Bio statement

Peter Pennefather is a professor in the Leslie Dan Faculty of Pharmacy and is is a director of the Laboratory for Collaborative Diagnostics (www.lcd.utoronto.ca). His training is in pharmacology, neuroscience and biophysics. His current research focuses on the process of collaborative diagnostics and data sharing infrastructure used in health systems, with a particular focus on mapping physical signature data associated with attributes and qualities of biological systems as well as medicinal products. He is also outreach director for the UofT Knowledge Media and Design Institute (www.kmdi.utoronto.ca).

Conferences

Privacy settings prevent us from displaying this information.
You may be able to see more details if you login

Accepted Abstracts

Medicine 2.0'08 (Toronto, Canada)

BioTIFF: Articulating Self-Documenting Personal Health Digital Information Artefacts
Peter Pennefather*, West Suhanic

Although the Web 2.0 world of participatory, web-based collaboration and social networking has just been launched, a Web 3.0 is emerging where ubiquitous data and virally distributed free and open applications for interacting with that data, is leading to new and useful forms of socio-technical articulation. The University of Toronto Laboratory for Collaborative Diagnostics (www.lcd.utoronto.ca) is developing an electronic personal health record (ePHR) system that anticipates Web 3.0. The Bi...

Medicine 2.0'12 (Boston, USA)

Creating Ignorant Data for Healthy People: an Essential Intermediate Step for Making Digitally Accessible Records of Diagnostic Events for Support of Patient-Centred Clinical Judgements.
Peter Pennefather*, West Suhanic

Background. The Latin verb ignoro refers to the action of not knowing. Data almost by definition does not know anything. However, for rhetorical purposes we propose the term “Ignorant Data” to refer to a new way to format digital diagnostic data so as to maximize its utility. We will demonstrate how the method enables primary digital diagnostic data to be recorded in a way that can remain ignorant of why or how that data will be used yet still enable someone knowledgeable about the diagno...

This user's work may be related to

saif

michael_dahlweid