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 Exit Disclaimer, 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 Exit Disclaimer 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:

  • Completeness
  • Correctness
  • Consistency
  • Currency

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.

9 Comments

  1. Margaret Foley says:

    The hearing should be very interesting.

    What is the fifth frequently cited attributes of high data quality? The article listed four:
    Completeness
    Correctness
    Consistency
    Currency

    • ONC Blog Team says:

      Margaret,

      Thank you for your interest in the HITPC hearing. The reference to five attributes was a typo. There are actually only four frequently cited attributes of high data quality. We have amended to the post to reflect this. Thank you for pointing out the discrepancy.

  2. Jesse, thanks for letting the community know about this meeting. I’ll be in attendance and I’m excited to participate in the conversation as I believe it’s an important one. As a clinician, I know the severity of the clinical documentation burden placed on physicians who are clearly focused on providing first-rate patient care vs. capturing the patient note – and rightly so; that’s why we took the job in the first place! Still, advancements in technology have helped to simplify this “burden” and the benefits associated with being able leverage reliable patient information throughout the care continuum which has dramatically increased as a result of the move to EMRs. And while worries surrounding fraud and potential upcoding are understandable, my biggest worry is how much time is actually being lost today with doctors who are documenting to in an attempt to receive appropriate reimbursement for the care they provide. How much of that time could instead be devoted to patient care. Additionally, one question, who specifically will be presenting? Thanks.
    Nick van Terheyden, MD
    Nick van Terheyden, MD
    CMIO – Clinical Language Understanding, Nuance Communications
    nvt@nuance.com

  3. Katherine McGraw says:

    Where can I find the transcript from this meeting? Thanks.

  4. Mary Carr says:

    What a pleasure to see a D.C.-based discussion on data quality. I trust that in such a discussion, the values of medical device integration were addressed, given this solution’s ability to quickly (the aforementioned “currency” attribute of high-quality data) and accurately (the “correctness” part) sync device data with electronic records.

    As Dr. Terheyden has pointed out in his comment above, there is a great value at stake in these sorts of discussions, and that is clinician efficiency. After all, study after study reveals the power of direct care when it comes to patient outcomes. I’ll add, though, that best of breed medical device integration providers build solutions that get device data to the record and decrease the amount of time clinicians spend documenting—as time study after time study has also shown.

  5. Sarah says:

    I missed the HITPC workgroups doing this health project. Next time i will look into those clinical data. Great inormation, Thanks

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