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Summary Objectives: Provide an overview of the emerging themes and notable papers which were published in 2020 in the field of Bioinformatics and Translational Informatics (BTI) for the International Medical Informatics Association Yearbook. Methods: A team of 16 individuals scanned the literature from the past year. Using a scoring rubric, papers were evaluated on their novelty, importance, and objective quality. 1,224 Medical Subject Headings (MeSH) terms extracted from these papers were used to identify themes and research focuses. The authors then used the scoring results to select notable papers and trends presented in this manuscript. Results: The search phase identified 263 potential papers and central themes of coronavirus disease 2019 (COVID-19), machine learning, and bioinformatics were examined in greater detail. Conclusions: When addressing a once in a centruy pandemic, scientists worldwide answered the call, with informaticians playing a critical role. Productivity and innovations reached new heights in both TBI and science, but significant research gaps remain.

Objective The goals of this study are to describe the value and impact of Project HealthDesign (PHD), a program of the Robert Wood Johnson Foundation that applied design thinking to personal health records, and to explore the applicability of the PHD model to another challenging translational informatics problem: the integration of AI into the healthcare system. Materials and Methods We assessed PHD’s impact and value in 2 ways. First, we analyzed publication impact by calculating a PHD h-index and characterizing the professional domains of citing journals. Next, we surveyed and interviewed PHD grantees, expert consultants, and codirectors to assess the program’s components and the potential future application of design thinking to artificial intelligence (AI) integration into healthcare. Results There was a total of 1171 unique citations to PHD-funded work (collective h-index of 25). Studies citing PHD span medical, legal, and computational journals. Participants stated that this project transformed their thinking, altered their career trajectory, and resulted in technology transfer into the commercial sector. Participants felt, in general, that the approach would be valuable in solving contemporary challenges integrating AI in healthcare including complex social questions, integrating knowledge from multiple domains, implementation, and governance. Conclusion Design thinking is a systematic approach to problem-solving characterized by cooperation and collaboration. PHD generated significant impacts as measured by citations, reach, and overall effect on participants. PHD’s design thinking methods are potentially useful to other work on cyber–physical systems, such as the use of AI in healthcare, to propose structural or policy-related changes that may affect adoption, value, and improvement of the care delivery system.

Objectives: Summarize recent research and select the best papers published in 2019 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association Yearbook. Methods: A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the section editors to select a list of 15 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole Yearbook editorial committee was organized to finally decide on the selection of the best papers. Results: Among the 931 retrieved papers covering the various subareas of BTI, the review process selected four best papers. The first paper presents a logical modeling of cancer pathways. Using their tools, the authors are able to identify two known behaviours of tumors. The second paper describes a deep-learning approach to predicting resistance to antibiotics in Mycobacterium tuberculosis. The authors of the third paper introduce a Genomic Global Positioning System (GPS) enabling comparison of genomic data with other individuals or genomics databases while preserving privacy. The fourth paper presents a multi-omics and temporal sequence-based approach to provide a better understanding of the sequence of events leading to Alzheimer’s Disease. Conclusions: Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to personalized medicine.

PurposeThe progress of life science and social science research is contingent on effective modes of data storage, data sharing and data reproducibility. In the present digital era, data storage and data sharing play a vital role. For productive data-centric tasks, findable, accessible, interoperable and reusable (FAIR) principles have been developed as a standard convention. However, FAIR principles have specific challenges from computational implementation perspectives. The purpose of this paper is to identify the challenges related to computational implementations of FAIR principles. After identification of challenges, this paper aims to solve the identified challenges.Design/methodology/approachThis paper deploys Petri net-based formal model and Petri net algebra to implement and analyze FAIR principles. The proposed Petri net-based model, theorems and corollaries may assist computer system architects in implementing and analyzing FAIR principles.FindingsTo demonstrate the use of derived petri net-based theorems and corollaries, existing data stewardship platforms – FAIRDOM and Dataverse – have been analyzed in this paper. Moreover, a data stewardship model – “Datalection” has been developed and conversed about in the present paper. Datalection has been designed based on the petri net-based theorems and corollaries.Originality/valueThis paper aims to bridge information science and life science using the formalism of data stewardship principles. This paper not only provides new dimensions to data stewardship but also systematically analyzes two existing data stewardship platforms FAIRDOM and Dataverse.

Parkinson's disease (PD) is a common neurological disease in elderly people, and its morbidity and mortality are increasing with the advent of global ageing. The traditional paradigm of moving from small data to big data in biomedical research is shifting toward big data-based identification of small actionable alterations. To highlight the use of big data for precision PD medicine, we review PD big data and informatics for the translation of basic PD research to clinical applications. We emphasize some key findings in clinically actionable changes, such as susceptibility genetic variations for PD risk population screening, biomarkers for the diagnosis and stratification of PD patients, risk factors for PD, and lifestyles for the prevention of PD. The challenges associated with the collection, storage, and modelling of diverse big data for PD precision medicine and healthcare are also summarized. Future perspectives on systems modelling and intelligent medicine for PD monitoring, diagnosis, treatment, and healthcare are discussed in the end. Keywords: Parkinson's disease, Healthcare, Disease biomarker, Translational informatics, Systems modelling

