Evaluating case studies of community-oriented integrated care.

This paper summarises a ten-year conversation within London Journal of Primary Care about the nature of community-oriented integrated care (COIC) and how to develop and evaluate it. COIC means integration of efforts for combined disease-treatment and health-enhancement at local, community level. COIC is similar to the World Health Organisation concept of a Community-Based Coordinating Hub - both require a local geographic area where different organisations align their activities for whole system integration and develop local communities for health. COIC is a necessary part of an integrated system for health and care because it enables multiple insights into 'wicked problems', and multiple services to integrate their activities for people with complex conditions, at the same time helping everyone to collaborate for the health of the local population. The conversation concludes seven aspects of COIC that warrant further attention

Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings

Standardization Framework for Sustainability from Circular Economy 4.0

The circular economy (CE) is widely known as a way to implement and achieve sustainability, mainly due to its contribution towards the separation of biological and technical nutrients under cyclic industrial metabolism. The incorporation of the principles of the CE in the links of the value chain of the various sectors of the economy strives to ensure circularity, safety, and efficiency. The framework proposed is aligned with the goals of the 2030 Agenda for Sustainable Development regarding the orientation towards the mitigation and regeneration of the metabolic rift by considering a double perspective. Firstly, it strives to conceptualize the CE as a paradigm of sustainability. Its principles are established, and its techniques and tools are organized into two frameworks oriented towards causes (cradle to cradle) and effects (life cycle assessment), and these are structured under the three pillars of sustainability, for their projection within the proposed framework. Secondly, a framework is established to facilitate the implementation of the CE with the use of standards, which constitute the requirements, tools, and indicators to control each life cycle phase, and of key enabling technologies (KETs) that add circular value 4.0 to the socio-ecological transition

Supporting Collaborative Health Tracking in the Hospital: Patients' Perspectives

The hospital setting creates a high-stakes environment where patients' lives depend on accurate tracking of health data. Despite recent work emphasizing the importance of patients' engagement in their own health care, less is known about how patients track their health and care in the hospital. Through interviews and design probes, we investigated hospitalized patients' tracking activity and analyzed our results using the stage-based personal informatics model. We used this model to understand how to support the tracking needs of hospitalized patients at each stage. In this paper, we discuss hospitalized patients' needs for collaboratively tracking their health with their care team. We suggest future extensions of the stage-based model to accommodate collaborative tracking situations, such as hospitals, where data is collected, analyzed, and acted on by multiple people. Our findings uncover new directions for HCI research and highlight ways to support patients in tracking their care and improving patient safety

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Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in