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The COVID-19 pandemic has accelerated the adoption of telemedicine in healthcare. This study explores the feasibility of telemedicine for foot and ankle care in primary settings, using a mixed-methods approach with online questionnaires, focus groups, and interviews. Stakeholders, including patients, podiatrists, and senior healthcare managers, agreed on the need for a telemedicine service. Recommendations include creating evidence-based guidelines, providing professional training, and enhancing community education. The research highlights the necessity for structured telemedicine services, identifying gaps in existing pandemic responses and the need for further guidelines and training. [ABSTRACT FROM AUTHOR]

Machine Translation (MT) has emerged as a crucial tool in bridging language barriers. In health settings, MT is increasingly relevant due to the diversity of patient populations, the dominance of English in medical research, and the limited availability of human translation services. Improvements in MT accuracy have prompted a re-evaluation of its suitability in contexts where it was once deemed impractical. This scoping review with meta-analysis delved into the appropriateness and limitations of MT in health, including in medical education, literature translation, and patient-provider communication. A keyword search in PubMed, PubMed Central, and IEEE Xplore produced peer-reviewed literature that focused on MT in a health context, published from 2018 to 2023. Analysis and mapping of full-text articles revealed 33 studies among 2,589 returned abstracts, indicating that MT is still unsuitable for direct use in patient interactions, due to clinical risks linked to insufficient accuracy. However, MT was showing promise further away from patients, for translation of medical articles, terminology, and educational content. Further research in improving MT performance in these contexts, coverage of understudied languages, and study of the existing usages of MT are recommended. [ABSTRACT FROM AUTHOR]

Sleep quality is a critical factor in human health and well-being, with implications for various physiological and psychological processes. Traditional methods of sleep data collection are often limited by the quality and reliability of the data due to issues such as recall bias and subjective interpretation. This research aims to propose a novel framework that objectively measures and evaluates sleep quality using smart thermostats equipped with motion sensors, providing noninvasive and effortless sleep monitoring. The study conducts a comprehensive analysis of sleep patterns, exploring the relationship between activity sensors and sleep quality. By analyzing behavioral characteristics, the study identifies periods or clusters of days that require attention in terms of health and stress levels. The approach ensures privacy, ease of access, and integrates environmental factors, enabling a comprehensive understanding of an individual’s sleep health. The findings suggest that this zero-effort technology can significantly enhance sleep monitoring at both individual and population levels, with implications for health monitoring, stress management, and personalized healthcare interventions. Future work will focus on expanding the data set, incorporating more variables, and integrating contextual data to further improve sleep quality analysis and support real-time health interventions. [ABSTRACT FROM AUTHOR]

In Canada, extreme heat occurrences present significant risks to public health, particularly for vulnerable groups like older individuals and those with preexisting health conditions. Accurately predicting indoor temperatures during these events is crucial for informing public health strategies and mitigating the adverse impacts of extreme heat. While current systems rely on outdoor temperature data, incorporating real-time indoor temperature estimations can significantly enhance decision-making and strengthen overall health system responses. Sensor-based technologies, such as ecobee smart thermostats installed in homes, enable effortless collection of indoor temperature and humidity data. This study evaluates the efficacy of deep learning models in predicting indoor temperatures during heat waves using smart thermostat data, to enhance public health responses. Utilizing ecobee smart thermostats, we analyzed indoor temperature trends and developed forecasting models. Our findings indicate the potential of integrating IoT and deep learning into health warning systems, enabling proactive interventions, and improving sustainable health care practices in extreme heat scenarios. This approach highlights the role of digital health innovations in creating the resilient and sustainable healthcare systems against climate-related health adversities. [ABSTRACT FROM AUTHOR]

The growing challenges of healthcare systems pose a unique opportunity to leverage evidence-based digital health interventions. The WHO's SMART (Standards-based, Machine-readable, Adaptive, Requirements-based, and Testable) guidelines represent a significant advancement in this domain. This paper aims to summarize SMART guidelines authoring and implementation process, drawing on a comprehensive literature analysis. Our findings highlight critical success factors for national implementation, including stakeholder engagement, customization to local contexts, and leveraging international standards and digital technologies. We conclude with recommendations for countries aiming to implement WHO SMART guidelines, underscoring the need for a multi-disciplinary approach and the potential challenges to be navigated. [ABSTRACT FROM AUTHOR]

