Operational data integrity during electronic health record implementation in the ED

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“Objective: Operational data are often used to make systems changes in real time. Inaccurate data, however, transiently, can result in inappropriate operational decision making. Implementing electronic health records (EHRs) is fraught with the possibility of data errors, but the frequency and magnitude of transient errors during this fast-evolving systems upheaval are unknown. This study was done to assess operational data quality in an emergency department (ED) immediately before and after an EHR implementation. Methods: Direct observations of standard ED timestamps (arrival, bed placement, clinician evaluation, disposition decision, and exit from ED) were conducted in a suburban ED for 4 weeks immediately before and 4 weeks after EHR implementation. Direct observations were compared with electronic timestamps to assess data quality. Differences in proportions and medians with 95% confidence intervals (CIs) were used to estimate the magnitude of effect. Results: There were 260 observations: 122 before and 138 after implementation. We found that more systematic data errors were introduced after EHR implementation. The proportion of discrepancies where the observed and electronic timestamp differed by more than 10 minutes was reduced for the disposition timestamp (29.3% vs 16.1%; difference in proportions,−13.2%; 95% CI,−24.4% to−1.9%). The accuracy of the clinician-evaluation timestamp was reduced after implementation (median difference of 3 minutes earlier than observed; 95% CI, −5.02 to −0.98). Multiple service intervals were less accurate after implementation. Conclusion: This single-center study raises questions about operational data quality in the peri-implementation period of EHRs. Using electronic timestamps for operational assessment and decision making following implementation should recognize the magnitude and compounding of errors when computing service times.” [1]


The purpose of this article was to study the accuracy of electronic timestamps during the implementation of an EHR in the emergency department.


The study ran for a total of 4 weeks prior and 4 weeks after EHR implementation. Study assistants manually measured the following data time points: arrival, bed placement, clinician evaluation, disposition decision and exit from ED. These were then compared with the corresponding timestamps from the patient tracking system (pre-EHR) and EHR.


There were 4 types of systematic errors found in the electronic data. These ranged from no timestamps documented, to incorrect discharge timestamps set at midnight, and to arrival and bed timestamps being identical. In many instances, the difference between the timestamp and observation worsened after the EHR implementation. The worst two being the clinician evaluation difference which increased by 3 minutes and the bed-to-clinician interval which increased by 4 minutes.


The authors noted that this data led to the realization that there were programming errors in the EHR software since the bed and arrival timestamps were identical. They underscore why it is so imperative to check data before and after implementation so problems like these can be found and corrected.


Even though this is a small study, this article really drove home the last point for me. It is essential to always be periodically checking data to make sure all is working properly. That way, if something is beginning to be a problem, you can catch it hopefully before it becomes a bigger problem and affects clinical care.


  1. Ward, M. J., Froehle, C. M., Hart, K. W., & Lindsell, C. J. (2013). Operational data integrity during electronic health record implementation in the ED. The American Journal of Emergency Medicine, 31(7), 1029–1033. http://doi.org/10.1016/j.ajem.2013.03.027