Exploring a Data-Augmented Approach for Improved Module Driver Analysis

2021 ◽  
pp. 677-685
Author(s):  
Rasmus Andersen ◽  
Thomas D. Brunoe ◽  
Kjeld Nielsen
Keyword(s):  
Author(s):  
Michael Trzesniowski ◽  
Philipp Eder
Keyword(s):  

BMJ Open ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. e021575 ◽  
Author(s):  
Fraser D Rubens ◽  
Diana M Rothwell ◽  
Amal Al Zayadi ◽  
Sudhir Sundaresan ◽  
Tim Ramsay ◽  
...  

ObjectiveTo determine the role of patient demographics, care domains and self-perceived health status in the analysis and interpretation of results from the Canadian Patient Experience Survey–Inpatient Care.DesignCross-sectional survey.SettingSingle large Canadian two campus tertiary care academic centre.ParticipantsRandom sampling of hospital patients postdischarge.Intervention and main outcome measuresLogistic regression models were developed to analyse topbox scoring on four questions of global care (rate experience, recommend hospital, rate hospital, overall helped). Means of each composite domain were correlated to the four overall scores at the patient level to determine Spearman’s rank correlation coefficients which were plotted against the overall (hospital) domain score for the key driver analysis.ResultsTopbox scoring was decreased with worse degrees of perceived physical and mental health in all four global questions (p<0.05). Female gender and higher levels of education were associated with worse scoring on rate experience, recommend hospital and rate hospital (p<0.001). Whereas there was a significant difference between hospital departments in unadjusted measures, these differences were no longer evident after adjustment with patient covariates. Key driver analysis identified person-centred care, care transition and the domain related to emergency admission as areas of highest potential for improvement.ConclusionsGlobal measures of overall care are influenced by patient-perceived physical and mental health. Caution should be exercised in using patient-satisfaction surveys to compare performance between different healthcare provision entities, as apparent differences could be explained by variation in patient mix rather than variation in performance.


Rheumatology ◽  
2020 ◽  
Author(s):  
Seung Min Jung ◽  
Kyung-Su Park ◽  
Ki-Jo Kim

Abstract Objective RA encompasses a complex, heterogeneous and dynamic group of diseases arising from molecular and cellular perturbations of synovial tissues. The aim of this study was to decipher this complexity using an integrative systems approach and provide novel insights for designing stratified treatments. Methods An RNA sequencing dataset of synovial tissues from 152 RA patients and 28 normal controls was imported and subjected to filtration of differentially expressed genes, functional enrichment and network analysis, non-negative matrix factorization, and key driver analysis. A naïve Bayes classifier was applied to the independent datasets to investigate the factors associated with treatment outcome. Results A matrix of 1241 upregulated differentially expressed genes from RA samples was classified into three subtypes (C1–C3) with distinct molecular and cellular signatures. C3 with prominent immune cells and proinflammatory signatures had a stronger association with the presence of ACPA and showed a better therapeutic response than C1 and C2, which were enriched with neutrophil and fibroblast signatures, respectively. C2 was more occupied by synovial fibroblasts of destructive phenotype and carried highly expressed key effector molecules of invasion and osteoclastogenesis. CXCR2, JAK3, FYN and LYN were identified as key driver genes in C1 and C3. HDAC, JUN, NFKB1, TNF and TP53 were key regulators modulating fibroblast aggressiveness in C2. Conclusions Deep phenotyping of synovial heterogeneity captured comprehensive and discrete pathophysiological attributes of RA regarding clinical features and treatment response. This result could serve as a template for future studies to design stratified approaches for RA patients.


2017 ◽  
Vol 10 (2) ◽  
pp. 290-298
Author(s):  
Charles A. Scherbaum ◽  
Justin Black ◽  
Sara P. Weiner

Cucina, Walmsley, Gast, Martin, and Curtin (2017) raise an important issue in evaluating whether our current approaches for key driver analysis on employee opinion survey data are indeed best practices. As has been argued elsewhere (Putka & Oswald, 2016; Scherbaum, Putka, Naidoo, & Youssefnia, 2010), there is and can be misalignment between current and best practices. We agree with Cucina et al. that our field should engage in larger discussion of these issues. That discussion is critical, as industrial and organizational (I-O) psychologists are competing with those outside our field who have either little knowledge of best practices in data analysis (but who have been empowered by technology that automates the analysis) or little knowledge of psychology (but a great deal of knowledge in big data analytical techniques). I-O psychologists are in the vanguard of survey data analysis (Ducey et al., 2015), and we have a responsibility to maintain the standards of our field as well as to wield our influence to guide other practitioners outside our field on sound theoretical and analytical approaches.


2016 ◽  
Vol 19 (4) ◽  
pp. 492-527 ◽  
Author(s):  
Yariv Taran ◽  
Christian Nielsen ◽  
Marco Montemari ◽  
Peter Thomsen ◽  
Francesco Paolone

Purpose Despite the common understanding that business model (BM) innovation is of vital importance for securing competitive positioning in the market place, managers still seem to lack appropriate frameworks and tools which can support them in renewing and rejuvenating their company’s existing BM. The purpose of this paper is to develop a structural and comprehensive toolbox of available BM configurations, from which companies can choose, to innovate their BM upon, and to design an appropriate BM innovation framework which can facilitate them in re-designing, selecting, and implementing new BM configuration possibilities. Design/methodology/approach A structured literature review is conducted to identify all the relevant BM configurations. Then, a value driver analysis is performed to group these BM configurations into appropriate categories. Finally, an ontological classification scheme and a structural and workable process, i.e. a BM innovation framework, are inductively developed. Findings The paper systematically develops a list of 71 BM configurations and groups them into an ontological classification scheme according to five groups: Value Proposition, Value Segment, Value Configuration, Value Network, and Value Capture. The paper illustrates how the BM innovation framework, enabled by this ontological classification scheme, provides a platform for identifying BM innovation routes for companies, allowing managers to envisage radical, disruptive, and new-to-the-world BM configuration ideas, or apply existing configurations from other industrial settings in what may be deemed new-to-the-industry innovation. Originality/value The paper enriches the amount of potential BM configurations available for managers to choose from when innovating their BMs, and extends the analysis to five core BM configuration categories. Moreover, the BM innovation framework suggested highlights the strong relationships among the value drivers, thus presenting the opportunity for managers to assess potential conflicts or synergies between various value drivers, and to align the BM management process as a whole.


2017 ◽  
Vol 10 (2) ◽  
pp. 268-277
Author(s):  
William H. Macey ◽  
Diane L. Daum

In contrast to the view that survey key driver analysis (SKDA) is a misused and blind empirical process, we suggest it is a reasonable, hypothesis-driven approach that builds on cumulative knowledge drawn from both the literature and practice, and requires reasoned judgment about the relationships of individual items to the constructs they represent and the criteria of interest. The logic of key driver analysis in applied settings is no different than the logic of its application in fundamental research regarding employee attitudes (e.g., Dalal, Baysinger, Brummel, & LeBreton, 2012). However, there are important survey design and analysis issues with respect to how key driver analyses are best conducted. Just some of these are discussed below.


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