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Categorizing patients in a forced-choice triad task: the integration of context in patient management.

Devantier SL, Minda JP, Goldszmidt M, Haddara W - PLoS ONE (2009)

Bottom Line: A linear mixed effects model indicated that novices were less likely to make deep matches than experts (t(68) = -3.63, p = .0006), while intermediates did not differ from experts (t(68) = -0.24, p = .81).We also found that the number of years in practice correlated with performance on diagnostic (r = .39, p = .02), but not management triads (r = .17, p = .34).We found that experts were more likely than novices to match patients based on deep features, and that this pattern held for both diagnostic and management triads.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, The University of Western Ontario, London, Ontario, Canada. sdevanti@uwo.ca

ABSTRACT

Background: Studies of experts' problem-solving abilities have shown that experts can attend to the deep structure of a problem whereas novices attend to the surface structure. Although this effect has been replicated in many domains, there has been little investigation into such effects in medicine in general or patient management in particular.

Methodology/principal findings: We designed a 10-item forced-choice triad task in which subjects chose which one of two hypothetical patients best matched a target patient. The target and its potential matches were related in terms of surface features (e.g., two patients of a similar age and gender) and deep features (e.g., two diabetic patients with similar management strategies: a patient with arthritis and a blind patient would both have difficulty with self-injected insulin). We hypothesized that experts would have greater knowledge of management categories and would be more likely to choose deep matches. We contacted 130 novices (medical students), 11 intermediates (medical residents), and 159 experts (practicing endocrinologists) and 15, 11, and 8 subjects (respectively) completed the task. A linear mixed effects model indicated that novices were less likely to make deep matches than experts (t(68) = -3.63, p = .0006), while intermediates did not differ from experts (t(68) = -0.24, p = .81). We also found that the number of years in practice correlated with performance on diagnostic (r = .39, p = .02), but not management triads (r = .17, p = .34).

Conclusions: We found that experts were more likely than novices to match patients based on deep features, and that this pattern held for both diagnostic and management triads. Further, management and diagnostic triads were equally salient for expert physicians suggesting that physicians recognize and may create management-oriented categories of patients.

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Related in: MedlinePlus

Proportion of deep-feature matches for each group of subjects.Proportions are shown for all triads (left set of bars) and for the management and diagnostic triads separately (center and right sets respectively). Significant differences at p<.05 are indicated with *. Error bars indicate the Standard Error of the Mean (SEM).
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pone-0005881-g002: Proportion of deep-feature matches for each group of subjects.Proportions are shown for all triads (left set of bars) and for the management and diagnostic triads separately (center and right sets respectively). Significant differences at p<.05 are indicated with *. Error bars indicate the Standard Error of the Mean (SEM).

Mentions: Our primary hypothesis concerned subjects' ability to recognize the deep-structure matches inherent in the triads. Accordingly, we calculated the average proportion-deep score for each subject across all the items. Figure 2 shows the performance by the three groups of subjects on all triads. The data show a general effect of expertise in which the experts chose the greatest proportion of deep responses, followed by the intermediates and novices. In order to analyze the overall effects, we fit a linear mixed effects model, using the proportion deep score as the dependant variable, expertise level (novice, intermediate, and expert) as a fixed effect, and question type (diagnostic or management) as a random effect within subject. The results indicated that novices' performance differed from experts', t(68) = −3.63, p = .0006, but that intermediates did not differ significantly from experts, t(68) = −0.24, p = .81. These results confirmed the existence of an expertise effect on proportion deep responding. In order to analyze this effect in greater detail, we entered the proportion-deep scores for each subject into an ANOVA with expertise as a between subjects factor. We found a significant effect of expertise, F(2, 31) = 8.01, MSE = 0.252, p = .002. A Tukey HSD test indicated the performance by the experts exceeded that of the novices (M's = .56 and .30, p = .002). The performance of the intermediates (M = .51) also exceeded performance of the novices (p = .027). For the experts and intermediates, the difference in performance did not achieve significance (p = .810).


