The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterpri...The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterprise data center requires a significant amount of time and human effort. Following a major disruption, the recovery process involves multiple stages, and during each stage, the partially recovered infrastructures can provide limited services to users at some degraded service level. However, how fast and efficiently an enterprise infrastructure can be recovered de- pends on how the recovery mechanism restores the disrupted components, considering the inter-dependencies between services, along with the limitations of expert human operators. The entire problem turns out to be NP- hard and rather complex, and we devise an efficient meta-heuristic to solve the problem. By considering some real-world examples, we show that the proposed meta-heuristic provides very accurate results, and still runs 600-2800 times faster than the optimal solution obtained from a general purpose mathematical solver [1].展开更多
Background:Telehealth has emerged as a powerful tool for managing chronic diseases and mental health conditions,offering increased access to care and improved patient outcomes.However,inequities in digital connectivit...Background:Telehealth has emerged as a powerful tool for managing chronic diseases and mental health conditions,offering increased access to care and improved patient outcomes.However,inequities in digital connectivity and technological resources have created significant disparities in access to these potentially life-changing services,disproportionately impacting marginalized and minoritized communities across the globe.Methods:Data on 473,716 telehealth encounters occurring between January 1,2022,and June 30,2023 were retrieved from the electronic health records(EHR)system used by University Hospitals.These encounters were classified into three groups:attended,canceled,and no-show.Relative risk was calculated based on age,sex,and race,and a multivariate linear regression was performed with age,sex,and race as inputs,to determine their effect on the encounter outcome.Results:Our analysis identified significant differences in relative risk between demographic groups.Patients 20-39 years of age had a high relative risk of cancellation and no-show,and Black patients demonstrated the highest relative risk for cancellation and no-show.The regression analysis illustrated a statistically significant link between no-shows and patients with a cellular plan with no other internet subscription(p<0.001),smartphone ownership(p<0.001),and not having a computer(p<0.05).Conclusions:This study highlights the clinical repercussions of the digital divide,as patients relying on a mobile phone and data plan to attend telehealth visits were more likely to no-show.Current disparities in digital connectivity for historically marginalized populations heightens the risk of creating a digital underclass.There is evidence this study may be applicable in multiple countries across the world.Further research on the causes of the observed no-shows is necessary to ensure equitable delivery of digital healthcare services.展开更多
This paper discusses the quality of Data Management Plans(DMPs)in the health sector and assesses remove the researchers’perceptions of DMPs.We applied qualitative methods to examine publicly available DMPs in healthc...This paper discusses the quality of Data Management Plans(DMPs)in the health sector and assesses remove the researchers’perceptions of DMPs.We applied qualitative methods to examine publicly available DMPs in healthcare,analyzing researchers’views and practices for creating these plans.The study combines three research methods:analysis of DMPs in the health sector,semi-structured questionnaires,and interviews.Our findings reveal that researchers are generally unaware of the importance and usefulness of DMPs,and acknowledge various inconsistencies and challenges in their development.In this paper,we identified that data management practices need to be improved and advocate for automating them and making DMPs machine-actionable.We also recommend more educational programs,such as workshops and courses,in data management especially for researchers.Finally,we recommend defining clear,accessible guidelines for researchers to effectively elaborate DMPs,and institutionalizing data management within organizations by establishing data(or digital)competence centers.展开更多
A growing number of research funding organizations(RFOs)are taking responsibility to increase the scientific and social impact of research output.Also reusable research data are recognized as relevant output for gaini...A growing number of research funding organizations(RFOs)are taking responsibility to increase the scientific and social impact of research output.Also reusable research data are recognized as relevant output for gaining impact.RFOs are therefore promoting FAIR research data management and stewardship(RDM)in their research funding cycle.However,the implementation of FAIR RDM still faces important obstacles and challenges.To solve these,stakeholders work together to develop innovative tools and practices.Here we elaborate on the role of RFOs in developing a FAIR funding model to support the FAIR RDM in the funding cycle,integrated with research community specific guidance,criteria and metadata,and enabling automatic assessments of progress and output from RDM.The model facilitates to create research data with a high level of FAIRness that are meaningful for a research community.To fully benefit from the model,RFOs,research institutions and service providers need to implement machine actionability in their FAIR RDM tools and procedures.As many stakeholders still need to get familiar with“human actionable”FAIR data practices,the introduction of the model will be stepwise,with an active role of the RFOs in driving FAIR RDM processes as effectively as possible.展开更多
文摘The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterprise data center requires a significant amount of time and human effort. Following a major disruption, the recovery process involves multiple stages, and during each stage, the partially recovered infrastructures can provide limited services to users at some degraded service level. However, how fast and efficiently an enterprise infrastructure can be recovered de- pends on how the recovery mechanism restores the disrupted components, considering the inter-dependencies between services, along with the limitations of expert human operators. The entire problem turns out to be NP- hard and rather complex, and we devise an efficient meta-heuristic to solve the problem. By considering some real-world examples, we show that the proposed meta-heuristic provides very accurate results, and still runs 600-2800 times faster than the optimal solution obtained from a general purpose mathematical solver [1].
