Search

Translational research is a bidirectional process that involves multidisciplinary integration among basic, clinical, practice, population, and policy-based research. The goal of translational research is to speed up scientific discovery into patient and community benefit. This chapter provides an overview of key concepts, methods, and trial types in translational research. The primary focus is on dissemination and implementation (D&I) research, or the systematic study of processes and factors that lead to widespread use and successful integration of evidence-based interventions into real-world clinical and community settings. This chapter will emphasize D&I methodological considerations as well as common frameworks and measures used in D&I research. Research concepts and resources pertinent for nutrition researchers and practitioners are provided.

Many tools and approaches can be applied in translational bioinformatics to support the translation of scientific discoveries into better clinical results. Some of the important steps are data integration, data standardization, data mining, knowledge discovery (KD), and decision support. These methods are useful for both basic analyses, such as genomic studies, and clinical practice, such as electronic health records (EHRs). The advantages of data integration have been demonstrated by various real-world applications. Data integration can be applied in several steps starting from the recognition, gathering, and selection of various data sources. For more effective decision support in both clinical and experimental environments involving various knowledge domains, standardization is the key solution for interoperability problems. To meet the goals of translational bioinformatics, the integration of biological and medical informatics would be needed. The ethical, legal, privacy, and societal issues should also be addressed. Based on data integration and management, data mining approaches can be applied for KD, such as finding patterns and correlations. Decision support is the key element of translational bioinformatics, from data access to communication, from drug design to treatment recommendations.

Designed to advance the two-way translational process between basic research and clinical practice, translational medicine has become one of the most important areas in biomedicine. The quantitative evaluation of translational medicine is valuable for the decision making of global translational medical research and funding. Using the scientometric analysis and information extraction techniques, this study quantitatively analyzed the scientific articles on translational medicine. The results showed that translational medicine had significant scientific output and impact, specific core field and institute, and outstanding academic status and benefit. While it is not considered in this study, the patent data are another important indicators that should be integrated in the relevant research in the future.

Engineering human health and healthcare delivery is one of the most significant open frontiers for progress by computational scientists. This article focuses on the opportunities and challenges of Big Data in transforming healthcare delivery.

SummaryBackground: In 2009, Applied Clinical Informatics (ACI) – focused on applications in clinical informatics – was launched as a companion journal to Methods of Information in Medicine (MIM). Both journals are official journals of the International Medical Informatics Association.Objectives: To explore which congruencies and interdependencies exist in publications from theory to practice and from practice to theory and to determine existing gaps. Major topics discussed in ACI and MIM were analyzed. We explored if the intention of publishing companion journals to provide an information bridge from informatics theory to informatics practice and vice versa could be supported by this model. In this manuscript we will report on congruencies and interdependences from practice to theory and on major topics in MIM.Methods: Retrospective, prolective observational study on recent publications of ACI and MIM. All publications of the years 2012 and 2013 were indexed and analyzed.Results: Hundred and ninety-six publications were analyzed (ACI 87, MIM 109). In MIM publications, modelling aspects as well as methodological and evaluation approaches for the analysis of data, information, and knowledge in biomedicine and health care were frequently raised – and often discussed from an interdisciplinary point of view. Important themes were ambient-assisted living, anatomic spatial relations, biomedical informatics as scientific discipline, boosting, coding, computerized physician order entry, data analysis, grid and cloud computing, health care systems and services, health-enabling technologies, health information search, health information systems, imaging, knowledge-based decision support, patient records, signal analysis, and web science. Congruencies between journals could be found in themes, but with a different focus on content. Interdependencies from practice to theory, found in these publications, were only limited.Conclusions: Bridging from informatics theory to practice and vice versa remains a major component of successful research and practice as well as a major challenge.Citation: Haux R, Lehmann CU. From bed to bench: Bridging from informatics practice to theory – an exploratory analysis. Appl Clin Inf 2014; 5: 907–915http://dx.doi.org/10.4338/ACI-2014-10-RA-0095

Despite the foundation of the Clinical and Translational Science Award (CTSA) program and the National Center for Advancing Translational Science (NCATS), the process to bring novel tools to the clinical setting is hindered by administrative challenges. Technology offers solutions to these obstacles and has the potential to drive the field of translational science forward. In this commentary, we discuss diagnostic technology, clinical informatics, and bioinformatics and their roles in the development of this field.

