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A Systematic Review of Conceptual Frameworks of Medical Complexity and New Model Development

Overview of attention for article published in Journal of General Internal Medicine, September 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 (83rd percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

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17 X users

Citations

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57 Dimensions

Readers on

mendeley
150 Mendeley
citeulike
1 CiteULike
Title
A Systematic Review of Conceptual Frameworks of Medical Complexity and New Model Development
Published in
Journal of General Internal Medicine, September 2015
DOI 10.1007/s11606-015-3512-2
Pubmed ID
Authors

Leah L. Zullig, Heather E. Whitson, Susan N. Hastings, Chris Beadles, Julia Kravchenko, Igor Akushevich, Matthew L. Maciejewski

Abstract

Patient complexity is often operationalized by counting multiple chronic conditions (MCC) without considering contextual factors that can affect patient risk for adverse outcomes. Our objective was to develop a conceptual model of complexity addressing gaps identified in a review of published conceptual models. We searched for English-language MEDLINE papers published between 1 January 2004 and 16 January 2014. Two reviewers independently evaluated abstracts and all authors contributed to the development of the conceptual model in an iterative process. From 1606 identified abstracts, six conceptual models were selected. One additional model was identified through reference review. Each model had strengths, but several constructs were not fully considered: 1) contextual factors; 2) dynamics of complexity; 3) patients' preferences; 4) acute health shocks; and 5) resilience. Our Cycle of Complexity model illustrates relationships between acute shocks and medical events, healthcare access and utilization, workload and capacity, and patient preferences in the context of interpersonal, organizational, and community factors. This model may inform studies on the etiology of and changes in complexity, the relationship between complexity and patient outcomes, and intervention development to improve modifiable elements of complex patients.

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 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 150 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 149 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 24%
Researcher 21 14%
Student > Master 13 9%
Other 11 7%
Professor 8 5%
Other 30 20%
Unknown 31 21%
Readers by discipline Count As %
Medicine and Dentistry 40 27%
Nursing and Health Professions 19 13%
Social Sciences 13 9%
Business, Management and Accounting 12 8%
Psychology 7 5%
Other 17 11%
Unknown 42 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 09 March 2018.
All research outputs
#3,312,558
of 23,911,072 outputs
Outputs from Journal of General Internal Medicine
#2,383
of 7,806 outputs
Outputs of similar age
#45,005
of 277,809 outputs
Outputs of similar age from Journal of General Internal Medicine
#16
of 53 outputs
Altmetric has tracked 23,911,072 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,806 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.8. This one has gotten more attention than average, scoring higher than 69% 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 277,809 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 53 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.