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Amazon Link Companion Website for 14th Ed. Sadalage, P. NoSQL distilled: a brief guide to the emerging world of polyglot persistence. Hoboken, New Jersey: Pearson. Amazon Li nk. Legal Ethical Aspects of Health Informatics. Required Textbook McWay, D. Legal and ethical aspects of health information management 4th ed. Ethics, computing, and medicine: Informatics and the transformation of health care.

Cambridge: Cambridge University Press. Foundations of Biomedical Information Sciences I. Kimberly A. Required Textbook Shortliffe, E. Coiera, E. Python for Everybody: Exploring Data in Python 3. Essential Writing Skills for College and Beyond. Xiaoqian Jiang, PhD. Recommended Textbook Hoyt, R.


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Health informatics: Practical guide 7th ed. Quality and Outcome Improvement in Healthcare. Introduction to healthcare quality management 3rd ed. System Analysis and Project Management. Systems analysis and design 11th ed. Effective project management: Traditional, agile, extreme 7th ed. Indianapolis, IN: Wiley. Workflow Process Modeling.

Methods in Biomedical Informatics: A Pragmatic Approach by Indra Neil Sarkar - jyqykuxuha.tk

Workflow modeling: Tools for process improvement and applications development 2nd ed. Norwood, MA: Artech House. Health Informatics: A practical guide for healthcare and informatics technology professionals 6th ed. Recommended Textbook Friedman, C. Evaluation methods in biomedical informatics 2nd ed. Introduction to Bioinformatics. Jim Zheng, PhD. Recommended Textbooks Campbell, A. Discovering genomics, proteomics, and bioinformatics 2nd ed. Bioinformatics and functional genomics 3rd ed.

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Chichester Wiley-Blackwell. Systems Medicine: Principles and Practice. Xiaobo Zhou, Phd. Recommended Readings Yankeelov, T. Annals of biomedical engineering, 44 9 , — Systems Medicine. DOI: Hardcover ISBN: Softcover ISBN: Series ISSN: Research Design and Evaluation in Biomedical Informatics. Required Textbook Jacobsen, K. Introduction to health research methods: A practical guide 2nd ed. We argue that distributed cognition theory can have descriptive, rhetorical, inferential and application power.

For evidence-based health informatics it can lead to design changes and hypotheses that can be tested. This chapter introduces Actor-Network Theory, a sociotechnical approach to studying health information technology implementation. The chapter is intended as a pragmatic introduction to the field, acknowledging that there are many contested features of an Actor-Network Theory informed methodology. Nevertheless, the approach can be usefully drawn on to help to focus data collection and sampling.

In doing so, this chapter offers a reflexive account of how Actor-Network Theory has provided a nuanced analysis of how the implementation of national electronic health records affected different stakeholders, organizations and technology. High reliability organisations operate safely in situations of high risk by organising for collective mindfulness. They do so through five ongoing processes geared towards anticipating, containing, and making sense of the unexpected.

The five processes are: preoccupation with failure, reluctance to simplify interpretations, sensitivity to operations, commitment to resilience, and deference to expertise. In health informatics, the theory of collective mindfulness can be used to explain health information technology IT development and implementation, across its life cycle, and inform guidance towards mindful management of IT projects.

Further, the theory challenges a static and linear understanding of success or failure of health IT initiatives, supporting instead an argument for outcomes — be it reliability and safety, or IT project success — as collective, complex and dynamic achievements of mindful organising practices.

The accumulation of medical knowledge, technology and expertise has provided people with more and more options to improve their health and increase longevity.

However, healthcare options typically come with benefits as well as harms and often involve important and complex, high-stakes trade-offs. Its vision is to equip individuals with competences, for instance improved risk literacy, to empower them to make well-informed choices when facing a difficult choice, such as decisions about health issues.

Application of the boosting framework to personal health choices and the SDM process unveils new and promising horizons for future research and could inform the design and evaluation of health informatics interventions such as decision support systems. Inadequate communication is a factor in suboptimal junior doctor management of deteriorating ward patients.

