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Clin Orthop Relat Res. 2013 Nov;471(11):3496-503. doi: 10.1007/s11999-013-3194-1.

Challenges in outcome measurement: clinical research perspective.

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1
Department of Health and Human Performance, University of Houston, 3855 Holman, Garrison 104, Houston, TX, 77204-6015, USA, dpoconno@central.uh.edu.

Abstract

Comparative effectiveness research evaluates treatments as actually delivered in routine clinical practice, shifting research focus from efficacy and internal validity to effectiveness and external validity ("generalizability"). Such research requires accurate assessments of the numbers of patients treated and the completeness of their followup, their clinical outcomes, and the setting in which their care was delivered. Choosing measures and methods for clinical outcome research to produce meaningful information that may be used to improve patient care presents a number of challenges. WHERE ARE WE NOW?: Orthopaedic surgery research has many stakeholders, including patients, providers, payers, and policy makers. A major challenge in orthopaedic surgery outcome measurement and clinical research is providing all of these users with valid information for their respective decision making. At present, no plan exists for capturing data on such a broad scale and scope. WHERE DO WE NEED TO GO?: Practical challenges include identifying and obtaining resources for widespread data collection and merging multiple data sources. Challenges of study design include sampling to obtain representative data, timing of data collection in the episode of care, and minimizing missing data and study dropout. HOW DO WE GET THERE?: Resource limitations may be addressed by repurposing existing clinical resources and capitalizing on technologic advances to increase efficiencies. Increasing use of rigorous, well-designed observational research designs can provide information that may be unattainable in clinical trials. Such study designs should incorporate methods to minimize missing data, to sample multiple providers, facilities, and patients, and to include evaluation of potential confounding variables to minimize bias and allow generalization to broad populations.

PMID:
23884806
PMCID:
PMC3792254
DOI:
10.1007/s11999-013-3194-1
[Indexed for MEDLINE]
Free PMC Article
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