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Presentation of Diagnostic Information to Doctors May Change Their Interpretation and Clinical Management: A Web-Based Randomised Controlled Trial.

Ben-Shlomo Y, Collin SM, Quekett J, Sterne JA, Whiting P - PLoS ONE (2015)

Bottom Line: 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).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.

View Article: PubMed Central - PubMed

Affiliation: School of Social & Community Medicine, University of Bristol, Canynge Hall, Bristol, United Kingdom.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

Different modes of data presentation to help interpret the results of the index test (A) Fagan’s nomogram (B) Probability modifying plot (C) Natural frequency tree.
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getmorefigures.php?uid=PMC4492926&req=5

pone.0128637.g001: Different modes of data presentation to help interpret the results of the index test (A) Fagan’s nomogram (B) Probability modifying plot (C) Natural frequency tree.

Mentions: The same short text summary as for (a) supported by a nomogram (Fig 1A) for which we provided the following instructions “… enables you to convert the pre-test probability to a post-test probability by connecting the values for the pre-test probability and appropriate likelihood ratio and extrapolating a straight line to the post-test probability column.”


Presentation of Diagnostic Information to Doctors May Change Their Interpretation and Clinical Management: A Web-Based Randomised Controlled Trial.

Ben-Shlomo Y, Collin SM, Quekett J, Sterne JA, Whiting P - PLoS ONE (2015)

Different modes of data presentation to help interpret the results of the index test (A) Fagan’s nomogram (B) Probability modifying plot (C) Natural frequency tree.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4492926&req=5

pone.0128637.g001: Different modes of data presentation to help interpret the results of the index test (A) Fagan’s nomogram (B) Probability modifying plot (C) Natural frequency tree.
Mentions: The same short text summary as for (a) supported by a nomogram (Fig 1A) for which we provided the following instructions “… enables you to convert the pre-test probability to a post-test probability by connecting the values for the pre-test probability and appropriate likelihood ratio and extrapolating a straight line to the post-test probability column.”

Bottom Line: 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).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.

View Article: PubMed Central - PubMed

Affiliation: School of Social & Community Medicine, University of Bristol, Canynge Hall, Bristol, United Kingdom.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus