Decades ago, Congress banned the U.S. Department of Health and Human Services from spending federal money on a national patient number identification system. This left health care companies to maintain their own system for patient identification, matching, and data updates. As the number of firms that handle patient health care data grows, it has become increasingly more challenging to ensure that data is current, accurate, and correctly matched to the patient. To address this patient-matching problem, the Office of the National Coordinator for Health Information Technology (ONC) has collaborated with the CMMI Institute to develop the Patient Demographic Data Quality (PDDQ) Framework.
The Patient Data Problem
Incomplete, inaccurate, and mismatched health care data within and across health care companies leads to misdiagnoses, treatment errors, and other patient safety issues. In addition, inconsistently matched health records can create privacy and security concerns. In response to this growing risk, ONC and CMMI Institute came up with a way to “support health systems, large practices, health information exchanges, and payers in improving their patient demographic data quality” through the PDDQ Framework.
The Framework Solution
The goal of the PDDQ Framework is to increase the accuracy and improve the completeness of patient health records, working toward improved patient safety and data security within and between health care companies. The framework helps healthcare organizations (HCOs) determine where, how, and who creates and modifies patient information. This enables HCOs to assess how they currently manage data, identify gaps, and develop a concrete path to better data quality and management. Through the PDDQ Framework, HCOs can accomplish goals like standardizing demographic attributes and best practices for capturing and identify quality patient data.
Additional PDDQ Benefits
Through the process of implementing the PDDQ Framework, HCOs can harness benefits in addition to improved patient safety and data quality. Processes such as auditing, gap identification, and sound data management practice implementation provide companies with better data for analytics, better data security, and a sense of shared responsibility internally and throughout the health care sector.