Categorizing patients in a forced-choice triad task: the integration of context in patient management.

Devantier SL, Minda JP, Goldszmidt M, Haddara W - PLoS ONE (2009)

Proportion of deep-feature matches for each group of subjects.Proportions are shown for all triads (left set of bars) and for the management and diagnostic triads separately (center and right sets respectively). Significant differences at p<.05 are indicated with *. Error bars indicate the Standard Error of the Mean (SEM).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2690657&req=5

pone-0005881-g002: Proportion of deep-feature matches for each group of subjects.Proportions are shown for all triads (left set of bars) and for the management and diagnostic triads separately (center and right sets respectively). Significant differences at p<.05 are indicated with *. Error bars indicate the Standard Error of the Mean (SEM).
Mentions: Our primary hypothesis concerned subjects' ability to recognize the deep-structure matches inherent in the triads. Accordingly, we calculated the average proportion-deep score for each subject across all the items. Figure 2 shows the performance by the three groups of subjects on all triads. The data show a general effect of expertise in which the experts chose the greatest proportion of deep responses, followed by the intermediates and novices. In order to analyze the overall effects, we fit a linear mixed effects model, using the proportion deep score as the dependant variable, expertise level (novice, intermediate, and expert) as a fixed effect, and question type (diagnostic or management) as a random effect within subject. The results indicated that novices' performance differed from experts', t(68) = −3.63, p = .0006, but that intermediates did not differ significantly from experts, t(68) = −0.24, p = .81. These results confirmed the existence of an expertise effect on proportion deep responding. In order to analyze this effect in greater detail, we entered the proportion-deep scores for each subject into an ANOVA with expertise as a between subjects factor. We found a significant effect of expertise, F(2, 31) = 8.01, MSE = 0.252, p = .002. A Tukey HSD test indicated the performance by the experts exceeded that of the novices (M's = .56 and .30, p = .002). The performance of the intermediates (M = .51) also exceeded performance of the novices (p = .027). For the experts and intermediates, the difference in performance did not achieve significance (p = .810).

Bottom Line: A linear mixed effects model indicated that novices were less likely to make deep matches than experts (t(68) = -3.63, p = .0006), while intermediates did not differ from experts (t(68) = -0.24, p = .81).We also found that the number of years in practice correlated with performance on diagnostic (r = .39, p = .02), but not management triads (r = .17, p = .34).We found that experts were more likely than novices to match patients based on deep features, and that this pattern held for both diagnostic and management triads.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, The University of Western Ontario, London, Ontario, Canada. sdevanti@uwo.ca

ABSTRACT

Background: Studies of experts' problem-solving abilities have shown that experts can attend to the deep structure of a problem whereas novices attend to the surface structure. Although this effect has been replicated in many domains, there has been little investigation into such effects in medicine in general or patient management in particular.

Methodology/principal findings: We designed a 10-item forced-choice triad task in which subjects chose which one of two hypothetical patients best matched a target patient. The target and its potential matches were related in terms of surface features (e.g., two patients of a similar age and gender) and deep features (e.g., two diabetic patients with similar management strategies: a patient with arthritis and a blind patient would both have difficulty with self-injected insulin). We hypothesized that experts would have greater knowledge of management categories and would be more likely to choose deep matches. We contacted 130 novices (medical students), 11 intermediates (medical residents), and 159 experts (practicing endocrinologists) and 15, 11, and 8 subjects (respectively) completed the task. A linear mixed effects model indicated that novices were less likely to make deep matches than experts (t(68) = -3.63, p = .0006), while intermediates did not differ from experts (t(68) = -0.24, p = .81). We also found that the number of years in practice correlated with performance on diagnostic (r = .39, p = .02), but not management triads (r = .17, p = .34).

Conclusions: We found that experts were more likely than novices to match patients based on deep features, and that this pattern held for both diagnostic and management triads. Further, management and diagnostic triads were equally salient for expert physicians suggesting that physicians recognize and may create management-oriented categories of patients.

Show MeSH
Related in: MedlinePlus