BACKGROUND While the pharmacokinetic (PK) mechanisms for many drug interactions (DDIs) have been established, pharmacovigilance studies related to these PK DDIs are limited. Using a large surveillance database, a translational informatics approach can systematically screen adverse drug events (ADEs) for many DDIs with known PK mechanisms. METHODS We collected a set of substrates and inhibitors related to the cytochrome P450 (CYP) isoforms, as recommended by the United States Food and Drug Administration (FDA) and Drug Interactions Flockhart table™. The FDA's Adverse Events Reporting System (FAERS) was used to obtain ADE reports from 2004 to 2018. The substrate and inhibitor information were used to form PK DDI pairs for each of the CYP isoforms and Medical Dictionary for Regulatory Activities (MedDRA) preferred terms used for ADEs in FAERS. A shrinkage observed-to-expected ratio (Ω) analysis was performed to screen for potential PK DDI and ADE associations. RESULTS We identified 149 CYP substrates and 62 CYP inhibitors from the FDA and Flockhart tables. Using FAERS data, only those DDI-ADE associations were considered that met the disproportionality threshold of Ω > 0 for a CYP substrate when paired with at least two inhibitors. In total, 590 ADEs were associated with 2085 PK DDI pairs and 38 individual substrates, with ADEs overlapping across different CYP substrates. More importantly, we were able to find clinical and experimental evidence for the paclitaxel-clopidogrel interaction associated with peripheral neuropathy in our study. CONCLUSION In this study, we utilized a translational informatics approach to discover potentially novel CYP-related substrate-inhibitor and ADE associations using FAERS. Future clinical, population-based and experimental studies are needed to confirm our findings.

The conduct of clinical research is critical to the advancement of health care delivery. Clinical research studies provide the basis for generating high quality and empirically grounded evidence that can be used at the patient and population levels. The design and conduct of contemporary clinical research studies is data, information, and knowledge intensive, requiring the collection, management, integration, analysis, and dissemination of diverse and multi-scale data types. Further, the information needs associated with the design and conduct of clinical studies are impacted by ethical, legal, privacy, and reporting requirements, thus amplifying the complexity of such endeavors. In this chapter, we review the basic principles of clinical study design, the role of various information systems in terms of supporting those designs, and the impact of syntactic and semantic standards in regard to enabling reproducible and rigorous data management and analyses. We will then conclude with an assessment of the current and future state of the field of Clinical Research Informatics (CRI) and open research and development questions therein.

Background: Prostate cancer (PCa) is occurred with increasing incidence and heterogeneous pathogenesis. Although clinical strategies are accumulated for PCa prevention, there is still a lack of sensitive biomarkers for the holistic management in PCa occurrence and progression. Based on systems biology and artificial intelligence, translational informatics provides new perspectives for PCa biomarker prioritization and carcinogenic survey.Methods: In this study, gene expression and miRNA-mRNA association data were integrated to construct conditional networks specific to PCa occurrence and progression, respectively. Based on network modeling, hub miRNAs with significantly strong single-line regulatory power were topologically identified and those shared by the condition-specific network systems were chosen as candidate biomarkers for computational validation and functional enrichment analysis.Results: Nine miRNAs, i.e., hsa-miR-1-3p, hsa-miR-125b-5p, hsa-miR-145-5p, hsa-miR-182-5p, hsa-miR-198, hsa-miR-22-3p, hsa-miR-24-3p, hsa-miR-34a-5p, and hsa-miR-499a-5p, were prioritized as key players for PCa management. Most of these miRNAs achieved high AUC values (AUC > 0.70) in differentiating different prostate samples. Among them, seven of the miRNAs have been previously reported as PCa biomarkers, which indicated the performance of the proposed model. The remaining hsa-miR-22-3p and hsa-miR-499a-5p could serve as novel candidates for PCa predicting and monitoring. In particular, key miRNA-mRNA regulations were extracted for pathogenetic understanding. Here hsa-miR-145-5p was selected as the case and hsa-miR-145-5p/NDRG2/AR and hsa-miR-145-5p/KLF5/AR axis were found to be putative mechanisms during PCa evolution. In addition, Wnt signaling, prostate cancer, microRNAs in cancer etc. were significantly enriched by the identified miRNAs-mRNAs, demonstrating the functional role of the identified miRNAs in PCa genesis.Conclusion: Biomarker miRNAs together with the associated miRNA-mRNA relations were computationally identified and analyzed for PCa management and carcinogenic deciphering. Further experimental and clinical validations using low-throughput techniques and human samples are expected for future translational studies.

Cardiovascular disease (CVD) is the most wide-spread disorder all over the world. The personalized and precision diagnosis, treatment and prevention of CVD is still a challenge. With the developing of metagenome sequencing technologies and the paradigm shifting to data-driven discovery in life science, the computer aided microbiota biomarker discovery for CVD is becoming reality. We here summarize the data resources, knowledgebases and computational models available for CVD microbiota biomarker discovery, and review the present status of the findings about the microbiota patterns associated with the therapeutic effects on CVD. The future challenges and opportunities of the translational informatics on the personalized drug usages in CVD diagnosis, prognosis and treatment are also discussed.

Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.

Drug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research.

As a concept and as a form of practice in the biomedical sciences, “translational research” is both everywhere and yet nowhere, and the challenges to translational success are significant. This essay introduces the notion of translational research in its contemporary sociopolitical context and proceeds to identify problems of communication at the core of translational research. Widely discussed and yet poorly understood, even by those who conduct it, translational research would benefit from the sustained attention of scholars working at the intersection of medical/health humanities and health communication studies.

