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Leveraging healthcare utilization to explore outcomes from musculoskeletal disorders: methodology for defining relevant variables from a health services data repository

Overview of attention for article published in BMC Medical Informatics and Decision Making, January 2018
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  • Good Attention Score compared to outputs of the same age (68th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
6 tweeters

Citations

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12 Dimensions

Readers on

mendeley
22 Mendeley
Title
Leveraging healthcare utilization to explore outcomes from musculoskeletal disorders: methodology for defining relevant variables from a health services data repository
Published in
BMC Medical Informatics and Decision Making, January 2018
DOI 10.1186/s12911-018-0588-8
Pubmed ID
Authors

Daniel I. Rhon, Derek Clewley, Jodi L. Young, Charles D. Sissel, Chad E. Cook

Abstract

Large healthcare databases, with their ability to collect many variables from daily medical practice, greatly enable health services research. These longitudinal databases provide large cohorts and longitudinal time frames, allowing for highly pragmatic assessment of healthcare delivery. The purpose of this paper is to discuss the methodology related to the use of the United States Military Health System Data Repository (MDR) for longitudinal assessment of musculoskeletal clinical outcomes, as well as address challenges of using this data for outcomes research. The Military Health System manages care for approximately 10 million beneficiaries worldwide. Multiple data sources pour into the MDR from multiple levels of care (inpatient, outpatient, military or civilian facility, combat theater, etc.) at the individual patient level. To provide meaningful and descriptive coding for longitudinal analysis, specific coding for timing and type of care, procedures, medications, and provider type must be performed. Assumptions often made in clinical trials do not apply to these cohorts, requiring additional steps in data preparation to reduce risk of bias. The MDR has a robust system in place to validate the quality and accuracy of its data, reducing risk of analytic error. Details for making this data suitable for analysis of longitudinal orthopaedic outcomes are provided. Although some limitations exist, proper preparation and understanding of the data can limit bias, and allow for robust and meaningful analyses. There is the potential for strong precision, as well as the ability to collect a wide range of variables in very large groups of patients otherwise not captured in traditional clinical trials. This approach contributes to the improved understanding of the accessibility, quality, and cost of care for those with orthopaedic conditions. The MDR provides a robust pool of longitudinal healthcare data at the person-level. The benefits of using the MDR database appear to outweigh the limitations.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Professor 3 14%
Researcher 3 14%
Student > Master 3 14%
Student > Ph. D. Student 3 14%
Professor > Associate Professor 2 9%
Other 5 23%
Unknown 3 14%
Readers by discipline Count As %
Medicine and Dentistry 9 41%
Computer Science 3 14%
Nursing and Health Professions 3 14%
Psychology 2 9%
Biochemistry, Genetics and Molecular Biology 2 9%
Other 0 0%
Unknown 3 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 08 October 2018.
All research outputs
#3,550,574
of 13,595,754 outputs
Outputs from BMC Medical Informatics and Decision Making
#407
of 1,227 outputs
Outputs of similar age
#108,682
of 351,198 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 5 outputs
Altmetric has tracked 13,595,754 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,227 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has gotten more attention than average, scoring higher than 66% 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 351,198 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 5 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