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

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          Abstract

          Background

          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.

          Methods

          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.

          Results

          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.

          Conclusion

          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.

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          Most cited references42

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          Missing data: our view of the state of the art.

          Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.
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            Big data analytics in healthcare: promise and potential

            Objective To describe the promise and potential of big data analytics in healthcare. Methods The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. Results The paper provides a broad overview of big data analytics for healthcare researchers and practitioners. Conclusions Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome.
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              Making trials matter: pragmatic and explanatory trials and the problem of applicability

              Randomised controlled trials are the best research design for decisions about the effect of different interventions but randomisation does not, of itself, promote the applicability of a trial's results to situations other than the precise one in which the trial was done. While methodologists and trialists have rightly paid great attention to internal validity, much less has been given to applicability. This narrative review is aimed at those planning to conduct trials, and those aiming to use the information in them. It is intended to help the former group make their trials more widely useful and to help the latter group make more informed decisions about the wider use of existing trials. We review the differences between the design of most randomised trials (which have an explanatory attitude) and the design of trials more able to inform decision making (which have a pragmatic attitude) and discuss approaches used to assert applicability of trial results. If we want evidence from trials to be used in clinical practice and policy, trialists should make every effort to make their trial widely applicable, which means that more trials should be pragmatic in attitude.
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                Author and article information

                Contributors
                daniel_rhon@baylor.edu
                derek.clewley@duke.edu
                jodiyoung@atsu.edu
                charles.d.sissel.civ@mail.mil
                chad.cook@duke.edu
                Journal
                BMC Med Inform Decis Mak
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central (London )
                1472-6947
                31 January 2018
                31 January 2018
                2018
                : 18
                : 10
                Affiliations
                [1 ]ISNI 0000 0004 0450 5663, GRID grid.416653.3, Center for the Intrepid, Brooke Army Medical Center, ; 3551 Roger Brooke Drive, San Antonio, TX 78234 USA
                [2 ]ISNI 0000 0001 2111 2894, GRID grid.252890.4, Baylor University, ; 3630 Stanley Road, Bldg 2841, Suite 1301; Joint Base San Antonio - Fort Sam Houston, San Antonio, TX 78234 USA
                [3 ]ISNI 0000 0004 1936 7961, GRID grid.26009.3d, Division of Physical Therapy, Department of Orthopedics, , Duke University, ; 2200 W. Main Street, Durham, NC 27701 USA
                [4 ]Department of Physical Therapy, Arizona School of Health Sciences, 5850 E. Still Circle, Mesa, AZ 85206 USA
                [5 ]ISNI 0000 0001 0689 287X, GRID grid.481489.8, Headquarters, U.S. Army Medical Command, Analysis & Evaluation Division, ; 3630 Stanley Road; Joint Base San Antonio - Fort Sam Houston, San Antonio, TX 78234 USA
                Author information
                http://orcid.org/0000-0002-4320-990X
                Article
                588
                10.1186/s12911-018-0588-8
                5793373
                29386010
                ddfd3ca3-e23e-40cd-8c58-74b981d2d9b7
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 5 May 2017
                : 17 January 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100009898, Defense Health Agency;
                Award ID: W911QY-15-1-0016
                Categories
                Database
                Custom metadata
                © The Author(s) 2018

                Bioinformatics & Computational biology
                database research,hip,arthroscopic surgery,healthcare utilization

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