Data Is Difficult to Measure Part IIby admin on 04/10/2015 10:49 AM
The data is structured and unstructured.
Dan LeSueur, Vice President of Client and Technical Operations
Posted in Data: Quality, Management, Governance and Data Warehouse / EDW.
Electronic medical record software has provided a platform for consistent data capture, but the reality is data capture is anything but consistent. For years, documenting clinical facts and findings on paper has trained an industry to capture data in whatever way is most convenient for the care provider with little regard for how this data could eventually be aggregated and analyzed. EMRs attempt to standardize the data capture process, but care providers are reluctant to adopt a one-size-fits-all approach to documentation. Thus, unstructured data capture is often allowed to appease the frustrated EMR users and avoid hindering the care delivery process. As a result, much of the data captured in this manner is difficult to aggregate and analyze in any consistent manner. As EMR products improve, as users become trained to standard workflows, and as care providers become more accustomed to entering data in structured fields as designed, we will have more and better data for analytics.
An example of the above phenomenon is found in a recent initiative to reduce unnecessary C-sections at a large health system in the Northwest. The first task for the team was to understand how the indications for C-section were documented in the EMR. It turned out that there were only two options to choose from: 1) fetal indication and 2) maternal indication. Because these were the only two options, delivering clinicians would often choose to document the true indication for C-section in a free text form, while others did not document it at all. Well, this was not conducive to understanding the root cause of unnecessary C-sections. So, the team worked with an analyst to modify the list of available options in the EMR so that more detail could be added. After making this slight modification to the data capture process, the team gained tremendous insight, and identified opportunities to standardize care delivery and reduce unnecessary C-sections.
To be continued in July 2015
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