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Background: Physicians report outpatient quality measures from data in electronic health records to facilitate care improvement and qualify for incentive payments., Objective: To determine the frequency and validity of exceptions to quality measures and to test a system for classifying the reasons for these exceptions., Design: Cross-sectional observational study., Setting: 5 internal medicine or cardiology practices., Participants: 47,075 patients with coronary artery disease between 2006 and 2007., Measurements: Counts of adherence with and exceptions to 4 quality measures, on the basis of automatic reports of recommended drug therapy by computer software and separate manual reviews of electronic health records., Results: 3.5% of patients who had a drug recommended had an exception to the drug and were not prescribed it (95% CI, 3.4% to 3.7%). Clinicians did prescribe the recommended drug for many other patients with exceptions. In 538 randomly selected records, 92.6% (CI, 90.3% to 94.9%) of the exceptions reported automatically by computer software were also exceptions during manual review. Most medical exceptions were clinical contraindications, drug allergies, or drug intolerances. In 592 randomly selected records, an unreported exception or a drug prescription was found during manual review for 74.6% (CI, 71.1% to 78.1%) of patients for whom automatic reporting recorded a quality failure., Limitation: The study used a convenience sample of practices, nonstandardized data extraction methods, only drug-related quality measures, and no financial incentives., Conclusion: Exceptions to recommended therapy occur infrequently and are usually valid. Physicians frequently prescribed drugs even when exceptions were present. Automated reports of quality failure often miss critical information., Primary Funding Source: Agency for Healthcare Research and Quality.

Background: Nationally endorsed, clinical performance measures are available that allow for quality reporting using electronic health records (EHRs). To our knowledge, how well they reflect actual quality of care has not been studied. We sought to evaluate the validity of performance measures for coronary artery disease (CAD) using an ambulatory EHR., Methods: We performed a retrospective electronic medical chart review comparing automated measurement with a 2-step process of automated measurement supplemented by review of free-text notes for apparent quality failures for all patients with CAD from a large internal medicine practice using a commercial EHR. The 7 performance measures included the following: antiplatelet drug, lipid-lowering drug, beta-blocker following myocardial infarction, blood pressure measurement, lipid measurement, low-density lipoprotein cholesterol control, and angiotensin-converting enzyme inhibitor or angiotensin receptor blocker for patients with diabetes mellitus or left ventricular systolic dysfunction., Results: Performance varied from 81.6% for lipid measurement to 97.6% for blood pressure measurement based on automated measurement. A review of free-text notes for cases failing an automated measure revealed that misclassification was common and that 15% to 81% of apparent quality failures either satisfied the performance measure or met valid exclusion criteria. After including free-text data, the adherence rate ranged from 87.5% for lipid measurement and low-density lipoprotein cholesterol control to 99.2% for blood pressure measurement., Conclusions: Profiling the quality of outpatient CAD care using data from an EHR has significant limitations. Changes in how data are routinely recorded in an EHR are needed to improve the accuracy of this type of quality measurement. Validity testing in different settings is required.