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BackgroundContinuous content management of health information portals is a feature vital for its sustainability and widespread acceptance. Knowledge and experience of a domain expert is essential for content management in the health domain. The rate of generation of online health resources is exponential and thereby manual examination for relevance to a specific topic and audience is a formidable challenge for domain experts. Intelligent content discovery for effective content management is a less researched topic. An existing expert-endorsed content repository can provide the necessary leverage to automatically identify relevant resources and evaluate qualitative metrics. ObjectiveThis paper reports on the design research towards an intelligent technique for automated content discovery and ranking for health information portals. The proposed technique aims to improve efficiency of the current mostly manual process of portal content management by utilising an existing expert-endorsed content repository as a supporting base and a benchmark to evaluate the suitability of new content MethodsA model for content management was established based on a field study of potential users. The proposed technique is integral to this content management model and executes in several phases (ie, query construction, content search, text analytics and fuzzy multi-criteria ranking). The construction of multi-dimensional search queries with input from Wordnet, the use of multi-word and single-word terms as representative semantics for text analytics and the use of fuzzy multi-criteria ranking for subjective evaluation of quality metrics are original contributions reported in this paper. ResultsThe feasibility of the proposed technique was examined with experiments conducted on an actual health information portal, the BCKOnline portal. Both intermediary and final results generated by the technique are presented in the paper and these help to establish benefits of the technique and its contribution towards effective content management. ConclusionsThe prevalence of large numbers of online health resources is a key obstacle for domain experts involved in content management of health information portals and websites. The proposed technique has proven successful at search and identification of resources and the measurement of their relevance. It can be used to support the domain expert in content management and thereby ensure the health portal is up-to-date and current.

This study presents an ontology that scopes the digital health ecosystem from a consumer–centered perspective. We used a mixed-method analysis on a set of papers collected for a comprehensive review to identify common themes, components, and patterns that repeatedly emerge within Australian-based digital health studies. Three major and four child themes were identified as the foundational aspects of the proposed ontology. The child themes have more precise concept definitions, inherited and distinguishing attributes. Out of 179 recognized concepts, 33 were related to the Healthcare theme; 23 concepts formed a cluster of employed devices under the Technology theme; 40 concepts were associated with Use and Usability factors. 60 other concepts formed the cluster of the consumer–user theme. The theme of Digital Health was seen as being connected to 2 independent clusters. The main cluster embodied 21 extracted concepts, semantically related to "data, information, and knowledge," whilst the second cluster embodied concepts related to "healthcare." Different stakeholders can utilize this ontology to define their landscape of digitally enabled healthcare. The novelty of this work resides in capturing a consumer–centered perspective and the method we used in deriving the ontology – formalizing the results of a systematic review based on data-driven analysis methods. [ABSTRACT FROM AUTHOR]

Decision support systems (DSS) have been traditionally developed to assist with unstructured and semi-structured problems. Early DSS researchers explored a broad range of techniques for supporting human cognition as part of decision making. Cognition during decision making was viewed in terms of two competing, and sometimes cooperating, systems: one that was automatic and fast, and one that was deliberative and slow. The aim of this research is to trace historical studies on cognitive aspects of decision support and determine the theoretical underpinnings of DSS support for cognition. We analysed articles drawing on the seminal literature to derive the relevant dimensions, including the classical Gorry & Scott Morton (1989) framework. This analysis identified opportunities for future research relevant to providing better support for cognition by highlighting some design parameters for information systems. [ABSTRACT FROM AUTHOR]