The practice of cytopathology has dramatically changed due to advances in genomics and information technology. Cytology laboratories have accordingly become increasingly dependent on pathology informatics support to meet the emerging demands of precision medicine. Pathology informatics deals with information technology in the laboratory, and the impact of this technology on workflow processes and staff who interact with these tools. This article covers the critical role that laboratory information systems, electronic medical records, and digital imaging plays in patient-centered personalized medicine. The value of integrated diagnostic reports, clinical decision support, and the use of whole-slide imaging to better evaluate cytology samples destined for molecular testing is discussed. Image analysis that offers more precise and quantitative measurements in cytology is addressed, as well as the role of bioinformatics tools to cope with Big Data from next-generation sequencing. This article also highlights the barriers to the widespread adoption of these disruptive technologies due to regulatory obstacles, limited commercial solutions, poor interoperability, and lack of standardization. Cancer Cytopathol 2017;125(6 suppl):494-501. © 2017 American Cancer Society.

This chapter introduces fundamental concepts of pharmacogenomics, including drug metabolism, and provides an informatics workflow-based perspective inspired by a learning healthcare system framework. Our intent is that the reader sees pharmacogenomics as a foundation of precision medicine, which is reliant upon informatics to deliver actionable patient-tailored knowledge at the point of care. Further, pharmacogenomics knowledge is poised to be further developed so as to be amenable to multi-drug comorbid disease treatment necessitated by precision medicine practice. Upon reviewing this chapter, we hope the reader understands how informatics is uniquely suited to i) enhance clinic-based precision medicine practice through appropriate dissemination of patient-tailored actionable pharmacogenomics knowledge, and ii) to advance the knowledge base underpinning pharmacogenomics by gleaning insights from real world outcomes of these same clinic-based populations. The methodologic considerations highlighted within this workflow-base perspective encompass end-to-end forward and reverse translational informatics activities, designed to both appropriately deploy existing pharmacogenomics knowledge, as well as contribute to its advancement by harnessing insights from real-world data.

Objectives: To summarize recent research and select the best papers published in 2018 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association (IMIA) Yearbook. Methods: A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the two section editors to select a list of 14 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole IMIA Yearbook editorial committee was organized to finally decide on the selection of the best papers. Results: Among the 636 retrieved papers published in 2018 in the various subareas of BTI, the review process selected four best papers. The first paper presents a computational method to identify molecular markers for targeted treatment of acute myeloid leukemia using multi-omics data (genome-wide gene expression profiles) and in vitro sensitivity to 160 chemotherapy drugs. The second paper describes a deep neural network approach to predict the survival of patients suffering from glioma on the basis of digitalised pathology images and genomics biomarkers. The authors of the third paper adopt a pan-cancer approach to take benefit of multi-omics data for drug repurposing. The fourth paper presents a graph-based semi-supervised method to accurate phenotype classification applied to ovarian cancer. Conclusions: Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to personalized medicine.

Networking of medical institutions by means of a capable data infrastructure has the potential to open up vast amounts of routine data to translational cancer research. However, the secondary use of information collected independently in several institutions is a challenging task of data integration. In this review, we discuss the requirements and common challenges involved in the establishment of such a platform. We present methods and tools from the field of medical informatics as solutions to semantic and technical heterogeneity, questions of data protection and record linkage, as well as issues of trust and data ownership. We also describe the architecture of an existing cancer research network as an exemplary application of these methods.

This article reviews some recent achievements reported in the area of Translational Bioinformatics (TBI), which has evolved rapidly as result of the Human Genome Project and subsequent -omic projects. Our goal is to support the understanding and enhancement of informatics research and applications at the intersection between medicine and the -omics fields. We discuss current progress and directions in the road ahead for this field, which already involves a significant number of dedicated professionals in research projects and conferences. Through a literature review, a list of topics of informatics research in TBI has been created, including decision support systems, natural language processing, standards, information retrieval, data, text and opinion mining, electronic health records (EHRs), and data integration. We also describe examples of the most challenging categories for research, such as discovery in EHRs, pharmacogenomics, drug repurposing, and genomic testing for individuals. We conclude with an overview of some of the challenges and opportunities presented by this field for research and education, particularly from the perspective of precision medicine.

Given the rapid advances in biomedical discoveries, the growth of the human population, and the escalating costs of health care, there is an ever increasing need for clinical research that will enable the testing and implementation of cost-effective therapies at the exclusion of those that are not. The fundamentally information-intensive nature of such clinical research endeavors begs for the solutions offered by CRI. As a result, the demand for informatics professionals who focus on the increasingly important field of clinical and translational research will only grow. New models, tools, and approaches need to be developed to achieve this, and this innovation is what will drive the field forward in the coming years.

Purpose The discipline of pharmacovigilance is rooted in the aftermath of the thalidomide tragedy of 1961. It has evolved as a result of collaborative efforts by many individuals and organizations, including physicians, patients, Health Authorities, universities, industry, the World Health Organization, the Council for International Organizations of Medical Sciences, and the International Conference on Harmonisation. Biomedical informatics is rooted in technologically based methodologies and has evolved at the speed of computer technology. The purpose of this review is to bring a novel lens to pharmacovigilance, looking at the evolution and development of the field of pharmacovigilance from the perspective of biomedical informatics, with the explicit goal of providing a foundation for discussion of the future direction of pharmacovigilance as a discipline. Methods For this review, we searched [publication trend for the log10 value of the numbers of publications identified in PubMed] using the key words [informatics (INF), pharmacovigilance (PV), phar-macovigilance þ informatics (PV þ INF)], for [study types] articles published between [1994-2015]. We manually searched the reference lists of identified articles for additional information. Implications Biomedical informatics has made significant contributions to the infrastructural development of pharmacovigilance. However, there has not otherwise been a systematic assessment of the role of biomedical informatics in enhancing the field of pharmacovigilance, and there has been little cross-discipline scholarship. Rapidly developing innovations in biomedical informatics pose a challenge to pharmacovigilance in finding ways to include new sources of safety information, including social media, massively linked databases, and mobile and wearable wellness applications and sensors. With biomedical informatics as a lens, it is evident that certain aspects of pharmacovigilance are evolving more slowly. However, the high levels of mutual interest in both fields and intense global and economic external pressures offer opportunities for a future of closer collaboration.

