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Enabling claims-based decision support through non-interruptive capture of admission diagnoses and provider billing codes.

Overview of attention for article published in AMIA Annual Symposium Proceedings, November 2014
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Title
Enabling claims-based decision support through non-interruptive capture of admission diagnoses and provider billing codes.
Published in
AMIA Annual Symposium Proceedings, November 2014
Pubmed ID
Authors

Colin G Walsh, David K Vawdrey, Peter D Stetson, Matthew R Fred, George Hripcsak

Abstract

The patient problem list, like administrative claims data, has become an important source of data for decision support, patient cohort identification, and alerting systems. A two-fold intervention to increase capture of problems on the problem list automatically - with minimal disruption to admitting and provider billing workflows - is described. For new patients with no prior data in the electronic health record, the intervention resulted in a statistically significant increase in the number of problems recorded to the problem list (3.8 vs 2.9 problems post-and pre-intervention respectively, p value 2×10(-16)). The majority of problems were recorded in the first 24 hours of admission. The proportion of patients with at least one problem coded to the problem list within the first 24 hours increased from 94% to 98% before and after intervention (chi square 344, p value 2×10(-16)). ICD9 "V codes" connoting circumstances beyond disease were captured at a higher rate post intervention than before. Deyo/Charlson comorbidities derived from problem list data were more similar to those derived from claims data after the intervention than before (Jaccard similarity 0.3 post- vs 0.21 pre-intervention, p value 2×10(-16)). A workflow-sensitive, non-interruptive means of capturing provider-entered codes early in admission can improve both the quantity and content of problems on the patient problem list.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 7%
Unknown 13 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 29%
Other 2 14%
Student > Master 2 14%
Student > Ph. D. Student 1 7%
Professor > Associate Professor 1 7%
Other 1 7%
Unknown 3 21%
Readers by discipline Count As %
Medicine and Dentistry 6 43%
Computer Science 1 7%
Pharmacology, Toxicology and Pharmaceutical Science 1 7%
Decision Sciences 1 7%
Design 1 7%
Other 0 0%
Unknown 4 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 03 October 2015.
All research outputs
#17,289,387
of 25,382,440 outputs
Outputs from AMIA Annual Symposium Proceedings
#476
of 912 outputs
Outputs of similar age
#161,570
of 269,854 outputs
Outputs of similar age from AMIA Annual Symposium Proceedings
#25
of 53 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 912 research outputs from this source. They receive a mean Attention Score of 4.5. 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 269,854 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
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 is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.