Journal article
Outcomes Knowledge May Bias Radiological Decision-making
Academic radiology, Vol.23(6), pp.760-767
06/2016
DOI: 10.1016/j.acra.2016.01.006
PMID: 26905454
Abstract
This research investigates whether an expectation of abnormality and prior knowledge might potentially influence the decision-making of radiologists, and discusses the implications for radiological expert witness testimony.
This study was a web-based perception experiment. A total of 12 board-certified radiologists were asked to interpret 40 adult chest images (20 abnormal) twice and decide if pulmonary lesions were present. Before the first viewing, a general clinical history was given for all images: cough for 3+ weeks. This was called the "defendants read." Two weeks later, the radiologists were asked to view the same dataset (unaware that the dataset was unchanged). For this reading, the radiologists were given the following information for all images: "These images were reported normal but all of these patients have a lung tumour diagnosed on a subsequent radiograph 6 months later." They were also given the lobar location of the newly diagnosed tumor. This was called the "expert witness read."
There was a significant difference in location-based sensitivity (W = -45, P = 0.02) between the two conditions with nodule detection increasing under the second condition. Specificity increased outside the lobe of interest (W = 727, P = < 0.0001) and decreased within the lobe of interest (W = -237, P = 0.03) significantly in the "expert witness" read. Case-based sensitivity and case-based specificity were unaffected.
This study showed evidence that increased clinical information affects the performance of radiologists. This effect may bias expert witnesses in radiological malpractice litigation.
Details
- Title: Subtitle
- Outcomes Knowledge May Bias Radiological Decision-making
- Creators
- Stephen Littlefair - Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, Room M213, 75 East Street, Lidcombe, Sydney, NSW 2141, Australia. Electronic address: s.littlefair@usyd.edu.auPatrick Brennan - Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, Room M213, 75 East Street, Lidcombe, Sydney, NSW 2141, AustraliaClaudia Mello-Thoms - Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, Room M213, 75 East Street, Lidcombe, Sydney, NSW 2141, AustraliaPhuong Dung - Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, Room M213, 75 East Street, Lidcombe, Sydney, NSW 2141, AustraliaMarius Pietryzk - Institute of Physics, London, United KingdomRoland Talanow - Chrissie Tomlinson Memorial Hospital, George Town, Cayman Islands; University of Bern, Bern, SwitzerlandWarren Reed - Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, Room M213, 75 East Street, Lidcombe, Sydney, NSW 2141, Australia
- Resource Type
- Journal article
- Publication Details
- Academic radiology, Vol.23(6), pp.760-767
- Publisher
- United States
- DOI
- 10.1016/j.acra.2016.01.006
- PMID
- 26905454
- ISSN
- 1076-6332
- eISSN
- 1878-4046
- Language
- English
- Date published
- 06/2016
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984051769502771
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