Imaging has become a prevalent tool in the diagnosis and treatment of many diseases, providing a unique in vivo, multi-scale view of anatomic and physiologic processes. With the increased use of imaging and its progressive technical advances, the role of imaging informatics is now evolving--from one of managing images, to one of integrating the full scope of clinical information needed to contextualize and link observations across phenotypic and genotypic scales. Several challenges exist for imaging informatics, including the need for methods to transform clinical imaging studies and associated data into structured information that can be organized and analyzed. We examine some of these challenges in establishing imaging-based observational databases that can support the creation of comprehensive disease models. The development of these databases and ensuing models can aid in medical decision making and knowledge discovery and ultimately, transform the use of imaging to support individually-tailored patient care.

Summary Background: Clinical decision support (CDS) is a key tool for enabling evidence-based medicine and improving the quality of healthcare. However, effective CDS faces a variety of challenges, including those relating to knowledge synthesis, capture, transformation, localization and maintenance. If not properly addressed, these challenges can limit the effectiveness of CDS, and potentially risk inaccurate or inappropriate interventions to clinicians. Objectives: (1) To describe an approach to CDS development using evidence as a basis for clinical decision support systems that promote effective care; (2) To review recent evidence regarding the effectiveness of selected clinical decision support systems. Method: Review and analysis of recent literature with identification of trends and best practices. Results: The state-of-the-art in CDS has advanced significantly, and many recent trials have shown CDS to be effective, although the results are mixed overall. Issues related to knowledge capture and synthesis, problems in knowledge transformation at the interface between knowledge authors and CDS developers, and problems specific to local CDS design and implementation can interfere with CDS development. Best practices, tools and techniques to manage them are described. Conclusions: CDS, when used well, can be effective, but further research is needed for it to reach its full potential.

Summary Objective: The field of clinical informatics has expanded substantially in the six decades since its inception. Early research focused on simple demonstrations that health information technology (HIT) such as electronic health records (EHRs), computerized provider order entry (CPOE), and clinical decision support (CDS) systems were feasible and potentially beneficial in clinical practice. Methods: In this review, we present recent evidence on clinical informatics in the United States covering three themes: 1) clinical informatics systems and interventions for providers, including EHRs, CPOE, CDS, and health information exchange; 2) consumer health informatics systems, including personal health records and web-based and mobile HIT; and 3) methods and governance for clinical informatics, including EHR usability; data mining, text mining, natural language processing, privacy, and security. Results: Substantial progress has been made in demonstrating that various clinical informatics methodologies and applications improve the structure, process, and outcomes of various facets of the healthcare system. Conclusion: Over the coming years, much more will be expected from the field. As we move past the “early adopters” in Rogers' diffusion of innovations' curve through the “early majority” and into the “late majority,” there will be a crucial need for new research methodologies and clinical applications that have been rigorously demonstrated to work (i.e., to improve health outcomes) in multiple settings with different types of patients and clinicians.

Summary Objectives: To summarize current excellent medical informatics research in the field of human factors and organizational issues. Methods: Using PubMed, a total of 3,024 papers were selected from 17 journals. The papers were evaluated on the basis of their title, keywords, and abstract, using several exclusion and inclusion criteria. 15 preselected papers were carefully evaluated by six referees using a standard evaluation grid. Results: Six best papers were selected exemplifying the central role cognitive and social sciences can play in medical informatics research. Among other contributions, those studies: (i) make use of the distributed cognition paradigm to model and understand clinical care situations; (ii) take into account organizational issues to analyse the impact of HIT on information exchange and coordination processes; (iii) illustrate how models and empirical data from cognitive psychology can be used in medical informatics; and (iv) highlight the need of qualitative studies to analyze the unexpected side effects of HIT on cognitive and work processes. Conclusion: The selected papers demonstrate that paradigms, methodologies, models, and results from cognitive and social sciences can help to bridge the gap between HIT and end users, and contribute to limit adoption failures that are reported regularly.

