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Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design

Overview of attention for article published in BMC Medical Informatics and Decision Making, November 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

blogs
1 blog
twitter
8 tweeters

Citations

dimensions_citation
37 Dimensions

Readers on

mendeley
158 Mendeley
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Title
Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design
Published in
BMC Medical Informatics and Decision Making, November 2015
DOI 10.1186/s12911-015-0221-z
Pubmed ID
Authors

Roosan Islam, Charlene R. Weir, Makoto Jones, Guilherme Del Fiol, Matthew H. Samore

Abstract

Clinical experts' cognitive mechanisms for managing complexity have implications for the design of future innovative healthcare systems. The purpose of the study is to examine the constituents of decision complexity and explore the cognitive strategies clinicians use to control and adapt to their information environment. We used Cognitive Task Analysis (CTA) methods to interview 10 Infectious Disease (ID) experts at the University of Utah and Salt Lake City Veterans Administration Medical Center. Participants were asked to recall a complex, critical and vivid antibiotic-prescribing incident using the Critical Decision Method (CDM), a type of Cognitive Task Analysis (CTA). Using the four iterations of the Critical Decision Method, questions were posed to fully explore the incident, focusing in depth on the clinical components underlying the complexity. Probes were included to assess cognitive and decision strategies used by participants. The following three themes emerged as the constituents of decision complexity experienced by the Infectious Diseases experts: 1) the overall clinical picture does not match the pattern, 2) a lack of comprehension of the situation and 3) dealing with social and emotional pressures such as fear and anxiety. All these factors contribute to decision complexity. These factors almost always occurred together, creating unexpected events and uncertainty in clinical reasoning. Five themes emerged in the analyses of how experts deal with the complexity. Expert clinicians frequently used 1) watchful waiting instead of over- prescribing antibiotics, engaged in 2) theory of mind to project and simulate other practitioners' perspectives, reduced very complex cases into simple 3) heuristics, employed 4) anticipatory thinking to plan and re-plan events and consulted with peers to share knowledge, solicit opinions and 5) seek help on patient cases. The cognitive strategies to deal with decision complexity found in this study have important implications for design future decision support systems for the management of complex patients.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 158 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
South Africa 1 <1%
Unknown 156 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 28 18%
Student > Ph. D. Student 24 15%
Student > Bachelor 22 14%
Researcher 20 13%
Other 14 9%
Other 31 20%
Unknown 19 12%
Readers by discipline Count As %
Medicine and Dentistry 48 30%
Nursing and Health Professions 16 10%
Computer Science 16 10%
Engineering 10 6%
Psychology 6 4%
Other 38 24%
Unknown 24 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 August 2019.
All research outputs
#1,227,280
of 15,691,914 outputs
Outputs from BMC Medical Informatics and Decision Making
#88
of 1,428 outputs
Outputs of similar age
#33,401
of 366,621 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#6
of 151 outputs
Altmetric has tracked 15,691,914 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,428 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 366,621 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 151 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.