TU-F-BRD-01: Biomedical Informatics for Medical Physicists



Biomedical informatics encompasses a very large domain of knowledge and applications. This broad and loosely defined field can make it difficult to navigate. Physicists often are called upon to provide informatics services and/or to take part in projects involving principles of the field. The purpose of the presentations in this symposium is to help medical physicists gain some knowledge about the breadth of the field and how, in the current clinical and research environment, they can participate and contribute.

Three talks have been designed to give an overview from the perspective of physicists and to provide a more in-depth discussion in two areas. One of the primary purposes, and the main subject of the first talk, is to help physicists achieve a perspective about the range of the topics and concepts that fall under the heading of “informatics”. The approach is to de-mystify topics and jargon and to help physicists find resources in the field should they need them.

The other talks explore two areas of biomedical informatics in more depth. The goal is to highlight two domains of intense current interest--databases and models--in enough depth into current approaches so that an adequate background for independent inquiry is achieved. These two areas will serve as good examples of how physicists, using informatics principles, can contribute to oncology practice and research.

Learning Objectives:

  • 1.To understand how the principles of biomedical informatics are used by medical physicists.
  • 2.To put the relevant informatics concepts in perspective with regard to biomedicine in general.
  • 3.To use clinical database design as an example of biomedical informatics.
  • 4.To provide a solid background into the problems and issues of the design and use of data and databases in radiation oncology.
  • 5.To use modeling in the service of decision support systems as an example of modeling methods and data use.
  • 6.To provide a background into how uncertainty in our data and knowledge can be incorporated into modeling methods.