Advances in E-Health and Mobile 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 [...].

Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining.

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.

Investigating Individuals' Perceptions Regarding the Context Around the Low Back Pain Experience: Topic Modeling Analysis of Twitter Data.

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.

Mining social media data to investigate patient perceptions regarding DMARD pharmacotherapy for rheumatoid arthritis.

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.

Electronic Skin Wearable Sensors for Detecting Lumbar-Pelvic Movements.

BACKGROUND: A nanomaterial-based electronic-skin (E-Skin) wearable sensor has been successfully used for detecting and measuring body movements such as finger movement and foot pressure. The ultrathin and highly sensitive characteristics of E-Skin sensor make it a suitable alternative for continuously out-of-hospital lumbar-pelvic movement (LPM) monitoring. Monitoring these movements can help medical experts better understand individuals' low back pain experience. However, there is a lack of prior studies in this research area. Therefore, this paper explores the potential of E-Skin sensors to detect and measure the anatomical angles of lumbar-pelvic movements by building a linear relationship model to compare its performance to clinically validated inertial measurement unit (IMU)-based sensing system (ViMove). METHODS: The paper first presents a review and classification of existing wireless sensing technologies for monitoring of body movements, and then it describes a series of experiments performed with E-Skin sensors for detecting five standard LPMs including flexion, extension, pelvic tilt, lateral flexion, and rotation, and measure their anatomical angles. The outputs of both E-Skin and ViMove sensors were recorded during each experiment and further analysed to build the comparative models to evaluate the performance of detecting and measuring LPMs. RESULTS: E-Skin sensor outputs showed a persistently repeating pattern for each movement. Due to the ability to sense minor skin deformation by E-skin sensor, its reaction time in detecting lumbar-pelvic movement is quicker than ViMove by ~1 s. CONCLUSIONS: E-Skin sensors offer new capabilities for detecting and measuring lumbar-pelvic movements. They have lower cost compared to commercially available IMU-based systems and their non-invasive highly stretchable characteristic makes them more comfortable for long-term use. These features make them a suitable sensing technology for developing continuous, out-of-hospital real-time monitoring and management systems for individuals with low back pain.

Exploring reasoning mechanisms in ward rounds: a critical realist multiple case study.

BACKGROUND: Ward rounds are an important and ubiquitous element of hospital care with a history extending well over a century. Although originally intended as a means of educating medical trainees and junior doctors, over time they have become focused on supporting clinical practice. Surprisingly, given their ubiquity and importance, they are under-researched and inadequately understood. This study aims to contribute knowledge in human reasoning within medical teams, meeting a pressing need for research concerning the reasoning occurring in rounds. METHODS: The research reported here aimed to improve the understanding of ward round reasoning by conducting a critical realist case study exploring the collaborative group reasoning mechanisms in the ward rounds of two hospitals in Victoria, Australia. The data collection involved observing rounds, interviewing medical practitioners and holding focus group meetings. RESULTS: Nine group reasoning mechanisms concerning sharing, agreeing and recording information in the categories of information accumulation, sense-making and decision-making were identified, together forming a program theory of ward round reasoning. In addition, themes spanning across mechanisms were identified, further explaining ward round reasoning and suggesting avenues for future exploration. Themes included the use of various criteria, tensions involving mechanisms, time factors, medical roles and hierarchies. CONCLUSIONS: This paper contributes to the literature by representing rounds in a manner that strengthens understanding of the form of the group reasoning occurring within, thus supporting theory-based evaluation strategies, redesigned practices and training enhancements.

Sharpening clinical decision support alert and reminder designs with MINDSPACE: A systematic review.

