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Clinicians can independently predict 30-day hospital readmissions as well as the LACE index

Overview of attention for article published in BMC Health Services Research, January 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)

Mentioned by

blogs
1 blog
twitter
5 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
48 Mendeley
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Title
Clinicians can independently predict 30-day hospital readmissions as well as the LACE index
Published in
BMC Health Services Research, January 2018
DOI 10.1186/s12913-018-2833-3
Pubmed ID
Authors

William Dwight Miller, Kimngan Nguyen, Sitaram Vangala, Erin Dowling

Abstract

Significant effort has been directed at developing prediction tools to identify patients at high risk of unplanned hospital readmission, but it is unclear what these tools add to clinicians' judgment. In our study, we assess clinicians' abilities to independently predict 30-day hospital readmissions, and we compare their abilities with a common prediction tool, the LACE index. Over a period of 50 days, we asked attendings, residents, and nurses to predict the likelihood of 30-day hospital readmission on a scale of 0-100% for 359 patients discharged from a General Medicine Service. For readmitted versus non-readmitted patients, we compared the mean and standard deviation of the clinician predictions and the LACE index. We compared receiver operating characteristic (ROC) curves for clinician predictions and for the LACE index. For readmitted versus non-readmitted patients, attendings predicted a risk of 48.1% versus 31.1% (p < 0.001), residents predicted 45.5% versus 34.6% (p 0.002), and nurses predicted 40.2% versus 30.6% (p 0.011), respectively. The LACE index for readmitted patients was 11.3, versus 10.1 for non-readmitted patients (p 0.003). The area under the curve (AUC) derived from the ROC curves was 0.689 for attendings, 0.641 for residents, 0.628 for nurses, and 0.620 for the LACE index. Logistic regression analysis suggested that the LACE index only added predictive value to resident predictions, but not attending or nurse predictions (p < 0.05). Attendings, residents, and nurses were able to independently predict readmissions as well as the LACE index. Improvements in prediction tools are still needed to effectively predict hospital readmissions.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 23%
Researcher 5 10%
Student > Doctoral Student 5 10%
Student > Postgraduate 4 8%
Other 4 8%
Other 10 21%
Unknown 9 19%
Readers by discipline Count As %
Medicine and Dentistry 13 27%
Nursing and Health Professions 12 25%
Business, Management and Accounting 2 4%
Agricultural and Biological Sciences 2 4%
Computer Science 2 4%
Other 5 10%
Unknown 12 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 24 January 2018.
All research outputs
#1,184,207
of 12,413,055 outputs
Outputs from BMC Health Services Research
#532
of 4,108 outputs
Outputs of similar age
#51,305
of 339,523 outputs
Outputs of similar age from BMC Health Services Research
#1
of 1 outputs
Altmetric has tracked 12,413,055 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,108 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has done well, scoring higher than 87% 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 339,523 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 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them