文摘Background:Telehealth has emerged as a powerful tool for managing chronic diseases and mental health conditions,offering increased access to care and improved patient outcomes.However,inequities in digital connectivity and technological resources have created significant disparities in access to these potentially life-changing services,disproportionately impacting marginalized and minoritized communities across the globe.Methods:Data on 473,716 telehealth encounters occurring between January 1,2022,and June 30,2023 were retrieved from the electronic health records(EHR)system used by University Hospitals.These encounters were classified into three groups:attended,canceled,and no-show.Relative risk was calculated based on age,sex,and race,and a multivariate linear regression was performed with age,sex,and race as inputs,to determine their effect on the encounter outcome.Results:Our analysis identified significant differences in relative risk between demographic groups.Patients 20-39 years of age had a high relative risk of cancellation and no-show,and Black patients demonstrated the highest relative risk for cancellation and no-show.The regression analysis illustrated a statistically significant link between no-shows and patients with a cellular plan with no other internet subscription(p<0.001),smartphone ownership(p<0.001),and not having a computer(p<0.05).Conclusions:This study highlights the clinical repercussions of the digital divide,as patients relying on a mobile phone and data plan to attend telehealth visits were more likely to no-show.Current disparities in digital connectivity for historically marginalized populations heightens the risk of creating a digital underclass.There is evidence this study may be applicable in multiple countries across the world.Further research on the causes of the observed no-shows is necessary to ensure equitable delivery of digital healthcare services.
文摘This paper discusses the quality of Data Management Plans(DMPs)in the health sector and assesses remove the researchers’perceptions of DMPs.We applied qualitative methods to examine publicly available DMPs in healthcare,analyzing researchers’views and practices for creating these plans.The study combines three research methods:analysis of DMPs in the health sector,semi-structured questionnaires,and interviews.Our findings reveal that researchers are generally unaware of the importance and usefulness of DMPs,and acknowledge various inconsistencies and challenges in their development.In this paper,we identified that data management practices need to be improved and advocate for automating them and making DMPs machine-actionable.We also recommend more educational programs,such as workshops and courses,in data management especially for researchers.Finally,we recommend defining clear,accessible guidelines for researchers to effectively elaborate DMPs,and institutionalizing data management within organizations by establishing data(or digital)competence centers.
文摘A growing number of research funding organizations(RFOs)are taking responsibility to increase the scientific and social impact of research output.Also reusable research data are recognized as relevant output for gaining impact.RFOs are therefore promoting FAIR research data management and stewardship(RDM)in their research funding cycle.However,the implementation of FAIR RDM still faces important obstacles and challenges.To solve these,stakeholders work together to develop innovative tools and practices.Here we elaborate on the role of RFOs in developing a FAIR funding model to support the FAIR RDM in the funding cycle,integrated with research community specific guidance,criteria and metadata,and enabling automatic assessments of progress and output from RDM.The model facilitates to create research data with a high level of FAIRness that are meaningful for a research community.To fully benefit from the model,RFOs,research institutions and service providers need to implement machine actionability in their FAIR RDM tools and procedures.As many stakeholders still need to get familiar with“human actionable”FAIR data practices,the introduction of the model will be stepwise,with an active role of the RFOs in driving FAIR RDM processes as effectively as possible.
