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Random or predictable?: Adoption patterns of chronic care management practices in physician organizations

Overview of attention for article published in Implementation Science, August 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • Average Attention Score compared to outputs of the same age and source

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Title
Random or predictable?: Adoption patterns of chronic care management practices in physician organizations
Published in
Implementation Science, August 2017
DOI 10.1186/s13012-017-0639-z
Pubmed ID
Authors

Isomi M. Miake-Lye, Emmeline Chuang, Hector P. Rodriguez, Gerald F. Kominski, Elizabeth M. Yano, Stephen M. Shortell

Abstract

Theories, models, and frameworks used by implementation science, including Diffusion of Innovations, tend to focus on the adoption of one innovation, when often organizations may be facing multiple simultaneous adoption decisions. For instance, despite evidence that care management practices (CMPs) are helpful in managing chronic illness, there is still uneven adoption by physician organizations. This exploratory paper leverages this natural variation in uptake to describe inter-organizational patterns in adoption of CMPs and to better understand how adoption choices may be related to one another. We assessed a cross section of national survey data from physician organizations reporting on the use of 20 CMPs (5 each for asthma, congestive heart failure, depression, and diabetes). Item response theory was used to explore patterns in adoption, first considering all 20 CMPs together and then by subsets according to disease focus or CMP type (e.g., registries, patient reminders). Mokken scale analysis explored whether adoption choices were linked by disease focus or CMP type and whether a consistent ordering of adoption choices was present. The Mokken scale for all 20 CMPs demonstrated medium scalability (H = 0.43), but no consistent ordering. Scales for subsets of CMPs sharing a disease focus had medium scalability (0.4 < H < 0.5), while subsets sharing a CMP type had strong scalability (H > 0.5). Scales for CMP type consistently ranked diabetes CMPs as most adoptable and depression CMPs as least adoptable. Within disease focus scales, patient reminders were ranked as the most adoptable CMP, while clinician feedback and patient education were ranked the least adoptable. Patterns of adoption indicate that innovation characteristics may influence adoption. CMP dissemination efforts may be strengthened by encouraging traditionally non-adopting organizations to focus on more adoptable practices first and then describing a pathway for the adoption of subsequent CMPs. Clarifying why certain CMPs are "less adoptable" may also provide insights into how to overcome CMP adoption constraints.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 71 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 15%
Student > Master 11 15%
Researcher 9 13%
Student > Bachelor 6 8%
Student > Doctoral Student 5 7%
Other 15 21%
Unknown 14 20%
Readers by discipline Count As %
Medicine and Dentistry 16 23%
Nursing and Health Professions 9 13%
Psychology 8 11%
Social Sciences 7 10%
Business, Management and Accounting 3 4%
Other 8 11%
Unknown 20 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 12 January 2023.
All research outputs
#2,379,365
of 23,524,722 outputs
Outputs from Implementation Science
#528
of 1,728 outputs
Outputs of similar age
#46,622
of 318,216 outputs
Outputs of similar age from Implementation Science
#23
of 34 outputs
Altmetric has tracked 23,524,722 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,728 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.8. 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 318,216 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 85% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.