History of Medical Informatics at Utah

book chapterBiomedical Informatic

On the road to personalised and precision geomedicine: medical geology and a renewed call for interdisciplinarity

Our health depends on where we currently live, as well as on where we have lived in the past and for how long in each place. An individual’s place history is particularly relevant in conditions with long latency between exposures and clinical manifestations, as is the case in many types of cancer and chronic conditions. A patient’s geographic history should routinely be considered by physicians when diagnosing and treating individual patients. It can provide useful contextual environmental information (and the corresponding health risks) about the patient, and should thus form an essential part of every electronic patient/health record. Medical geology investigations, in their attempt to document the complex relationships between the environment and human health, typically involve a multitude of disciplines and expertise. Arguably, the spatial component is the one factor that ties in all these disciplines together in medical geology studies. In a general sense, epidemiology, statistical genetics, geoscience, geomedical engineering and public and environmental health informatics tend to study data in terms of populations, whereas medicine (including personalised and precision geomedicine, and lifestyle medicine), genetics, genomics, toxicology and biomedical/health informatics more likely work on individuals or some individual mechanism describing disease. This article introduces with examples the core concepts of medical geology and geomedicine. The ultimate goals of prediction, prevention and personalised treatment in the case of geology-dependent disease can only be realised through an intensive multiple-disciplinary approach, where the various relevant disciplines collaborate together and complement each other in additive (multidisciplinary), interactive (interdisciplinary) and holistic (transdisciplinary and cross-disciplinary) manners

Multi-disciplinary medical case study development for first year medical students

This poster will describe the history of the medical informatics course and the process of designing the case studies to fit into the new course management system, and will review the experiences of the librarians involved

The shifting sands of nursing informatics education: from content to connectivity

This chapter considers the development of nurse education over the past 50 years and ventures a view towards 2020. A link will be made to the introduction of informatics to nursing curricula. It is clear when looking over the recent history of nurse education that it has moved from a medical model and content driven apprentice mode to that of a reflective agile professional mode where autonomous practice allows for collaboration in care and connectivity between health professionals. Parallel to these pedagogical changes are the introduction of informatics across healthcare, starting with computer skills and moving through information management to decision support. The chapter will conclude with some thoughts around the next possible steps forward for nursing informatics education

A Deep Learning Approach to Integrate Medical Big Data for Improving Health Services in Indonesia

Medical Informatics to support health services in Indonesia is proposed in this paper. The focuses of paper to the analysis of Big Data for health care purposes with the aim of improving and developing clinical decision support systems (CDSS) or assessing medical data both for quality assurance and accessibility of health services. Electronic health records (EHR) are very rich in medical data sourced from patient. All the data can be aggregated to produce information, which includes medical history details such as, diagnostic tests, medicines and treatment plans, immunization records, allergies, radiological images, multivariate sensors device, laboratories, and test results. All the information will provide a valuable understanding of disease management system. In Indonesia country, with many rural areas with limited doctor it is an important case to investigate. Data mining about large-scale individuals and populations through EHRs can be combined with mobile networks and social media to inform about health and public policy. To support this research, many researchers have been applied the Deep Learning (DL) approach in data-mining problems related to health informatics. However, in practice, the use of DL is still questionable due to achieve optimal performance, relatively large data and resources are needed, given there are other learning algorithms that are relatively fast but produce close performance with fewer resources and parameterization, and have a better interpretability. In this paper, the advantage of Deep Learning to design medical informatics is described, due to such an approach is needed to make a good CDSS of health services

Open source and international health informatics: placebo or panacea?

The authors explore the history of open source software and how the future of this paradigm can affect global changes to healthcare informatics. They identify four key requirements: 1 to establish an international health informatics open source (IHI-OS) community 2 to develop a kernel that is broad enough but also provides sufficient detail to be usable across international boundaries and across medical disciplines 3 to develop a business case for international health informatics open source 4 to develop international standards

Information Technology to Support Improved Care For Chronic Illness

BackgroundIn populations with chronic illness, outcomes improve with the use of care models that integrate clinical information, evidence-based treatments, and proactive management of care. Health information technology is believed to be critical for efficient implementation of these chronic care models. Health care organizations have implemented information technologies, such as electronic medical records, to varying degrees. However, considerable uncertainty remains regarding the relative impact of specific informatics technologies on chronic illness care.ObjectiveTo summarize knowledge and increase expert consensus regarding informatics components that support improvement in chronic illness care.DesignA systematic review of the literature was performed. "Use case" models were then developed, based on the literature review, and guidance from clinicians and national quality improvement projects. A national expert panel process was conducted to increase consensus regarding information system components that can be used to improve chronic illness care.ResultsThe expert panel agreed that informatics should be patient-centered, focused on improving outcomes, and provide support for illness self-management. They concurred that outcomes should be routinely assessed, provided to clinicians during the clinical encounter, and used for population-based care management. It was recommended that interactive, sequential, disorder-specific treatment pathways be implemented to quickly provide clinicians with patient clinical status, treatment history, and decision support.ConclusionsSpecific informatics strategies have the potential to improve care for chronic illness. Software to implement these strategies should be developed, and rigorously evaluated within the context of organizational efforts to improve care

Лаборатория биомедицинской информатики СПИИРАН

The meaning of terms "bioinformatics", "medical informatics", "biomedical informatics" is discussed as applied to the latter's goals, problems and methods. Justification is given to the definition of biomedical informatics that is in our opinion most complete. The milestones in the history of Russian biomedical informatics are listed as well as the main scientific schools within this line of investigation headed by Russia’s leading scientists. The article provides an examination of the activity of the laboratory of biomedical informatics and the characteristics of solving the problems of biomedical informatics at SPIIRASОбсуждается смысл терминов «биоинформатика», «медицинская информатика», «биомедицинская информатика» применительно к целям, задачам и методам последней. Обосновывается наиболее полное на наш взгляд определение биомедицинской информатики. Приводятся вехи истории биомедицинской информатики в России и основные научные школы по этому направлению, возглавляемые лидерами отечественной науки. Рассматриваются деятельность лаборатории биомедицинской информатики и особенности решения задач биомедицинской информатики в СПИИРА