BACKGROUND: Clinical decision support (CDS) alerts and reminders aim to influence clinical decisions, yet they are often designed without considering human decision-making behaviour. While this behaviour is comprehensively described by behavioural economics (BE), the sheer volume of BE literature poses a challenge to designers when identifying behavioural effects with utility to alert and reminder designs. This study tackles this challenge by focusing on the MINDSPACE framework for behaviour change, which collates nine behavioural effects that profoundly influence human decision-making behaviour: Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitment, and Ego. METHOD: A systematic review searching MEDLINE, Embase, PsycINFO, and CINAHL Plus to explore (i) the usage of MINDSPACE effects in alert and reminder designs and (ii) the efficacy of those alerts and reminders in influencing clinical decisions. The search queries comprised ten Boolean searches, with nine focusing on the MINDSPACE effects and one focusing on the term mindspace. RESULTS: 50 studies were selected from 1791 peer-reviewed journal articles in English from 1970 to 2022. Except for ego, eight of nine MINDSPACE effects were utilised to design alerts and reminders, with defaults and norms utilised the most in alerts and reminders, respectively. Overall, alerts and reminders informed by MINDSPACE effects showed an average 71% success rate in influencing clinical decisions (alerts 73%, reminders 69%). Most studies utilised a single effect in their design, with higher efficacy for alerts (64%) than reminders (41%). Others utilised multiple effects, showing higher efficacy for reminders (28%) than alerts (9%). CONCLUSION: This review presents sufficient evidence demonstrating the MINDSPACE framework's merits for designing CDS alerts and reminders with human decision-making considerations. The framework can adequately address challenges in identifying behavioural effects pertinent to the effective design of CDS alerts and reminders. The review also identified opportunities for future research into other relevant effects (e.g., framing).

Understanding the drivers on medical workloads: an analysis of spectators at the Australian Football League.

OBJECTIVE: The present study was designed to further understand the psychosocial drivers of crowds impacting on the demand for healthcare. This involved analysing different spectator crowds for medical usage at mass gatherings; more specifically, did different football team spectators (of the Australian Football League) generate different medical usage rates. METHODS: In total, 317 games were analysed from 10 venues over 2 years. Data were analysed by the ANOVA and Pearson correlation tests. RESULTS; Spectators who supported different football teams generated statistically significant differences in patient presentation rates (PPR) (F15, 618=1.998, P=0.014). The present study confirmed previous findings that there is a positive correlation between the crowd size and PPR at mass gatherings but found a negative correlation between density and PPR (r = -0.206, n=317, P<0.0005). CONCLUSIONS: The present study has attempted to scientifically explore psychosocial elements of crowd behaviour as a driver of demand for emergency medical care. In measuring demand for emergency medical services there is a need to develop a more sophisticated understanding of a variety of drivers in addition to traditional metrics such as temperature, crowd size and other physical elements. In this study we saw that spectators who supported different football teams generated statistically significant differences in PPR. What is known about this topic? Understanding the drivers of emergency medical care is most important in the mass gathering setting. There has been minimal analysis of psychological 'crowd' variables. What does this paper add? This study explores the psychosocial impact of supporting a different team on the PPR of spectators at Australian Football League matches. The value of collecting and analysing these types of data sets is to support more balanced planning, better decision support and knowledge management, and more effective emergency medical demand management. What are the implications for practitioners? This information further expands the body of evidence being created to understand the drivers of emergency medical demand and usage. In addition, it supports the planning and management of emergency medical and health-related requirements by increasing our understanding of the effect of elements of 'crowd' that impact on medical usage and emergency healthcare.

A digital health ecosystem ontology from the perspective of Australian consumers: a mixed-method literature analysis.

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.

Vaccine Adverse Event Mining of Twitter Conversations: 2-Phase Classification Study.

