Implementation of EHR-Based Strategies to Improve Outpatient CAD Care
Published Online: October 25, 2012
Stephen D. Persell, MD, MPH; Janardan Khandekar, MD; Thomas Gavagan, MD; Nancy C. Dolan, MD; Sue Levi, RN, MBA; Darren Kaiser, MS; Elisha M. Friesema, BA, CCRP; Ji Young Lee, MS; and David W. Baker, MD, MPH
We retrospectively calculated the performance for the 4 CAD measures for each month from September 2007 through March 2011. At each time point, patients were eligible for a measure if they had an office visit with a physician from 1 of the 4 included practices during the preceding 18 months and had a qualifying International Classification of Diseases, Ninth Revision, Clinical Modification code used on their active problem list, past medical history, or as an encounter diagnosis. We used Structured Query Language to retrieve data from an enterprise data warehouse that contains data copied daily from the EHR. For each time point, all patients were classified for each measure for which they were eligible as: a) satisfied, b) did not satisfy but had an exception, or c) did not satisfy and had no documented exception. The primary outcome for each measure was calculated as the number of patients who satisfied the measure divided by the total number of eligible patients excluding those with an exception. As an equation, the primary outcome = number satisfied / [number eligible – number not satisfied with an exception]. We also analyzed separately for each measure the proportion of eligible patients who satisfied the measure (were given the medication) and the proportion of all eligible patients who did not satisfy the measure and had exceptions.
We performed peer review beginning in September 2009 of medical exceptions recorded in the EHR and continued the review process for exceptions entered within the first 10 months of the intervention. One physician reviewed medical records to collect the reason for the exception and additional clinical information needed to judge the validity of the exception. When the clinical reasoning was unclear, the peer reviewer would request clarification from the treating clinician. Two board-certified internists and 1 board-certified family medicine physician met regularly to review the exceptions and judged them as appropriate, inappropriate (including when no contraindication to the medication was evident on physician chart review or cases where clarifying information was requested from the primary care physician but was not provided), or of uncertain appropriateness by consensus. When a consensus was not reached or the appropriateness was uncertain, 1 physician reviewed the medical literature, requested advice from specialists when needed, and the group discussed the case again until consensus was reached. Practice physicians received e-mail or telephone feedback for medical exceptions that were judged to be inappropriate.
We used interrupted time series analysis to examine changes in the primary and secondary outcomes over time for 2 different interventions: before and after July 2008 (the transition between baseline and Phase 1), and before and after September 2009 (the transition between Phase 1 and Phase 2). We calculated the primary and secondary outcomes for each of the performance measures for each month from September 2007 through August 2010. A linear model was fit to each series including a continuous time variable, a dichotomous indicator of the intervention, and the interaction term of time and intervention as covariates. The individual data points used for each time period are depicted in the figures. Next, we determined the autoregressive order of the model residuals by minimizing Akaike’s information criterion.10 Finally, we fit a linear regression model with autoregressive errors (using the appropriate number of autoregressive parameters, if any were necessary) to each series. These fitted models were used to test statistical significance.11 To ensure model validity, we examined several residual diagnostics, the Jarque-Bera and the Shapiro-Wilk tests for normality of residuals, and normal Q-Q and autocorrelation plots.12-14 Analyses used SAS version 9.2 (SAS Institute Inc, Cary, North Carolina) and R software package version 2.13.1 (R Foundation for Statistical Computing, Vienna, Austria).
Patients and Their Characteristics
The number of patients eligible for the coronary disease measures and their characteristics are provided in Table 1. The number of eligible patients increased over time from 779 CAD and 218 MI patients in October 2007 to 1099 CAD and 332 MI patients by October 2010. Their characteristics changed little during the 3 years we examined (Table 1).
Performance During Baseline Period
Median performance during the baseline period was 78.9% for antiplatelet treatment, 85.3% for statin treatment, 77.0% for beta-blocker after MI, and 67.2% for ACE inhibitor or ARB after MI (Table 2). Performance on the antiplatelet measure was increasing significantly during the baseline period. Performance on the other 3 measures did not change significantly during the baseline period (Figures 1 and 2).
Performance During Phase 1
During Phase 1, overall performance continued to increase for the antiplatelet measure at a rate that was similar to the rate of improvement observed during the baseline period. There were statistically significant, but very small, increases in overall performance for the statin and ACE inhibitor/ARB measures (Table 2 and Figure 1). However, there were no significant increases in the rates of patients given medication for these 2 measures, and there was a decrease in patients given beta-blockers during Phase 1 compared with baseline (Table 2 and Figure 2). Physicians recorded exceptions to these measures during Phase 1 for small percentages of eligible patients (Figure 3).
Performance During Phase 2
There were significant increases in measured performance for all 4 measures during Phase 2 compared with Phase 1 (Table 2 and Figure 1). For 3 of the 4 measures, there was an increase in the documentation of exceptions (Table 2 and Figure 3) but not medication prescribing (Figure 2). For the antiplatelet measure there was a significant increase in both medication prescribing and exception documentation (Table 2, Figures 2 and 3).
By March 2011, overall performance was 99.7%, 91.1%, 86.8%, and 78.9% for antiplatelet treatment, statin treatment, beta-blocker after MI, and ACE inhibitor or ARB after MI, respectively.
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