Background: Patient flow describes the progression of patients along a pathway of care such as the journey from hospital inpatient admission to discharge. Poor patient flow has detrimental effects on health outcomes, patient satisfaction and hospital revenue. There has been an increasing adoption of health information systems (HISs) in various healthcare settings to address patient flow issues, yet there remains limited evidence of their overall impacts. Objective: To systematically review evidence on the impacts of HISs on patient flow management including what HISs have been used, their application scope, features, and what aspects of patient flow are affected by the HIS adoption. Methods: A systematic search for English-language, peer-review literature indexed in MEDLINE and EMBASE, CINAHL, INSPEC, and ACM Digital Library from the earliest date available to February 2022 was conducted. Two authors independently scanned the search results for eligible publications, and reporting followed the PRISMA guidelines. Eligibility criteria included studies that reported impacts of HIS on patient flow outcomes. Information on the study design, type of HIS, key features and impacts was extracted and analysed using an analytical framework which was based on domain-expert opinions and literature review. Results: Overall, 5996 titles were identified, with 44 eligible studies, across 17 types of HIS. 22 studies (50%) focused on patient flow in the department level such as emergency department while 18 studies (41%) focused on hospital-wide level and four studies (9%) investigated network-wide HIS. Process outcomes with time-related measures such as 'length of stay' and 'waiting time' were investigated in most of the studies. In addition, HISs were found to address flow problems by identifying blockages, streamlining care processes and improving care coordination. Conclusion: HIS affected various aspects of patient flow at different levels of care; however, how and why they delivered the impacts require further research. [ABSTRACT FROM AUTHOR]

Length of Stay (LOS) is an important performance metric in Australian Emergency Departments (EDs). Recent evidence suggests that an LOS in excess of 4 h may be associated with increased mortality, but despite this, the average LOS continues to remain greater than 4 h in many EDs. Previous studies have found that Data Mining (DM) can be used to help hospitals to manage this metric and there is continued research into identifying factors that cause delays in ED LOS. Despite this, there is still a lack of specific research into how DM could use these factors to manage ED LOS. This study adds to the emerging literature and offers evidence that it is possible to predict delays in ED LOS to offer Clinical Decision Support (CDS) by using DM. Sixteen potentially relevant factors that impact ED LOS were identified through a literature survey and subsequently used as predictors to create six Data Mining Models (DMMs). An extract based on the Victorian Emergency Minimum Dataset (VEMD) was used to obtain relevant patient details and the DMMs were implemented using the Weka Software. The DMMs implemented in this study were successful in identifying the factors that were most likely to cause ED LOS > 4 h and also identify their correlation. These DMMs can be used by hospitals, not only to identify risk factors in their EDs that could lead to ED LOS > 4 h, but also to monitor these factors over time. [ABSTRACT FROM AUTHOR]

The proposed framework with integrated features of case-based reasoning and fuzzy multicriteria decision making techniques along with its usefulness for intelligent decision support and tropical cyclone forecasting is discussed.

Background: Type 2 diabetes (T2D) is increasingly prevalent in society, in part because of behavioral issues, with sedentary behavior, reduced exercise, and the consumption of foods with a high glycemic index being major contributors. There is evidence for the efficacy of mobile apps in promoting behavior change and lifestyle improvements in people with T2D. Many mobile phone apps help to monitor the condition of people with T2D and inform them about their health. Some of these digital interventions involve patients using apps on their own or in conjunction with health care professionals. Objective: This study aimed to test the acceptability of receiving app-based, daily physician feedback for patients with T2D that is informed by the continuous monitoring of their activity, food choices, and glucose profiles, with the aim of encouraging healthier behavior. The GLOOK! app was designed and developed by an academic research team and pilot-tested at an Australian public hospital. Methods: A total of 15 patients diagnosed with T2D wore a glucose monitor and an Apple Watch for 12 days. The uploaded data were integrated into the GLOOK! app on the patients’ smartphones, which also enabled the recording of activity and consumed food. A physician provided daily feedback to each individual through the app based on their data from each of the 12 days. At the beginning and end of the study, data were collected on vital signs, anthropometry, hemoglobin A1c level, fructosamine level, and fasting lipids level. Participants were also interviewed at the beginning and end of the study to assess the acceptability of the intervention and its potential impact on promoting positive behavior change. Results: Over the 12 days of the study, there was a significant reduction of 0.22% (P=.004) in hemoglobin A1c level. There were favorable changes in fructosamine and lipid fractions; however, none reached significance. There was also a fall of 0.65 kg in body weight and falls in blood pressure and pulse rate that did not reach significance. Patient feedback on the GLOOK! system was positive. Of the 15 participants, 13 (87%) were enthusiastic about continuing to use the app system if some usability and reliability aspects were improved. All participants regarded the personalized physician feedback as supportive and helpful in understanding their own health behavior. Of the 15 participants, 4 (27%) felt that using the system encouraged long-term behavior changes. Conclusions: A mobile app system that provides people with T2D daily, physician-generated, personalized feedback can produce favorable changes in glycemic and cardiovascular risk parameters—even in the short term—and encourage better self-management of their condition. Study participants found the experience of using the mobile app system acceptable and were motivated to establish longer-term lifestyle improvements through behavior changes. [ABSTRACT FROM AUTHOR]