BACKGROUND As clinical data are increasingly collected and stored electronically, their potential use for comparative effectiveness research (CER) grows. Despite this promise, challenges face those wishing to leverage such data. In this paper we aim to enumerate some of the knowledge management and informatics issues common to such data reuse. DESIGN After reviewing the current state of knowledge regarding biomedical informatics challenges and best practices related to CER, we then present 2 research projects at our institution. We analyze these and highlight several common themes and challenges related to the conduct of CER studies. Finally, we represent these emergent themes. RESULTS The informatics challenges commonly encountered by those conducting CER studies include issues related to data information and knowledge management (eg, data reuse, data preparation) as well as those related to people and organizational issues (eg, sociotechnical factors and organizational factors). Examples of these are described in further detail and a formal framework for describing these findings is presented. CONCLUSIONS Significant challenges face researchers attempting to use often diverse and heterogeneous datasets for CER. These challenges must be understood in order to be dealt with successfully and can often be overcome with the appropriate use of informatics best practices. Many research and policy questions remain to be answered in order to realize the full potential of the increasingly electronic clinical data available for such research.

Summary Objectives: To present the importance of Evidence-based Health Informatics (EBHI) and the ethical imperative of this approach; to highlight the work of the IMIA Working Group on Technology Assessment and Quality Improvement and the EFMI Working Group on Assessment of Health Information Systems; and to introduce the further important evaluation and evidence aspects being addressed. Methods: Reviews of IMIA, EFMA and other initiatives, together with literature reviews on evaluation methods and on published systematic reviews. Results: Presentation of the rationale for the health informatics domain to adopt a scientific approach by assessing impact, avoiding harm, and empirically demonstrating benefit and best use; reporting of the origins and rationale of the IMIA- and EQUATOR-endorsed Statement on Reporting of Evaluation Studies in Health Informatics (STARE-HI) and of the IMIA WG's Guideline for Good Evaluation Practice in Health Informatics (GEP-HI); presentation of other initiatives for objective evaluation; and outlining of further work in hand on usability and indicators; together with the case for development of relevant evaluation methods in newer applications such as telemedicine. The focus is on scientific evaluation as a reliable source of evidence, and on structured presentation of results to enable easy retrieval of evidence. Conclusions: EBHI is feasible, necessary for efficiency and safety, and ethically essential. Given the significant impact of health informatics on health systems, care delivery and personal health, it is vital that cultures change to insist on evidence-based policies and investment, and that emergent global moves for this are supported.

Summary Objectives: To provide a survey of the field of clinical research informatics (CRI), focusing in particular on significant developments over the past 3 years and the insights they provide about the progress and state of the field. Methods: An iterative “scoping review” of the literature published in scientific journals and conference proceedings that are relevant to CRI, from late 2009 to early 2013. Results: 212 articles were identified, and 64 were selected to illustrate recent advances in the field. Based on those, six categories of CRI activity were identified: Data and Knowledge Management, Discovery and Standards; Clinical Data Re-Use for Research; Researcher Support and Resources; Participant Recruitment; Patients/Consumers and CRI; Policy, Regulatory and Fiscal Matters. Conclusions: This survey demonstrates that the field of CRI has matured and is now well established. The ongoing work is essential to overcome many of the challenges the clinical research enterprise is facing and more work is needed. Even as work continues to establish necessary infrastructure, methods, and best practices, CRI researchers should strive for more rigorous study designs to evaluate the impacts of the work in the field. There is little doubt that the field is poised for rapid growth, and that the CRI literature will continue to reflect that growth in years to come.

The epilepsy community increasingly recognizes the need for a modern classification system that can also be easily integrated with effective informatics tools. The 2010 reports by the United States President's Council of Advisors on Science and Technology (PCAST) identified informatics as a critical resource to improve quality of patient care, drive clinical research, and reduce the cost of health services. An effective informatics infrastructure for epilepsy, which is underpinned by a formal knowledge model or ontology, can leverage an ever increasing amount of multimodal data to improve (1) clinical decision support, (2) access to information for patients and their families, (3) easier data sharing, and (4) accelerate secondary use of clinical data. Modeling the recommendations of the International League Against Epilepsy (ILAE) classification system in the form of an epilepsy domain ontology is essential for consistent use of terminology in a variety of applications, including electronic health records systems and clinical applications. In this review, we discuss the data management issues in epilepsy and explore the benefits of an ontology-driven informatics infrastructure and its role in adoption of a “data-driven” paradigm in epilepsy research.

