Objective: To describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions.Design: Systematic review and meta-analysis.Data source: Medline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020.Eligibility criteria for selecting studies: Studies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months. Primary and secondary outcome measures: Model discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. Meta-regression and subgroup analyses were performed to investigate predefined sources of heterogeneity. Outcome measures from models reported in multiple independent cohorts and similarly defined risk predictors were pooled.Results: Sixty studies describing 81 models were included: 43 models were newly developed, and 38 were externally validated. Included populations were mainly patients with heart failure (HF) (n=29). The average age ranged between 56.5 and 84 years. The incidence of readmission ranged from 3% to 43%. Risk of bias (RoB) was high in almost all studies. The c-statistic was <0.7 in 72 models, between 0.7 and 0.8 in 16 models and >0.8 in 5 models. The study population, data source and number of predictors were significant moderators for the discrimination. Calibration was reported for 27 models. Only the GRACE (Global Registration of Acute Coronary Events) score had adequate discrimination in independent cohorts (0.78, 95% CI 0.63 to 0.86). Eighteen predictors were pooled. Conclusion: Some promising models require updating and validation before use in clinical practice. The lack of independent validation studies, high RoB and low consistency in measured predictors limit their applicability.PROSPERO registration number: CRD42020159839.
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The building and construction industry, which is responsible for 39% of global carbon emissions, is far off track in achieving its net-zero emission targets. Product-service system (PSS) business models are one of the instruments used by the industry in the transition toward reaching these targets. A PSS business model is designed around an end-of-life solution that minimizes material usage and maximizes energy efficiency. It is provided to customers as a marketable set of products and services, jointly capable of fulfilling a customer’s needs. There are signals from practice however, that suggest that the implementation of this type of business model is falling behind. This study investigates this and seeks to identify key challenges and opportunities for sustainable PSS business models in the built environment. Using a grounded theory approach, data from 13 semi-structured interviews across five companies is used to identify challenges and opportunities that suppliers are facing in selling their products through PSS business models. Our preliminary data analysis points to nine challenges and opportunities for PSS business models. We discuss these in the context of the current economic transition toward a sustainable and circular built environment and provide suggestions for further research that could help to overcome resistance toward the implementation of PSS business models. The contribution of this research to researchers and practitioners is that it provides insights into the adoption of new business models in fragmented and competitive business environments.
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The growing availability of data offers plenty of opportunities for data driven innovation of business models for SMEs like interactive media companies. However, SMEs lack the knowledge and processes to translate data into attractive propositions and design viable data-driven business models. In this paper we develop and evaluate a practical method for designing data driven business models (DDBM) in the context of interactive media companies. The development follows a design science research approach. The main result is a step-by-step approach for designing DDBM, supported by pattern cards and game boards. Steps consider required data sources and data activities, actors and value network, revenue model and implementation aspects. Preliminary evaluation shows that the method works as a discussion tool to uncover assumptions and make assessments to create a substantiated data driven business model.
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Nederland heeft in het Natura 2000 Beheerplan Deltawateren richtlijnen vastgelegd voor natuurbehoud en biodiversiteit. De Nederlandse wateren en de deltagebieden maken tweederde uit van de Natura 2000 gebieden en vormen een belangrijk leefgebied voor kustbroedvogels en zijn voor trekvogels onmisbaar als rustgebied en plek om te foerageren. Om natuurbeheer effectiever te kunnen laten verlopen, is monitoring van de dynamiek van estuariene natuur in de deltabeheercyclus van groot belang. Het biedt publieke professionals mogelijkheden om systeemontwerpen en/of systeemingrepen (tijdig) aan te passen. Voor projectmonitoring wordt gebruik gemaakt van conventionele meettechnieken die veelal arbeidsintensief en dus kostbaar zijn. Doel van dit project is te onderzoeken of het monitoren van natuurherstelprojecten efficiënter kan. Kernvraag is of door de inzet van nieuwe meettechnieken meer of andersoortige data tegen lagere kosten, over grotere arealen en met betere temporele resoluties kan worden vergaard. Oftewel meer systeembegrip. Op drie locaties in de Westerschelde (Baalhoek, Knuitershoek en Perkpolder) wordt geëxperimenteerd met innovatieve meettechnieken om beter inzicht te krijgen op factoren die van invloed zijn op het functioneren van getijdenecosystemen. Data van negen kernparameters wordt ingewonnen: (1) vogelaantallen, (2) benthos als vogelvoedsel, (3) benthos als bioturbator, (4) middelgrootte schaal morfologie, (5) grootschalige morfologie, (6) korte termijn (dagelijkse) veranderingen in sedimenthoogte, (7) bodemdichtheid, (8) hydrodynamiek: stroming /golven en (9) sedimentconcentraties in water. Het activiteitenplan bestaat uit zes werkpakketten: (1)het fysiek inrichten van de meetlocaties, (2) data-acquisitie op zowel conventionele- als innovatieve wijze, (3) data-analyse door vergelijkend onderzoek, (4) het ontwikkelen van een afwegingskader voor publieke professionals, (5) een plan van doorwerking en (6) projectmanagement. Na afronding van elke meetcampagne worden data geanalyseerd en vergeleken met modellen en kennis die tot dan toe bekend is. Kennis en expertise wordt op de DeltaExpertise-site (HZ Body of Knowledge) gestructureerd en ontsloten met behulp van de Expertise Management Methodologie en de Soft Systems Methodologie.
