Expert-novice differences in mammogram interpretation

Expert-Novice Differences in Mammogram Interpretation Roger Azevedo Department of Psychology, Institute for Intelligent Systems, University of Memphis 3693 Norriswood Avenue, Memphis, TN, 38152, USA {razevedo@memphis.edu} Sonia Faremo and Susanne P. Lajoie McGill University, Applied Cognitive Science Research Group, 3700 McTavish Street, Montreal, Quebec, H3A 1Y2, Canada Abstract be investigated in order to lessen the impact of this disease. As cognitive scientists we have taken a critical initial step towards improving training in this area by conducting research that examines the cognitive components that constitute proficiency in mammogram interpretation. This paper links the results of two parallel studies in order to provide a comprehensive characterization of expert-novice differences in mammogram interpretation. Our intention is to subsequently use these research-based results to improve the training of future medical professionals (e.g., Azevedo & Lajoie, 1998; Lajoie & Azevedo, 2000; Crowley et al., 2005; Taylor, 2006). This paper examines the results of two initial studies of the problem- solving strategies used by more and less skilled medical professionals during mammogram interpretation. The first study examined the cognitive processing of staff radiologists and radiology residents, while the second looked at surgical residents and medical students‚ as they individually solved a set of breast disease cases. Analyses of 100 verbal protocols from the two studies resulted in the development of a problem-solving model of mammogram interpretation and a characterization of novice expert differences based on performance measures. Results revealed that with increasing levels of expertise there were significant increases in the number of radiological observations and findings, proportion of correct diagnoses, use of data-driven problem solving, and diagnostic planning. The analysis provides a valuable initial characterization of mammogram interpretation across a broad range of expertise levels with implications for the design of computer- based learning environments aimed to train medical professionals to interpret mammograms. Cognitive Science Studies in Radiology A few studies in radiology have been conducted by cognitive scientists focusing on the interpretation of chest x-rays. For example, Lesgold and colleagues (1981, 1988) provide one of the few existing explicit cognitive accounts of problem- solving strategies used by radiology residents and staff radiologists during chest x-ray interpretation. Their principal contribution was to demonstrate that experts extensively use “top-down” or “knowledge-based” processing. In this way, x- ray diagnosis is similar to but not the same as problem solving in non-perceptual domains. Other relevant research includes a study of chest radiography interpretation characterizing the interplay between perceptual and cognitive (knowledge-based) processing and a model of visual interaction (Rogers, 1992). This study identified three types of errors: (a) detection errors (failure to detect an abnormality), (b) labeling errors (mislabeling an abnormality), and (c) integration errors (correctly labeling an abnormality but failing to use it in the generation of a diagnostic hypothesis). Rogers was unable to examine expertise effects because of limited availability of participants in varying levels of expertise. A subsequent study by Raufaste and colleagues (1998) tested a model about how a human expert’s cognitive system learns to detect, and does detect, pertinent data and hypotheses via a process called pertinence generation. Their results suggest two qualitative different kinds of expertise, basic and super. Basic experts are those who make routine daily diagnostic decisions by using all kinds of patient data (e.g., routine x-rays, clinical data) while super experts do the same but they also deal with Keywords: expertise; radiology; problem solving; diagnostic reasoning; think-aloud protocols; medical training Cognitive Science in the Real World: Improving Mammography Training Based on Expertise Studies Breast cancer is the second leading cause of cancer deaths in women today after skin cancer (American Cancer Society [ACS], 2006). According to the World Health Organization (WHO), more than 1.2 million people will be diagnosed with breast cancer this year worldwide. Breast cancer is the leading cause of death among women 40 to 55 years of age and causes 18% of all cancer deaths in women. In 2007, an estimated 178,480 women in the US will be diagnosed with breast cancer and about 40,460 will die because of the disease (ACS, 2006). In the last decade, incidence rates stabilized probably because mammographic screening became a critical means of substantially reducing breast cancer mortality (ACS, 2000). Nevertheless, 11% to 25% of cancers are overlooked by radiologists on initial screening mammograms (Goergen et al., 1997). Given the scope and seriousness of breast cancer, it is evident that any promising means for alleviating it should be investigated. Societal, ethical, and training issues should