ONC launches framework to improve patient data matching

ONC has launched a new patient demographic tool aimed at helping organizations match patient data to prevent errors. 

The Department of Health and Human Services Office of the National Coordinator for Health IT partnered with the Pittsburgh-based CMMI Institute to develop the Patient Demographic Data Quality framework, which seeks to better match patient data both internally and between different organizations, according to an announcement

The framework includes best practices in 19 areas that are designed to determine an individual organization's strengths and weakness in data management. It also aims to provide clear steps for improvement and foster better collaboration among healthcare stakeholders. 

The framework evaluates data management across a number of areas, including laboratory, pharmacy, patient intake and claims and billing. 

ONC selected CMMI as a partner because its existing Data Management Maturity evaluation model offered a "fact-based approach and built-in path for capability growth" that is "aligned with the healthcare industry's need for a more comprehensive standard," Lee Stevens, ONC's director of state and interoperability policy, said in the announcement. 

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Patient data errors are not uncommon and can lead to serious safety lapses. Hospitals misidentify as many as 10% of patients in electronic health records. The announcement notes that 86% of healthcare providers either witnessed or are aware of a medical mistake that was caused by patient identification error. 

The new tool comes a month after the College of Health Informatics Management Executives (CHIME) abandoned its National Patient ID Challenge, an initiative that was closely followed by officials at ONC. Senators recently asked the Government Accountability Office to expand its review of patient matching issues within EHRs. 

The PDDQ framework can also be applied to improve alignment on healthcare organization's other data-related goals, according to the announcement, such as: 

  • Supporting access to greater interoperability
  • Using more effective data governance protocols
  • Improving data quality
  • Additional mapping of data dependencies