Background: The capability of an interprofessional healthcare team to reach a shared understanding through group reasoning is critical to good healthcare delivery. Models for clinical reasoning have proved useful but remain focused on individual cognitive processes. Whilst interprofessional education has steadily gained real-world traction, it is unclear how interprofessional student groups practice group reasoning when performing online tasks. Method: We analyzed the group reasoning processes with two teams of health professional students in an online interprofessional education task (n = 13). Two simulated interprofessional team meetings about a palliative case were audio recorded, transcribed, and deductively analyzed to determine the mechanisms of team deliberation using a previously published study of group reasoning. Results: The reasoning mechanisms outlined in a previous study (informationaccumulating, sense-making, and decision-making) were evident in an analysis of student group reasoning. In particular, students focused on sharing and agreeing on information, and to a lesser extent, recording information. Conclusion: Attention to the mechanisms of action may be useful to facilitate teaching interprofessional reasoning. Group reasoning may benefit from focusing student attention on these stages: 1) prioritizing and sequencing of options, methods for exposing agreement about shared information, shared understanding of the situation, and options; 2) techniques for critically evaluating information so that opportunities arise to identify when information may disrupt existing understandings; and 3) development of documentation tools to assist recording of the process. [ABSTRACT FROM AUTHOR]

Background: Low back pain (LBP) remains the leading cause of disability worldwide. A better understanding of the beliefs regarding LBP and impact of LBP on the individual is important in order to improve outcomes. Although personal experiences of LBP have traditionally been explored through qualitative studies, social media allows access to data from a large, heterogonous, and geographically distributed population, which is not possible using traditional qualitative or quantitative methods. As data on social media sites are collected in an unsolicited manner, individuals are more likely to express their views and emotions freely and in an unconstrained manner as compared to traditional data collection methods. Thus, content analysis of social media provides a novel approach to understanding how problems such as LBP are perceived by those who experience it and its impact.Objective: The objective of this study was to identify contextual variables of the LBP experience from a first-person perspective to provide insights into individuals' beliefs and perceptions.Methods: We analyzed 896,867 cleaned tweets about LBP between January 1, 2014, and December 31, 2018. We tested and compared latent Dirichlet allocation (LDA), Dirichlet multinomial mixture (DMM), GPU-DMM, biterm topic model, and nonnegative matrix factorization for identifying topics associated with tweets. A coherence score was determined to identify the best model. Two domain experts independently performed qualitative content analysis of the topics with the strongest coherence score and grouped them into contextual categories. The experts met and reconciled any differences and developed the final labels.Results: LDA outperformed all other algorithms, resulting in the highest coherence score. The best model was LDA with 60 topics, with a coherence score of 0.562. The 60 topics were grouped into 19 contextual categories. "Emotion and beliefs" had the largest proportion of total tweets (157,563/896,867, 17.6%), followed by "physical activity" (124,251/896,867, 13.85%) and "daily life" (80,730/896,867, 9%), while "food and drink," "weather," and "not being understood" had the smallest proportions (11,551/896,867, 1.29%; 10,109/896,867, 1.13%; and 9180/896,867, 1.02%, respectively). Of the 11 topics within "emotion and beliefs," 113,562/157,563 (72%) had negative sentiment.Conclusions: The content analysis of tweets in the area of LBP identified common themes that are consistent with findings from conventional qualitative studies but provide a more granular view of individuals' perspectives related to LBP. This understanding has the potential to assist with developing more effective and personalized models of care to improve outcomes in those with LBP. [ABSTRACT FROM AUTHOR]

