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Background: There is little evidence on how best to present diagnostic information to doctors and whether this makes any difference to clinical management. We undertook a randomised controlled trial to see if different data presentations altered clinicians' decision to further investigate or treat a patient with a fictitious disorder ("Green syndrome") and their ability to determine post-test probability. Methods: We recruited doctors registered with the United Kingdom's largest online network for medical doctors between 10 July and 6" November 2012. Participants were randomised to one of four arms: (a) text summary of sensitivity and specificity, (b) Fagan's nomogram, (c) probability-modifying plot (PMP), (d) natural frequency tree (NFT). The main outcome measure was the decision whether to treat, not treat or undertake a brain biopsy on the hypothetical patient and the correct post-test probability. Secondary outcome measures included knowledge of diagnostic tests. Results: 917 participants attempted the survey and complete data were available from 874 (95.3%). Doctors randomized to the PMP and NFT arms were more likely to treat the patient than those randomized to the text-only arm. (ORs 1.49, 95% CI 1.02, 2.16) and 1.43, 95% CI 0.98, 2.08 respectively). More patients randomized to the PMP (87/218-39.9%) and NFT (73/207-35.3%) arms than the nomogram (50/194-25.8%) or text only (30/255-11.8%) arms reported the correct post-test probability (p <0.001). Younger age, postgraduate training and higher self-rated confidence all predicted better knowledge performance. Doctors with better knowledge were more likely to view an optional learning tutorial (OR per correct answer 1.18, 95% CI 1.06, 1.31). Conclusions: Presenting diagnostic data using a probability-modifying plot or natural frequency tree influences the threshold for treatment and improves interpretation of tests results compared to text summary of sensitivity and specificity or Fagan's nomogram.
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- 1 Finished
1/05/09 → 1/11/12