Accurately and consistently matching patient data both within and across organizations is pivotal to ensuring safe and effective care to patients. When patient data is not accurately matched, treatment and diagnosis decisions are made in the absence of valuable information, and patients could be subject to adverse events and significant harm1. To facilitate the best possible treatment and care coordination among a care team, exchanging and linking patient data from across the care continuum is critical.
In the absence of a nationwide, voluntary unique identifier, patient demographic data is the primary key used for matching patient records. Unfortunately, patient demographic data has historically been of poor data quality, resulting in both inaccurate matching of patient records and low match rates, particularly when data is exchanged across organizations. Over the last few years, the industry has considered a number of solutions to help address the problem, including:
Defining and adopting standards for data, and implementing sound data management processes requires increased awareness, effective collaboration, and a cultural evolution towards shared responsibility, both within and across health care organizations. To support these goals, the Office of the National Coordinator for Health Information Technology (ONC) worked with the CMMI Institute to develop the Patient Demographic Data Quality (PDDQ) Framework. The goal of the PDDQ Framework is to help organizations ensure that formulation, agreements, approvals, and implementation of adopted standards and processes will be effective and sustainable and support the minimization of the number of duplicate records across the industry, ultimately improving patient safety.