Smartphones and their built-in sensors provide many opportunities for health monitoring. E-health as a new industrial phenomenon and a field of research integrates medical informatics, public health and healthcare business, aiming to facilitate the provision of more accessible healthcare services, such as remote health monitoring, reducing healthcare costs and enhancing patient experience. Conclusions This Special Issue was created to collate original research papers and review articles that explore new techniques, solutions and applications in e-health and mobile health monitoring. [Extracted from the article]

Background: Digital health (DH) and the benefits of related services are fairly well understood. However, it still is critical to map the digital health care landscape including the key elements that define it as an ecosystem. Particularly, knowing the perspectives of citizens on this digital transformation is an important angle to capture. In this review we aim to analyze the relevant studies to identify how DH is understood and experienced by Australian citizens and what they may require from DH platforms. Materials and methods: A scoping literature review was conducted across several electronic databases (ACM Digital Library, OVID, PubMed, Scopus, IEEE, Science Direct, SAGE), as well as grey literature. Additionally, citation mining was conducted to identify further relevant studies. Identified studies were subjected to eligibility criteria and the final set of articles was independently reviewed, analyzed, discussed and interpreted by three reviewers. Results: Of 3811 articles, 98 articles met the inclusion criteria with research-based articles–as opposed to review articles or white papers– comprising the largest proportion (72%) of the selected literature. The qualitative analysis of the literature revealed five key elements that capture the essence of the digital health ecosystem interventions from the viewpoint of the Australian citizens. The identified elements were "consumer/user", "health care", "technology", "use and usability", "data and information". These elements were further found to be associated with 127 subcategories. Conclusions: This study is the first of its kind to analyze and synthesize the relevant literature on DH ecosystems from the citizens' perspective. Through the lens of two research questions, this study defines the key components that were found crucial to understanding citizens' experiences with DH. This understanding lays a strong foundation for designing and fostering DH ecosystem. The results provide a solid ground for empirical testing. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to assess the opportunity for a distributed, networked open biomedical repository (OBR) using a knowledge management system (KMS) conceptual framework. An innovative KMS conceptual framework is proposed to guide the transition from a traditional, siloed approach to a sustainable OBR. Design/methodology/approach: This paper reports on a cycle of action research, involving literature review, interviews and focus group with leaders in biomedical research, open science and librarianship, and an audit of elements needed for an Australasian OBR; these, along with an Australian KM standard, informed the resultant KMS framework. Findings: The proposed KMS framework aligns the requirements for an OBR with the people, process, technology and content elements of the KM standard. It identifies and defines nine processes underpinning biomedical knowledge – discovery, creation, representation, classification, storage, retrieval, dissemination, transfer and translation. The results comprise an explanation of these processes and examples of the people, process, technology and content dimensions of each process. While the repository is an integral cog within the collaborative, distributed open science network, its effectiveness depends on understanding the relationships and linkages between system elements and achieving an appropriate balance between them. Research limitations/implications: The current research has focused on biomedicine. This research builds on the worldwide effort to reduce barriers, in particular paywalls to health knowledge. The findings present an opportunity to rationalize and improve a KMS integral to biomedical knowledge. Practical implications: Adoption of the KMS framework for a distributed, networked OBR will facilitate open science through reducing duplication of effort, removing barriers to the flow of knowledge and ensuring effective management of biomedical knowledge. Social implications: Achieving quality, permanency and discoverability of a region's digital assets is possible through ongoing usage of the framework for researchers, industry and consumers. Originality/value: The framework demonstrates the dependencies and interplay of elements and processes to frame an OBR KMS. [ABSTRACT FROM AUTHOR]

