EP04.04 - Validating Data Increases Workload but Not Data Quality in EMRs
e-Health ePoster Library. Hudson D. Jun 7, 2016; 131554; EP04.04
Darren Hudson
Darren Hudson
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Abstract
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Purpose/Objectives: One of the purposes of electronic medical records is to gather large streams of data. These systems collect data blind to whether these points represent 'true' events and rely on humans to filter out unwanted data and to select a data point that represents the true value. Requiring humans to select datapoints from a data stream creates the potential for bias and increases workload. Studies using clinical data rarely describe how the data was validated. There are no agreed standards for nurses on validation so policy is left to institutions. Biased data has significant impact on the calculation of severity scores. Reviewing electronic medical records for quality assurance is also problematic as the detail may not reflect the entire event. Considering the importance of the representative data for the medical record, quality assurance and clinical research, it is critical to fully understand the quality of the data being selected as valid. Methodology/Approach: After obtaining institutional and ethics approval, all patients admitted to all critical care units using a province-wide critical care information system (eCritical) at a single point in time had data extracted. Basic demographic data (age, gender, duration of admission) as well as physiologic data (heart rate (HR), mean arterial pressure (MAP), and oxygen saturation (SpO2) were extracted. These parameters stream continuously from the physiological monitors and need to be validated as per nursing policy. These records were imported into a SQL Server 2012 Database (Microsoft, Redmond, MA). Datapoint were marked as validated if a nurse had selected that datapoint as valid at some point in their shift. To determine how reflective each validated data point is of the patient's condition, the data points in the surrounding hour (30 minutes before and 30 minutes after) were analyzed. The mean, median and mode of this surrounding data were calculated for each validated data point using custom designed SQL queries. The difference from the validated data point and the mean, median, mode of this surrounding data was also calculated. Finding/Results: A total of 162 patients from 13 critical care units werecollected totaling 4.2 million data points; 96,609 validated. Total Average for Total (Range Validated Only Average for Validated (Range) MAP (mmHg) 1,122,259 83.1 (0-249) 22,833 82.3 (0-248) Heart Rate (bpm) 1,622,287 92.4 (0-268) 34,067 93.26 (0-205) SpO2 (%) 1,476,125 97 (23-100) 39,709 97 (40-100) Average Difference between All and Validated Data Median Mean Mode MAP (mmHg) 0.6 -0.2 1.7 Heart Rate (bpm) 0.5 0.3 1.6 SpO2 (%) -0.1 0 -0.2 Conclusion/Implication/Recommendations: This is the first to examine data validation by humans. We were able to examine a large set of validated data.The distribution curves are not normal, skewed towards 'normal' values as would be expected where treatment is focused on maintaining normal values. The data suggest that validation is not necessary and may be substituted for averaging techniques or leaving the data as is. This does not negate the importance of a provider reviewing and acting upon the data and it may be appropriate to allow providers to exclude data if erroreous. 140 Character Summary: Data validation in EMRs increases nursing workload. This study demonstrates that the data from the monitors is essentially equal to the validated data.
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