The US public health infrastructure has been historically underfunded, a condition that was exacerbated by the COVID-19 pandemic. This was especially noted in the area of public health informatics. It was also acknowledged that the lack of a diverse public health workforce made it more difficult to address biases and disparities effectively. In 2021 the Office of the National Coordinator awarded $73 million to 10 awardees to develop public health informatics and technology (PHIT) workforce training. The Gaining Equity in Training for Public Health Informatics and Technology (GET PHIT) award utilizes various methods to train and engage minority and underserved populations in the field of public health informatics. Evaluations of the bootcamps and internships to date have shown generally positive results, both in terms of skills acquired and overall experiences. These results indicate that integrating the fields of public health and data science in non-degree, short-term experiences can have positive outcomes. [ABSTRACT FROM AUTHOR]

COVID-19 remains an important focus of study in the field of public health informatics. COVID-19 designated hospitals have played an important role in the management of patients affected by the disease. In this paper we describe our modelling of the needs and sources of information for infectious disease practitioners and hospital administrators used to manage a COVID-19 outbreak. Infectious disease practitioner and hospital administrator stakeholders were interviewed to learn about their information needs and where they obtained their information. Stakeholder interview data were transcribed and coded to extract use case information. The findings indicate that participants used many and varied sources of information in the management of COVID-19. The use of multiple, differing sources of data led to considerable effort. In modelling participants' activities, we identified potential subsystems that could be used as a basis for developing an information system specific to the public health needs of hospitals providing care to COVID-19 patients. [ABSTRACT FROM AUTHOR]

Our initiative aims to enhance the public health informatics infrastructure for surveillance of maternal and child health (MCH) using data captured from electronic health records (EHRs), public health information systems, and administrative health data. Our work includes development, validation, and application of linkage algorithms across records for mothers and children; integration of data across myriad sources; design of routine surveillance reports; and design of longitudinal studies to examine determinants and outcomes in MCH populations. Our work is conducted in partnership with governmental public health agencies, health care providers, academic institutions, and community-based organizations. Future work will build on the enhanced informatics infrastructure to draw from additional public health data sources and/or expand surveillance efforts to include prioritized MCH outcomes. We will further translate knowledge gained from surveillance into action, working with our partners to improve and sustain better MCH equitably in our population. [ABSTRACT FROM AUTHOR]

Visualizations form an important part of public health informatics (PHI) communications. Visualizing data facilitates discussion, aids understanding, makes patterns apparent, promotes analysis, and fosters recall. How rare are novel visualizations in the PHI literature? In Phase 1, we used a rapid review methodology to test the commonness of the Sankey diagram in the PHI theory literature via an automated text search for key terms. In Phase 2, we prototype an uncommon chart type. A total of 27 relvant papers were searched and a computer-generated Sankey diagram was prototyped. PHI professionals have access to visualization tools emerging from social media and niche systems. PHI literature underutilizes uncommon visualizations requiring programming expertise. The authors advocate for: multi-disciplinary teamwork, technical education, the use of open visualization tools, and further adoption of visualization for public health professionals. [ABSTRACT FROM AUTHOR]

In many developing countries like India, there is a widespread lack of general awareness about the importance of good oral health, which causes dental patients to neglect their oral hygiene, thus precipitating many long-term ailments. We developed an application that promotes the significance of regular dental checkups and oral health care by explaining to patients how these are intrinsic to overall health. Our application, in essence, extracts relevant health information from published scientific studies according to a patient's medical history and shares it with the patient at the discretion of the supervising dentist, thereby empowering patients to make more informed decisions. We present a detailed overview of our semiautonomous machine learning-based solution, along with the complex challenges involved in the design, development, and real-world deployment of our application. Finally, we conducted a randomized parallel-group study in India with 224 dental patients over two years to assess the utility of our proposed solution. Results show our application improved the patient recall rate from 21.1% to 37.8% (p-value = 0.024). [ABSTRACT FROM AUTHOR]

In response to epidemics and pandemics, access to authentic sources of information plays a critical role in informing public health practices. However, infodemic, i.e., an overabundance of health information, misinformation, and disinformation, impede implementing best public health policies during a public health crisis such as COVID-19. In this bibliometric study, we aim to report on concept mapping of infodemic literature, and in line with the World Health Organization (WHO)'s repeated calls for actions in managing infodemic, we highlight fruitful avenues for future directions. Through a visualization approach on a set of 414 records, a concept mapping was carried out. This map revealed 42 infodemic-related nodes in five clusters. We also propose an infodemic research platform in which a combination of the research nodes (e.g., COVID, pandemic, disinformation, fake news, post-truth, fact-checking, social networks, Facebook, WhatsApp, and lockdown) with impactful questions suggest future directions. [ABSTRACT FROM AUTHOR]

