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Abstract Real-time locating systems (RTLS, also known as real-time location systems) have become an important component of many existing ubiquitous location aware systems. While GPS (global positioning system) has been quite successful as an outdoor real-time locating solution, it fails to repeat this success indoors. A number of RTLS technologies have been used to solve indoor tracking problems. The ability to accurately track the location of assets and individuals indoors has many applications in healthcare. This paper provides a condensed primer of RTLS in healthcare, briefly covering the many options and technologies that are involved, as well as the various possible applications of RTLS in healthcare facilities and their potential benefits, including capital expenditure reduction and workflow and patient throughput improvements. The key to a successful RTLS deployment lies in picking the right RTLS option(s) and solution(s) for the application(s) or problem(s) at hand. Where this application-technology match has not been carefully thought of, any technology will be doomed to failure or to achieving less than optimal results.

Abstract 'Wikification of GIS by the masses' is a phrase-term first coined by Kamel Boulos in 2005, two years earlier than Goodchild's term 'Volunteered Geographic Information'. Six years later (2005-2011), OpenStreetMap and Google Earth (GE) are now full-fledged, crowdsourced 'Wikipedias of the Earth' par excellence, with millions of users contributing their own layers to GE, attaching photos, videos, notes and even 3-D (three dimensional) models to locations in GE. From using Twitter in participatory sensing and bicycle-mounted sensors in pervasive environmental sensing, to creating a 100,000-sensor geo-mashup using Semantic Web technology, to the 3-D visualisation of indoor and outdoor surveillance data in real-time and the development of next-generation, collaborative natural user interfaces that will power the spatially-enabled public health and emergency situation rooms of the future, where sensor data and citizen reports can be triaged and acted upon in real-time by distributed teams of professionals, this paper offers a comprehensive state-of-the-art review of the overlapping domains of the Sensor Web, citizen sensing and 'human-in-the-loop sensing' in the era of the Mobile and Social Web, and the roles these domains can play in environmental and public health surveillance and crisis/disaster informatics. We provide an in-depth review of the key issues and trends in these areas, the challenges faced when reasoning and making decisions with real-time crowdsourced data (such as issues of information overload, "noise", misinformation, bias and trust), the core technologies and Open Geospatial Consortium (OGC) standards involved (Sensor Web Enablement and Open GeoSMS), as well as a few outstanding project implementation examples from around the world.

Abstract This paper covers the use of depth sensors such as Microsoft Kinect and ASUS Xtion to provide a natural user interface (NUI) for controlling 3-D (three-dimensional) virtual globes such as Google Earth (including its Street View mode), Bing Maps 3D, and NASA World Wind. The paper introduces the Microsoft Kinect device, briefly describing how it works (the underlying technology by PrimeSense), as well as its market uptake and application potential beyond its original intended purpose as a home entertainment and video game controller. The different software drivers available for connecting the Kinect device to a PC (Personal Computer) are also covered, and their comparative pros and cons briefly discussed. We survey a number of approaches and application examples for controlling 3-D virtual globes using the Kinect sensor, then describe Kinoogle, a Kinect interface for natural interaction with Google Earth, developed by students at Texas A&M University. Readers interested in trying out the application on their own hardware can download a Zip archive (included with the manuscript as additional files 1, 2, &3) that contains a 'Kinnogle installation package for Windows PCs'. Finally, we discuss some usability aspects of Kinoogle and similar NUIs for controlling 3-D virtual globes (including possible future improvements), and propose a number of unique, practical 'use scenarios' where such NUIs could prove useful in navigating a 3-D virtual globe, compared to conventional mouse/3-D mouse and keyboard-based interfaces. Additional file 1 Installation package for Kinoogle (part 1 of 3). Compressed (zipped) archive containing Kinoogle's installation package for Microsoft Windows operating systems. Download and unzip the contents of Additional file 1, Additional file 2, and Additional file 3 to the same hard drive location, then run 'Additional_file.part1.exe' from that location. Click here for file Additional file 2 Installation package for Kinoogle (part 2 of 3). Compressed (zipped) archive containing Kinoogle's installation package for Microsoft Windows operating systems. Download and unzip the contents of Additional file 1, Additional file 2, and Additional file 3 to the same hard drive location, then run 'Additional_file.part1.exe' from that location. Click here for file Additional file 3 Installation package for Kinoogle (part 3 of 3). Compressed (zipped) archive containing Kinoogle's installation package for Microsoft Windows operating systems. Download and unzip the contents of Additional file 1, Additional file 2, and Additional file 3 to the same hard drive location, then run 'Additional_file.part1.exe' from that location. Click here for file