Purpose Informatics is a relatively new interdisciplinary field which is not very well understood outside of specific disciplinary communities. With a review of the history of informatics and a discussion of the various branches of informatics related to health-care practice, the paper aims to provide an overview designed to enhance the understanding of an information professional interested in this field. Design/methodology/approach The paper is designed to provide a basic introduction to the topic of informatics for information professionals unfamiliar with the field. Using a combination of historical and current sources, the role of informatics in the health professions is explored through its history and development. Findings The emergence of informatics as a discipline is a relatively recent phenomenon. Informatics is neither information technology (IT) nor information science but shares many common interests, concerns and techniques with these other two fields. The role of the informaticist is to transform data to knowledge and information. Consequently, while the outcomes may be different, there are many commonalities in informatics with the work information professionals perform. Originality/value Most introductions to informatics assume the reader is either an IT professional or a clinical practitioner in one of the health science fields. This paper takes a unique approach by positioning the discussion of the history and application of informatics in the health sciences from the perspective of the information professional.

The field of biomedical informatics experienced a productive 2015 in terms of research. In order to highlight the accomplishments of that research, elicit trends, and identify shortcomings at a macro level, a 19-person team conducted an extensive review of the literature in clinical and consumer informatics. The result of this process included a year-in-review presentation at the American Medical Informatics Association Annual Symposium and a written report (see supplemental data). Key findings are detailed in the report and summarized here. This article organizes the clinical and consumer health informatics research from 2015 under 3 themes: the electronic health record (EHR), the learning health system (LHS), and consumer engagement. Key findings include the following: (1) There are significant advances in establishing policies for EHR feature implementation, but increased interoperability is necessary for these to gain traction. (2) Decision support systems improve practice behaviors, but evidence of their impact on clinical outcomes is still lacking. (3) Progress in natural language processing (NLP) suggests that we are approaching but have not yet achieved truly interactive NLP systems. (4) Prediction models are becoming more robust but remain hampered by the lack of interoperable clinical data records. (5) Consumers can and will use mobile applications for improved engagement, yet EHR integration remains elusive.

Since the inception of the Clinical and Translational Science Award (CTSA) program in 2006, leaders in education across CTSA sites have been developing and updating core competencies for Clinical and Translational Science (CTS) trainees. By 2009, 14 competency domains, including biomedical informatics, had been identified and published. Since that time, the evolution of the CTSA program, changes in the practice of CTS, the rapid adoption of electronic health records (EHRs), the growth of biomedical informatics, the explosion of big data, and the realization that some of the competencies had proven to be difficult to apply in practice have made it clear that the competencies should be updated. This paper describes the process undertaken and puts forth a new set of competencies that has been recently endorsed by the Clinical Research Informatics Workgroup of AMIA. In addition to providing context and background for the current version of the competencies, we hope this will serve as a model for revision of competencies over time.

Purpose – This paper aims to describe the emerging field of data science, its significance in the larger information landscape and some issues that distinguish the problems of data science and informatics from traditional approaches in the information sciences. Design/methodology/approach – Through a general overview of the topic, the author discusses some of the major aspects of how work in the data sciences and informatics differ from traditional library and information science. Findings – Data science and informatics, as emerging fields, are expanding our understanding of how the massive amount of information currently being generated can be collected, managed and used. While these may not be traditional “library” problems, the contributions of the library and information science communities are critical to help address aspects of these issues. Originality/value – The emerging fields of data science and informatics have not been extensively explored from the perspective of the information professional. This paper is designed to help information professionals better understand some of the implications of data science in a changing information environment.

Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care.

SummaryObjectives: To reflect on the recent rise of Digital Medicine, as well as to analyse main research opportunities in this area. Through the use of several examples, this article aims to highlight the new role that Biomedical Informatics (BMI) can play to facilitate progress in research fields such as participatory and precision medicine. This paper also examines the potential impact and associated risks for BMI due to the development of digital medicine and other recent trends. Lastly, possible strategies to place BMI in a better position to face these challenges are suggested. Methods: The core content of this article is based on a recent invited keynote lecture delivered by one of the authors (Martin- Sanchez) at the Medical Informatics Europe conference (MIE 2015) held in Madrid in May 2015. Both authors (Lopez-Campos and Martin-Sanchez) have collaborated during the last four years in projects such as the ones described in section 3 and have also worked in reviewing relevant articles and initiatives to prepare this talk. Results and Conclusions: Challenges for BMI posed by the rise of technologically driven fields such as Digital Medicine are explored. New opportunities for BMI, in the context of two main avenues for biomedical and clinical research (participatory and precision medicine) are also emphasised. Several examples of current research illustrate that BMI plays a key role in the new area of Digital Medicine. Embracing these opportunities will allow academic groups in BMI to maintain their leadership, identify new research funding opportunities and design new educational programs to train the next genera -tion of BMI scientists.