Emphasis has been placed on assessing the efficiency of clinical and translational research as part of the National Institutes of Health (NIH) goal to "improve human health." Improvements identified and implemented by individual organizations cannot address the research infrastructure needs of all clinical and translational research conducted. NIH's National Center for Advancing Translational Sciences (NCATS) has brought together 61 Clinical and Translational Science Award (CTSA) sites creating a virtual national laboratory that reflects the diversity and breadth of academic medical centers to collectively improve clinical and translational science. The annual Clinical Research Management workshop is organized by the CTSA consortium with participation from CTSA awardees, NIH, and others with an interest in clinical research management. The primary objective of the workshop is to disseminate information that improves clinical research management although the specific objectives of each workshop evolve within the consortium. The fifth annual workshop entitled "Learning by doing; applying evidence-based tools to re-engineer clinical research management" took place in June 2012. The primary objective of the 2012 workshop was to utilize data to evaluate, modify, and improve clinical research management. This report provides a brief summary of the workshop proceedings and the major themes discussed among the participants.

Translational medical research literatures have increased rapidly in last decades and there have been fewer attempts or efforts to map global research context of translational medical related research. The main purpose of this study is to evaluate the global progress and to assess the current quantitatively trends on translational medical research by using a scientometric approach to survey translational medicine related literatures in Science Citation Index Expanded (SCI-E), Social Science Citation Index and PubMed database from 1992 to 2012. The scientometric methods and knowledge visualization technologies were employed in this paper. The document types, languages, publication patterns, subject categories, journals, geographic and institutional distributions, top cited papers, and the distribution of keywords as well as MeSH terms were thoroughly examined. Translational medicine research has increased rapidly over past 20 years, most notably in the last 4 years. In total, there are currently 3,627 research articles in 1,062 journals listed in 91 SCI-E subject categories. The top 20 productive countries and institutes were analyzed herein, where 11 key papers in translational medical research and research foci were identified. Research outputs descriptors have suggested that the presence of a solid development in translational medical research, where research in this field has mainly focused on experimental medicine, general internal medicine, and medical laboratory technologies. All these outcomes have been concentrated in several journals such as Translational Research, Translational Oncology, Translational Stroke Research, and Translational Neuroscience. G7 countries make up the leading nations for translational medical research, where the center is located in USA. American institutions have made great advances in paper productions, citations, and cooperation, with overall great strengths and good development prospects. Moreover, the evolution pathway of translational medical research has been summarized as bellows: problems emerged, causes analyzed, challenges faced and solutions proposed, translational medical research programs been formally established, theoretical and applied research, all of which was in full swing. During this process, neoplasms and genomics, interdisciplinary communication between academic medical centers/institutes, drug design and development, cardiovascular and brain diseases, and even biomedical research have been identified as mainstream topics in translational medical research fields.

SummaryIntroduction: This article is part of a For-Discussion-Section of Methods of Information in Medicine on “Biomedical Informatics: We are what we publish“. It is introduced by an editorial and followed by a commentary paper with invited comments. In subsequent issues the discussion may continue through letters to the editor.Objective: Informatics experts have attempted to define the field via consensus projects which has led to consensus statements by both AMIA. and by IMIA. We add to the output of this process the results of a study of the Pubmed publications with abstracts from the field of Biomedical Informatics.Methods: We took the terms from the AMIA consensus document and the terms from the IMIA definitions of the field of Biomedical Informatics and combined them through human review to create the Health Infor -matics Ontology. We built a terminology server using the Intelligent Natural Language Processor (iNLP). Then we downloaded the entire set of articles in Medline identified by searching the literature by “Medical Informatics” OR “Bioinformatics”. The articles were parsed by the joint AMIA / IMIA terminology and then again using SNOMED CT and for the Bioinformatics they were also parsed using HGNC Ontology.Results: We identified 153,580 articles using “Medical Informatics” and 20,573 articles using “Bioinformatics”. This resulted in 168,298 unique articles and an overlap of 5,855 articles. Of these 62,244 articles (37%) had titles and abstracts that contained at least one concept from the Health Infor -matics Ontology. SNOMED CT indexing showed that the field interacts with most all clinical fields of medicine.Conclusions: Further defining the field by what we publish can add value to the consensus driven processes that have been the mainstay of the efforts to date. Next steps should be to extract terms from the literature that are uncovered and create class hierarchies and relationships for this content. We should also examine the high occurring of MeSH terms as markers to define Biomedical Informatics. Greater understanding of the Biomedical Informatics Literature has the potential to lead to improved self-awareness for our field.

The volumes of current patient data as well as their complexity make clinical decision making more challenging than ever for physicians and other care givers. This situation calls for the use of biomedical informatics methods to process data and form recommendations and/or predictions to assist such decision makers. The design, implementation, and use of biomedical informatics systems in the form of computer-aided decision support have become essential and widely used over the last two decades. This paper provides a brief review of such systems, their application protocols and methodologies, and the future challenges and directions they suggest.