Abstract The goal of visual analytics is to facilitate the discourse between the user and the data by providing dynamic displays and versatile visual interaction opportunities with the data that can support analytical reasoning and the exploration of data from multiple user-customisable aspects. This paper introduces geospatial visual analytics, a specialised subtype of visual analytics, and provides pointers to a number of learning resources about the subject, as well as some examples of human health, surveillance, emergency management and epidemiology-related geospatial visual analytics applications and examples of free software tools that readers can experiment with, such as Google Public Data Explorer. The authors also present a practical demonstration of geospatial visual analytics using partial data for 35 countries from a publicly available World Health Organization (WHO) mortality dataset and Microsoft Live Labs Pivot technology, a free, general purpose visual analytics tool that offers a fresh way to visually browse and arrange massive amounts of data and images online and also supports geographic and temporal classifications of datasets featuring geospatial and temporal components. Interested readers can download a Zip archive (included with the manuscript as an additional file) containing all files, modules and library functions used to deploy the WHO mortality data Pivot collection described in this paper.

Abstract Background The 1980s marked the occasion when Geographical Information System (GIS) technology was broadly introduced into the geo-spatial community through the establishment of a strong GIS industry. This technology quickly disseminated across many countries, and has now become established as an important research, planning and commercial tool for a wider community that includes organisations in the public and private health sectors. The broad acceptance of GIS technology and the nature of its functionality have meant that numerous datasets have been created over the past three decades. Most of these datasets have been created independently, and without any structured documentation systems in place. However, search and retrieval systems can only work if there is a mechanism for datasets existence to be discovered and this is where proper metadata creation and management can greatly help. This situation must be addressed through support mechanisms such as Web-based portal technologies, metadata editor tools, automation, metadata standards and guidelines and collaborative efforts with relevant individuals and organisations. Engagement with data developers or administrators should also include a strategy of identifying the benefits associated with metadata creation and publication. Findings The establishment of numerous Spatial Data Infrastructures (SDIs), and other Internet resources, is a testament to the recognition of the importance of supporting good data management and sharing practices across the geographic information community. These resources extend to health informatics in support of research, public services and teaching and learning. This paper identifies many of these resources available to the UK academic health informatics community. It also reveals the reluctance of many spatial data creators across the wider UK academic community to use these resources to create and publish metadata, or deposit their data in repositories for sharing. The Go-Geo! service is introduced as an SDI developed to provide UK academia with the necessary resources to address the concerns surrounding metadata creation and data sharing. The Go-Geo! portal, Geodoc metadata editor tool, ShareGeo spatial data repository, and a range of other support resources, are described in detail. Conclusions This paper describes a variety of resources available for the health research and public health sector to use for managing and sharing their data. The Go-Geo! service is one resource which offers an SDI for the eclectic range of disciplines using GIS in UK academia, including health informatics. The benefits of data management and sharing are immense, and in these times of cost restraints, these resources can be seen as solutions to find cost savings which can be reinvested in more research.