The vision for the 2015–2020 American Society for Clinical Pharmacology and Therapeutics (ASCPT) strategic plan is that ASCPT's influence and leadership make it the authority on the science and practice of translational medicine, building on a foundation of clinical pharmacology and therapeutics [http://www.ascpt.org/About-ASCPT/ASCPT-Strategic-Plan]. The vision for Clinical and Translational Science (CTS) is well-suited for ASCPT.(1) Our vision is to become the beacon and organizing principle for the field of translational medicine, as well as to provide a vehicle for ASCPT member publications. The ASCPT definition of translational medicine(1) serves as a framework for the CTS strategy. In brief, “From ASCPT's perspective, translational medicine is a multi-faceted discipline with a focus on translational therapeutics. In a broad sense, translational medicine bridges across the discovery, development, regulation, and utilization spectrum. For clinical pharmacology, the focus of translational research is on the discovery, development, regulation and use of pharmacologic agents to improve clinical outcome, and inform optimal use of therapeutics in patients.” This article is protected by copyright. All rights reserved

The innovative work (Research and development) pipeline is a huge cost for pharmaceutical Organizations. In spite of the requirement for more advancement, Research and development profitability has vegetated or decayed over various years.1-3 More present, the industry has not exhaustively evaluated the effect of new developments in pharmaceutical improvement and market get to particularly as far as basic achievement measurements, for example, clinical preliminary productivity, the probability of medication dispatch and patient access. To invigorate activity on this diagnostic issue, we accumulated and translated hard confirmation on the effect of chosen developments estimated against particular achievement measurements. The general objective of the investigation is to invigorate expansive dialog on how the business can utilize inventive methodologies in medicate advancement and market access to enhance proficiency, revive profitability and revitalize supportability. It is unmistakable in openly evaluating the effect of the most encouraging advancements in sedate improvement on preliminary productivity and accomplishment in dispatch and getting model endorsement around the world. We recommend that it makes convincing, information-driven case for expediting the selection of new market get to forms for drugs. In particular, it demonstrates that the four developments assessed---adaptive trial designs, patient-centric trials, precision medicine trials and real-world data trials reliably convey in contrast to industry achievement touchstone.

Many things have changed but much has remained the same as we have seen a dramatic increase in the generation of genetics, genomics and a variety of clinical data leading to increased data density and continued challenges in organizing and managing that data in pursuit of personalized medicine. Simultaneously, we have seen an increase in commercial and open-source solutions, and marked movement toward open sharing of tools and data in public–private partnerships, yet still few examples of traditional companion diagnostics for personalized medicine products. Most encouraging are examples of focused public and private efforts that have resulted in knowledge leading to critical assessment of existing therapies and the development of new therapies. These examples lay highly emulatable informatics foundations for rapid advances in personalized medicine.

