The Dimensions of Clinical Data Quality

As we move into the next phase of EMR / EHR maturity, these systems will increasingly shine a light on problems with a) the quality of data captured by health providers and b) the quality of care delivered.  Without a mechanism for measurement of clinical data quality we can’t start this conversation that we need to have.

 

Regular readers of this blog will be familiar with my views and focus on the role of data quality in the emerging models of care made possible through Digital Health.  Without high quality, structured, atomic, coded data using health informatics standards to create a shared semantics (to the extent that is possible), the vision of greater use of Clinical Decision Support at the front-line of healthcare, artificial intelligence, analytics, etc., will be greatly diluted.  We need high quality data to enable the vision and promise of Digital Health.

But how do we define and measure data quality?

I wanted to share with you a framework that I’ve been using for a while that helps with thinking about the dimensions of clinical data quality.  This framework comes from Weiskopf and Chan (2011), “Methods and dimensions of electronic health record data quality assessment: enabling resuse for clinical research“.

  • Completeness – Is a truth about a patient present?
  • Correctness – Is an element that is present true?
  • Concordance – Is there agreement between elements, and with external data sources?
  • Plausability – Does an element make sense in light of other knowledge about what that element is measuring?
  • Currency – Is an element a relevant representation of the patient state at a given point in time?

 

It is critical that we start measuring clinical data quality using frameworks such as this to achieve a degree of quantification.  As we move into the next phase of EMR / EHR maturity, these systems will increasingly shine a light on problems with a) the quality of data captured by health providers and b) the quality of care delivered.  Without a mechanism for measurement of clinical data quality we can’t start this conversation that we need to have.  If we don’t start the conversation, experience tells me that health providers are quick to blame the IT system or its implementation, without understanding the impact that poor quality data has on its operation.

Unfortunately, I haven’t seen many health organisations that understand this need.  In Australia, the My Health Record (our national EHR) has poor data governance, little regular clinical data quality measurement and quantification of clinical data quality, and no national dialogue on the role that clinical data capture and the point of care plays in an efficient and effective health system.  The owners of the system are seeking to build new capabilities that aggregate data from multiple parts of a patient record, offer Clinical Decision Support and other secondary uses of data.  However, without a greater focus on clinical data quality they are building on a house of cards.  It won’t take long before poor data aggregated with other poor data causes clinical safety issues, significantly eroding trust in the system.

It’s time to focus on clinical data quality…

 

One Response to“The Dimensions of Clinical Data Quality”

  1. December 25, 2016 at 12:13 pm #

    Wow that was unusual. I just wrote an very long comment but after I clicked submit my comment didn’t show up.
    Grrrr… well I’m not writing all that over again. Anyways, just
    wanted to say fantastic blog!

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