In response to findings that many “safety-related events were caused by or related to incorrect patient identification”, the Office of the National Coordinator for Health Information Technology (ONC) worked with CMMI to develop the PDDQ Framework in order help organizations implement effective, sustainable data management practices around patient data management.
Groundbreaking? Yes. As a lean framework appropriate for small business the PDDQ shows how you can rightsize the Data Management Maturity Model to match your situation. That it is freely available demonstrates CMMI’s commitment to improving data quality in healthcare.
The PDDQ is Lean
The framework aims at a single target: “minimization of the number of duplicate patient records”. As a result, the PDDQ comprises five rather than the DMM’s six process categories, and 19 rather than DMM’s 26 process areas. Assessment questions are likewise condensed to a usefully targeted subset. For example, DMM’s 16 questions related to Data Integration are rendered down to only three that are concrete and concise. Here’s one:
- Does the organization apply quality rules to the integration of patient demographic data from multiple sources?
To me, the PDDQ shows that DMM assessments could benefit from an initial step that condenses the model to meet an organization’s industry area and maturity objective. Such a preparation step could substantially improve assessment acceptance and quality of participation.
The PDDQ is Accessible
Sure, it is a stripped down model, but let’s not forget that many handlers of detailed patient data are small businesses that haven’t made data quality per se a priority. With the assessment consisting of a web page presenting all the questions and calculating results on the fly, the PDDQ serves as an easy on ramp to the world of data quality for businesses that don’t have a lot of time.
And, not to be discounted in a world where at least one data quality framework closely restricts access, it’s free.
Kudos to ONC and CMMI for presenting a lean, accessible framework to reduce potentially life threatening treatment errors by helping improve patient data quality.