- The Capability Maturity Model Integration (CMMI) Institute recently announced its Patient Demographic Data Quality (PDDQ) framework is now publically available to help healthcare organizations reduce patient matching errors.
Developed in partnership with ONC, the framework is designed to improve patient safety by enabling healthcare organizations to accurately match patient data both internally and between organizations.
The initiative is an effort to reduce medical errors caused by patient misidentification. According to a 2016 National Patient Misidentification Report released by the Ponemon Institute, 86 percent of physicians have witnessed or heard of a medical error caused by patient misidentification.
“Patients and practitioners alike need to have the assurance that medical errors do not occur due to improperly recorded data,” said CMMI Institute CEO Kirk Botula in a public statement.
The framework will also help to reduce duplicative testing, incorrect treatments or diagnoses, and billing errors.
“The ONC Community of Practice analyzed available frameworks and selected the CMMI Institute’s Data Management Maturity (DMM) model as the baseline for developing the PDDQ framework of best practices,” said ONC Director of State and Interoperability Policy Lee Stevens. “The DMM’s fact-based approach and built-in path for capability growth is aligned with the healthcare industry’s need for a comprehensive standard.”
The PDDQ framework consists of five primary categories including data governance, data quality, data operations, platforms and standards, and supporting processes. All told, the categories contain best practices in 19 data management process areas intended to evaluate a healthcare organization’s capabilities and gaps. These best practices provide insight into areas in need of quality improvements.
“Process areas serve as the principal mechanisms to communicate the themes, context, benefits, and example work products of the model, focused around the key evaluation questions contained within each section,” wrote authors of the framework. “Fulfilling practices assists an organization to chart its path and progress in building capabilities.”
The framework will encourage collaboration between stakeholders to ensure health data is aligned across organizations and care settings.
“The PDDQ provides a comprehensive evaluation of data management practices for patient data through all health care process areas, including registration, patient care, laboratory, pharmacy, claims and billing,” said CMMI Institute Director of Data Management Products and Services Melanie Mecca.
Developers worked to ensure the PDDQ framework is flexible to accommodate a variety of healthcare organizations. Healthcare organizations are encouraged to fully or partially apply the framework in accordance with their specific needs and capabilities.
The framework also promotes organization-wide alignment in the following areas:
CMMI Institute is also available to assist healthcare organizations with framework implementation.
Data management experts can provide organizations with a DMM assessment to evaluate their current patient data matching capabilities, maturity levels, and practice areas where additional capabilities are needed.
In addition to ONC, several other organizations have attempted to resolve problems with patient misidentification in recent years.
The College of Healthcare Information Management Executives (CHIME) attempted to award $1 million to any health IT innovator that could successfully develop a solution ensuring 100 percent accuracy in patient EHR matching.
However, CHIME suspended the patient ID challenge initiative in November after nearly two years of work.
“Though we’ve made great progress and moved the industry forward in many ways through the Challenge, we ultimately did not achieve the results we sought to this complex problem,” CHIME said in a public statement. “We have consequently decided the best course for addressing this patient safety hazard is to redirect our attention and resources to another strategy.”
Efforts to improve patient matching such as the PDDQ framework can help to reduce misidentification errors while developers continue to work on solutions to improve matching accuracy.