Background:Many patients show deterioration in functioning and increased care needs in the last year of life. End-of-life care needs and health care utilization might differ between groups of acutely hospitalized older patients.Aim:To investigate differences in geriatric conditions, advance care planning, and health care utilization in patients with cancer, organ failure, or frailty, who died within 1 year after acute hospitalization.Design:Prospective cohort study conducted between 2002 and 2008, with 1-year follow-up.Setting:University teaching hospital in the Netherlands.Participants:Aged ⩾65 years, acutely hospitalized for ⩾48 h, and died within 1 year after hospitalization. At admission, all patients received a systematic comprehensive geriatric assessment. Hospital records were searched for advance care planning information and health care utilization. Differences between patient groups were calculated.Results:In total, 306 patients died within 1 year after acute admission (35%) and were included; 151 with cancer, 98 with end-stage organ failure, and 57 frail older persons. At hospital admission, 72% of the frail group had delirium and/or severe pre-existing cognitive impairment. The frail and organ failure group had many pre-existing disabilities. Three months post-discharge, 75% of the frail and organ failure group had died, 45% of these patients had an advance care plan in their hospital records.Conclusion:Patients with frailty and organ failure had highest rates of geriatric conditions at hospital admission and often had missing information on advance care planning in the hospital records. There is a need to better identify end-of-life needs for these groups.
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The prevention and diagnosis of frailty syndrome (FS) in cardiac patients requires innovative systems to support medical personnel, patient adherence, and self-care behavior. To do so, modern medicine uses a supervised machine learning approach (ML) to study the psychosocial domains of frailty in cardiac patients with heart failure (HF). This study aimed to determine the absolute and relative diagnostic importance of the individual components of the Tilburg Frailty Indicator (TFI) questionnaire in patients with HF. An exploratory analysis was performed using machine learning algorithms and the permutation method to determine the absolute importance of frailty components in HF. Based on the TFI data, which contain physical and psychosocial components, machine learning models were built based on three algorithms: a decision tree, a random decision forest, and the AdaBoost Models classifier. The absolute weights were used to make pairwise comparisons between the variables and obtain relative diagnostic importance. The analysis of HF patients’ responses showed that the psychological variable TFI20 diagnosing low mood was more diagnostically important than the variables from the physical domain: lack of strength in the hands and physical fatigue. The psychological variable TFI21 linked with agitation and irritability was diagnostically more important than all three physical variables considered: walking difficulties, lack of hand strength, and physical fatigue. In the case of the two remaining variables from the psychological domain (TFI19, TFI22), and for all variables from the social domain, the results do not allow for the rejection of the null hypothesis. From a long-term perspective, the ML based frailty approach can support healthcare professionals, including psychologists and social workers, in drawing their attention to the nonphysical origins of HF.
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Rationale To improve the quality of exercise-based cardiac rehabilitation (CR) in patients with chronic heart failure (CHF) a practice guideline from the Dutch Royal Society for Physiotherapy (KNGF) has been developed. Guideline development A systematic literature search was performed to formulate conclusions on the efficacy of exercise-based intervention during all CR phases in patients with CHF. Evidence was graded (1–4) according the Dutch evidence-based guideline development criteria. Clinical and research recommendations Recommendations for exercise-based CR were formulated covering the following topics: mobilisation and treatment of pulmonary symptoms (if necessary) during the clinical phase, aerobic exercise, strength training (inspiratory muscle training and peripheral muscle training) and relaxation therapy during the outpatient CR phase, and adoption and monitoring training after outpatient CR. Applicability and implementation issues This guideline provides the physiotherapist with an evidence-based instrument to assist in clinical decision-making regarding patients with CHF. The implementation of the guideline in clinical practice needs further evaluation. Conclusion This guideline outlines best practice standards for physiotherapists concerning exercise-based CR in CHF patients. Research is needed on strategies to improve monitoring and follow-up of the maintenance of a physical active lifestyle after supervised CR.
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Denim Democracy from the Alliance for Responsible Denim (ARD) is an interactive exhibition that celebrates the journey and learning of ARD members, educates visitors about sustainable denim and highlights how companies collaborate together to achieve results. Through sight, sound and tactile sensations, the visitor experiences and fully engages sustainable denim production. The exhibition launches in October 2018 in Amsterdam and travels to key venues and locations in the Netherlands until April 2019. As consumers, we love denim but the denim industry, like other sub-sectors in the textile, apparel and footwear industries, faces many complex sustainability challenges and has been criticized for its polluting and hazardous production practices. The Alliance for Responsible Denim project brought leading denim brands, suppliers and stakeholders together to collectively address these issues and take initial steps towards improving the ecological sustainability impact of denim production. Sustainability challenges are considered very complex and economically undesirable for individual companies to address alone. In denim, small and medium sized denim firms face specific challenges, such as lower economies of scale and lower buying power to affect change in practices. There is great benefit in combining denim companies' resources and knowledge so that collective experimentation and learning can lift the sustainability standards of the industry and lead to the development of common standards and benchmarks on a scale that matters. If meaningful, transformative industrial change is to be made, then it calls for collaboration between denim industry stakeholders that goes beyond supplier-buyer relations and includes horizontal value chain collaboration of competing large and small denim brands. However collaboration between organizations, and especially between competitors, is highly complex and prone to failure. The research behind the Alliance for Responsible Denim project asked a central research question: how do competitors effectively collaborate together to create common, industry standards on resource use and benchmarks for improved ecological sustainability? To answer this question, we used a mixed-method, action research approach. The Alliance for Responsible Denim project mobilized and facilitated denim brands to collectively identify ways to reduce the use of water and chemicals in denim production and then aided them to implement these practices individually in their respective firms.
In the past decades, we have faced an increase in the digitization, digitalization, and digital transformation of our work and daily life. Breakthroughs of digital technologies in fields such as artificial intelligence, telecommunications, and data science bring solutions for large societal questions but also pose a new challenge: how to equip our (future)workforce with the necessary digital skills, knowledge, and mindset to respond to and drive digital transformation?Developing and supporting our human capital is paramount and failure to do so may leave us behind on individual (digital divide), organizational (economic disadvantages), and societal level (failure in addressing grand societal challenges). Digital transformation necessitates continuous learning approaches and scaffolding of interdisciplinary collaboration and innovation practices that match complex real-world problems. Research and industry have advocated for setting up learning communities as a space in which (future) professionals of different backgrounds can work, learn, and innovate together. However, insights into how and under which circumstances learning communities contribute to accelerated learning and innovation for digital transformation are lacking. In this project, we will study 13 existing and developing learning communities that work on challenges related to digital transformation to understand their working mechanisms. We will develop a wide variety of methods and tools to support learning communities and integrate these in a Learning Communities Incubator. These insights, methods and tools will result in more effective learning communities that will eventually (a) increase the potential of human capital to innovate and (b) accelerate the innovation for digital transformation
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
