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Segmentation of High-Cost Adults in an Integrated Healthcare System Based on Empirical Clustering of Acute and Chronic Conditions

Overview of attention for article published in Journal of General Internal Medicine, September 2018
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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 (84th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

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1 news outlet
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6 X users

Citations

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28 Dimensions

Readers on

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60 Mendeley
Title
Segmentation of High-Cost Adults in an Integrated Healthcare System Based on Empirical Clustering of Acute and Chronic Conditions
Published in
Journal of General Internal Medicine, September 2018
DOI 10.1007/s11606-018-4626-0
Pubmed ID
Authors

Anna C. Davis, Ernest Shen, Nirav R. Shah, Beth A. Glenn, Ninez Ponce, Donatello Telesca, Michael K. Gould, Jack Needleman

Abstract

High-cost patients are a frequent focus of improvement projects based on primary care and other settings. Efforts to characterize high-cost, high-need patients are needed to inform care planning, but such efforts often rely on a priori assumptions, masking underlying complexities of a heterogenous population. To define recognizable subgroups of patients among high-cost adults based on clinical conditions, and describe their survival and future spending. Retrospective observational cohort study. Within a large integrated delivery system with 2.7 million adult members, we selected the top 1% of continuously enrolled adults with respect to total healthcare expenditures during 2010. We used latent class analysis to identify clusters of alike patients based on 53 hierarchical condition categories. Prognosis as measured by healthcare spending and survival was assessed through 2014 for the resulting classes of patients. Among 21,183 high-cost adults, seven clinically distinctive subgroups of patients emerged. Classes included end-stage renal disease (12% of high-cost population), cardiopulmonary conditions (17%), diabetes with multiple comorbidities (8%), acute illness superimposed on chronic conditions (11%), conditions requiring highly specialized care (14%), neurologic and catastrophic conditions (5%), and patients with few comorbidities (the largest class, 33%). Over 4 years of follow-up, 6566 (31%) patients died, and survival in the classes ranged from 43 to 88%. Spending regressed to the mean in all classes except the ESRD and diabetes with multiple comorbidities groups. Data-driven characterization of high-cost adults yielded clinically intuitive classes that were associated with survival and reflected markedly different healthcare needs. Relatively few high-cost patients remain persistently high cost over 4 years. Our results suggest that high-cost patients, while not a monolithic group, can be segmented into few subgroups. These subgroups may be the focus of future work to understand appropriateness of care and design interventions accordingly.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 13%
Researcher 8 13%
Student > Master 8 13%
Student > Doctoral Student 3 5%
Professor 3 5%
Other 11 18%
Unknown 19 32%
Readers by discipline Count As %
Medicine and Dentistry 12 20%
Nursing and Health Professions 11 18%
Social Sciences 6 10%
Psychology 2 3%
Business, Management and Accounting 1 2%
Other 5 8%
Unknown 23 38%
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 27 September 2021.
All research outputs
#2,757,715
of 25,191,684 outputs
Outputs from Journal of General Internal Medicine
#2,020
of 8,115 outputs
Outputs of similar age
#53,998
of 341,260 outputs
Outputs of similar age from Journal of General Internal Medicine
#37
of 127 outputs
Altmetric has tracked 25,191,684 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 8,115 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.1. This one has done well, scoring higher than 75% 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 341,260 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 84% of its contemporaries.
We're also able to compare this research output to 127 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 71% of its contemporaries.