Objectives: We hypothesise that patients have a positive sentiment regarding biological/targeted synthetic disease modifying anti-rheumatic drugs (b/tsDMARDs) and a negative sentiment towards conventional synthetic agents (csDMARDs). We analysed discussions on social media platforms regarding DMARDs to understand the collective sentiment expressed towards these medications.Methods: Treato analytics were used to download all available posts on social media about DMARDs in the context of rheumatoid arthritis. Strict filters ensured that user generated content was downloaded. The sentiment (positive or negative) expressed in these posts was analysed for each DMARD using sentiment analysis. We also analysed the reason(s) for this sentiment for each DMARD, looking specifically at efficacy and side effects.Results: Computer algorithms analysed millions of social media posts and included 54 742 posts about DMARDs. We found that both classes had an overall positive sentiment. The ratio of positive to negative posts was higher for b/tsDMARDs (1.210) than for csDMARDs (1.048). Efficacy was the most commonly mentioned reason in posts with a positive sentiment and lack of efficacy was the most commonly mentioned reason for a negative sentiment. These were followed by the presence/absence of side effects in negative or positive posts, respectively.Conclusions: Public opinion on social media is generally positive about DMARDs. Lack of efficacy followed by side effects were the most common themes in posts with a negative sentiment. There are clear reasons why a DMARD generates a positive or negative sentiment, as the sentiment analysis technology becomes more refined, targeted studies could be done to analyse these reasons and allow clinicians to tailor DMARDs to match patient needs. [ABSTRACT FROM AUTHOR]

Social media is playing an increasingly central role in patient's decision-making process. Advances in technology have enabled meaningful interpretation of discussions on social media. We conducted a scoping review to assess whether Sentiment Analysis (SA), a big data analytic tool, could be used to extract meaningful themes from social media discussions on pharmacotherapy. A keyword search strategy was used on the following databases: One Search, PubMed, Medline, EMBASE, and Cochrane. One hundred and ninety-four titles were identified of which 10 studies were included. We extracted themes about uses and implications of SA of social media discussions on pharmacotherapy. Twitter was the most frequently analyzed platform. Assessment of public sentiment about a particular medication was the most common use of SA followed by detection of adverse drug reactions. Studies also revealed a significant impact of news media on public sentiment. Implications for real world practice include identifying reasons for a negative sentiment, detecting adverse drug reactions and using the impact of news media on social media sentiment to drive public health initiatives. The lack of a consistent approach to SA between the studies reflects the lack of a gold standard for the technology and consequently the need for future research. Sentiment Analysis is a promising technology that can allow us to better understand patient opinion regarding pharmacotherapy. This knowledge can be used to improve patient safety, patient-physician interaction, and also enhance the delivery of public health measures. [ABSTRACT FROM AUTHOR]

Scholarly publishing has undergone major changes over the past 50 years. Funder mandates and organisational reporting obligations have heralded the creation of open access repositories, such as institutional and subject repositories. This research draws upon the US PubMed Central (PMC) and Europe PMC, also known as PMC International, as a role model to inform the concept and opportunity for an Australasia open access biomedical repository. PMC International is a leader in making citations and research output, which link to research data, Findable, Accessible, Interoperable and Reusable (FAIR). As repositories approach two decades of development, this paper reports on the potential for an Australasia open access biomedical repository through a knowledge management lens and explores the opportunities for future open access biomedical repositories. [ABSTRACT FROM AUTHOR]