Background and objective: The number of publications on the use of chatbots for health is recently increasing, however to our knowledge, there are no publications summarizing what is known about using chatbots for public health yet. The objective of this work is to provide an overview of the existing scientific literature on the use of chatbots for public health, for which purpose have chatbots been used, and whether health-related outcomes have been reported. Methods: We carried out a literature review on this topic across 5 databases: Pubmed, EMBASE, PsychINFO, SCOPUS, and IEEE Xplore. Identified papers were classified according to their underlying technology, application area, and study design. Results: A total of 15 relevant papers were identified: eight of these papers were developmental studies that tested the feasibility or usability of a chatbot, and seven were interventional studies. All the interventional studies reported positive healthrelated outcomes associated with the chatbot use. Discussion: The first studies testing chatbots for public health seem very promising; however, there are various aspects that should be improved, including the chatbots' designs, studies' methods, and analysis and reporting of results. More high-quality studies and improved reporting of chatbots' use are needed. [ABSTRACT FROM AUTHOR]

Radiation therapy interruptions drive cancer treatment failures; they represent an untapped opportunity for improving outcomes and narrowing treatment disparities. This research reports on the early development of the X-CART platform, which uses explainable AI to model cancer treatment outcome metrics based on high-dimensional associations with our local social determinants of health dataset to identify and explain causal pathways linking social disadvantage with increased radiation therapy interruptions. [ABSTRACT FROM AUTHOR]

The US public health infrastructure has been historically underfunded, a condition that was exacerbated by the COVID-19 pandemic. This was especially noted in the area of public health informatics. It was also acknowledged that the lack of a diverse public health workforce made it more difficult to address biases and disparities effectively. In 2021 the Office of the National Coordinator awarded $73 million to 10 awardees to develop public health informatics and technology (PHIT) workforce training. The Gaining Equity in Training for Public Health Informatics and Technology (GET PHIT) award utilizes various methods to train and engage minority and underserved populations in the field of public health informatics. Evaluations of the bootcamps and internships to date have shown generally positive results, both in terms of skills acquired and overall experiences. These results indicate that integrating the fields of public health and data science in non-degree, short-term experiences can have positive outcomes.

Radiation therapy interruptions drive cancer treatment failures; they represent an untapped opportunity for improving outcomes and narrowing treatment disparities. This research reports on the early development of the X-CART platform, which uses explainable AI to model cancer treatment outcome metrics based on high-dimensional associations with our local social determinants of health dataset to identify and explain causal pathways linking social disadvantage with increased radiation therapy interruptions.

The present work aims at describing a viable "protocol" for unobtrusive direct/indirect monitoring of biometric parameters for the estimation of body conditions on Mediterranean Buffalo populations, using low-cost automated systems i.e., smart cameras endowed with depth perception capabilities. [ABSTRACT FROM AUTHOR]

Colorectal cancer screening access within a rural and remote health care environment represents a complex systems problem. Existing modeling approaches are inadequate in their representation of health system complexity. A combined Collaborative Information Behavior (CIB) and Continuity of Care framework was developed to model the health care processes involved in screening access over time. This framework highlighted necessary information behavior supports and system gaps in screening access, supporting development of targeted informatics solutions to improve screening access and cancer outcomes. [ABSTRACT FROM AUTHOR]

Portable Healthcare Clinic (PHC) is a mobile healthcare system comprising of medical sensors and health assessment criteria. It has been applied in Bangladesh for the last two years as a pilot program to identify non-communicable diseases. In this study, we adapted PHC to fit post-disaster conditions. The PHC health assessment criteria are redesigned to deal with emergency cases and healthcare worker insufficiency. A new algorithm makes an initial assessment of age, symptoms, and whether the person is seeing a doctor. These changes will make the turn-around time shorter and will enable reaching the most affected patients better. We tested the operability and turn-around time of the adapted system at the debris flow disaster shelters in Hiroshima, Japan. Changing the PHC health assessment criteria and other solutions such as a list of medicine preparation makes the PHC system switch into an emergency mode more smoothly following a natural disaster. [ABSTRACT FROM AUTHOR]