However, junior doctors are already dissatisfied with existing technologies for general hospital communication. The Deterioration Communication Management Theory DCMT provides a means to approach these issues by uniting two themes: 1 factors affecting the properties of ICT used to communicate to junior doctors; and 2 factors affecting junior doctor interpretation of communication about deteriorating hospital patients. ICT factors include how the combination of physical devices and mode of usage affect user perception of system reliability and efficiency.

Junior doctors interpret clinician communication about patient deterioration in terms of risk, which is affected by their contextual responsibility and experience. Perceived risk and contextual experience in turn affects their communication efficiency. Combining these themes gives more options to explain junior doctor communication in this clinical context and to design ICT systems to improve it. This chapter presents an overview of Resilient Health Care RHC , introducing two aspects of RHC that are important for designing sustainable digital health systems and for considering implementation outcomes: 1 understanding how normal variation in everyday work can affect implementation of digital health interventions, and 2 the role of information systems in coping with unexpected events.

The importance of considering how variation in everyday work can lead to wanted and unwanted outcomes when designing information systems is illustrated through a case study of implementation of a telehealth intervention. We examine how normal variation in everyday work can lead to both safety and error, and discuss how consideration of system resilience when designing and implementing health informatics applications can contribute to improving safety for patients in the future.

How health information systems can assist organisations in coping with the unexpected is illustrated through a second case study, of a thunderstorm asthma event in Melbourne, Australia. We briefly present the thunderstorm asthma case, and discuss the role of healthcare informatics in preparing for future unexpected events affecting population health.

The rising use of the Internet and information technology has made computerized interventions an attractive channel for providing advice and support for behaviour change. Health behaviour and behaviour change theories are a family of theories which aim to explain the mechanisms by which human behaviours change and use that knowledge to promote change. We discuss three examples of how behaviour change theories have been applied in computer-based interventions: a system to aid users to quit smoking, a decision aid for choice of breast cancer therapy, and an internet-based exercise program for reducing cardiovascular risk.

We also discuss misapplication of theory, and reflect on how these theories can best be used. Behaviour change theory can be applied in health informatics interventions in several ways; for example, to select participants for a particular intervention, to shape the content of the intervention to effectively influence behaviour, or to tailor content to individual needs. Control theory is about the processes underlying the behaviour of self-regulating agents.

It proposes that behaviour is regulated by a negative feedback loop, in which the agent compares the perception of its current state against a goal state and will strive to reduce perceived discrepancies by modifying its behaviour. Although studies in health informatics often do not report the use of this theory, the principle of a negative feedback loop underlies many applications in the field.

This chapter describes how control theory fits within health informatics, discussing its role in the development and assessment of audit and feedback interventions in healthcare. Control theory has been used to synthesise evidence of audit and feedback, and to design and evaluate interventions to improve the quality of blood transfusion practice, cardiac rehabilitation, and intensive care. This has driven progress in our understanding of the underlying mechanisms of audit and feedback for improving health care, and has helped to design better interventions. Successful implementation of health informatics systems depends not only on efficient performance of intended tasks, but also integration into existing working relationships and environments.

Implementation is an understudied area in health informatics research, and relevant empirical evidence is often absent from strategic decision making. Implementation theories such as Normalization Process Theory NPT can help address this gap by providing explanations for relevant phenomena, proposing important research questions, and framing collection and analysis of data. NPT identifies, characterizes, and explains mechanisms that have been empirically demonstrated to affect implementation processes and outcomes.

These explanations are generalizable and facilitate comparative investigations. The first section of this chapter introduces the four main constructs of NPT coherence, cognitive participation, collective action, and reflexive monitoring and their constituent components. Each component is discussed with reference to a real-world example, and relationships between the four constructs are explored. The second section explores how NPT has been applied in both prospective planning of interventions and their evaluation, as well as retrospective exploration of factors promoting or inhibiting successful implementation.

