Design trade-offs in self-management technology: the HeartMan case

ABSTRACT

Health self-management technology has the potential to significantly improve the Quality of Life of patients suffering from chronic diseases. However, designing the technology involves numerous highly context-dependent design decisions. In this paper, we analyse a case study of self-monitoring technology in the field of congestive heart failure. We analyse the design process of the technology from the perspective of design trade-offs. Three important trade-offs related to health self-monitoring technology are described in detail, related to patient autonomy, technology appropriation, and patient well-being. For each of the trade-offs, various mediating factors that influence design decisions are described in detail. On a practical level, this analysis can inform future developments in self-management technology. In addition, this design trade-off analysis provides intermediary knowledge that can contribute to a better theoretical understanding of health self-management technology.     

Medical savings accounts: opportunities for cost savings?

Interest in medical savings accounts (MSAs) as a potential tool to reduce healthcare costs has been widespread. A small number of countries have either implemented or run pilot programs of MSAs, and vigorous policy debates have taken place in several other countries about the potential merits of introducing MSAs as a method of paying for health care. In this paper we develop a model to assess the cost saving potential of MSAs in a publicly funded healthcare system. We assume that the public healthcare payer may choose between reimbursing healthcare expenditures through an MSA or through a form of third‐party payer insurance. We use the model to identify the conditions under which MSAs may reduce costs. We illustrate using data on healthcare expenditures from Canada.     

Hemodialysis in the United Kingdom

Hemodialysis was a neglected aspect of nephrology in the UK. At the request of the Renal Association, the first UK Haemodialysis Masterclass was organized in 2007. The articles in this supplement arose from that meeting. Here, an overview of UK hemodialysis services and nephrology training is presented as background. Government‐funded dialysis should be provided to all UK citizens who require it. In 2005, there were 17,645 patients receiving hemodialysis, 5057 on peritoneal dialysis and 19,074 with kidney transplants, looked after by 359 nephrologists working in 73 National Health Service renal units. Renal replacement therapy incidence and prevalence remain comparatively low, at 108 and 694 per million population, respectively. Whether this represents inadequate provision or genuinely lower need remains unclear. The Renal Association sets clinical practice guidelines for dialysis, and audits performance via the UK Renal Registry. Postgraduate medical education is undergoing radical change in the UK. This is driven by the reduction in trainee doctors' working hours to 48 hr/week (mandated by the European Working Time Directive), and the governments' wish to reduce the duration of training, but also by a desire to formalize training, Our challenge is to continue to produce talented clinical nephrologists educated in breadth and depth, despite the reduced emphasis on clinical experience and omission of period of scientific research. The future for hemodialysis services in the UK is, however, promising with an expansion in the number of specialists and dialysis centers, and a growing interest in dialysis practice and research.     

Formal verification of collaboration patterns in healthcare

We propose a computer-based framework for the formal verification of collaboration patterns in healthcare teams. In this, the patterns are constructed diagrammatically as compositions of keystones that are viewed as abstract processes. The approach provides mechanisms for ensuring that safety properties are enforced and exceptional events are handled systematically. Additionally, a fully verified, executable model is obtained as an end product, enabling a simulation of its associated collaboration scenarios.     

医学知识增强的肿瘤分期多任务学习模型

肿瘤分期是指从病人的电子病历文本中推测肿瘤对应阶段的过程。在电子病历数据中存在类别严重不均衡现象,因此使用深度学习方法进行肿瘤分期具有一定的挑战性。该文提出医学知识增强的多任务学习KEMT(knowledge enhanced multi-task) 模型,将肿瘤分期问题视作面向医疗电子病历的文本分类任务,同时引入医生在人工预测肿瘤分期时参考的医学属性,提出基于医学问题的机器阅读理解任务,对上述两种任务进行联合学习。我们与医疗机构合作构建了真实场景下的肿瘤分期的数据集,实验结果显示,KEMT模型可以将医学知识与神经网络结合起来,预测准确率高于传统的文本分类模型。在数据分布不均衡的条件下,在小样本类别上的准确率提升了4.2个百分点,同时模型也具有一定的解释性。     

一种基于无线传感器网络安全的社区卫生保健监护系统设计

设计并实现了一种基于无线传感器网络带安全机制的社区卫生保健监护系统.为了避免无线网络通信中信息泄漏和受到攻击,在现有传感器网络节点资源受限情况下,采用椭圆曲线密码体制及相关算法,有效地实现了加密系统中复杂的密钥管理功能,通过椭圆曲线加密和数字签名技术有效防止人体生理参数等敏感数据的泄漏,同时提出了一个切实可行的系统架构并加以实现.实验结果表明,系统能抵御网络潜在的无线信号窃听和内部攻击.     

