A New Model for Evaluating the Future Options of Integrating Ground Source Heat Pumps in Building Construction

Decision-making for effective infrastructure integration is challenging because the performances of long-lasting objects often depends on conditions which are either outside the control of the designer or difficult to foresee at the design stage. In this paper we examine a new approach to estimating the range of cost-effective solutions for integrating the construction/retrofit of two or more different types of infrastructure. Infrastructure integration has many perceived benefits, but also faces serious new challenges and doubts from practitioners, particularly in sectors with complex construction process, long asset lives, uncertain cost parameters, and slow and unwieldy decision-making, such as is common with civil engineering works. We test all main options in integrating a ground source heat pump (GSHP) system with the construction and retrofit of an archetypal, office building. A new simulation model is developed and parameterized using actual data in the UK. We incorporate unavoidable uncertainties and randomness in how the decisions are triggered, and test the effectiveness of proactive measures to embed future options. The model highlights how sensitive the range of cost-effective solutions is to the setting of renewable energy incentives, discount rates, technical performance and life-cycle asset management of interdependent infrastructure. This points to a clear need for establishing appropriate regulatory standards. We expect this model to find increasing applications in the planning and designing of integrated complexes of buildings, transport facilities, renewable energy supply, water supply and waste management in dense urban areas, which are an increasingly key part of sustainable urban development

The health care utility model: a novel approach to doing business

The growth of the Civica Rx health care utility model of addressing shortages of pharmaceutical supplies for hospitals has been gathering momentum since its launch in 2018. The concept is to bypass the major drug makers and to manufacture or subcontract for generic pharmaceuticals that its 1,400-plus member hospitals need. What’s unique about the venture is that it is established as a nonstock, nonprofit 501(c)(4) social welfare organization. The money to run the operation comes from the customers (the hospitals) and philanthropic organizations, not banks or investors in need of a healthy return. The aim is to combine the efficiency of a pro-competitive private enterprise with the equitable mission of a pro–social welfare organization. This new organizational construct represents an opportunity that could be adapted and implemented in other areas of the health care landscape

Empirical research in healthcare operations: past research, present understanding, and future opportunities

We examine the published empirical literature in healthcare operations management over the last 20 years. We note several unique characteristics of the research in healthcare operations, including a focus on operational and organizational variables, an interest in the underlying mechanisms that explain operational causal pathways, and an interest in economic and managerial implications. We organize the prior findings under five distinct themes: importance of operational variables, importance of volume, routing patients through healthcare systems, to err is human, and managing the improvement process. We also identify several key areas of future research, including personalized medicine, value based healthcare, and connected health. We conclude with a call to action for greater engagement with the medical community in areas where tools and insights of operations management can bring about improvements in healthcare delivery

Role of hyperpolarization-activated cyclic nucleotide-gated ion channels in neuropathic pain: a proof-of-concept study of ivabradine in patients with chronic peripheral neuropathic pain.

INTRODUCTION: Hyperpolarization-activated cyclic nucleotide-gated (HCN) ion channels mediate repetitive action potential firing in the heart and nervous system. The HCN2 isoform is expressed in nociceptors, and preclinical studies suggest a critical role in neuropathic pain. Ivabradine is a nonselective HCN blocker currently available for prescription for cardiac indications. Mouse data suggest that ivabradine in high concentrations is equianalgesic with gabapentin. We sought to translate these findings to patients with chronic peripheral neuropathic pain. OBJECTIVES: We sought to translate these findings to patients with chronic peripheral neuropathic pain. METHODS: We adopted an open-label design, administering increasing doses of ivabradine to target a heart rate of 50 to 60 BPM, up to a maximum of 7.5 mg twice daily. All participants scored their pain on an 11-point numerical rating scale (NRS). RESULTS: Seven (7) participants received the drug and completed the study. There was no significant treatment effect on the primary endpoint, the difference between the mean score at baseline and at maximum dosing (mean reduction = 0.878, 95% CI = -2.07 to 0.31, P = 0.1). Exploratory analysis using linear mixed models, however, revealed a highly significant correlation between ivabradine dose and pain scores (χ2(1) = 74.6, P < 0.001), with a reduction of 0.12 ± 0.01 (SEM) NRS points per milligram. The 2 participants with painful diabetic neuropathy responded particularly well. CONCLUSION: This suggests that ivabradine may be efficacious at higher doses, particularly in patients with diabetic neuropathic pain. Importantly, participants reported no adverse effects. These data suggest that ivabradine, a peripherally restricted drug (devoid of central nervous system side effects), is well tolerated in patients with chronic neuropathic pain. Ivabradine is now off-patent, and its analgesic potential merits further investigation in clinical trials.This research was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). The authors acknowledge support from the East of England NIHR Clinical Research Network who facilitated identification of participants, and the staff at the Cambridge NIHR Clinical Investigation Ward, who cared for our participants during their visit. The authors are grateful to Mr Abhishek Dixit who built and maintained OpenClinica for data capture. The in-house development and use of FAST-diary are supported by Evelyn Trust (RECORD-Pain) and AAGBI (Anaesthesia-Wiley) research grants