In this paper, we apply a Hidden Markov Model (HMM) to analyze time-series personal health checkup data. HMM is widely used for data having continuation and extensibility such as time-series health checkup data. Therefore, using HMM as probabilistic model to model the health checkup data is considered to be suitable, and HMM can express the process of health condition changes of a person. In this paper, a HMM with six states placed in a 2×3 matrix was prepared. We collected training features including the time-series health checkup data. Each feature consists of eight inspection parameters such as BMI, SBP, and TG. The HMM was then built using the training features. In the experiments, we built five HMMs for different gender and age conditions (e.g. male 50's) using thousands of training feature vectors, respectively. Investigating the HMMs we found that the HMMs can model three health risk levels. The models can also represent health transitions or changes, indicating the possibility of estimating the risk of lifestyle-related diseases. [ABSTRACT FROM AUTHOR]

Public health surveillance of oral health might benefit from increased access to and analysis of electronically available data including systematic collection, analysis, interpretation, and dissemination of outcome-specific data for use in public health action to improve oral health. This study aimed to develop and evaluate a new Health-oriented Electronic Oral Health Record (Health-EOHR) that integrated new oral health status graphical user interface, the health-oriented status and intervention model to facilitate oral health surveillance. We designed an experiment using focus groups and a Delphi process to develop health-oriented status and intervention model and graphical user interface. The Health-EOHR was implemented and integrated into the existing Electronic Health Record widely used in community hospitals. The study on usefulness for oral health surveillance was conducted. Overall, the dentists were significantly satisfied with the Health-EOHR compared to the existing EOHR (p < 0.001). The dentists found it easy to use and were generally satisfied with the function and the impact on their work, oral health services and surveillance. [ABSTRACT FROM AUTHOR]

Information that is persistently collected, unrelated to direct clinical care may be a Pandora's Box for health improvement and monitoring. The use and linkage of disparate data sources, particularly those relating to geo-location and social media present enormous opportunities and challenges for healthcare. To date these sources have been difficult to harness and make sense of, yet they are now beginning to be exploited. It is the innovation in analytic methodology that may hold the key if the issues that may confound the results can be addressed. These issues are encompassed in privacy, and with the scientific quality and completeness of the data. The future for alternative application of geo-location and social media data is here and now, and if they can be harnessed carefully, healthcare may be the biggest beneficiary. [ABSTRACT FROM AUTHOR]

Denmark have unique health informatics databases e.g. 'The Children's Database', which since 2009 holds data on all Danish children from birth until 17 years of age. In the current set-up a number of potential sources of errors exist - both technical and human-which means that the data is flawed. This gives rise to erroneous statistics and makes the data unsuitable for research purposes. In order to make the data usable, it is necessary to develop new methods for validating the data generation process at the municipal/regional/national level. In the present ongoing research project, two research areas are combined: Public Health Informatics and Computer Science, and both ethnographic as well as system engineering research methods are used. The project is expected to generate new generic methods and knowledge about electronic data collection and transmission in different social contexts and by different social groups and thus to be of international importance, since this is sparsely documented in the Public Health Informatics perspective. This paper presents the preliminary results, which indicate that health information technology used ought to be subject for redesign, where a thorough insight into the work practices should be point of departure. [ABSTRACT FROM AUTHOR]

Public health organizations in different nations face similar needs for gathering and analyzing population health data to detect and manage infectious disease outbreaks, including outbreaks of the 2009 Novel H1N1 Influenza A virus or “swine flu.” This paper presents our progress to date on the design and assessment of a multi-national public health informatics infrastructure for data collection and disease surveillance. This initial work, under the aegis of an open health tools collaborative, lays the foundation for best practices in patient care and public health preparedness in the national health IT sector. This multinational collaboration is the first to identify essential electronic health record (EHR) data sets as well as standard public health informatics indicators to electronically monitor a notifiable public health condition internationally. [ABSTRACT FROM AUTHOR]

