What patients want to ask their doctors: Data analysis from Question Builder, an online question prompt list tool.

OBJECTIVES: Question prompt lists (QPLs) are one strategy to increase patient participation in healthcare decisions but the extent to which consumers might access them in the 'real world' is largely unknown. This study measured usage of a passively-promoted, government-funded web-based patient-generated QPL tool, called Question Builder (Australia) (QB) hosted on healthdirect.gov.au, a consumer health information website. METHODS: 12.5months of post-launch Google Analytics data from QB were analysed. Two existing coding frameworks (RIAS and ACEPP) were used to code QB questions thematically and 107 user-generated lists were analysed further to determine the questions chosen and prioritised. RESULTS: QB was accessed 8915 times, 4000 question lists were commenced and 1271 lists completed. Most lists were for general practice (GP) consultations (2444) rather than specialist consultations (1556). The most frequently chosen question was "Do I need any tests?". Shared decision-making questions (SDM) made up 40% of questions prioritised e.g. "Do I need any treatment and what are my treatment options?" CONCLUSIONS: There is active use of this online QPL, with strong interest in creating lists for GP consultations. Question Builder users prioritised questions which facilitate SDM. PRACTICE IMPLICATIONS: More research is required to assess the utilisation of QB in practice and health professionals' views of QB.

TOWARDS EARLY DISCOVERY OF SALIENT HEALTH THREATS: A SOCIAL MEDIA EMOTION CLASSIFICATION TECHNIQUE.

Online social media microblogs may be a valuable resource for timely identification of critical ad hoc health-related incidents or serious epidemic outbreaks. In this paper, we explore emotion classification of Twitter microblogs related to localized public health threats, and study whether the public mood can be effectively utilized in early discovery or alarming of such events. We analyse user tweets around recent incidents of Ebola, finding differences in the expression of emotions in tweets posted prior to and after the incidents have emerged. We also analyse differences in the nature of the tweets in the immediately affected area as compared to areas remote to the events. The results of this analysis suggest that emotions in social media microblogging data (from Twitter in particular) may be utilized effectively as a source of evidence for disease outbreak detection and monitoring.