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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.

SUBMITTER: Ward MJ

PROVIDER: S-EPMC4560638 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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

Ward Michael J MJ Self Wesley H WH Froehle Craig M CM

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine 20150820 9

<h4>Objectives</h4>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.<h4>Methods</h4>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 use ...[more]

PMID: 26291051

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Project description:Traditional multiple-group confirmatory factor analysis (multiple-group CFA) is usually criticized for having too restrictive model assumption, namely the scalar measurement invariance. The new multiple-group analysis methodology, alignment, has become an effective alternative. The alignment evaluates measurement invariance and more importantly, permits factor mean comparisons without requiring scalar invariance which is usually required in traditional multiple-group CFA. Some simulation studies and empirical studies have investigated the applicability of alignment under different conditions, but some areas remain unexplored. Based on the simulation studies of Asparouhov and Muthén and of Flake and McCoach, this current simulation study is broken into two sections. The first study investigates the minimal group sizes required for alignment in three-factor models. The second study compares the performance of multiple-group CFA, multiple-group exploratory structural equation model (multiple-group ESEM), and alignment by including different proportions and magnitudes of cross-loadings in the models. Study 1 shows that when the model has no noninvariant parameters, the alignment requires relatively lower group sizes. Explicitly, the minimal group size required for alignment was 250 when the amount of groups was three, the minimal group size was 150 when the amount of groups was nine, and 200 when the amount of groups was 15. When there are noninvariant parameters in the model and the amount of groups is low, a group size of 350 is a safe rule of thumb. When there are noninvariant parameters in the model and the amount of groups is high, a group size of 250 is required for trustworthy results. The magnitude of noninvariance and the noninvariance rate do not affect the minimal group size required for alignment. Study 2 shows that multiple-group CFA provides accurate factor mean estimates when each factor had 20% factor loading (1 factor loading) with small-sized cross-loading. Multiple-group ESEM provides accurate factor mean estimates when the magnitude of cross-loading is small or when each factor had 20% factor loading (1 factor loading) with medium-sized cross-loading. Alignment provides accurate factor mean estimates when there are only small-sized cross-loadings in the model. The parameter estimates, coverage rates and ratios of average standard error to standard deviation for each methodology are not influenced by the amount of groups. Recommendations are concluded for using multiple-group CFA, multiple-group ESEM, traditional alignment and aligned ESEM (AESEM) based on the results. Multiple-group CFA is more suitable for use when scalar invariance is established. Multiple-group ESEM works best when there are small-sized or only a few medium-sized cross-loadings in the model. Traditional alignment allows for small-sized cross-loadings and a few noninvariant parameters in the model. AESEM integrates the advantages of alignment and ESEM, can provide accurate estimates when noninvariant parameters and cross-loadings both exist in the model. Compared to multiple-group CFA, multiple-group ESEM, the alignment methodology performs well in more situations.

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Dataset Information

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

Publications

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

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