HITPC Workgroups to Hold a Hearing Nov. 30 on Clinical Data Quality Improvement
Garbage in, garbage out is a familiar adage to many who work with information: If you start with bad data you are going to end up with a bad conclusion. The same is certainly true for clinical data captured by providers who use health IT in an effort to help patients live healthier lives.
In this spirit, Health IT Policy Committee (HITPC) workgroups are holding a public hearing on November 30, 2012 to help pave the path toward an environment in which clinical data used in health IT are of the highest possible quality. The workgroups want to hear from experts about the challenges and opportunities in this domain.
The workgroups also want to gather feedback from scientists, clinicians, researchers, and others about potential pitfalls or problems, and how we can thread the needle of optimal clinical data documentation complexity (reducing the burden of clinical documentation) while retaining sufficient granularity so that clinical data documentation can be leveraged as a tool in measuring the quality of care.
Open Meeting on Clinical Data Quality Improvement
The HITPC Quality Measures and Certification and Adoption Workgroups will convene a joint public hearing on November 30, 2012 at the Dupont Circle Hotel , located at 1500 New Hampshire Avenue N.W., in Washington D.C.
A pair of panels, “Current State of EHR-Generated Data Quality for Clinical Quality Measurement” and “Addressing Barriers to EHR-Generated Data Quality,” will open the discussion from 9:30 a.m. to 12:30 p.m. EST.
Jacob Reider, ONC’s Chief Medical Officer, looks forward to the meeting, and observes: “As care providers, we have a remarkable opportunity to leverage electronic health records to make clinical documentation more efficient, more comprehensive, and more accurate. This hearing will be the first of several public discussions in which we will work to better define our nation’s path toward these objectives. The reliability and credibility of any electronic clinical quality measure is dependent on the quality of the data used to compute that measure.
Data Quality Terms, Defined
JM Juran classically described data as being of high quality if they are “fit for intended uses” in decision-making, operations, and planning. Although the terminology ascribed to data quality varies, there are four frequently cited attributes of high data quality:
For the purpose of this hearing, the workgroups will define high-quality data as being fit for secondary use and “free of defect.” While “complete data” are free of omissions; “correct data” are valid and reliable; and “consistent data” agree with “related data,” and “current data” are up-to-date.
Obtaining public feedback and suggestions concerning the future of clinical data quality measures is just as important as the discussion generated by the expert panelists. As such, there will be an opportunity for public comment after the panelists conclude their presentations.
ONC Wants to Hear From You
Can’t make the hearing? Use the comment section below to share your thoughts, or send your comments to ONC.Policy@hhs.gov under the heading: Clinical Data Quality Hearing – Public Comment.
Please be advised that all comments will be available publicly.
For more information on health information technology, visit HealthIT.gov.