Performance feedback is an important mechanism of adaptation in learning theories, as it provides one of the motivations for organizations to learn (Pettit, Crossan, and Vera 2017). Embedded in the behavioral theory of the firm, organizational learning from performance feedback predicts the probability for organizations to change with an emphasis on organizational aspirations, which serve as a threshold against which absolute performance is evaluated (Cyert and March 1963; Greve 2003). It postulates that performance becomes a ‘problem’, or the trigger to search for alternative procedures, strategies, products and behaviors, when performance is below that threshold. This search is known as problemistic search. Missing from this body of research, is empirically grounded understanding if the characteristics of performance feedback over time matter for the triggering function of the feedback. I explore this gap. This investigation adds temporality as a dimension of the performance feedback concept guided by a worldview of ongoing change and flux where conditions and choices are not given, but made relevant by actors and enacted upon (Tsoukas and Chia 2002). The general aim of the study is to complement the current knowledge of performance feedback as a trigger for problemistic search with an explicit process temporal approach. The main question guiding this project is how temporal patterns of performance feedback influence organizational change, which I answer in four chapters, each zooming into one sub-question.First, I focus on the temporal order of performance feedback by examining performance feedback and change sequences organizations go through. In this section time is under study and the goal is to explore how feedback patterns have evolved over time, just as the change states organizations pass through. Second, I focus on the plurality of performance feedback by investigating performance feedback from multiple aspiration levels (i.e. multiple qualitatively different metrics and multiple reference points) and how over time clusters of performance feedback sequences have evolved. Next, I look into the rate and scope of change relative to performance feedback sequences and add an element of signal strength to the feedback. In the last chapter, time is a predictor (in the sequences), and, it is under study (in the timing of responses). I focus on the timing of organizational responses in relation to performance feedback sequences of multiple metrics and reference points.In sum, all chapters are guided by the timing problem of performance feedback, meaning that performance feedback does not come ‘available’ at a single point in time. Similarly to stones with unequal weight dropped in the river, performance feedback with different strength comes available at multiple points in time and it is plausible that sometimes it is considered by decision-makers as problematic and sometimes it is not, because of the sequence it is part of. Overall, the investigation is grounded in the general principles of organizational learning from performance feedback, and the concept of time as duration, sequences and timing, with a focus on specification of when things happen. The context of the study is universities of applied sciences and hotels in The Netherlands. Project partner: Tilburg University, School of Social and Behavioral Sciences, Department of Organization Studies
Nature-based coastal management is mainstream in the Netherlands. About 12 Mm3 of sand is added annually to the coast to compensate coastal erosion and maintain high safety levels against flooding. This amount will likely increase to compensate for accelerated sea level rise. (Mega-)Nourishments may also strengthen and support biodiversity and recreational values of the coastal zone and associated wetland areas. However, the ecological and societal impacts of mega-nourishments on open coasts are not well established, hampering comparison of pros and cons of different nourishment strategies. This knowledge gap is largely due to the lack of suitable methods to monitor and predict the spreading of nourishment sand along the coast and into tidal basins. Ameland Inlet provides us with a unique opportunity to develop and test novel approaches to fill this knowledge gap in close collaboration with our consortium and stakeholders. In 2018 the first tidal inlet mega-nourishment (5 Mm3) was placed in the Ameland Inlet ebb-tidal delta, and geomorphic and biotic responses nearby are closely monitored in the Kustgenese 2.0 and SEAWAD programmes. Our research builds on the insights gained, will gather new data to investigate off-site effects (linked with SIBES/SIBUS sampling), and build a common knowledge-base with stakeholders. We will develop novel luminescence-based methods to monitor the temporal and spatial dispersal of nourishment sand. These insights will be combined with an inventory of off-site biotic responses to nourishment and the role biota play in the mixing of nourishment sand with natural sediments. Combined results will be used to develop and validate models to trace transport paths of individual grains and improve morphodynamic predictions. Throughout the project, we will collaborate and interact intensely with coastal managers and (local) stakeholders to address concerns and exchange insights, creating a platform for co-assessment and optimization of nourishment designs and strategies.
