@article{31,
  keywords = {User-Centered Design, treatment plans, temporal uncertainty, protocol-based care, patient data, information visualization, clinical guidelines},
  author = {Wolfgang Aigner and Silvia Miksch},
  title = {{CareVis: Integrated Visualization of Computerized Protocols and Temporal Patient Data}},
  abstract = {Objective: Currently, visualization support for patient data analysis is mostly limited to the representation of directly measured data. Contextual information on performed treatment steps is an important source to find reasons and explanations for certain phenomena in the measured patient data, but is mostly spared out in the analysis process. This work aims to fill this gap via integrating classical data visualization and visualization of treatment information. Methods and Material: We considered temporal as well as logical data as- pects and applied a user-centered development approach that was guided by user input gathered via a user study, design reviews, and prototype evaluations. Further- more, we investigated the novel PlanningLine glyph, that is used to represent plans in the temporal domain, via a comparative empirical user study. Results: Our interactive visualization approach CareVis provides multiple simul- taneous views to cover different aspects of the complex underlying data structure of treatment plans and patient data. The tightly coupled views use visualization methods well-known to domain experts and are designed to facilitate users' tasks. The views are based on the concepts of clinical algorithm maps and LifeLines which have been extended in order to cope with the powerful and expressive plan rep- resentation language Asbru. Initial feedback of physicians was encouraging and is accompanied by empirical evidence which verifies that PlanningLines are well suited to manage temporal uncertainty. Conclusion: The interactive integration of different visualization methods forms a novel way of combining, relating, and analyzing different kinds of medical data and information that otherwise would be separated.
},
  year = {2006},
  journal = {Artifical Intelligence in Medicine (AIIM)},
  volume = {37},
  number = {3},
  pages = {203–218},
  month = {July},
  publisher = {Elsevier},
  url = {http://www.cvast.tuwien.ac.at/sites/default/files/publications/PDF/2006/aiim_2006-37-3/aigner_2006_aiim_carevis-preprint.pdf},
  doi = {doH:10.1016/j.artmed.2006.04.002},
  language = {eng},
}