In 1986, the National Library of Medicine began a long-term research and development project to build the Unified Medical Language System® (UMLS®). The purpose of the UMLS is to improve the ability of computer programs to “understand” the biomedical meaning in user inquiries and to use this understanding to retrieve and integrate relevant machine-readable information for users. Underlying the UMLS effort is the assumption that timely access to accurate and up-to-date information will improve decision making and ultimately the quality of patient care and research. The development of the UMLS is a distributed national experiment with a strong element of international collaboration. The general strategy is to develop UMLS components through a series of successive approximations of the capabilities ultimately desired. Three experimental Knowledge Sources, the Metathesaurus®, the Semantic Network, and the Information Sources Map have been developed and are distributed annually to interested researchers, many of whom have tested and evaluated them in a range of applications. The UMLS project and current developments in high-speed, high-capacity international networks are converging in ways that have great potential for enhancing access to biomedical information.

Introduction:Translational research is central to international health policy, research and funding initiatives. Despite increasing use of the term, the translation of basic science discoveries into clinical practice is not straightforward. This systematic search and narrative synthesis aimed to examine factors enabling or hindering translational research from the perspective of basic and clinician scientists, a key stakeholder group in translational research, and to draw policy-relevant implications for organisations seeking to optimise translational research opportunities.Methods and Results:We searched SCOPUS and Web of Science from inception until April 2015 for papers reporting scientists’ views of the factors they perceive as enabling or hindering the conduct of translational research.We screened 8,295 papers from electronic database searches and 20 papers from hand searches and citation tracking, identifying 26 studies of qualitative, quantitative or mixed method designs. We used a narrative synthesis approach and identified the following themes: 1) differing concepts of translational research 2) research processes as a barrier to translational research; 3) perceived cultural divide between research and clinical care; 4) interdisciplinary collaboration as enabling translation research, but dependent on the quality of prior and current social relationships; 5) translational researchas entrepreneurial science. Across all five themes, factors enabling or hindering translational research were largely shaped by wider social, organisational, and structural factors.Conclusion:To optimise translational research, policy could consider refining translational research models to better reflect scientists’ experiences, fostering greater collaboration and buy in from all types of scientists. Organisations could foster cultural change, ensuring that organisational practices and systems keep pace with the change in knowledge production brought about by the translational research agenda.

This special issue on precision medicine informatics flowed from the AMIA 2015 Translational Bioinformatics Summit theme of “Accelerating Precision Medicine”1 and President Obama’s 2015 State of the Union call “to give all of us access to the personalized information we need to keep ourselves and our families healthier.”2 The goal is to focus on the inherent translational informatics challenges, concerns, and opportunities afforded by precision medicine to provide an accurate, personalized characterization of patient populations based on various characteristics including molecular (eg, genomic, proteomic), clinical (eg, comorbidities), environmental exposures, lifestyle, patient preferences, and other information.4–13 Informatics is a necessary component in a comprehensive initiative to tackle precision medicine. Its roles include (1) managing big data,15–17 (2) creating learning systems for knowledge generation,18–21 (3) providing access for individual involvement,22–25 and (4) ultimately supporting optimal delivery of precision treatments derived from translational research.26–29 The papers submitted for this special issue cover these 4 areas and expand on the themes of informatics emerging in the field of precision medicine. Data become “big” when the scale of the data and analyses challenge our traditional systems in size, velocity, or complexity.8 The large scale of the Million Veterans Program is an example, with systems and analysis methods being developed for application in a repository that has established policies and procedures. The Million Veterans Program can serve as a model for involving thousands of individuals in precision medicine research.30 Similarly, the Precision Medicine Initiative Cohort …