OBJECTIVES/SPECIFIC AIMS: Translational researchers often require the use of informatics methods in their work. Lack of an understanding of key informatics principles and methods limits the abilities of translational researchers to successfully implement Findable, Accessible, Interoperable, Reusable (FAIR) principles in grant proposal submissions and performed studies. In this study we describe our work in addressing this limitation in the workforce by developing a competency-based, modular course in informatics to meet the needs of diverse translational researchers. METHODS/STUDY POPULATION: We established a Translational Research Informatics Education Collaborative (TRIEC) consisting of faculty at the University of Utah (UU) with different primary expertise in informatics methods, and working in different tiers of the translational spectrum. The TRIEC, in collaboration with the Foundation of Workforce Development of the Utah Center for Clinical and Translational Science (CCTS), gathered informatics needs of early investigators by consolidating requests for informatics services, assistance provided in grant writing, and consultations. We then reviewed existing courses and literature for informatics courses that focused on clinical and translational researchers [3–9]. Using the structure and content of the identified courses, we developed an initial draft of a syllabus for a Translational Research Informatics (TRI) course which included key informatics topics to be covered and learning activities, and iteratively refined it through discussions. The course was approved by the UU Department of Biomedical Informatics, UU Graduate School and the CCTS. RESULTS/ANTICIPATED RESULTS: The TRI course introduces informatics PhD students, clinicians, and public health practitioners who have a demonstrated interest in research, to fundamental principles and tools of informatics. At the completion of the course, students will be able to describe and identify informatics tools and methods relevant to translational research and demonstrate inter-professional collaboration in the development of a research proposal addressing a relevant translational science question that utilizes the state-of-the-art in informatics. TRI covers a diverse set of informatics content presented as modules: genomics and bioinformatics, electronic health records, exposomics, microbiomics, molecular methods, data integration and fusion, metadata management, semantics, software architectures, mobile computing, sensors, recruitment, community engagement, secure computing environments, data mining, machine learning, deep learning, artificial intelligence and data science, open source informatics tools and platforms, research reproducibility, and uncertainty quantification. The teaching methods for TRI include (1) modular didactic learning consisting of presentations and readings and face-to-face discussions of the content, (2) student presentations of informatics literature relevant to their final project, and (3) a final project consisting of the development, critique and chalk talk and formal presentations of informatics methods and/or aims of an National Institutes of Health style K or R grant proposal. For (3), the student presents their translational research proposal concept at the beginning of the course, and works with members of the TRIEC with corresponding expertise. The final course grade is a combination of the final project, paper presentations and class participation. We offered TRI to a first cohort of students in the Fall semester of 2018. DISCUSSION/SIGNIFICANCE OF IMPACT: Translational research informatics is a sub-domain of biomedical informatics that applies and develops informatics theory and methods for translational research. TRI covers a diverse set of informatics topics that are applicable across the translational spectrum. It covers both didactic material and hands-on experience in using the material in grant proposals and research studies. TRI’s course content, teaching methodology and learning activities enable students to initially learn factual informatics knowledge and skills for translational research correspond to the ‘Remember, Understand, and Apply’ levels of the Bloom’s taxonomy [10]. The final project provides opportunity for applying these informatics concepts corresponding to the ‘Analyze, Evaluate, and Create’ levels of the Bloom’s taxonomy [10]. This inter-professional, competency-based, modular course will develop an informatics-enabled workforce trained in using state-of-the-art informatics solutions, increasing the effectiveness of translational science and precision medicine, and promoting FAIR principles in research data management and processes. Future work includes opening the course to all Clinical and Translational Science Award hubs and publishing the course material as a reference book. While student evaluations for the first cohort will be available end of the semester, true evaluation of TRI will be the number of trainees taking the course and successful grant proposal submissions. References: 1. Wilkinson MD, Dumontier M, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016 Mar 15. 2. National Center for Advancing Translational Sciences. Translational Science Spectrum. National Center for Advancing Translational Sciences. 2015 [cited 2018 Nov 15]. Available from: https://ncats.nih.gov/translation/spectrum 3. Hu H, Mural RJ, Liebman MN. Biomedical Informatics in Translational Research. 1 edition. Boston: Artech House; 2008. 264 p. 4. Payne PRO, Embi PJ, Niland J. Foundational biomedical informatics research in the clinical and translational science era: a call to action. J Am Med Inform Assoc JAMIA. 2010;17(6):615–6. 5. Payne PRO, Embi PJ, editors. Translational Informatics: Realizing the Promise of Knowledge-Driven Healthcare. Softcover reprint of the original 1st ed. 2015 edition. Springer; 2016. 196 p. 6. Richesson R, Andrews J, editors. Clinical Research Informatics. 2nd ed. Springer International Publishing; 2019. (Health Informatics). 7. Robertson D, MD GHW, editors. Clinical and Translational Science: Principles of Human Research. 2 edition. Amsterdam: Academic Press; 2017. 808 p. 8. Shen B, Tang H, Jiang X, editors. Translational Biomedical Informatics: A Precision Medicine Perspective. Softcover reprint of the original 1st ed. 2016 edition. S.l.: Springer; 2018. 340 p. 9. Valenta AL, Meagher EA, Tachinardi U, Starren J. Core informatics competencies for clinical and translational scientists: what do our customers and collaborators need to know? J Am Med Inform Assoc. 2016 Jul 1;23(4):835–9. 10. Anderson LW, Krathwohl DR, Airasian PW, Cruikshank KA, Mayer RE, Pintrich PR, Raths J, Wittrock MC. A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives, Abridged Edition. 1 edition. New York: Pearson; 2000.

