Improvement in Detection of Wrong-Patient Errors When Radiologists Include Patient Photographs in Their Interpretation of Portable Chest Radiographs
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This study was conducted to determine whether facial photographs obtained simultaneously with radiographs improve radiologists’ detection rate of wrong-patient errors, when they are explicitly asked to include the photographs in their evaluation. Radiograph-photograph combinations were obtained from 28 patients at the time of portable chest radiography imaging. From these, pairs of radiographs were generated. Each unique pair consisted of one new and one old (comparison) radiograph. Twelve pairs of mismatched radiographs (i.e., pairs containing radiographs of different patients) were also generated. In phase 1 of the study, 5 blinded radiologist observers were asked to interpret 20 pairs of radiographs without the photographs. In phase 2, each radiologist interpreted another 20 pairs of radiographs with the photographs. Radiologist observers were not instructed about the purpose of the photographs but were asked to include the photographs in their review. The detection rate of mismatched errors was recorded along with the interpretation time for each session for each observer. The two-tailed Fisher exact test was used to evaluate differences in mismatch detection rates between the two phases. A p value of <0.05 was considered significant. The error detection rates without (0/20 = 0 %) and with (17/18 = 94.4 %) photographs were different (p = 0.0001). The average interpretation times for the set of 20 radiographs were 26.45 (SD 8.69) and 20.55 (SD 3.40) min, for phase 1 and phase 2, respectively (two-tailed Student t test, p = 0.1911). When radiologists include simultaneously obtained photographs in their review of portable chest radiographs, there is a significant improvement in the detection of labeling errors. No statistically significant difference in interpretation time was observed. This may lead to improved patient safety without affecting radiologists’ throughput.
KeywordsMedical errors Wrong-patient events
We would like to acknowledge and thank the patients and their families who allowed our team to photograph them during their ICU stays. Samuel Galgano obtained the patient data in 2011. Senthil Ramamurthy developed the software for the observer studies and helped conduct the observer studies. We also thank the five radiology observers who participated in this study. Srini Tridandapani was supported in part by the National Institute of Biomedical Imaging and Bioengineering (Award Number K23EB013221) and by the National Center for Advancing Translational Sciences (Award Number UL1TR000454) of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
- 1.Joint Commission on Accreditation of Healthcare Organizations. [Accessed May 13th, 2014] Hospital national patient safety goals. 2014. http://www.jointcommission.org/assets/1/6/OBS_NPSG_Chapter_2014.pdf
- 2.Kohn LT, Corrigan JM, Donaldson MS, (Institute of Medicine): To err is human: building a safer health system. National Academy Press, Washington, DC, 2000Google Scholar
- 4.Pennsylvania Patient Safety Authority, ECRI Institute, Institute for Safe Medication Practices: Pennsylvania patient safety advisory, applying the universal protocol to improve patient safety in radiology. Pa Pateint Saf Advis 8:63–69, 2011Google Scholar
- 5.Kumar N, Berg AC, Belhumeur PN, and Nayar SK: Attribute and simile classifiers for face verification. 12th IEEE International Conference on Computer Vision (ICCV), October 2009, 365–372Google Scholar
- 6.Huang G, Ramesh M, Berg T, and Learned-Miller E: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. UMass Amherst Technical Report 07–49, October 2007Google Scholar
- 8.Tridandapani S, Ramamurthy S, Provenzale JM, Obuchowski N, Evanoff M, Bhatti P: A multi-reader observer study of the effect of adding point-of-care patient photographs with portable radiographs: a means to reduce wrong-patient errors. Acad Radiol 21(8):1038–47, 2014PubMedCentralCrossRefPubMedGoogle Scholar
- 10.Taigman Y, Yang M, Ranzato M, Wolf L: DeepFace: closing the gap to human-level performance in face verification. Conference on Computer Vision and Pattern Recognition, March 2014Google Scholar