International audience; Medical Imaging Informatics has become a fast evolving discipline at the crossing of Informatics, Computational Sciences, and Medicine that is profoundly changing medical practices, for the patients' benefit. Keywords Medical Imaging Informatics, personalized computational patient, computational anatomy, computational medicine Yearb Med Inform 2016;xxx http://dx.doi.org/10.15265/IY-2016-002 Published online xxx In 2002, my preface to the IMIA Yearbook was entitled " From Digital Anatomy to Virtual Scalpels and Image-Guided Therapy ". It was announcing a revolution in medicine brought by the extensive use of medical image computing to better assist the diagnosis and therapy of the patient. Today, the promised revolution is here: Medical images are omnipresent at the hospital, and Medical Imaging Informatics is required more than ever to exploit their flood of information. All around the world, medical image computing is used to extract the clinically relevant information from medical images, and to present this information in a way that is clinically useful to the physician. This is mainly done through the construction of a computational and personalized model of the patient. Building a computational model of the human body requires dedicated algorithms that take into account a thorough knowledge of the human anatomy and physiology. Huge progress has been made during the last decades to describe and simulate the structure and functions of organs thanks to advanced mathematical, biological, physical, and chemical models of the living tissues at various scales from the nanoscopic (molecular) to microscopic (cellular), mesoscopic (tissue), and macroscopic (organic) scales. Computational models of the human body rely on a set of parameters that allow, for instance, to specify the structure and function of organs. Generic models are based on average parameters estimated over a population. Confronted to in vivo anatomical and functional images and signals of a singular patient, those parameters are adjusted by efficient personalization algorithms in order to reproduce more precisely the observed structures and functions leading to the personalized computational model of this particular patient. The personalized computational model of the patient is then used to provide quantitative and objective measurements on the patient's condition to better assess the diagnosis. It is also used to predict a pathological evolution resulting in a better assessment of the prognosis. Finally, the computational model of the patient is extensively used to plan and simulate the effect of a therapy, in order to optimize its actual delivery. These three steps-computer aided diagnosis, prognosis, and therapy-announce the fast development of the computational medicine at the service of the physician. The tremendous progress of Medical Imaging Informatics also accompanies the evolution of normative and reactive medicine towards a more personalized, precise, preventive, and predictive medicine. This progress relies on numerous algorithmic advances in medical image analysis and inverse problem solving. It also relies on continuous advances in the modeling of human anatomy and physiology. It benefits from the improvement of medical image acquisition techniques, and from the introduction of new imaging modalities at various scales. It is supported by the regular

Biomedical informatics has become a central focus for many academic medical centers and universities as biomedical research because increasingly reliant on the processing, analysis, and interpretation of large volumes of data, information, and knowledge. We posit here that this is the beginning of the golden era of biomedical informatics with opportunity for this maturing discipline to have a substantial impact on the biggest questions and challenges facing efforts to improve human health and the healthcare system.

Translational bioinformatics and clinical research (biomedical) informatics are the primary domains related to informatics activities that support translational research. Translational bioinformatics focuses on computational techniques in genetics, molecular biology, and systems biology. Clinical research (biomedical) informatics involves the use of informatics in discovery and management of new knowledge relating to health and disease. This article details 3 projects that are hybrid applications of translational bioinformatics and clinical research (biomedical) informatics: The Cancer Genome Atlas, the cBioPortal for Cancer Genomics, and the Memorial Sloan Kettering Cancer Center clinical variants and results database, all designed to facilitate insights into cancer biology and clinical/therapeutic correlations.

Molecular informatics (MI) is an evolving discipline that will support the dynamic landscape of molecular pathology and personalized medicine. MI provides a fertile ground for development of clinical solutions to bridge the gap between clinical informatics and bioinformatics. Rapid adoption of next generation sequencing (NGS) in the clinical arena has triggered major endeavors in MI that are expected to bring a paradigm shift in the practice of pathology. This brief review presents a broad overview of various aspects of MI, particularly in the context of NGS based testing.

The American Medical Informatics Association convened the 2014 Health Policy Invitational Meeting to develop recommendations for updates to current policies and to establish an informatics research agenda for personalizing medicine. In particular, the meeting focused on discussing informatics challenges related to personalizing care through the integration of genomic or other high-volume biomolecular data with data from clinical systems to make health care more efficient and effective. This report summarizes the findings ( n = 6) and recommendations ( n = 15) from the policy meeting, which were clustered into 3 broad areas: (1) policies governing data access for research and personalization of care; (2) policy and research needs for evolving data interpretation and knowledge representation; and (3) policy and research needs to ensure data integrity and preservation. The meeting outcome underscored the need to address a number of important policy and technical considerations in order to realize the potential of personalized or precision medicine in actual clinical contexts.

