Unwarranted clinical variation in health care: Definitions and proposal of an analytic framework.
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ABSTRACT: Rationale, aims, and objectives
Unwarranted clinical variation is a topic of heightened interest in health care systems around the world. While there are many publications and reports on clinical variation, few studies are conceptually grounded in a theoretical model. This study describes the empirical foundations of the field and proposes an analytic framework.Method
Structured construct mapping of published empirical studies which explicitly address unwarranted clinical variation.Results
A total of 190 studies were classified in terms of three key dimensions: perspective (assessing variation across geographical areas or across providers); criteria for assessment (measuring absolute variation against a standard, or relative variation within a comparator group); and object of analysis (using process, structure/resource, or outcome metrics).Conclusion
Consideration of the results of the mapping exercise-together with a review of adjustment, explanatory and stratification variables, and the factors associated with residual variation-informed the development of an analytic framework. This framework highlights the role that agency and motivation, evidence and judgement, and personal and organizational capacity play in clinical decision making and reveals key facets that distinguish warranted from unwarranted clinical variation. From a measurement perspective, it underlines the need for careful consideration of attribution, aggregation, models of care, and temporality in any assessment.
SUBMITTER: Sutherland K
PROVIDER: S-EPMC7317701 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
Publications
Unwarranted clinical variation in health care: Definitions and proposal of an analytic framework.
Sutherland Kim K Levesque Jean-Frederic JF
Journal of evaluation in clinical practice 20190528 3
<h4>Rationale, aims, and objectives</h4>Unwarranted clinical variation is a topic of heightened interest in health care systems around the world. While there are many publications and reports on clinical variation, few studies are conceptually grounded in a theoretical model. This study describes the empirical foundations of the field and proposes an analytic framework.<h4>Method</h4>Structured construct mapping of published empirical studies which explicitly address unwarranted clinical variati ...[more]
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Project description:Although coexpression analysis via pair-wise expression correlation is popularly used to elucidate gene-gene interactions at the whole-genome scale, many complicated multi-gene regulations require more advanced detection methods. Liquid association (LA) is a powerful tool to detect the dynamic correlation of two gene variables depending on the expression level of a third variable (LA scouting gene). LA detection from single transcriptomic study, however, is often unstable and not generalizable due to cohort bias, biological variation and limited sample size. With the rapid development of microarray and NGS technology, LA analysis combining multiple gene expression studies can provide more accurate and stable results.In this article, we proposed two meta-analytic approaches for LA analysis (MetaLA and MetaMLA) to combine multiple transcriptomic studies. To compensate demanding computing, we also proposed a two-step fast screening algorithm for more efficient genome-wide screening: bootstrap filtering and sign filtering. We applied the methods to five Saccharomyces cerevisiae datasets related to environmental changes. The fast screening algorithm reduced 98% of running time. When compared with single study analysis, MetaLA and MetaMLA provided stronger detection signal and more consistent and stable results. The top triplets are highly enriched in fundamental biological processes related to environmental changes. Our method can help biologists understand underlying regulatory mechanisms under different environmental exposure or disease states.A MetaLA R package, data and code for this article are available at http://tsenglab.biostat.pitt.edu/software.htm.ctseng@pitt.edu.Supplementary data are available at Bioinformatics online.
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Project description:BackgroundDigital technologies are increasingly used in health research to collect real-world data from wider populations. A new wave of digital health studies relies primarily on digital technologies to conduct research entirely remotely. Remote digital health studies hold promise to significant cost and time advantages over traditional, in-person studies. However, such studies have been reported to typically suffer from participant attrition, the sources for which are still largely understudied.ObjectiveTo contribute to future remote digital health study planning, we present a conceptual framework and hypotheses for study enrollment and completion. The framework introduces 3 participation criteria that impact remote digital health study outcomes: (1) participant motivation profile and incentives or nudges, (2) participant task complexity, and (3) scientific requirements. The goal of this study is to inform the planning and implementation of remote digital health studies from a person-centered perspective.MethodsWe conducted a scoping review to collect information on participation in remote digital health studies, focusing on methodological aspects that impact participant enrollment and retention. Comprehensive searches were conducted on the PubMed, CINAHL, and Web of Science databases, and additional sources were included in our study from citation searching. We included digital health studies that were fully conducted remotely, included information on at least one of the framework criteria during recruitment, onboarding or retention phases of the studies, and included study enrollment or completion outcomes. Qualitative analyses were performed to synthesize the findings from the included studies.ResultsWe report qualitative findings from 37 included studies that reveal high values of achieved median participant enrollment based on target sample size calculations, 128% (IQR 100%-234%), and median study completion, 48% (IQR 35%-76%). Increased median study completion is observed for studies that provided incentives or nudges to extrinsically motivated participants (62%, IQR 43%-78%). Reducing task complexity for participants in the absence of incentives or nudges did not improve median study enrollment (103%, IQR 102%-370%) or completion (43%, IQR 22%-60%) in observational studies, in comparison to interventional studies that provided more incentives or nudges (median study completion rate of 55%, IQR 38%-79%). Furthermore, there were inconsistencies in measures of completion across the assessed remote digital health studies, where only around half of the studies with completion measures (14/27, 52%) were based on participant retention throughout the study period.ConclusionsFew studies reported on participatory factors and study outcomes in a consistent manner, which may have limited the evidence base for our study. Our assessment may also have suffered from publication bias or unrepresentative study samples due to an observed preference for participants with digital literacy skills in digital health studies. Nevertheless, we find that future remote digital health study planning can benefit from targeting specific participant profiles, providing incentives and nudges, and reducing study complexity to improve study outcomes.
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