The traffic road accidents (ATT) are non-intentional events with an important magnitude worldwide, mainly in the urban centers. This article aims to analyzes data related to the victims of ATT recorded by the Justice Secretariat and Public Security (SEJUSP) in hospital morbidity and mortality incidence at the city of Cuiabá-MT during 2006, using data mining technology. An observational, retrospective and exploratory study of the secondary data bases was carried out. The three database selected were related using the probabilistic method, through the free software RecLink. One hundred and thirty-nine (139) real pairs of victims of ATT were obtained. In this related database the data mining technology was applied with the software WEKA using the Apriori algorithm. The result generated 10 best rules, six of them were considered according to the parameters established that indicated a useful and comprehensible knowledge to characterize the victims of accidents in Cuiabá. Finally, the findings of the associative rules showed peculiarities of the road traffic accident victims in Cuiabá and highlight the need of prevention measures in the collision accidents for males. [ABSTRACT FROM AUTHOR]

Risk factors discovery and prevention is an active research field within the biomedical domain. Despite abundant existing information on risk factors, as found in bibliographical databases or on several websites, accessing this information may be difficult. Methods from Natural Language Processing and Information Extraction can be helpful to access it more easily. Specifically, we show a procedure for analyzing massive amounts of scientific literature and for detecting linguistically marked associations between pathologies and risk factors. This approach allowed us to extract over 22,000 risk factors and associated pathologies. The performed evaluations pointed out that (1) over 88% of risk factors for coronary heart disease are correct, (2) associated pathologies, when they could be compared to MeSH indexing, are correct in about 70%, and (3) in existing terminologies links between risk factors and their pathologies are seldom recorded. [ABSTRACT FROM AUTHOR]

“Pharmacovigilance is the process and science of monitoring the safety of medicines, consisting in (i) collecting and managing data on the safety of medicines (ii) looking at the data to detect ‘signals’ (any new or changing safety issue)” [1]. Pharmacovigilance is mainly based on spontaneous reports: when suspecting an adverse drug reaction, health care practitioners send a report to a spontaneous reporting system (SRS). This produces huge databases containing numerous reports and their manual exploration is both cost and time prohibitive. Existing techniques that automatically extract relevant signals rely on statistics or Bayesian models but do not provide information to the experts about possible biases lying in the data, nor about the specificity of a signal to a particular patient profile. Our extraction method combines numerical methods from the state of the art with a qualitative approach that helps interpretation. We build a synthetic representation of the database that is used to (i) identify unexpected patterns and biases (ii) extract potentially relevant signals w.r.t. patient profiles (iii) provide traceability facilities between extracted signals and raw data. [ABSTRACT FROM AUTHOR]

Implementation of computerized systems in resource-constrained settings have been gaining traction as a means of improving the delivery of health care, the use and reuse of information, and providing a standards-based capacity for assessing the process and impact of health care. In a resource-constrained environment, systems are often implemented as stand-alone entities focused on specific care activities (for example, delivering antiretroviral therapy). As such, in many countries, taking a generalized approach to linking electronic medical record systems with laboratory information systems (EMR-LIS) is an important area in which to achieve interoperability. In this paper we describe a scenario of use and information interaction interoperability profile based on our experience implementing EMR-LIS integration in two resource-constrained settings. Of significance, the profile emphasizes queued matching in order to avoid mutual dependence while achieving interoperability between systems. [ABSTRACT FROM AUTHOR]

Many e-Health strategies rely on the secure integration of datasets that have previously resided in isolated locations, but can now in principle be accessed over the Internet. Of paramount importance in the health domain is the need for the security and privacy of data that is transmitted across these networks. One such collaboration, which spans several specialist centres across France, Germany, Italy and the UK, is ENSAT – the European Network for the Study of Adrenal Tumors. The rarity of the tumors under study means the value of accessing, aggregating and comparing data from many centres is great indeed. However this is especially challenging given that ENSAT require clinical and genomic data to be seamlessly linked, but in such a way that the information governance, ethics and privacy concerns of the patients and associated stakeholders involved are visibly satisfied. Key to this is the clear separation of clinical and genomic data sets and support for rigorous patient-identity protecting access control. This is especially challenging when such data sets exist across different organisational boundaries. In this paper we describe a prototype solution offering a security-oriented tailored portal supported by a layered encryption-driven linkage technology (VANGUARD) that offers precisely such patient-privacy protecting capabilities. We describe the architecture, implementation and use to date of this facility to support the ENSAT adrenal cancer research network. [ABSTRACT FROM AUTHOR]