Open biomedical repositories, such as PubMed Central (PMC), are a means to make research discoverable and permanently accessible. Assessing the potential interest of key stakeholders in an Australasia PubMed Central was the objective of this research. The investigation is novel, assisting in the development of open science infrastructure through its systematic analysis of the potential interest in, and viability of a biomedical repository for managing openly accessible research outputs for the Australasia region. The research adopted a qualitative approach based on semi-structured interviews and a focus group. Forty-four stakeholders located throughout Australia and New Zealand participated in the research. Participants expanded upon their experience of PubMed, MEDLINE, PMC and their use of information resources for research and clinical practice. The Evidence Based Healthcare (EBHC) pyramid was the theoretical model adopted to explain open biomedical repository processes. A strengths, weaknesses, opportunities and threats (SWOT) analysis identified support for exploring membership of an international PMC system, in particular Europe PMC. Lessons learnt from PMC US, Europe PMC and PMC Canada (collectively known as PubMed Central International) informed the investigation. A major strength identified was that PubMed Central International has been able to achieve high levels of compliance way beyond that of most institutional repositories. A great threat faced is overcoming the difficulties of working together with other major world bodies and financially sustaining an Australasia PMC. Improving Australasian biomedical knowledge management processes may be possible from adopting a PMC for retrieving and transferring research, linked to the data underlying the research. This in turn could help put regional research under a brighter spotlight, potentially leading to improvements in research quality. There is an opportunity for a potential Australasia PMC to harvest biomedical research from the National Library of Australia’s aggregator database, Trove and work closely with Europe PMC to avoid duplication of effort. Overall, establishment of an Australasia permanent biomedical digital open repository is perceived as important, with significant potential flow-on benefits to healthcare, industry and society. [ABSTRACT FROM AUTHOR]

The effectiveness, efficiency and reliability of health services depends on the quality of data in health information systems used by health care providers, managers and decision-makers. The aim of this research-in-progress paper is to propose a data quality framework, method and set of tools to help an information management team at a health care institution to monitor data quality as part of their business intelligence process. The proposed tool has the capacity to capture and visualise different stakeholder perceptions of data quality depending on the user role and the task they are responsible for. This study uses an action case method to develop a prototype of the tool and provide an illustration of its implementation and usage by multiple stakeholders. [ABSTRACT FROM AUTHOR]

During the last few decades, research into sustainability aspects of ICT grew rapidly for most organisational and business processes. Evolution towards the development of infrastructure for Smart Cities, Green ICT with application of decision support systems (DSSs) created opportunities for solving complex problems, for example, sensor-enabled smart waste management. This paper focuses on supporting decisions for sustainable garbage collection route planning and optimisation. The objective is to create a practical solution for reducing CO2 emissions and reduce negative impact on the environment as a result. From the design science perspective, a DSS is proposed, researched and developed to increase the effectiveness of municipal waste management. The paper also evaluates this approach against the Green IS framework. [ABSTRACT FROM AUTHOR]

An increasing number of individuals are now living with some form of chronic, long-term condition (LTC). The holistic perspective of LTCs makes it important to acknowledge that priorities and decisions are in fluctuation over the course of an individual’s life. The landscape of digital healthcare is full of information systems that capture individuals’ health data, clinical guidelines and/or advice on health conditions, which taken together can help create a comprehensive overview of suitable lifestyle choices to optimise health and well-being. Despite this, there is no evidence of existing frameworks to support individuals living with LTCs from a continuum of care perspective. In this paper, we propose such a multidimensional model for a decision support tool - LTC-Choices. This tool was developed from existing work conducted by the authors around use of multicriteria to support health decision-making. We illustrate how LTC-Choices can be implemented using the example of individuals living post-stroke. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated that Multi-Disciplinary Meetings (MDM) practiced in some medical contexts can contribute to positive health care outcomes. The group reasoning and decision-making in MDMs has been found to be most effective when deliberations revolve around the patient’s needs, comprehensive information is available during the meeting, core members attend and the MDM is effectively facilitated. This article presents a case study of the MDMs in cancer care in a region of Australia. The case study draws on a group reasoning model called the Reasoning Community model to analyse MDM deliberations to illustrate that many factors are important to support group reasoning, not solely the provision of pertinent information. The case study has implications for the use of data analytics in any group reasoning context. [ABSTRACT FROM PUBLISHER]