A H-QoS-demand personalized home physiological monitoring system over a wireless multi-hop relay network for mobile home healthcare applications

For the elderly and chronic patients with cardiovascular disease who live alone, it is necessary to constantly monitor their physiological parameters, especially the electrocardiogram (ECG), to effectively prevent and control their health condition and even to provide urgent treatment or care while an emergency such as the abnormal variation of heart rate (HR) occurs. In this paper, a wireless in-home physiological monitoring system, based on multi-hop relay communications, which can ubiquitously and continuously monitor the patient's ECG at any time or any place at home without space limit and the “dead spot” due to the extended communication coverage by multi-hop wireless connectivity, is proposed. The system consists of a mobile-care device, which is responsible for capturing and wirelessly sending the patient's ECG data, a wireless multi-hop relay network (WMHRN) that is in charge of relaying the data sent by the former, and a residential gateway (RG), which is responsible for gathering and uploading the received ECG data to the remote care server through the Internet to carry out the patient's health condition monitoring and the management of pathological data. However, in order to assure that the ECG data can be effectively and timely forwarded, from the mobile-care device to the RG through the WMHRN, to meet the healthcare quality of service (H-QoS) demand for reliable and real-time end-to-end ECG transmission, the analysis of WMHRN latency in data-forwarding stage and the deployment consideration of wireless relay nodes are investigated in detail in this work. Moreover, an emergency alert service using short message service (SMS), based on the detection of abnormal variation of HR, is also used in the RG to further enhance the healthcare service quality. A prototype of this system has been developed and implemented. Finally, the experimental results are presented to verify the feasibility of the proposed system.     

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code (ECEMAC) has been used to aggregate the parameters generated from the wearable sensor devices of the patient. The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO. Aggregation scheme will reduce the number of transmissions over the network. The aggregated data are preprocessed at edge node to remove the noise for better diagnosis. Edge node will reduce the overhead of cloud server. The aggregated data are forward to cloud server for central storage and diagnosis. This proposed smart diagnosis will reduce the transmission cost through aggregation scheme which will reduce the energy of the system. Energy cost for proposed system for 300 nodes is 0.34μJ. Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme (SPPDA), concealed data aggregation scheme for multiple application (CDAMA) and secure aggregation scheme (ASAS) are 1.3 μJ, 0.81 μJ and 0.51 μJ respectively. The optimization approaches and encryption method will ensure the data privacy.     

Network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders (E-HAE)

Contemporary attackers, mainly motivated by financial gain, consistently devise sophisticated penetration techniques to access important information or data. The growing use of Internet of Things (IoT) technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation, as it facilitates multiple new attack vectors to emerge effortlessly. As such, existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems. To address this problem, we designed a blended threat detection approach, considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence. We collectively refer to the convergence of different technology sectors as the internet of blended environment. The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder. An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02% detection accuracy. Furthermore, performance of the proposed approach was compared with various single model (autoencoder)-based network intrusion detection approaches: autoencoder, variational autoencoder, convolutional variational autoencoder, and long short-term memory variational autoencoder. The proposed model outperformed all compared models, demonstrating F1-score improvements of 4.99%, 2.25%, 1.92%, and 3.69%, respectively.     

中国健康服务业发展战略研究

为落实健康中国发展战略,推进中国健康服务业的发展,中国工程院于2015年启动"中国健康服务业发展战略研究"重大咨询项目。课题自启动以来,组织了28位院士和150余位专家,对京上广深等一至三线共12个城市进行了广泛的社会调查。本文在大量社会调查的基础上,从健康服务业的概念、内涵、发展特点入手,深入分析了国内健康服务业的发展现状;以全方位、全覆盖、全过程建立健康服务保障体系为目标,对健康服务业的定位、发展思路,城乡理想模式等进行了深入探讨;并针对健康管理体系建设、养生养老、医养结合、健康保险、社会办医、金融保障等重点问题,提出了为顺应中国健康服务业发展战略顺畅实施的保障措施和相关建议。