OBJECTIVES/SPECIFIC AIMS: The objective of this research was to identify and evaluate published research articles that highlight the use of CTSA institutional websites as a research tool or data source for translational science research. METHODS/STUDY POPULATION: A multifaceted systematic search process was engaged for this literature review process using standard literature database searching, digital journal database searching, and pearl growing. All U.S. based studies and reports from 2006 through the present that addressed the application of websites of CTSA institutions for translational science purposes were included in this review. Identified articles were collected, organized, and analyzed using an excel spreadsheet. There were 2 different data collection and organization protocols, one for studies the other for reports. The first data collection protocol was for identified studies that used individual CTSA Institutional websites as a data source for a research topic. The organization processes for each relevant study article included a customized data extraction process that looked to identify a standard group of key elements from each study: (1) The study’s NCATS CTSA Goal. (2) The type of data searched in the CTSA institutional website. (3) The number of CTSA institutional websites searched. (4) The number of sites that had the needed data. (6) The outcomes reported from the research. The second data collection protocol was for identified reports that referenced single CTSA Institutional websites as performing a specific translational informatics functionality either as a portal to Clinical and Translational Science Award tools and resources or as a direct information source. The organizational process for each relevant report article also included a customized data extraction process that looked to identify a standard group of key elements from each report: (1) NCATS / CTSA Goal (2) Tool or Functionality Promoted (3) Description (4) Website used as portal or direct tool. (5) Target Audience. RESULTS/ANTICIPATED RESULTS: The studies were summarized using the standard group of key elements identified for data extraction and summarized in a table. In 5 of the 6 studies, researchers relied on CTSA member individual website content to mine necessary data. One (1) of the studies employed a mixed methods approach to data acquisition and only relied on CTSA member individual website content for CTSA institutions that did not respond to a user survey. One (1) study used a survey to learn about CTSA website content rather than review the websites. In 5 of the 6 studies, researchers reviewed individual CTSA websites for the purposes of determining the number or percentages of CTSA institutions had specific data. One (1) study instead reviewed the individual websites to develop a broader picture of what the CTSA Consortium offered as a group. The percentage of CTSA websites that had the needed data of the researchers ranged from 32% to 100%. The median and mean scores for CTSA websites having the needed data was 66% and 66.5% respectively. One study did not provide specific information for assessment. All 6 studies included research that fell within at least 2 categories of the 5 NCATS CTSA Goal topics. The category most investigated was translational research processes where 5 of the 6 studies investigated how CTSA websites looked to improve the quality and efficiency of translational research. Three (3) studies investigated how CTSA’s cultivated and trained the clinical and translational science workforce. Two (2) studies investigated how CTSA’s engaged patients and communities in the translational research process. Two (2) studies investigated how CTSA’s promoted the integration of underserved populations. One (1) study investigated ways the CTSA’s used their websites to advance the use of cutting-edge informatics. The outcomes reported included (1) the percentage CTSA individual websites that provided information regarding patient recruitment. (2) A list of generic services provided across the CTSA Individual website medium. (3) The number of CTSA individual website education and training programs. (4) The number and quality of informed consent forms presented online. (5) Investigational New Drug (IND) / Investigational Device Exemption (IDE) training methods for CTSA Investigators. (6) The percentage of KL2 Awards used by Child Health Investigators at CTSA Institutions. The reports (rn=9) were also summarized using the standard group of key elements identified for data extraction and summarized in a table. All six articles reported using their Institutional CTSA website as either a portal or a tool to promote clinical and translational science as outlined through NCATS goals. A CTSA website is used as a portal when it provides links to other sites, tools, or programs. A CTSA Website is used as a tool when it provides the functionality within its web design like providing an online application or database, or interactive training pages. In 8 of the 9 articles, authors reported on CTSA institutional website as either a translational informatics portal or providing informatics functionalities. Four (4) of the articles reported the use of their website for engagement, on either the collaborator or patient level, such as advocacy, education, or subject enrollment. Two (2) articles reported the use of their CTSA website for the cultivation and training of a clinical and translational science workforce. Four (4) articles reported on the use of their CTSA website for the purposes of increasing the quality and efficiency of translational research. None of the articles reported how their sites were used to promote the integration of underserved populations. All the reports identified a CTSA institutional website as a tool to leverage or disseminate CTSA capabilities and functionality. The access point and or warehousing of these capabilities was the CTSA institutional website. The target audience for these publications included researchers, clinical research administrators, IT programmers, community collaborators, and research subjects. The articles that reported on the use of CTSA institutional websites for clinical and translational functionality included topics such as: (1) the introduction of an informatics tool that searches clinical notes to identify clinical data for research. (2) the promotion of an online research subject advocacy program. (3) the introduction of an informatics tool portal that allows researchers flexible, efficient and effective means of collaboration and interaction with data. (4) the promotion of a team development project tool. (5) the introduction of a research participant registry and study promotion and education tool. (6) the promotion of an independent informatics tool registry that could connect to all CTSA websites. DISCUSSION/SIGNIFICANCE OF IMPACT: This research shows that CTSA institutional website functionality and content contributes to the CTSA body of research and the advancement NIH translational science goals.

We live in the genomic era of medicine, where a patient's genomic/molecular data is becoming increasingly important for disease diagnosis, identification of targeted therapy, and risk assessment for adverse reactions. However, decoding the genomic test results and integrating it with clinical data for retrospective studies and cohort identification for prospective clinical trials is still a challenging task. In order to overcome these barriers, we developed an overarching enterprise informatics framework for translational research and personalized medicine called Synergistic Patient and Research Knowledge Systems (SPARKS) and a suite of tools called Oncology Data Retrieval Systems (OncDRS). OncDRS enables seamless data integration, secure and self-navigated query and extraction of clinical and genomic data from heterogeneous sources. Within a year of release, the system has facilitated more than 1500 research queries and has delivered data for more than 50 research studies.

The aim of this study is to identify and review literature that presented information about development and application of pharmacoinformatics in pharmaceutical and health sciences. The quality assessment tool for quantitative studies suggested by Cochrane Collaboration was adopted in this review. Independent assessment was conducted to evaluate the quality of the included studies. A databases used for this study and review was PubMed and Science Direct. Both databases search was conducted using the English key words, “pharmacoinformatics”, “pharmacy informatics”, “medical informatics”, “health informatics” and etc. The search strategy resulted in the inclusion of sources, the majority of which expert opinion and examines the pharmacoinformatics relevance from a theoretical point of view (PubMed, n=72). Based from the keyword of “informatics” and “pharmacy” on PubMed databases using advanced search, 59 articles was obtained with particular fields which is title and abstract. The articles are then being filtered by article type, publication dates and languages of articles. The article type which is clinical trial (n=2) and in review articles (n=7). The articles has the range of publication dates which is 10 years (n=34) and 15 years (n=72). The rapid development of internet has led to the pharmacoinformatics technologies to assist the pharmaceutical care and health-related outcomes. Based on the study, it can be concluded that pharmacoinformatics has a lot of advantages and uses especially in pharmaceutical and health sciences. Key words: Computer, Health informatics, Internet, Information system, Medical informatics, Pharmacy informatics.

The article studies the development process of medical informatics specialty terminology as the ground for further research into foreign countries’ experience, including the Canadian one, of specialists’ professional training in the field of MI. The study determines the origin and chief stages of the formation and development of the medical informatics terminological system. The author performs the comparative analysis of terms used by the world organizations on health care informatisation issues, particularly International Medical Informatics Association as well as medical informatics associations of the USA and Canada as the leading countries where qualified workforce in the medical informatics specialty is trained. The European and Ukrainian experience has also been taken into consideration. The results of the comparative study have shown that the English terms ‘medical informatics’, ‘biomedical informatics’ and ‘health informatics’ serve as the umbrella terms for professional training programs and include a set of subspecialties that identify diverse spheres of information technology applications to medical science and practice, namely ‘clinical informatics’, ‘bioinformatics’, ‘health care informatics’, ‘nursing informatics’, ‘imaging informatics’, etc.