Summary Objective: This paper presents the development of medical informatics education during the years from the establishment of the International Medical Informatics Association (IMIA) until today. Method: A search in the literature was performed using search engines and appropriate keywords as well as a manual selection of papers. The search covered English language papers and was limited to search on papers title and abstract only. Results: The aggregated papers were analyzed on the basis of the subject area, origin, time span, and curriculum development, and conclusions were drawn. Conclusions: From the results, it is evident that IMIA has played a major role in comparing and integrating the Biomedical and Health Informatics educational efforts across the different levels of education and the regional distribution of educators and institutions. A large selection of references is presented facilitating future work on the field of education in biomedical and health informatics.

The behavioral and psychological sciences have generated a robust knowledge base over the last two decades that can be leveraged to advance oncology informatics and improve cancer control. In this chapter, we contend that the behavioral and psychological sciences add value through articulating the most promising theoretical models and approaches for designing, developing, and evaluating digital health technologies and informatics solutions. Examples in clinical care settings are given that illustrate how fundamental behavioral and psychological constructs inform the design and evaluation of clinical decision support systems for tobacco cessation, emotional distress screening and management, physical activity promotion, and for creating automated conversational agents for support and assistance in understanding complex consent forms. The future success of oncology informatics is predicated on drawing from the best available scientific evidence and behavioral informatics can serve as a core resource.

Medical Imaging Informatics provides an overview of this growing discipline, which stems from an intersection of biomedical informatics, medical imaging, computer science and medicine. Supporting two complementary views, this volume explores the fundamental technologies and algorithms that comprise this field, as well as the application of medical imaging informatics to subsequently improve healthcare research. Clearly written in a four part structure, this introduction follows natural healthcare processes, illustrating the roles of data collection and standardization, context extraction and modeling, and medical decision making tools and applications. Medical Imaging Informatics identifies core concepts within the field, explores research challenges that drive development, and includes current state-of-the-art methods and strategies.

Biobanks are one of the most important biomedical research resources and contribute to the development of biomarker detection, molecular diagnosis, translational medicine, and multidisciplinary disease research, as well as studies of interactions between genetic and environmental or lifestyle factors. Aiming for the wide clinical application of biobanks, biobanking efforts have recently switched from a focus on accumulating samples to both formalizing and sustaining collections in light of the rapid progress in the fields of personalized medicine and bioinformatics analysis. With the emergence of novel molecular diagnostic technologies, although the bioinformatics platform of biobanks ensures reliable bioinformatics analysis of patient samples, there are a series of challenges facing biobanks in terms of the overall harmonization of policies, integrated processes, and local informatics solutions across the network. Further, there is a controversy regarding the increased role of ethical boards, governance, and accreditation bodies in ensuring that collected samples have sufficient informatics capabilities to be used in biobanks. In this volume, we present a selection of current issues on the inevitable challenges of the clinical application of biobanks in informatics.

Collecting and managing data for clinical and translational research presents significant challenges for clinical and translational researchers, many of whom lack needed access to data management expertise, methods, and tools. At many institutions, funding constraints result in differential levels of research informatics support among investigators. In addition, the lack of widely shared models and ontologies for clinical research informatics and health information technology hampers the accurate assessment of investigators’ needs and complicates the efficient allocation of crucial resources for research projects, ultimately affecting the quality and reliability of research. In this paper, we present a model for providing flexible, cost‐efficient institutional support for clinical and translational research data management and informatics, the research management team, and describe our initial experiences with deploying this model at our institution. Clin Trans Sci 2012; Volume 5: 464–469

SummarySummarize excellent current research in the field of Health Information Systems.Synopsis of the articles selected for the IMIA Yearbook 2012.Three papers from international peer reviewed journals have been selected for the section on health information systems.The selected articles illustrate current research regarding health IT impacts and evaluation and the latest developments in health information exchange.

SummaryTo review current excellent research and trend in the field of bioinformatics and translational informatics with direct application in the medical domain.Synopsis of the articles selected for the IMIA Yearbook 2012.Six excellent articles were selected in this Yearbook’s section on Bioinformatics and Translational Informatics. They exemplify current key advances in the use of patient information for translational research and health surveillance. First, two proof-of-concept studies demonstrated the cross-institutional and -geographic use of Electronic Health Records (EHR) for clinical trial subjects identification and drug safety signals detection. These reports pave ways to global large-scale population monitoring. Second, there is further evidence on the importance of coupling phenotypic information in EHR with genotypic information (either in biobank or in gene association studies) for new biomedical knowledge discovery. Third, patient data gathered via social media and self-reporting was found to be comparable to existent data and less labor intensive. This alternative means could potentially overcome data collection challenge in cohort and prospective studies. Finally, it can be noted that metagenomic studies are gaining momentum in bioinformatics and system-level analysis of human microbiome sheds important light on certain human diseases.The current literature showed that the traditional bench to bedside translational research is increasing being complemented by the reverse approach, in which bedside information can be used to provide novel biomedical insights.

