Abstract
BackgroundThere 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.MethodsWe 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.Results917 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).ConclusionsPresenting 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.
Highlights
Accurate diagnosis is fundamental to the provision of appropriate health care
Doctors randomized to the probability-modifying plot (PMP) and natural frequency tree (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
Summary
Accurate diagnosis is fundamental to the provision of appropriate health care. The volume of diagnostic tests performed in the UK National Health Service (NHS) has increased dramatically in recent years due to technological innovation and an ageing population [1]. Six previous trials [7,12,13,14,15,16] have compared health professionals’ ability to estimate post-test probability for different methods of presentation of diagnostic information. Four of these were small (
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