Display Omitted Cognitive informatics (CI) research has its foundations in cognitive science.Transformations seen in CI in JBI reflect the changes seen broadly in the field of CI.Key topics include decision-making, usability, comprehension, workflow and errors.Recent developments toward use of applied cognition for usability and HCI studies.Future trends point toward consumer health tools and the use of mobile technology. Cognitive Informatics (CI) is a burgeoning interdisciplinary domain comprising of the cognitive and information sciences that focuses on human information processing, mechanisms and processes within the context of computing and computer applications. Based on a review of articles published in the Journal of Biomedical Informatics (JBI) between January 2001 and March 2014, we identified 57 articles that focused on topics related to cognitive informatics. We found that while the acceptance of CI into the mainstream informatics research literature is relatively recent, its impact has been significant - from characterizing the limits of clinician problem-solving and reasoning behavior, to describing coordination and communication patterns of distributed clinical teams, to developing sustainable and cognitively-plausible interventions for supporting clinician activities. Additionally, we found that most research contributions fell under the topics of decision-making, usability and distributed team activities with a focus on studying behavioral and cognitive aspects of clinical personnel, as they performed their activities or interacted with health information systems. We summarize our findings within the context of the current areas of CI research, future research directions and current and future challenges for CI researchers.

In this article are considered the integrant importance of informatics and informational technologys includes the sciences and the humanities.There are a differences between scientifi c grounds of the various information orientations, which include physical informatics, bioinfomatics, technical and social informatics. Creation of a united theoretical base for these orientations is very problematical. The metodologically important issue of classifi cation different informatics is a part of the general informatics, the example of which are considered here.

This chapter presents a summary of a sample of research in the field of biomedical informatics. The topics include digital health research, medical decision support systems, Bayesian networks, tele-monitoring, preprocessing in high dimensional datasets.

Education in biomedical and health informatics (BMHI) has been established in many countries throughout the world. For degree programs in BMHI we can distinguish between those that are completely stand-alone or dedicated to the discipline vs. those that are integrated within another program. After running integrated degree medical informatics programs at TU Braunschweig for 10 years at the B.Sc. and for 15 years at the M.Sc level, we (1) report about this educational approach, (2) analyze recommendations on, implementations of, and experiences with degree educational programs in BMHI worldwide, (3) summarize our lessons learned with the integrated approach at TU Braunschweig, and (4) suggest an answer to the question, whether degree programs in biomedical and health informatics should be dedicated or integrated. According to our experience at TU Braunschweig and based on our analysis of publications, there is a clear dominance of dedicated degree programs in BMHI. The specialization in medical informatics within a computer science program, as offered at TU Braunschweig, may be a good way of implementing an integrated, informatics-based approach to medical informatics, in particular if a dual degree option can be chosen. The option of curricula leading to double degrees, i.e. in this case to two separate degrees in computer science and in medical informatics might, however, be a better solution.

The rise of precision medicine combined with the variety of biomedical data sources and their heterogeneous nature make the integration and exploration of information that they retain more complicated. In light of these issues, translational research platforms were developed as a promising solution. Research centers have used translational tools for the study of integrated data for hypothesis development and validation, cohort discovery and data-exploration. For this article, we reviewed the literature in order to determine the use of translational research platforms in precision medicine. These tools are used to support scientists in various domains regarding precision medicine research. We identified eight platforms: BRISK, iCOD, iDASH, tranSMART, the recently developed OncDRS, as well as caTRIP, cBio Cancer Portal and G-DOC. The last four platforms explore multidimensional data specifically for cancer research. We focused on tranSMART, for it is the most broadly used platform, since its development in 2012.

The field of Biomedical Informatics (BMI) is concerned with multimethod approaches to generating contextualized information and actionable knowledge from a variety of biological and healthcare-relevant data types. In doing so, BMI practitioners adopt and adapt a number of methods drawn from the computational, quantitative, and qualitative sciences. In this chapter, we provide an overview of such methods and a rationale for how they can be applied to address driving biological and clinical problems. Such use cases span a range from the biomolecular characterization of disease states to the comprehensive phenotyping of patients to the promotion of population health. In doing so, we hope to equip readers with the ability to critically understand and evaluate the use of multimethod approaches as are commonly encountered in the aforementioned application areas.

Health informatics is becoming more important to the delivery of health and health care and more diverse in the kinds of settings and expertise that health informatics professionals will need to be successful. Precision medicine initiatives will need to leverage translational informatics and data sciences to connect genomic and data insights with actionable clinical information. Consumer-focused health devices and electronic health records raise issues of interoperability, usability, and workflow integration that are essential to applied informatics professionals. In the health policy realm, changing models of reimbursement not only will require population health and data sciences expertise, but also will require knowing the right questions to ask and how to integrate the answers into health care. It is the informatics professionals, represented by AMIA’s 5,300-plus members, who possess the leadership and expertise to contribute across these domains and to make a real difference in how information is collected, analyzed, and used to improve the health of our society. But other organizations are seeing the importance of health informatics and the potential for financial and marketing opportunities. Programs are springing up that profess to teach health informatics, but are using faculty who lack informatics training and are teaching to a curriculum that does not include core informatics concepts. Associations are offering …