Summaryto select and summarize excellent research published during 2011 in the study of human factors in bio-medical informatics.we attempt to derive a synthetic overview of the activity and new trends in this field, from a wide selection of worldwide research papers published during 2011.We selected four papers. The first one presents an international effort aiming to design a guideline for good evaluation practice in health informatics (GEP-HI) [2]. The second reviews medical errors taxonomies from a human factor perspective [3]. The third one advocates the need to systematically perform a deep evaluation process after all healthcare information technologies project deployment [4]. The fourth one explores exit strategies performed by clinician using health record system and how/why we need to anticipate them [5].This papers selection will provide our readers with valuable evidences on past and existing research in the specific field of human factors in healthcare informatics. It can also act as a foundation for stakeholders in the healthcare industry that emphasize the significance of human factors and ergonomics in designing healthcare information systems of the future.

SummaryWe review the latest trends and major developments in translational bioinformatics in the year 2011-2012. Our emphasis is on highlighting the key events in the field and pointing at promising research areas for the future. The key take-home points are:• Translational informatics is ready to revolutionize human health and healthcare using large-scale measurements on individuals.• Data–centric approaches that compute on massive amounts of data (often called “Big Data”) to discover patterns and to make clinically relevant predictions will gain adoption.• Research that bridges the latest multimodal measurement technologies with large amounts of electronic healthcare data is increasing; and is where new breakthroughs will occur.

Clinical research informatics is the rapidly evolving sub-discipline within biomedical informatics that focuses on developing new informatics theories, tools, and solutions to accelerate the full translational continuum: basic research to clinical trials (T1), clinical trials to academic health center practice (T2), diffusion and implementation to community practice (T3), and 'real world' outcomes (T4). We present a conceptual model based on an informatics-enabled clinical research workflow, integration across heterogeneous data sources, and core informatics tools and platforms. We use this conceptual model to highlight 18 new articles in the JAMIA special issue on clinical research informatics.

The future of neonatal informatics will be driven by the availability of increasingly vast amounts of clinical and genetic data. The field of translational bioinformatics is concerned with linking and learning from these data and applying new findings to clinical care to transform the data into proactive, predictive, preventive, and participatory health. As a result of advances in translational informatics, the care of neonates will become more data driven, evidence based, and personalized.

SummaryBackground: In the past decade, Medical Informatics (MI) and Bioinformatics (BI) have converged towards a new discipline, called Biomedical Informatics (BMI) bridging informatics methods across the spectrum from genomic research to personalized medicine and global healthcare. This convergence still raises challenging research questions which are being addressed by researchers internationally, which in turn raises the question of how biomedical informatics publications reflect the contributions from around the world in documenting the research.Objectives: To analyse the worldwide participation of biomedical informatics researchers from professional groups and societies in the best-known scientific conferences in the field. The analysis is focused on their geographical affiliation, but also includes other features, such as the impact and recognition of the conferences.Methods: We manually collected data about authors of papers presented at three major MI conferences: Medinfo, MIE and the AMIA symposium. In addition, we collected data from a BI conference, ISMB, as a comparison. Finally, we analyzed the impact and recognition of these conferences within their scientific contexts.Results: Data indicate a predominance of local authors at the regional conferences (AMIA and MIE), whereas other conferences with a worldwide scope (Medinfo and ISMB) had broader participation. Our analysis shows that the influence of these conferences beyond the discipline remains somewhat limited.Conclusions: Our results suggest that for BMI to be recognized as a broad discipline, both in the geographical and scientific sense, it will need to extend the scope of collaborations and their interdisciplinary impacts worldwide.

Personalized medicine is often associated and discussed in the context of advances from the human genome project. It is true that significant breakthroughs and advancement of deep sequencing and other analytical technologies have greatly expanded the pool of available biological data, but integrating this data into medically meaningful knowledge via translational informatics remains an area of opportunity that is far from being fully realized. Significant opportunities remain for informatics to drive progress towards wide use and utility of personalized medicine by focusing and exploitation of multimodal biomarkers, precompetitive data sharing and a balance between high-content data and rich phenotypic data, such as real observational medical outcomes.