The 2009 H1N1 outbreak has demonstrated that continuing vigilance, planning, and strong public health research capability are essential defenses against emerging health threats. Molecular epidemiology of influenza virus strains provides scientists with clues about the temporal and geographic evolution of the virus. In the present paper, researchers from France and Vietnam are proposing a global surveillance network based on grid technology: the goal is to federate influenza data servers and deploy automatically molecular epidemiology studies. A first prototype based on AMGA and the WISDOM Production Environment extracts daily from NCBI influenza H1N1 sequence data which are processed through a phylogenetic analysis pipeline deployed on EGEE and AuverGrid e-infrastructures. The analysis results are displayed on a web portal (http://g-info.healthgrid.org) for epidemiologists to monitor H1N1 pandemics. [ABSTRACT FROM AUTHOR]

This chapter gives an educational overview of: • The origins and evolution of bioinformatics from its beginning where it was a discipline mainly oriented towards the resolution of problems in biology to the present where it shows a clear interest on the field of translational research in medicine. • The different aspects of bioinformatics, ranging from its application for the study of individual entities, such as a gene or a protein, through its role as support to the large sequencing projects, and the global approach to genomics to systems biology. • Bioinformatics resources most used by researchers, distinguishing between the different information resources (databases) and the tools (programs) used to process them. • The current relevance of bioinformatics in medicine, both in the field of biomedical research and in the fields of clinical practice and public health as well as its multiple connections with the field of medical informatics. • The main lines for research and development in translational medicine, its main applications in the field of genomics medicine and future challenges raised by the new trends in medicine. [ABSTRACT FROM AUTHOR]

This chapter gives an educational overview of: • Management implications of information technology in research, public health and policy development • Modern developments in death investigation, injury prevention and disaster management • The dangers of applying global analysis to individual health care situations and vice versa • Mechanisms that can be used to identify diffuse disaster in global and individual medical practice • The impact of informatics in areas of medical services which rely upon observational studies rather than validated clinical trials to develop knowledge bases • A paradigm shift in health care policy towards client driven services and the implications for medical informatics [ABSTRACT FROM AUTHOR]

This chapter gives an educational overview of: • many competing characteristics within national health systems • national primary information and knowledge flows between health care entities • the role of information technologies in assisting health organizations become sustainable enterprises • the business of maintaining healthy populations for any nation • desirable e-health strategy objectives [ABSTRACT FROM AUTHOR]

The Internet and social media are becoming ubiquitous technologies that are transforming the health sector. Social media has become an avenue for accessing, creating and sharing health information among patients and healthcare professionals. Furthermore, social media has become a key feature in many eHealth solutions, including wearable technologies, Big Data solutions, eLearning systems, Serious Games, Medical imaging, etc. These hyper-connected technologies are facilitating a paradigm shift towards more connected health. In this panel, representatives of different IMIA Working Groups will explore how both hope and hype contribute to social media's catalyzing role in creating connected health solutions. [ABSTRACT FROM AUTHOR]

We often restrict the analysis of eHealth services to a concept of privacy. In this article, we'll demonstrate that other legislation can apply to those services as Directive 2000/31/EC on Ecommerce. By creating telematic networks or infrastructure, eHealth services are offering information services. But what are the consequences with such concept? What are the duties and rights for the actors of the network(s)? We'll try to answer to some questions, even if it won't be exhaustive. [ABSTRACT FROM AUTHOR]

In the United States and Europe, electronic health records (EHRs) allow information technology and decision-support to facilitate the activities of clinicians and are considered an important component of health care improvement. However, actual adoption of EHRs by physicians has been slow and the use of decision support has been minimal. Part of the difficulty lies in the challenges that users face in capturing structured clinical information. Reference and administrative terminologies have been in use for many years and provide a critical infrastructure to support reimbursement, decision-support and data analysis. The problem is that physicians do not think and work using reference terminologies. Interface terminologies bridge the gap between information that is in the physician's mind and information that can be interpreted by computer applications. The maps from interface terminologies to appropriate reference terminologies enable advanced functionality in clinical information systems. The conflict between the need for timely adoption of health information technology and the need for standardization is also relevant to the problems faced by health information technology in Africa. The problem of clinicians having to communicate and/or record information in a format that is acceptable to someone else, somewhere else, leaves the true benefits of these systems beyond the reach of most in Africa. There is a growing effort in the United States to produce clinically-relevant interface terminologies mapped to standards. These interface terminologies can be expanded to incorporate the languages and clinical requirements of clinicians in Africa. The adoption of interface terminologies will help bring the value of standard terminology and the resulting benefits of decision-support, data analysis and information retrieval to parts of the world where they are needed most. [ABSTRACT FROM AUTHOR]