We examine two examples from published literature: firstly, prospective planning of an evaluation study on implementation of a digital health intervention for Type-2 diabetes; and secondly an evaluation of implementation of a new electronic preoperative information system within a surgical pre-assessment clinic. The chapter concludes with reflections on some limitations of NPT as a theoretical framework. Technologies are often viewed as the route to better, safer and more efficient care, but technology projects rarely deliver all the benefits expected of them.

Based on a literature review and empirical case studies, we developed a framework NASSS for studying the non-adoption, abandonment and challenges to scale-up, spread and sustainability of technology-supported change efforts in health and social care. Such projects meet problems usually because they are too complex — and because the complexity is sub-optimally handled. NASSS consists of six domains — the illness or condition, the technology, the value proposition, the individuals intended to adopt the technology, the organisation s and the wider system — along with a seventh domain that considers how all these evolve over time.

The NASSS framework incorporates a number of other theories and analytic approaches described elsewhere in this book. It is not intended to offer a predictive or formulaic solution to technology adoption. Rather, NASSS should be used to generate a rich and situated narrative of the multiple influences on a complex project; to identify parts of the project where complexity might be reduced; and to consider how individuals and organisations might be supported to handle the remaining complexities better.

In this chapter, we reflect on the aim and objectives of the textbook and address known gaps in our theory coverage. We reinforce the importance of theory in health informatics and review the varying disciplinary origins of the theories considered in the book. We discuss the question of what makes a good theory and how to know which one is relevant for a given study. Finally, we propose topics to form a research agenda for theory in health informatics.

Guest Access. Register Log in. As a guest user you are not logged in or recognized by your IP address. Series Studies in Health Technology and Informatics. Description The American Medical Informatics Association AMIA defines the term biomedical informatics BMI as: The interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.

Download complete PDF. Order hardcopy. Front Matter. Preface Philip J. Definitions of Health Informatics There is still no single universally agreed definition of health informatics, but we now seem to have a converging set of ideas. Structure of the Book We set our overall learning aim for the textbook as: What theories have been applied in health informatics and what difference have they made? The specific objectives were: To show where and how interdisciplinary theories have been applied in health informatics To identify theory developed specifically within health informatics To highlight where further work is necessary to develop theory-based approaches.

Suggested Use in Teaching We suggest that the specified learning objectives in each chapter might be used to construct a teaching plan for a given lecture or seminar. Acknowledgements The editors gratefully acknowledge all our colleagues who gave formative advice in the planning of this book and, of course, all our chapter authors and peer reviewers. References [1] K. Download PDF. Abstract This chapter introduces the idea of theories in health informatics, defines what we mean by theory and distinguishes theories from models, frameworks and predictive principles.

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Information Science and Technology Theories. Abstract General System Theory was proposed in the post-war period as a unifying framework for interdisciplinary science based on the idea that systems have a set of similar properties and characteristics regardless of discipline. Information Theory and Medical Decision Making. Abstract Information theory has gained application in a wide range of disciplines, including statistical inference, natural language processing, cryptography and molecular biology. Abstract Information value chain theory provides a straightforward approach to information system evaluation and design.

A Pragmatic Approach for Organizations to Measure Health Care Professional Well-Being

Abstract mHealth can offer great potential for the self-management of health conditions and facilitating health services. Part 2. Social and Psychological Theories. Abstract Distributed cognition theory posits that our cognitive tasks are so tightly coupled to the environment that cognition extends into the environment, beyond the skin and the skull. Abstract This chapter introduces Actor-Network Theory, a sociotechnical approach to studying health information technology implementation.

Course is taken during final semester of enrollment. Topics focus on environmental forces shaping the direction of the development of new therapeutics in the United States and include clinical and translational research as part of healthcare, the therapeutic development process, relevant federal agencies and regulations, and economic factors. No Prerequisites BMIG Clinical Data Standards 1 Credit This graduate course reviews the various standards used in healthcare, with special focus on how those standards are used in electronic health records.

