Effects of Common Data Errors in Electronic Health Records on Emergency Department Operational Performance Metrics: A Monte Carlo Simulation.

Abstract

The objective was to estimate how data errors in electronic health records (EHRs) can affect the accuracy of common emergency department (ED) operational performance metrics. Using a 3-month, 7,348-visit data set of electronic time stamps from a suburban academic ED as a baseline, Monte Carlo simulation was used to introduce four types of data errors (substitution, missing, random, and systematic bias) at three frequency levels (2, 4, and 7%). Three commonly used ED operational metrics (arrival to clinician evaluation, disposition decision to exit for admitted patients, and ED length of stay for admitted patients) were calculated and the proportion of ED visits that achieved each performance goal was determined. Even small data errors have measurable effects on a clinical organization's ability to accurately determine whether it is meeting its operational performance goals. Systematic substitution errors, increased frequency of errors, and the use of shorter-duration metrics resulted in a lower proportion of ED visits reported as meeting the associated performance objectives. However, the presence of other error types mitigated somewhat the effect of the systematic substitution error. Longer time-duration metrics were found to be less sensitive to data errors than shorter time-duration metrics. Infrequent and small-magnitude data errors in EHR time stamps can compromise a clinical organization's ability to determine accurately if it is meeting performance goals. By understanding the types and frequencies of data errors in an organization's EHR, organizational leaders can use data management best practices to better measure true performance and enhance operational decision-making.