The AMIA biomedical informatics (BMI) core competencies have been designed to support and guide graduate education in BMI, the core scientific discipline underlying the breadth of the field's research, practice, and education. The core definition of BMI adopted by AMIA specifies that BMI is '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.' Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine. The shared core informatics competencies of BMI draw on the practical experience of many specific informatics sub-disciplines. The AMIA BMI analysis highlights the central shared set of competencies that should guide curriculum design and that graduate students should be expected to master.

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n recent years there has been increasing interest in describing complicated information processing systems in terms of the knowledge they have, rather than by the details of their implementation. This requires a means of modeling the knowledge in a system. Several different approaches to knowledge modeling have been developed by researchers working in Artificial Intelligence (AI). Most of these approaches share the view that knowledge must be modeled with respect to a goal or task. In this article, we outline our modeling approach in terms of the notion of a task-structure, which recursively links a task to alternative methods and to their subtasks. Our emphasis is on the notion of modeling domain knowledge using tasks and methods as mediating concepts. We begin by tracing the development of a number of different knowledge-modeling approaches. These approaches share many features, but their differences make it difficult to compare systems that have been modeled using different approaches. We present these approaches and describe their similarities and differences. We then give a detailed description, based on the task structure, of our knowledge-modeling approach and illustrate it with task structures for diagnosis and design. Finally, we show how the task structure can be used to compare and unify the other approaches.

Many healthcare technology projects fail due to the lack of consideration of human issues, such as workflow, organizational change, and usability, during the design and implementation stages of a project's development process. Even when human issues are considered, the consideration is typically on designing better user interfaces. We argue that human-centered computing goes beyond a better user interface: it should include considerations of users, functions and tasks that are fundamental to human-centered computing. From this perspective, we integrated a previously developed human-centered methodology with a Project Design Lifecycle, and we applied this integration in the design of a complex distributed knowledge management system for the Biomedical Engineer (BME) domain in the Mission Control Center at NASA Johnson Space Center. We analyzed this complex system, identified its problems, generated systems requirements, and provided specifications of a replacement prototype for effective organizational memory and knowledge management. We demonstrated the value provided by our human-centered approach and described the unique properties, structures, and processes discovered using this methodology and how they contributed in the design of the prototype.

The explosive growth of biomedical complexity calls for a shift in the paradigm of medical decision making-from a focus on the power of an individual brain to the collective power of systems of brains. This shift alters professional roles and requires biomedical informatics and information technology (IT) infrastructure. The authors illustrate this future role of medical informatics with a vignette and summarize the evolving understanding of both beneficial and deleterious effects of informatics-rich environments on learning, clinical care, and research. The authors also provide a framework of core informatics competencies for health professionals of the future and conclude with broad steps for faculty development. They recommend that medical schools advance on four fronts to prepare their faculty to teach in a biomedical informatics-rich world: (1) create academic units in biomedical informatics; (2) adapt the IT infrastructure of academic health centers (AHCs) into testing laboratories; (3) introduce medical educators to biomedical informatics sufficiently for them to model its use; and (4) retrain AHC faculty to lead the transformation to health care based on a new systems approach enabled by biomedical informatics. The authors propose that embracing this collective and informatics-enhanced future of medicine will provide opportunities to advance education, patient care, and biomedical science.

This research focused on the design of a decision-support system to assist blood bankers in identifying alloantibodies in patients' blood. It was hypothesized that critiquing, a technique in which a computer monitors human performance for errors, would be an effective role for such a decision-support system if the error monitoring was unobtrusive and if the critiquing was in response to both intermediate and final conclusions made by the user. A prototype critiquing system monitored medical technologists for (a) errors of commission and errors of omission, b) failure to follow a complete protocol, (c) answers inconsistent with the data collected, and (d) answers inconsistent with prior probability information. Participants using the critiquing system had significantly better performance (completely eliminating misdiagnosis rates for 3 out of 4 test cases) than a comparable control group. Detailed analysis of the behavioral protocols provided insights into how specific design features influenced performance. Practical applications of this research include its use (after refinements) as a tool for routine antibody identification in blood banks.

Biomedical informatics lacks a clear and theoretically grounded definition. Many proposed definitions focus on data, information, and knowledge, but do not provide an adequate definition of these terms. Leveraging insights from the philosophy of information, we define informatics as the science of information, where information is data plus meaning. Biomedical informatics is the science of information as applied to or studied in the context of biomedicine. Defining the object of study of informatics as data plus meaning clearly distinguishes the field from related fields, such as computer science, statistics and biomedicine, which have different objects of study. The emphasis on data plus meaning also suggests that biomedical informatics problems tend to be difficult when they deal with concepts that are hard to capture using formal, computational definitions. In other words, problems where meaning must be considered are more difficult than problems where manipulating data without regard for meaning is sufficient. Furthermore, the definition implies that informatics research, teaching, and service should focus on biomedical information as data plus meaning rather than only computer applications in biomedicine.

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