Design with uncertainty: the role of future options for infrastructure integration

Decision making for effective infrastructure integration is challenging because the performance of long-lasting facilities is often difficult to foresee or well beyond the designer's control. We propose a new approach for integrating the construction/retrofitting of two or more types of facilities. Infrastructure integration has many perceived benefits, but practitioners also express serious doubts, particularly when it comes to civil engineering works. To substantiate this approach, we test all of the major options for integrating a ground source heat pump system with the construction/retrofitting of an archetypal office building. We use actual data from the United Kingdom, which represent a middle-of-the-road setting among major developed countries. The model highlights the sensitivity of the range of cost-effective solutions to the embedding of future options. The findings point to a clear need for appropriate standards for managing infrastructure integration. We expect this kind of model to find increasing applications among infrastructure complexes, particularly as cities become denser and more multifunctional.All authors wish to acknowledge the funding support of the EPSRC Centre for Smart Infrastructure and Construction at Cambridge University (EPSRC reference EP/K000314/1). Ying Jin would also like to acknowledge the funding support received from the Tsinghua–Cambridge–MIT Low-carbon University Alliance and the China Ministry of Education Key Lab of Eco-Planning & Green Building at Tsinghua University

Sub-optimal learning of tactile-spatial predictions in patients with complex regional pain syndrome.

In Complex Regional Pain Syndrome (CRPS), tactile sensory deficits have motivated the therapeutic use of sensory discrimination training. However, the hierarchical organisation of the brain is such that low-level sensory processing can be dynamically influenced by higher-level knowledge, e.g. knowledge learnt from statistical regularities in the environment. It is unknown whether the learning of such statistical regularities is impaired in CRPS. Here, we employed a hierarchical Bayesian model of predictive coding to investigate statistical learning of tactile-spatial predictions in CRPS. Using a sensory change-detection task, we manipulated bottom-up (spatial displacement of a tactile stimulus) and top-down (probabilistic structure of occurrence) factors to estimate hierarchies of prediction and prediction error signals, as well as their respective precisions or reliability. Behavioural responses to spatial changes were influenced by both the magnitude of spatial displacement (bottom-up) and learnt probabilities of change (top-down). The Bayesian model revealed that patients' predictions (of spatial displacements) were found to be less precise, deviating further from the ideal (statistical optimality) compared to healthy controls. This imprecision was less context-dependent, i.e. more enduring across changes in probabilistic context and less finely-tuned to statistics of the environment. This caused greater precision on prediction errors, resulting in predictions that were driven more by momentary spatial changes and less by the history of spatial changes. These results suggest inefficiencies in higher-order statistical learning in CRPS. This may have implications for therapies based on sensory re-training whose effects may be more short-lived if success depends on higher-order learning

From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles

The inference of network topologies from relational data is an important problem in data analysis. Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships based on co-occurrences of words in documents. Solving these problems requires techniques to infer significant links in noisy relational data. In this short paper, we propose a new statistical modeling framework to address this challenge. It builds on generalized hypergeometric ensembles, a class of generative stochastic models that give rise to analytically tractable probability spaces of directed, multi-edge graphs. We show how this framework can be used to assess the significance of links in noisy relational data. We illustrate our method in two data sets capturing spatio-temporal proximity relations between actors in a social system. The results show that our analytical framework provides a new approach to infer significant links from relational data, with interesting perspectives for the mining of data on social systems.Comment: 10 pages, 8 figures, accepted at SocInfo201

Sampling of temporal networks: methods and biases

Temporal networks have been increasingly used to model a diversity of systems that evolve in time; for example, human contact structures over which dynamic processes such as epidemics take place. A fundamental aspect of real-life networks is that they are sampled within temporal and spatial frames. Furthermore, one might wish to subsample networks to reduce their size for better visualization or to perform computationally intensive simulations. The sampling method may affect the network structure and thus caution is necessary to generalize results based on samples. In this paper, we study four sampling strategies applied to a variety of real-life temporal networks. We quantify the biases generated by each sampling strategy on a number of relevant statistics such as link activity, temporal paths and epidemic spread. We find that some biases are common in a variety of networks and statistics, but one strategy, uniform sampling of nodes, shows improved performance in most scenarios. Given the particularities of temporal network data and the variety of network structures, we recommend that the choice of sampling methods be problem oriented to minimize the potential biases for the specific research questions on hand. Our results help researchers to better design network data collection protocols and to understand the limitations of sampled temporal network data

A systematic review of the use of an expertise-based randomised controlled trial design

Acknowledgements JAC held a Medical Research Council UK methodology (G1002292) fellowship, which supported this research. The Health Services Research Unit, Institute of Applied Health Sciences (University of Aberdeen), is core-funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates. Views express are those of the authors and do not necessarily reflect the views of the funders.Peer reviewedPublisher PD