BACKGROUND: Traditional monitoring for adverse events following immunization (AEFI) relies on various established reporting systems, where there is inevitable lag between an AEFI occurring and its potential reporting and subsequent processing of reports. AEFI safety signal detection strives to detect AEFI as early as possible, ideally close to real time. Monitoring social media data holds promise as a resource for this. OBJECTIVE: The primary aim of this study is to investigate the utility of monitoring social media for gaining early insights into vaccine safety issues, by extracting vaccine adverse event mentions (VAEMs) from Twitter, using natural language processing techniques. The secondary aims are to document the natural language processing techniques used and identify the most effective of them for identifying tweets that contain VAEM, with a view to define an approach that might be applicable to other similar social media surveillance tasks. METHODS: A VAEM-Mine method was developed that combines topic modeling with classification techniques to extract maximal VAEM posts from a vaccine-related Twitter stream, with high degree of confidence. The approach does not require a targeted search for specific vaccine reaction-indicative words, but instead, identifies VAEM posts according to their language structure. RESULTS: The VAEM-Mine method isolated 8992 VAEMs from 811,010 vaccine-related Twitter posts and achieved an F1 score of 0.91 in the classification phase. CONCLUSIONS: Social media can assist with the detection of vaccine safety signals as a valuable complementary source for monitoring mentions of vaccine adverse events. A social media-based VAEM data stream can be assessed for changes to detect possible emerging vaccine safety signals, helping to address the well-recognized limitations of passive reporting systems, including lack of timeliness and underreporting.

Understanding the impacts of health information systems on patient flow management: A systematic review across several decades of research.

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.

Physician-Authored Feedback in a Type 2 Diabetes Self-management App: Acceptability Study.

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.

Understanding digital health ecosystem from Australian citizens' perspective: A scoping review.

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.

Sentiment analysis of social media posts on pharmacotherapy: A scoping review.

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: OneSearch, 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.

A systematic literature review on unnecessary diagnostic testing: The role of ICT use.

BACKGROUND: The negative impact of unnecessary diagnostic tests on healthcare systems and patients has been widely recognized. Medical researchers in various countries have been devoting effort to reduce unnecessary diagnostic tests by using different types of interventions, including information and communications technology-based (ICT-based) intervention, educational intervention, audit and feedback, the introduction of guidelines or protocols, and the reward and punishment of staff. We conducted a review of ICT based interventions and a comparative analysis of their relative effectiveness in reducing unnecessary tests. METHOD: A systematic Boolean search in PubMed, EMBase and EBSCOhost research databases was performed. Keyword search and citation analysis were also conducted. Empirical studies reporting ICT based interventions, and their implications on relative effectiveness in reducing unnecessary diagnostic tests (pathology tests or medical imaging) were evaluated independently by two reviewers based on a rigorously developed coding protocol. RESULTS: 92 research articles from peer-reviewed journals were identified as eligible. 47 studies involved a single-method intervention and 45 involved multi-method interventions. Regardless of the number of interventions involved in the studies, ICT-based interventions were utilized by 71 studies and 59 of them were shown to be effective in reducing unnecessary testing. A clinical decision support (CDS) tool appeared to be the most adopted ICT approach, with 46 out of 71 studies using CDS tools. The CDS tool showed effectiveness in reducing test volume in 38 studies and reducing cost in 24 studies. CONCLUSIONS: This review investigated five frequently utilized intervention methods, ICT-based, education, introduction of guidelines or protocols, audit and feedback, and reward and punishment. It provides in-depth analysis of the efficacy of different types of interventions and sheds insights about the benefits of ICT based interventions, especially those utilising CDS tools, to reduce unnecessary diagnostic testing. The replicability of the studies is limited due to the heterogeneity of the studies in terms of context, study design, and targeted types of tests.

'There is a very distinct need for education' among people with rotator cuff tendinopathy: An exploration of health professionals' attitudes.

BACKGROUND: Clinical practice guidelines recommend non-surgical care in the management of rotator cuff tendinopathy prior to considering imaging or surgery. However, this requires effective education to promote adherence to treatment. OBJECTIVES: To explore expert shoulder clinician's experiences with managing rotator cuff tendinopathy including practice beliefs towards providing education. DESIGN: An in-depth qualitative study. METHOD: We conducted interviews (n = 8) with an international sample of expert shoulder clinician-researchers. Data were analysed using an inductive thematic approach with constant comparison. RESULTS: Three key themes emerged: (1) The need for early, focused education: "Some beliefs can be detrimental to rehabilitation options", (2) Developing therapeutic alliance: "If a patients trust you then you are generally going to get much better results" and (3) What is required moving forward in current day RT management: "Maybe we can get better." CONCLUSIONS: Our findings highlight the importance of education to alleviate potential barriers to effective conservative care (including exercise) and self-management for rotator cuff tendinopathy. We also identified actionable ways to promote a collaborative therapeutic alliance however, this hinges on sufficient clinical time to educate patients adequately, which may be a barrier in busy clinical settings. Further, there is need for targeted education to facilitate development of clinical skills required to implement effective patient education strategies.