Development of strategies for mitigating the severity of a new influenza pandemic is a global public health priority. The aim of this study is to examine effects on simulation outcomes caused by variations in local socio-demographic data. We used a spatially explicit geo-physical model of a virtual city as a baseline and employed an ontology-modeling tool to construct alternative population distributions and household structures. We found that adjustment for the case when single parents in practice were cohabiting led to a higher reproduction rate than that observed for a population with the highest formally recorded share of households with >2 children. When antivirals sufficient to protect 10 percent of the population were administered to schoolchildren, a preliminary effect on the reproduction rate was observed. This effect was eliminated when the household structure was adjusted for cohabiting single parents. Nations have been encouraged to develop estimates of morbidity and mortality during a possible pandemic outbreak. In order to deal with ecological fallacy, the present results suggest that this recommendation can be extended also to local communities. [ABSTRACT FROM AUTHOR]

Sustainable health systems should understand information as a resource which also has to be managed efficiently. Today's electronic documentation is only the mandatory first step to automatic information exchange between hospitals, physician offices, pharmacies and other participants. The K-Box is a modular system which allows different monolithic information systems to be connected and integrated by using the clinical document architecture (CDA) as the standard document exchange format. A prototype K-Box has been implemented and a trial run is scheduled. The workflow selected for the trial produces discharge summaries in CDA format. [ABSTRACT FROM AUTHOR]

This paper describes the development and application of a medical pattern evaluation methodology. The ADAPT process aims to facilitate the increased use of data mining technologies in medicine by providing a method of translating the language of data mining outputs into that of the medical domain. The research behind this work was developed in response to issues raised both in the literature and in public forums regarding the difficulty of adapting data mining technologies for the unique needs of the medical domain. This paper also responds to the need for automated systems to reflect the methodologies and processes applied in the management and analysis of medical data. The solution presented here shows promise as a method of providing a bridge between traditional data mining and the needs of medical domain users. [ABSTRACT FROM AUTHOR]

Computer-based simulation of influenza outbreaks in local communities can help researchers, epidemiologists, and decision makers better understand the impact of the community structure on the reproduction rate of disease, and the relative benefits of different types of prevention and interventions. The goal of scenario modeling is to develop a description of scenario components, such as the disease, the community, and interventions. An ontology-based representation of the scenario model together with a modeling tool, which is based on an extension to Protégé, assist scenario developers in formulating simulation specifications. This approach allows the exploration of new ideas by rapidly formulating and reconstructing scenarios from novel components. [ABSTRACT FROM AUTHOR]

A “healthy communities” initiative in Louisiana led to creation of the Health Informatics Center of Acadiana(HICA) at The University of Louisiana at Lafayette, in the south central United States. Since hurricanes Katrina and Rita devastated the Louisiana coast in 2005, HICA's role has taken on heightened significance. HICA identifies vulnerable populations, documents their risk factors, and evaluates interventions intended to improve community health. HICA collaborates with the Louisiana Department of Health and Hospitals and the Lafayette Community Health Consortium (LCHC), the latter formed for coordination among local healthcare providers and agencies. Both HICA and LCHC were created when “Bonne Santé à Lafayette!” – a locally developed community health improvement plan – was implemented. This paper reports on methods and experiences of HICA and LCHC, offering these as models for addressing community concerns elsewhere. Of special interest is the discussion of Louisiana HABITS, a consumer survey methodology that HICA has developed to measure healthcare access barriers, to provide information that healthcare organizations and governments need to implement workable business strategy and public policy. [ABSTRACT FROM AUTHOR]

Objectives. Syndromic surveillance for early warning in military context needs a robust, scalable, flexible, ubiquitous, and interoperable surveillance system. Methods. We have designed our surveillance system as a collaborative network of web services on the basis of a skill oriented decomposition of the overall task and a formal model of epidemiological events. Results. The services (integration devices, epidemiologic receivers, information processing devices, GUI clients) are distributed in several locations in France and French Guiana using a secured network. Conclusions. This system is in operation since several months. It has already early detected two outbreaks before conventional surveillance systems. [ABSTRACT FROM AUTHOR]