BACKGROUND: At an emergency department (ED) in a tertiary care children's hospital with a level 1 pediatric trauma designation, unapproved abbreviations (UAAs) within electronic medical records (EMRs) were identified, and feedback was provided to providers regarding their types and use rates. METHODS: Existing EMRs, including the ED physicians' patient notes were used as templates to develop a UAA list and an abbreviation detector. The detector was validated against human-screened samples of electronic ED notes from 2003 and then applied to all existing data to generate baseline rates of UAA, before intervention/implementation. Next, the validated abbreviation detector was applied prospectively in screening all EMRs monthly during a six-month period. RESULTS: In validation, the abbreviation detector had a sensitivity of 89%, a specificity of 99.9%, and a positive predictive value of 89%. Some 475,613 EMRs were screened, with UAAs identified at a rate of 26.4 +/- 4 per 1,000 EMRs. The most common nonmedication UAA was "qd" [11.8/1,000 EMRs], and the most common medication UAA was "PCN" [4.2/1,000 EMRs]. A total of 27,282 patient notes from 74 physicians were screened between January 1, 2007, and June 30, 2007, and 392 monthly reports were generated. Aggregate UAA use decreased by 8% (95% confidence interval [CI]: 6%-14%) per month-from 19.3 to > 12.1/100 charts, for a 37.3% decrease in UAA use in the six-month period. The estimated monthly decrease per physician was 0.9/100 (95% CI: 0.86-0.94, p < .001.) After adjusting for secular trends, the decrease was 29% in the six-month study period (95% CI: 14%-44%, p < .0001). CONCLUSIONS: Use of the abbreviation detector for surveillance of newly created EMRs, followed by consistent education and feedback, led to a significant decrease in UAA use in the study period.
BACKGROUND: At an emergency department (ED) in a tertiary care children's hospital with a level 1 pediatric trauma designation, unapproved abbreviations (UAAs) within electronic medical records (EMRs) were identified, and feedback was provided to providers regarding their types and use rates. METHODS: Existing EMRs, including the ED physicians' patient notes were used as templates to develop a UAA list and an abbreviation detector. The detector was validated against human-screened samples of electronic ED notes from 2003 and then applied to all existing data to generate baseline rates of UAA, before intervention/implementation. Next, the validated abbreviation detector was applied prospectively in screening all EMRs monthly during a six-month period. RESULTS: In validation, the abbreviation detector had a sensitivity of 89%, a specificity of 99.9%, and a positive predictive value of 89%. Some 475,613 EMRs were screened, with UAAs identified at a rate of 26.4 +/- 4 per 1,000 EMRs. The most common nonmedication UAA was "qd" [11.8/1,000 EMRs], and the most common medication UAA was "PCN" [4.2/1,000 EMRs]. A total of 27,282 patient notes from 74 physicians were screened between January 1, 2007, and June 30, 2007, and 392 monthly reports were generated. Aggregate UAA use decreased by 8% (95% confidence interval [CI]: 6%-14%) per month-from 19.3 to > 12.1/100 charts, for a 37.3% decrease in UAA use in the six-month period. The estimated monthly decrease per physician was 0.9/100 (95% CI: 0.86-0.94, p < .001.) After adjusting for secular trends, the decrease was 29% in the six-month study period (95% CI: 14%-44%, p < .0001). CONCLUSIONS: Use of the abbreviation detector for surveillance of newly created EMRs, followed by consistent education and feedback, led to a significant decrease in UAA use in the study period.