Assessing explicit error reporting in the narrative electronic medical record using keyword searching.
BACKGROUND
Many types of medical errors occur in and outside of hospitals, some of which have very serious consequences and increase cost. Identifying errors is a critical step for managing and preventing them. In this study, we assessed the explicit reporting of medical errors in the electronic record.


METHODS
We used five search terms "mistake," "error," "incorrect," "inadvertent," and "iatrogenic" to survey several sets of narrative reports including discharge summaries, sign-out notes, and outpatient notes from 1991 to 2000. We manually reviewed all the positive cases and identified them based on the reporting of physicians.


RESULTS
We identified 222 explicitly reported medical errors. The positive predictive value varied with different keywords. In general, the positive predictive value for each keyword was low, ranging from 3.4 to 24.4%. Therapeutic-related errors were the most common reported errors and these reported therapeutic-related errors were mainly medication errors.


CONCLUSIONS
Keyword searches combined with manual review indicated some medical errors that were reported in medical records. It had a low sensitivity and a moderate positive predictive value, which varied by search term. Physicians were most likely to record errors in the Hospital Course and History of Present Illness sections of discharge summaries. The reported errors in medical records covered a broad range and were related to several types of care providers as well as non-health care professionals.
Cao
Hui
H
Department of Medical Informatics, Columbia University, New York, NY, USA. hui.cao@bdmi.columbia.edu
Stetson
Peter
P
Hripcsak
George
G
D000043:Abstracting and Indexing / Q000379:methods* / Q000706:statistics & numerical data; D004282:Documentation; D016247:Information Storage and Retrieval / Q000379:methods*; D008485:Medical Audit / Q000379:methods*; D019300:Medical Errors / Q000145:classification / Q000706:statistics & numerical data*; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D015203:Reproducibility of Results; D012308:Risk Management / Q000379:methods* / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity; D013358:Subject Headings; D009626:Terminology as Topic