Variation in routine electrocardiogram use in academic primary care practice.
BACKGROUND
Lack of practical consensus regarding routine electrocardiogram (ECG) ordering in primary care led us to hypothesize that nonclinical variations in ordering would exist among primary care providers.


METHODS
We used 2 computerized billing systems to measure ECG ordering at visits to providers in 10 internal medicine group practices affiliated with a large, urban teaching hospital from October 1, 1996, to September 30, 1997. To focus on screening or routine ECGs, patients with known cardiac disease or suggestive symptoms were excluded, as were providers with fewer than 200 annual patient visits. Included were 69 921 patients making 190 238 visits to 125 primary care providers. Adjusted rates of ECG ordering accounted for patient age, sex, and 5 key diagnoses. Logistic regression evaluated additional predictors of ECG ordering.


RESULTS
Electrocardiograms were ordered in 4.4% of visits to patients without reported cardiac disease. Among the 10 group practices, ECG ordering varied from 0.5% to 9.6% of visits (adjusted rates, 0.8%-8.6%). Variations between individual providers were even more dramatic: adjusted rates ranged from 0.0% to 24% of visits, with an interquartile range of 1.4% to 4.7% and a coefficient of variation of 88%. Significant predictors of ECG use were older patient age, male sex, and the presence of clinical comorbidities. Additional nonclinical predictors included Medicare as a payment source, older male providers, and providers who billed for ECG interpretation.


CONCLUSIONS
Variations in ECG ordering are not explained by patient characteristics. The tremendous nonclinical variations in ECG test ordering suggest a need for greater consensus about use of screening ECGs in primary care.
Stafford
R S
RS
Stanford Center for Research in Disease Prevention, 1000 Welch Rd, Palo Alto, CA 94304, USA. rstafford@stanford.edu
Misra
B
B
D000046:Academic Medical Centers / Q000706:statistics & numerical data*; D000328:Adult; D000367:Age Factors; D000368:Aged; D003955:Diagnostic Tests, Routine / Q000706:statistics & numerical data*; D004562:Electrocardiography / Q000706:statistics & numerical data*; D005260:Female; D006331:Heart Diseases / Q000503:physiopathology*; D006801:Humans; D007348:Insurance, Health / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data; D008875:Middle Aged; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data*; D011320:Primary Health Care / Q000706:statistics & numerical data*; D011331:Private Practice / Q000706:statistics & numerical data; D012737:Sex Factors