Evidence map›Paper›PMID 29386010›Full record

ArticleBMC medical informatics and decision making2018

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

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

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Daniel I RhonCenter for the Intrepid, Brooke Army Medical Center, 3551 Roger Brooke Drive, San Antonio, TX, 78234, USA. daniel_rhon@baylor.edu.ORCID 0000-0002-4320-990X
Derek ClewleyBaylor University, 3630 Stanley Road, Bldg 2841, Suite 1301; Joint Base San Antonio - Fort Sam Houston, San Antonio, TX, 78234, USA.
Jodi L YoungDivision of Physical Therapy, Department of Orthopedics, Duke University, 2200 W. Main Street, Durham, NC, 27701, USA.
Charles D SisselDepartment of Physical Therapy, Arizona School of Health Sciences, 5850 E. Still Circle, Mesa, AZ, 85206, USA.
Chad E CookHeadquarters, U.S. Army Medical Command, Analysis & Evaluation Division, 3630 Stanley Road; Joint Base San Antonio - Fort Sam Houston, San Antonio, TX, 78234, USA.

Funding

Defense Health Agency W911QY-15-1-0016
6 · The paper itself

Abstract

backgroundLarge 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.

methodsThe 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.

resultsAlthough 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.

conclusionThe 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.

Indexed as

Databases, FactualHealth Information SystemsOutcome Assessment, Health CarePatient Acceptance of Health CareUnited States Department of DefenseHumansMusculoskeletal DiseasesUnited Statesarthroscopic surgeryDatabase researchhealthcare utilizationhip

Identifiers

PMID29386010
PMCPMC5793373

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.