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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Charles, M. Miano, T.N. Xinhe Zhang Barnes, L.E. Lobo, J.M. |
| Copyright Year | 2015 |
| Description | Author affiliation: Univ. of Virginia, Charlottesville, VA, USA (Charles, M.; Miano, T.N.; Xinhe Zhang; Barnes, L.E.; Lobo, J.M.) |
| Abstract | The detection rate of adenomas in screening colonoscopies is an important quality indicator for endoscopists. Successful detection of adenomas is linked to reduced cancer incidence and mortality. This study focuses on evaluating the performance of endoscopists on adenoma detection rate (ADR), polyp detection rate (PDR), and scope withdrawal time. The substitution of PDR for ADR has been suggested due the reliance of ADR calculation on pathology reports. We compare these metrics to established clinical guidelines and to the performance of other individual endoscopists. Our analysis (n = 2730 screening colonoscopies) found variation in ADR for 14 endoscopists, ranging from 0.20 to 0.41. PDR ranged from 0.38 to 0.62. Controlling for age, sex, race, withdrawal time, and the presence of a trainee endoscopist accounted for 34% of variation in PDR but failed to account for any variation in ADR. The Pearson correlation between PDR and ADR is 0.82. These results suggest that PDR has significant value as a quality indicator. The reported variation in detection rates after controlling for case mix signals the need for greater scrutiny of individual endoscopist skill. Understanding the root cause of this variation could potentially lead to better patient outcomes. |
| Starting Page | 171 |
| Ending Page | 175 |
| File Size | 1388547 |
| Page Count | 5 |
| File Format | |
| e-ISBN | 9781479918324 |
| DOI | 10.1109/SIEDS.2015.7116968 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-04-24 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Colonoscopy Cancer Guidelines Logistics Endoscopes Predictive models Measurement Machine Learning Electronic Medical Records Health Data Physician Performance |
| Content Type | Text |
| Resource Type | Article |
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