The course covers the use of imaging information in several key medical specialties. Introduction to Biomedical Informatics BMIG Clinical Research Informatics 3 Credits This graduate course presents information-reliant processes in clinical research with an emphasis on major theories, principles, and methods used in practice and inquiry in Clinical Research Informatics. BMIG Comparative Microbial Genomics 3 Credits The aim for this graduate course is to teach about the comparison of massive availability of genome sequence of microbes and other organisms.

The course is designed to enable student to use computational tools through lectures and hands-on practical to extract biological meanings and discover novel features from the genomic data.

Under supervision of graduate faculty, an original research study will be designed and conducted with written dissertation following Graduate School guidelines. All coursework completed. Students completing this course should be able to locate reference genomes, computationally compare genomes of interest and clearly communicate the results of the investigation using three different formats: a journal club report critiquing a recently published paper, a poster, and finally by writing a scientific paper which is formatted and suitable for publication.

Topics focus on system functionality required to support care in inpatient and outpatient settings and associated data and workflows. No Prerequisites BMIG Healthcare in the US 1 Credit This graduate course presents the components of the healthcare system in the United States with a focus on current challenges and external forces shaping those challenges. Special emphasis is given to topics impacting or impacted by technology in healthcare. Topics focus on methods and tools to achieve the Institute of Medicine components of healthcare quality in clinical settings.

No Prerequisites BMIG Information Systems in Clinical Research 1 Credit This graduate course covers information systems used in Clinical Research with an emphasis on automation, system functionality, system integration, and information exchange. Common information-reliant and automated processes and methodology are explored.

The course will start with an overview of graphs; basic definitions and concepts, families of graphs, describe creating network graphs and analysis of network graph characteristics, statistical models for Network graphs and network topology inference. The course will concentrate on building correlation networks as an example.

Major topics include defining the discipline, information flow at the molecular and cellular level, declarative, probabilistic and procedural knowledge, Biomedical ontology, relational theory, and concepts involving development, implementation, use, and evaluation of computer systems in biomedicine. The lab portion of the course offers hands-on exposure to and experience with methods and tools used across the discipline.

After the course, the student should have a comfort level discussing areas of practice and digesting research findings. The introduction to the discipline is continued through additional key application areas, thought leaders, seminal work and methods common across Biomedical Informatics. Major topics covered include structured, semi-structured and unstructured data in biomedicine, computer-aided human use of data in biomedicine, and ethical, policy and legal issues in biomedical informatics.

This two-part course series serves as the foundation for the Biomedical Informatics Graduate Program educational goals by providing a survey of the discipline and general awareness of the major areas of practice and crosscutting concepts, theories and methods. The didactic portion of the course offers students the opportunity to internalize the goals and value of the discipline to human health. No Prerequisites BMIG Introduction to Human Computer Interaction 3 Credits This graduate course is a survey course covering select topics from cognitive science, human factors, human centered design, and usability relevant to biomedical informatics.

Topics covered include leadership models, interdisciplinary teams, effective communication, project management, change management, and strategic and financial planning for clinical information. No Prerequisites BMIG Neuroimaging Informatics and Connectomics 3 Credits This graduate course will explore in depth the use of advanced imaging techniques and quantitative analysis approaches in Neuroscience research.

The focus is distinct from clinical imaging and standard clinical practice. Pre-clinical and advanced imaging techniques not yet approved for the clinic will be explored. Topics include epistemology, concept, construct and theory development, qualitative and mixed methods approaches as well as experimental and quasi-experimental design. The purpose of this course is to aid students in selecting, articulating and defending appropriate research designs for thesis or doctoral research.

No Prerequisites BMIG Research Imaging Informatics 3 Credits This graduate course will explore in depth the use of advanced radiology and pathology imaging techniques and quantitative analysis approaches in biomedical research. Image creation, quantitative analysis and management technologies will be presented drawing on the primary literature and making full use of unique imaging resources at UAMS such as the Cancer Imaging Archive. Students completing this course should be able to present a scientific dataset in a clear, informative and reader-friendly manner. The course includes discussion of the figures of selected scientific publications.

Students will make criticisms on the figures to identify the strong and weak components and discuss the alternative ways to improve the visualization.