value-based care, patient safety, and care coordination. Duplicate or mismatched records can result in privacy risks, claim denials, redundant medical tests or procedures, and reporting errors.
The lack of accurate and reliable DQ in healthcare leads to dire consequences that are completely preventable, as shown in OwlDQ's troponin example below. Complete and accurate data is a vital component of our complex health system, and anything less is an unacceptable risk. OwlDQ provides the predictable data quality that healthcare organizations need to deliver high-quality care that we all strive to achieve.