Business Intelligence (BI) systems are an important part of many organisations’ IT portfolios. While the evolutionary nature of other kinds of decision support technology has been noted, there is little research investigating the evolutionary nature of BI systems. This paper presents a case study of a BI system development in a large Australian healthcare institution and uses evolutionary theories from decision support systems (DSS) to understand the system evolution observed. The paper concludes that the theories describing evolution in DSS can also be effectively applied to BI as well. BI practitioners and developers should be aware of evolutionary triggers, as well as the different kinds of evolution that can affect BI system evolution. [ABSTRACT FROM PUBLISHER]

Taxonomy learning is an important task for knowledge acquisition, sharing, and classification as well as application development and utilization in various domains. To reduce human effort to build a taxonomy from scratch and improve the quality of the learned taxonomy, we propose a new taxonomy learning approach, named TaxoFinder. TaxoFinder takes three steps to automatically build a taxonomy. First, it identifies domain-specific concepts from a domain text corpus. Second, it builds a graph representing how such concepts are associated together based on their co-occurrences. As the key method in TaxoFinder, we propose a method for measuring associative strengths among the concepts, which quantify how strongly they are associated in the graph, using similarities between sentences and spatial distances between sentences. Lastly, TaxoFinder induces a taxonomy from the graph using a graph analytic algorithm. TaxoFinder aims to build a taxonomy in such a way that it maximizes the overall associative strengths among the concepts in the graph to build a taxonomy. We evaluate TaxoFinder using gold-standard evaluation on three different domains: emergency management for mass gatherings, autism research, and disease domains. In our evaluation, we compare TaxoFinder with a state-of-the-art subsumption method and show that TaxoFinder is an effective approach significantly outperforming the subsumption method. [ABSTRACT FROM AUTHOR]

Location information is one of key pieces of information for real-time decision support systems that are used in emergency management. In the context of mass gatherings, mobile and location-aware Decision Support Systems (DSS) can significantly improve split-second decision making, facilitate coordination of team members and reduce medical response time. While existing location-aware DSS enable tracking patients, medical emergency staff and resources, they have limited or no support for semantic querying and reasoning. Semantic search provides the ability to produce more precise and accurate results compared to the syntax-based search. The use of ontologies in DSS not only enables capturing and sharing the domain knowledge but provides the capability to perform semantic querying and reasoning. In this paper, we introduce a mobile and location-aware DSS architecture for medical emergency management in mass gatherings that integrates a domain ontology and provides reasoning and semantic research capabilities. The implementation and evaluation of the proposed architecture using an Android phone are described. [ABSTRACT FROM PUBLISHER]

This paper explores inclusive research design (IRD) as an integral component in what we term community partnership research in the information management and systems field. It aims to unravel the challenges and complexities of the double hermeneutic spiral associated with designing and doing this type of research in order to identify principles and processes appropriate to the engagement of all partners – in the community that provides the setting for the research and in the academy – in the iterative, ongoing design of the research. The research reported in the paper was undertaken within an interpretivist framework, using literary warrant analysis, autoethnography and a second generation grounded theory approach to data analysis. It involved in-depth analysis of our experiences over the last 10 years in community partnership research relating to smart health information portals, including reflection on our particular roles and interests in the research, as well as our changing needs, values and expectations. The paper concludes by presenting an IRD framework for community partnership research in information management and systems. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports published within the issue on topics including community engagement in information studies, community collaborative research partnerships and inclusive research design.

Investment in knowledge management (KM) programmes is often contentious due to the challenge of meeting the need for continuous and sustainable benefits realization. In KM, the word 'sustainable' describes how programmes of strategies to leverage organizational knowledge remain productive over time: that they deliver strategic value to the organization. The focus of this paper falls on the constructs of the governance of KM and on its leadership, in KM strategy development and implementation, including risk management, financial controls and transparent evaluation mechanisms for continuous benefits realization. This paper presents a KM governance model and explores its recent validation through six case studies of large, distributed, multinational organizations, and through these introduces the granular constructs of KM governance. Exploration of the impact of KM governance on developing and implementing KM programmes in each case study organization demonstrates that there is a clear nexus between strategic KM governance and benefits realized from those programmes. [ABSTRACT FROM AUTHOR]