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A system dynamics model of clinical decision thresholds for the detection of developmental-behavioral disorders

Overview of attention for article published in Implementation Science, November 2016
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  • Good Attention Score compared to outputs of the same age (69th percentile)

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6 X users

Citations

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19 Dimensions

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97 Mendeley
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Title
A system dynamics model of clinical decision thresholds for the detection of developmental-behavioral disorders
Published in
Implementation Science, November 2016
DOI 10.1186/s13012-016-0517-0
Pubmed ID
Authors

R. Christopher Sheldrick, Dominic J. Breuer, Razan Hassan, Kee Chan, Deborah E. Polk, James Benneyan

Abstract

Clinical decision-making has been conceptualized as a sequence of two separate processes: assessment of patients' functioning and application of a decision threshold to determine whether the evidence is sufficient to justify a given decision. A range of factors, including use of evidence-based screening instruments, has the potential to influence either or both processes. However, implementation studies seldom specify or assess the mechanism by which screening is hypothesized to influence clinical decision-making, thus limiting their ability to address unexpected findings regarding clinicians' behavior. Building on prior theory and empirical evidence, we created a system dynamics (SD) model of how physicians' clinical decisions are influenced by their assessments of patients and by factors that may influence decision thresholds, such as knowledge of past patient outcomes. Using developmental-behavioral disorders as a case example, we then explore how referral decisions may be influenced by changes in context. Specifically, we compare predictions from the SD model to published implementation trials of evidence-based screening to understand physicians' management of positive screening results and changes in referral rates. We also conduct virtual experiments regarding the influence of a variety of interventions that may influence physicians' thresholds, including improved access to co-located mental health care and improved feedback systems regarding patient outcomes. Results of the SD model were consistent with recent implementation trials. For example, the SD model suggests that if screening improves physicians' accuracy of assessment without also influencing decision thresholds, then a significant proportion of children with positive screens will not be referred and the effect of screening implementation on referral rates will be modest-results that are consistent with a large proportion of published screening trials. Consistent with prior theory, virtual experiments suggest that physicians' decision thresholds can be influenced and detection of disabilities improved by increasing access to referral sources and enhancing feedback regarding false negative cases. The SD model of clinical decision-making offers a theoretically based framework to improve understanding of physicians' behavior and the results of screening implementation trials. The SD model is also useful for initial testing of hypothesized strategies to increase detection of under-identified medical conditions.

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The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Unknown 96 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 13%
Researcher 13 13%
Student > Master 11 11%
Student > Doctoral Student 11 11%
Other 9 9%
Other 23 24%
Unknown 17 18%
Readers by discipline Count As %
Medicine and Dentistry 23 24%
Psychology 13 13%
Nursing and Health Professions 11 11%
Social Sciences 5 5%
Mathematics 5 5%
Other 12 12%
Unknown 28 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 29 November 2016.
All research outputs
#6,838,576
of 22,758,963 outputs
Outputs from Implementation Science
#1,156
of 1,721 outputs
Outputs of similar age
#125,013
of 415,059 outputs
Outputs of similar age from Implementation Science
#29
of 37 outputs
Altmetric has tracked 22,758,963 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,721 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.7. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
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 415,059 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.