Protecting the privacy of individual general practice patient electronic records for geospatial epidemiology research

Background: General practitioner (GP) practices in Australia are increasingly storing patient information in electronic databases. These practice databases can be accessed by clinical audit software to generate reports that inform clinical or population health decision making and public health surveillance. Many audit software applications also have the capacity to generate de-identified patient unit record data. However, the de-identified nature of the extracted data means that these records often lack geographic information. Without spatial references, it is impossible to build maps reflecting the spatial distribution of patients with particular conditions and needs. Links to socioeconomic, demographic, environmental or other geographically based information are also not possible. In some cases, relatively coarse geographies such as postcode are available, but these are of limited use and researchers cannot undertake precision spatial analyses such as calculating travel times. Methods: We describe a method that allows researchers to implement meaningful mapping and spatial epidemiological analyses of practice level patient data while preserving privacy. Results: This solution has been piloted in a diabetes risk research project in the patient population of a practice in Adelaide. Conclusions and Implications: The method offers researchers a powerful means of analysing geographic clinic data in a privacy-protected manner

Whitelisting system state in windows forensic memory visualizations

Examiners in the field of digital forensics regularly encounter enormous amounts of data and must identify the few artifacts of evidentiary value. One challenge these examiners face is manual reconstruction of complex datasets with both hierarchical and associative relationships. The complexity of this data requires significant knowledge, training, and experience to correctly and efficiently examine. Current methods provide text-based representations or low-level visualizations, but levee the task of maintaining global context of system state on the examiner. This research presents a visualization tool that improves analysis methods through simultaneous representation of the hierarchical and associative relationships and local detailed data within a single page application. A novel whitelisting feature further improves analysis by eliminating items of less interest from view. Results from a pilot study demonstrate that the visualization tool can assist examiners to more accurately and quickly identify artifacts of interest