Abstract Background On 21 July 2004, the Healthcare Commission http://www.healthcarecommission.org.uk/ released its annual star ratings of the performance of NHS Primary Care Trusts (PCTs) in England for the year ending March 2004. The Healthcare Commission started work on 1 April 2004, taking over all the functions of the former Commission for Health Improvement http://www.chi.nhs.uk/, which had released the corresponding PCT ratings for 2002/2003 in July 2003. Results We produced two Web-based interactive maps of PCT star ratings, one for 2003 and the other for 2004 http://healthcybermap.org/PCT/ratings/, with handy functions like map search (by PCT name or part of it). The maps feature a colour-blind friendly quadri-colour scheme to represent PCT star ratings. Clicking a PCT on any of the maps will display the detailed performance report of that PCT for the corresponding year. Conclusion Using our Web-based interactive maps, users can visually appreciate at a glance the distribution of PCT performance across England. They can visually compare the performance of different PCTs in the same year and also between 2003 and 2004 (by switching between the synchronised 'PCT Ratings 2003' and 'PCT Ratings 2004' themes). The performance of many PCTs has improved in 2004, whereas some PCTs achieved lower ratings in 2004 compared to 2003. Web-based interactive geographical interfaces offer an intuitive way of indexing, accessing, mining, and understanding large healthcare information sets describing geographically differentiated phenomena. By acting as an enhanced alternative or supplement to purely textual online interfaces, interactive Web maps can further empower organisations and decision makers.

(1) Aims and Objectives: Mobile health (mHealth) is increasingly becoming a favorite healthcare delivery solution in underserved areas around the globe. This study aims to identify the influence of technology–organization–environment (TOE) factors on mHealth adoption and to assess the influence of mHealth on the reduction in health disparities in the context of healthcare delivery in low-resource settings. (2) Methods: A cross-sectional survey of physicians and nurses was carried out at six hospitals in the public and private health sectors in Pakistan. The survey's theoretical foundation is based on the technology–organization–environment (TOE) framework. TOE constructs (relative advantage, compatibility, management support, organizational readiness, external support, and government regulations) were used to develop hypotheses. The hypotheses were tested using structural equation modeling (SEM). (3) Results: Findings from this study show that management support and external support are the two main predictors of mHealth adoption among healthcare professionals. The study proposes an mHealth adoption model that can significantly contribute towards improving medical outcomes, reducing inefficiencies, expanding access, lowering costs, raising quality, making medicine more personalized for patients, and gaining advantages from mHealth solutions in order to reduce health disparities. (4) Conclusion: The study suggests that there is no single approach that could support mHealth adoption. Instead, a holistic approach is required that considers cultural, economic, technological, organizational, and environmental factors for successful mHealth adoption in low-resource settings. Our proposed mHealth model offers guidance to policymakers, health organizations, governments, and political leaders to make informed decisions regarding mHealth implementation plans. [ABSTRACT FROM AUTHOR]

Generative AI (artificial intelligence) refers to algorithms and models, such as OpenAI's ChatGPT, that can be prompted to generate various types of content. In this narrative review, we present a selection of representative examples of generative AI applications in medicine and healthcare. We then briefly discuss some associated issues, such as trust, veracity, clinical safety and reliability, privacy, copyrights, ownership, and opportunities, e.g., AI-driven conversational user interfaces for friendlier human-computer interaction. We conclude that generative AI will play an increasingly important role in medicine and healthcare as it further evolves and gets better tailored to the unique settings and requirements of the medical domain and as the laws, policies and regulatory frameworks surrounding its use start taking shape. [ABSTRACT FROM AUTHOR]

Digital games are an important class of eHealth interventions in diabetes, made possible by the Internet and a good range of affordable mobile devices (eg, mobile phones and tablets) available to consumers these days. Gamifying disease management can help children, adolescents, and adults with diabetes to better cope with their lifelong condition. Gamification and social in-game components are used to motivate players/patients and positively change their behavior and lifestyle. In this paper, we start by presenting the main challenges facing people with diabetes—children/adolescents and adults—from a clinical perspective, followed by three short illustrative examples of mobile and desktop game apps and platforms designed by Ayogo Health, Inc. (Vancouver, BC, Canada) for type 1 diabetes (one example) and type 2 diabetes (two examples). The games target different age groups with different needs—children with type 1 diabetes versus adults with type 2 diabetes. The paper is not meant to be an exhaustive review of all digital game offerings available for people with type 1 and type 2 diabetes, but rather to serve as a taster of a few of the game genres on offer today for both types of diabetes, with a brief discussion of (1) some of the underpinning psychological mechanisms of gamified digital interventions and platforms as self-management adherence tools, and more, in diabetes, and (2) some of the hypothesized potential benefits that might be gained from their routine use by people with diabetes. More research evidence from full-scale evaluation studies is needed and expected in the near future that will quantify, qualify, and establish the evidence base concerning this gamification potential, such as what works in each age group/patient type, what does not, and under which settings and criteria.

