The Pareto Principle

Harvey, H. Benjamin; Sotardi, Susan

doi:10.1016/j.jacr.2018.02.026

Cited by 82 publications

(45 citation statements)

References 1 publication

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“…-None of the discussed DM tools will cover all medical circumstances. Applying the Pareto principle to using DM, we could expect that targeting 20% of all the causes leading the patients to the doctor will cover 80% of all patients [58]. The remaining 20% are patients with complex diseases, indifferent symptoms or simply fall out of the scope of the respective DM strategy.…”

Section: What Are the Requirements For Successful Implementation Of Tmentioning

confidence: 99%

Managing inappropriate utilization of laboratory resources

et al. 2018

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Background The inappropriate use of laboratory resources, due to excessive number of tests not really necessary for patient care or by failure to order the appropriate diagnostic test, may lead to wrong, missed or delayed diagnosis, thus potentially jeopardizing patient safety. It is estimated that 5-95% of tests are currently used inappropriately, depending on the appropriateness criteria, thus significantly contributing to the potential of generating medical errors, the third leading cause of death in the US. Content In this review, we discuss the reasons as well as the medical and financial consequences of inappropriate utilization of laboratory tests. We then provide demand management (DM) tools as a means for overcoming this issue and also discuss their benefits, challenges, limitations and requirements for successful implementation. Summary and outlook When based on current evidence, adapted to local conditions and developed in close collaboration with clinicians, DM is a reasonable strategy for progressing toward better management of over- and underuse of laboratory resources.

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Section: What Are the Requirements For Successful Implementation Of Tmentioning

confidence: 99%

Managing inappropriate utilization of laboratory resources

et al. 2018

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“…For blind validation, the original data were randomly split into two sub-datasets by using systematic random sampling. Following the 80/20 rule [19,20], the training dataset consisted of 66 patients (80%) and independent blind dataset of 16 (20%). All qualified patients were enrolled in a chronological order and selected at a fixed interval.…”

Section: Discussionmentioning

confidence: 99%

A Method for the Prediction of Clinical Outcome Using Diffusion Magnetic Resonance Imaging: Application on Parkinson’s Disease

Tsai

Lin

et al. 2020

JCM

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Robust early prediction of clinical outcomes in Parkinson’s disease (PD) is paramount for implementing appropriate management interventions. We propose a method that uses the baseline MRI, measuring diffusion parameters from multiple parcellated brain regions, to predict the 2-year clinical outcome in Parkinson’s disease. Diffusion tensor imaging was obtained from 82 patients (males/females = 45/37, mean age: 60.9 ± 7.3 years, baseline and after 23.7 ± 0.7 months) using a 3T MR scanner, which was normalized and parcellated according to the Automated Anatomical Labelling template. All patients were diagnosed with probable Parkinson’s disease by the National Institute of Neurological Disorders and Stroke criteria. Clinical outcome was graded using disease severity (Unified Parkinson’s Disease Rating Scale and Modified Hoehn and Yahr staging), drug administration (levodopa equivalent daily dose), and quality of life (39-item PD Questionnaire). Selection and regularization of diffusion parameters, the mean diffusivity and fractional anisotropy, were performed using least absolute shrinkage and selection operator (LASSO) between baseline diffusion index and clinical outcome over 2 years. Identified features were entered into a stepwise multivariate regression model, followed by a leave-one-out/5-fold cross validation and additional blind validation using an independent dataset. The predicted Unified Parkinson’s Disease Rating Scale for each individual was consistent with the observed values at blind validation (adjusted R2 0.76) by using 13 features, such as mean diffusivity in lingual, nodule lobule of cerebellum vermis and fractional anisotropy in rolandic operculum, and quadrangular lobule of cerebellum. We conclude that baseline diffusion MRI is potentially capable of predicting 2-year clinical outcomes in patients with Parkinson’s disease on an individual basis.

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“…It is also reasonable to expect that most features would not be especially useful. Considering the Pareto principle [53][54][55]-if most variation can truly be explained by a relatively small number of variables, then it is natural to expect increasing numbers of features to be classified in the lower value categories.…”

Section: Discussionmentioning

confidence: 99%

A hierarchical, fuzzy inference approach to data filtration and feature prioritization in the connected manufacturing enterprise

2018

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Rockwell Automation, a global manufacturing and consultation corporation headquartered in Milwaukee, WI, employs a term, The Connected Enterprise (CE), to describe its strategy of corporate shared vision for the future of industrial automation [1]. CE strategies address the problem of disconnect by linking people, equipment, and processes for real-time learning of enterprise status in order to enable informed, adaptive, and proactive decisions [2]. The term "big data" may be loosely defined as information such that the size, structure, or variety strain the capability of traditional software or database tools to capture, store, manage, and analyze it [3, 4]. Not only does big data pose a challenge to software systems and tools, but also the volume of data challenges the ability of human operators, analysts, and leaders to grasp, consume, and understand the critical pieces. Research in fields as diverse as psychology [5], economics [6], and literature [7] have identified limitations in human ability to process, visualize, and synthesize meaning from data. George

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The Pareto Principle

Cited by 82 publications

References 1 publication

Managing inappropriate utilization of laboratory resources

Managing inappropriate utilization of laboratory resources

A Method for the Prediction of Clinical Outcome Using Diffusion Magnetic Resonance Imaging: Application on Parkinson’s Disease

A hierarchical, fuzzy inference approach to data filtration and feature prioritization in the connected manufacturing enterprise

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