From Insights to INTEL: Evaluating Process Mining Insights with Healthcare Professionals

As healthcare organisations are looking for ways to improve their processes, process mining techniques are increasingly being used. Current process mining methods do not offer support for translating process mining insights into actionable improvement ideas. By performing action research at two healthcare organisations, we introduce and illustrate the INTEL funnel, a novel three-staged method consisting of process familiarisation, domain explanation and improvement ideation. Our method complements existing process mining methods and constitutes the first attempt to open the black box regarding the path from process mining insights to actionable process improvement ideas. In this way, it can contribute to a more systematic uptake of process mining in healthcare practice

Mirabegron-induced brown fat activation does not exacerbate atherosclerosis in mice with a functional hepatic ApoE-LDLR pathway

Activation of brown adipose tissue (BAT) with the β3-adrenergic receptor agonist CL316,243 protects mice from atherosclerosis development, and the presence of metabolically active BAT is associated with cardiometabolic health in humans. In contrast, exposure to cold or treatment with the clinically used β3-adrenergic receptor agonist mirabegron to activate BAT exacerbates atherosclerosis in apolipoprotein E (ApoE)- and low-density lipoprotein receptor (LDLR)-deficient mice, both lacking a functional ApoE-LDLR pathway crucial for lipoprotein remnant clearance. We, therefore, investigated the effects of mirabegron treatment on dyslipidemia and atherosclerosis development in APOE*3-Leiden.CETP mice, a humanized lipoprotein metabolism model with a functional ApoE-LDLR clearance pathway. Mirabegron activated BAT and induced white adipose tissue (WAT) browning, accompanied by selectively increased fat oxidation and attenuated fat mass gain. Mirabegron increased the uptake of fatty acids derived from triglyceride (TG)-rich lipoproteins by BAT and WAT, which was coupled to increased hepatic uptake of the generated cholesterol-enriched core remnants. Mirabegron also promoted hepatic very low-density lipoprotein (VLDL) production, likely due to an increased flux of fatty acids from WAT to the liver, and resulted in transient elevation in plasma TG levels followed by a substantial decrease in plasma TGs. These effects led to a trend toward lower plasma cholesterol levels and reduced atherosclerosis. We conclude that BAT activation by mirabegron leads to substantial metabolic benefits in APOE*3-Leiden.CETP mice, and mirabegron treatment is certainly not atherogenic. These data underscore the importance of the choice of experimental models when investigating the effect of BAT activation on lipoprotein metabolism and atherosclerosis.</p

Process mining for healthcare: Characteristics and challenges

[EN] Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.This work is partially supported by ANID FONDECYT 1220202, Direccion de Investigacion de la Vicerrectoria de Investigacion de la Pontificia Universidad Catolica de Chile-PUENTE [Grant No. 026/2021] ; and Agencia Nacional de Investigacion y Desarrollo [Grant Nos. ANID-PFCHA/Doctorado Nacional/2019-21190116, ANID-PFCHA/Doctorado Nacional/2020-21201411] . With regard to the co-author Hilda Klasky, this manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE) . The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan)Munoz Gama, J.; Martin, N.; Fernández Llatas, C.; Johnson, OA.; Sepúlveda, M.; Helm, E.; Galvez-Yanjari, V.... (2022). Process mining for healthcare: Characteristics and challenges. Journal of Biomedical Informatics. 127:1-15. https://doi.org/10.1016/j.jbi.2022.10399411512

Post-MFA Adjustment in the Philippine Garments Sector: Women's Cooperatives amid Manufacturing Decline

La fin des Accords Multi- Fibres (AMF) en 2005 a donné lieu à une reconfiguration des chaînes de valeur internationales ainsi qu'à des transformations importantes dans la fabrication de vêtements, qui ont produit des gagnants et des perdants, tant parmi les pays que les travailleurs. Sur la base d'informations collectées au moyen d'entretiens ouverts et d'un questionnaire auprès d'ouvriers, cet article se penche sur la survie de l'industrie du vêtement aux Philippines dans l'ère post-AMF. Une attention particulière est portée à trois groupes de femmes ayant formé des coopératives de fabrication à Manille afin de sauvegarder leur emploi dans ce secteur en déclin. L'article démontre comment celles-ci ont pu en conséquence maintenir un emploi, et identifie les leçons de cette expérience pour la main d'oeuvre féminine dans des situations semblables ailleurs. Ce faisant, il contribue à un agenda de recherche sur la manière dont les dynamiques productives internationales peuvent avoir un impact sur la situation de la main d'oeuvre féminine travaillant en aval des chaînes de valeur internationales.European Journal of Development Research (2009) 21, 362–376. doi:10.1057/ejdr.2009.10

Editorial: The Geography of the COVID-19 Pandemic

status: publishe

The changing role of the nation-state and regulation: Workplace bullying legislation in The Netherlands

Workplace bullying literature has focused mainly on actions of individual targets of mistreatment, undertaken to address the problem, and on analyses of the effectiveness of responses. Less attention has been paid to the efficacy of state regulation in establishing a climate of prevention as well as redress. We examine the role of the Dutch Working Conditions Act as a means of mitigating workplace bullying from the perspective of legislative intention, processes and outcomes. Semi-structured interviews with stakeholders involved in creating, influencing and implementing the Act are analysed thematically to highlight how contextual, employer and phenomenon-specific factors affect the effectiveness of legislation with regard to workplace bullying. The findings indicate that state involvement, organisational commitment and collective action are all important contributors in reducing workplace bullying, but that even in the context of neoliberalism, the role of the nation-state is of critical importance, notwithstanding initiatives by employers. JEL Codes: J58, J78, J81, M54

Process Mining for Healthcare: Characteristics and Challenges

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future

Process Mining for Healthcare: Characteristics and Challenges

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future