With open science gaining traction, do we need an Australasia PubMed Central (PMC)? A qualitative investigation.

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.

Personalized Mobile Tool AskPCOS Delivering Evidence-Based Quality Information about Polycystic Ovary Syndrome.

Despite polycystic ovary syndrome (PCOS) being the most common endocrine condition affecting reproductive-aged women, studies have shown the information needs of PCOS consumers are not currently met. The expressed need by women with PCOS for accessible, evidence-based personalized PCOS information informed the design and development of the PCOS mobile tool-AskPCOS. The App provides a range of unique features such as: evidence-based PCOS health information, self-diagnostic function, a question prompt list to optimize health practitioner engagement, and a commonly asked questions list. A five-phase App development process involved extensive stakeholder consultation, system architecture design, development of the content repository, system prototyping, and evaluation. AskPCOS is the first evidence-based, consumer-driven mobile App developed by women and for women with PCOS and utilizes innovative technology to empower PCOS consumers and optimize health outcomes. The App content repository is enhanced by the best available evidence and expertise extracted from the International Evidence-Based Guideline in PCOS (2018). The AskPCOS App has extensive data capture capability through the usage of data gathering functions and backend analytics to optimize responsiveness to consumer needs. In addition, the multilingual content and not-for-profit cost model is designed to facilitate worldwide adoption of the tool, and address PCOS information inequities in developing countries.

Investigating Subjective Experience and the Influence of Weather Among Individuals With Fibromyalgia: A Content Analysis of Twitter.

BACKGROUND: Little is understood about the determinants of symptom expression in individuals with fibromyalgia syndrome (FMS). While individuals with FMS often report environmental influences, including weather events, on their symptom severity, a consistent effect of specific weather conditions on FMS symptoms has yet to be demonstrated. Content analysis of a large number of messages by individuals with FMS on Twitter can provide valuable insights into variation in the fibromyalgia experience from a first-person perspective. OBJECTIVE: The objective of our study was to use content analysis of tweets to investigate the association between weather conditions and fibromyalgia symptoms among individuals who tweet about fibromyalgia. Our second objective was to gain insight into how Twitter is used as a form of communication and expression by individuals with fibromyalgia and to explore and uncover thematic clusters and communities related to weather. METHODS: Computerized sentiment analysis was performed to measure the association between negative sentiment scores (indicative of severe symptoms such as pain) and coincident environmental variables. Date, time, and location data for each individual tweet were used to identify corresponding climate data (such as temperature). We used graph analysis to investigate the frequency and distribution of domain-related terms exchanged in Twitter and their association strengths. A community detection algorithm was applied to partition the graph and detect different communities. RESULTS: We analyzed 140,432 tweets related to fibromyalgia from 2008 to 2014. There was a very weak positive correlation between humidity and negative sentiment scores (r=.009, P=.001). There was no significant correlation between other environmental variables and negative sentiment scores. The graph analysis showed that "pain" and "chronicpain" were the most frequently used terms. The Louvain method identified 6 communities. Community 1 was related to feelings and symptoms at the time (subjective experience). It also included a list of weather-related terms such as "weather," "cold," and "rain." CONCLUSIONS: According to our results, a uniform causal effect of weather variation on fibromyalgia symptoms at the group level remains unlikely. Any impact of weather on fibromyalgia symptoms may vary geographically or at an individual level. Future work will further explore geographic variation and interactions focusing on individual pain trajectories over time.