Background: Often combined with other traditional and non-traditional types of data, geospatial sensing data have a crucial role in public health studies. We conducted a systematic narrative review to broaden our understanding of the usage of big geospatial sensing, ancillary data, and related spatial data infrastructures in public health studies. Methods: English-written, original research articles published during the last ten years were examined using three leading bibliographic databases (i.e., PubMed, Scopus, and Web of Science) in April 2022. Study quality was assessed by following well-established practices in the literature. Results: A total of thirty-two articles were identified through the literature search. We observed the included studies used various data-driven approaches to make better use of geospatial big data focusing on a range of health and health-related topics. We found the terms 'big' geospatial data and geospatial 'big data' have been inconsistently used in the existing geospatial sensing studies focusing on public health. We also learned that the existing research made good use of spatial data infrastructures (SDIs) for geospatial sensing data but did not fully use health SDIs for research. Conclusions: This study reiterates the importance of interdisciplinary collaboration as a prerequisite to fully taking advantage of geospatial big data for future public health studies. [ABSTRACT FROM AUTHOR]

BackgroundGoogle AdWords are increasingly used to recruit people into research studies and clinical services. They offer the potential to recruit from targeted control areas in cluster randomized controlled trials (RCTs), but little is known about the feasibility of accurately targeting ads by location and comparing with control areas. ObjectiveTo examine the accuracy and contamination of control areas by a location-targeted online intervention using Google AdWords in a pilot cluster RCT. MethodsBased on previous use of online cognitive behavioral therapy for depression and population size, we purposively selected 16 of the 121 British postcode areas and randomized them to three intervention and one (do-nothing) control arms. Two intervention arms included use of location-targeted AdWords, and we compared these with the do-nothing control arm. We did not raise the visibility of our research website to normal Web searches. Users who clicked on the ad were directed to our project website, which collected the computer Internet protocol (IP) address, date, and time. Visitors were asked for their postcode area and to complete the Patient Health Questionnaire (depression). They were then offered links to several online depression resources. Google Analytics largely uses IP methods to estimate location, but AdWords uses additional information. We compared locations assessed by (1) Analytics, and (2) as self-identified by users. ResultsAds were shown 300,523 times with 4207 click-throughs. There were few site visits except through AdWord click-throughs. Both methods of location assessment agreed there was little contamination of control areas. According to Analytics, 69.75% (2617/3752) of participants were in intervention areas, only 0% (8/3752) in control areas, but 30.04% (1127/3752) in other areas. However, according to user-stated postcodes, only 20.7% (463/2237) were in intervention areas, 1% (22/2236) in control areas, but 78.31% (1751/2236) in other areas. Both location assessments suggested most leakage from the intervention arms was to nearby postcode areas. Analytics data differed from postcodes reported by participants. Analysis of a subset of 200/2236 records over 10 days comparing IP-estimated location with stated postcode suggested that Google AdWords targeted correctly in just half the cases. Analytics agreed with our assessment that, overall, one-third were wrongly targeted by AdWords. There appeared little evidence that people who bothered to give their postcode did not answer truthfully. ConclusionsAlthough there is likely to be substantial leakage from the targeted areas, if intervention and control areas are a sufficient distance apart, it is feasible to conduct a cluster RCT using online ads to target British postcode areas without significant contamination. Trial RegistrationClinicaltrials.gov NCT01469689; http://clinicaltrials.gov/ct2/show/NCT01469689 (Archived by WebCite at http://www.webcitation.org/681iro5OU)