A Look at the Quality Data Model

Here is a post on the Quality Data Model from Anne Bobb, RPh, who is a member of the HIMSS National Quality Forum Task Force. Take a moment to read her post. Jonathan French, Director, Healthcare Information Systems, HIMSS.

The Health Information Technology Expert Panel, or HITEP, was convened in 2007 to envision the electronic health record platform necessary for future quality and performance measurement. HITEP I issued recommendations in 2008 for a framework to facilitate the development, use and reporting of quality measures from EHR systems.

The recommendations, titled “Common Data Types and Prioritized Performance Measures for Electronic Health Information Systems,” proposed 39 data types for a set of 84 high priority quality measures.

HITEP-II was launched in 2009 in an effort to build on HITEP-I’s recommendations. The HITEP-II Panel developed the Quality Data Model (QDM) to create standardized, less-burdensome quality measurement and reporting for providers, researchers, measure developers and vendors.

QDM describes the data elements and the states, or contexts, in which the data elements are expected to exist in clinical information systems.

Each QDM element is composed of:

a concept,
the state in which the concept is expected to be used, and
a value set of codes in a defined taxonomy to specify which instance of the concept is expected.

Additional attributes include timing, actor, data flow and concept-specific.

Each iteration further refines the model and improves the ability to understand and implement. Providers may be challenged by the highly structured requirements of the QDM. Much of the data is captured in EHRs as a by-product of care provided; however, some data is likely in free-text physician notes and not in the EHR field defined in the QDM, such as a structured problem list.

The meaningful use federal initiative to generate quality measures directly from the EHR is dependent on the discrete data defined in the QDM. In traditional quality measurement and reporting, some of the data is manually abstracted from charts retrospectively by quality nurses. These nurses look in the defined data field as well as free-text notes.

With e-reporting, if the data element does not exist in the correct location and state, it will not be captured. Providers will need to work closely with vendors and measure developers to ensure efficient clinical workflow and key data capture are harmonious.

QDM Version 3.0 is currently available for public comment. The slides associated with this topic go into greater detail on the HITEP project as well as the Quality Data Model.

In our next blog, we will focus on the NQF Measure Authoring Tool. Please continue to read our blogs so that you stay abreast of how NQF is supporting the industry, public and government in improving healthcare quality.

Anne M Bobb, RPh, is Director of Informatics Quality at Children’s Memorial Hospital. She is responsible for strategies that result in the effective design, implementation and adoption of the electronic health record at CMH, a free-standing academic children’s hospital in Chicago. Her scope covers aligning the EHR with the CMH quality agenda, mission and values and includes oversight of meaningful use for the hospital and eligible providers. Contact Anne at abobb@childrensmemorial.org.

Please note: This is the fifth blog in the weekly series on NQF Health IT Projects and the impact on the healthcare industry. Previous posts are:

Keeping Tabs on Quality, by Zahid Butt, MD, Chair, NQF Task Force
Understanding Quality Reporting/Performance Measurement, by Amy Thorpe, Member, NQF Task Force
Retooling of Paper-Based Measures, by Zahid Butt, MD, Chair, NQF Task Force
eMeasures Feedback and What it Means, by Ferdinand Velasco, MD, Chair, QCS Committee

Each blog post is linked to a slide deck on the topic which elaborates on the importance of the project and connects dots to the other NQF HIT projects. All of the blog posts plus the related slide deck are posted on the HIMSS NQF Web site