CHI Series
Exploring How to Motivate Assistive Health Application Use
Date: Tuesday 24th November 2009
Time: 11:00am - 12:00pm
Venue: AIHI Board Room, Level 1 AGSM.
Speaker: Katie A. Siek
Abstract
Researchers in human computer interaction and health informatics are studying how information communication technology can be used to empower people to monitor everything from medication compliance to mood swings. Currently, it is unclear how individuals will use these assistive health applications throughout their everyday lives and reflect on the data to improve their health. We must not only help people collect data about their health when they are well, we must also provide individuals with tools to understand how their actions and behaviors affect their overall health so that they can reduce their risk of illness or improve their wellbeing during times of illness. In addition, this information is invaluable to health professionals who rely on individual and aggregate data to track illness trends and prepare wellness educational materials for specific populations.
We are exploring how to motivate individuals to monitor their health and what feedback would be most useful to reflect on her individual and family health during quick episodic and longitudinal intervals. In this talk, we provide brief overviews of three health informatics projects that explore monitoring, self-reflection on data, and adoption in everyday life. In the first project, I review our findings from a Dietary Intake Monitoring Application (DIMA) that assists low-literacy chronic kidney disease patients monitor their fluid and nutrient intake with a Personal Digital Assistant application. The second project will provide an overview of the Colorado Care Tablet – a touch screen personal health application (PHA) that interoperates with a Personal Health Record to assist older adults manage their medications and share health information with caregivers and healthcare professionals. In the third project, we discuss Health Bridge, a new project that aims to design a family and community-based personalized nutrition system for low-socioeconomic populations through design workshops and community design activities. Based on the findings from these projects, we found that individuals want a balance of automatic sensing and manual inputs into health assistive applications. In exchange for these inputs, individuals want to share the data with trusted people, including health professionals, and receive authoritative, culturally relevant feedback. We conclude the talk with a discussion about what is needed from a technological and design standpoint to meet the needs of populations who could benefit from using assistive technologies.
Speaker Profile
Katie A. Siek is an assistant professor in Computer Science at the University of Colorado at Boulder where she leads the Wellness Innovation and Interaction Lab. Her primary research interests are in human computer interaction, health informatics, and ubiquitous computing. More specifically, she is interested in how sociocentric technology interventions affect personal health and well being. Her research is supported by the National Institutes of Health, the Robert Wood Johnson Foundation, and the National Science Foundation including a five-year NSF CAREER award. Prior to her appointment at Colorado, she completed her Ph.D. and M.S. at Indiana University – Bloomington in computer science and her B.S. in computer science at Eckerd College. She was a National Physical Science Consortium Fellow and was a Ford Apprentice Scholar at Eckerd College. Siek is a member of the ACM-W Council and on the College Board AP Computing Advisory Group. (More information: http://www.cs.colorado.edu/~ksiek)
Clinical Pharmacology and Drug Interactions, Room for Improvement
Date: Thursday 3rd December 2009
Time: 3:00 - 4:00pm
Venue: AIHI Board Room, Level 1 AGSM.
Speaker: Dr Matt Doogue
Staff Specialist in Clinical Pharmacology & Endocrinology, Southern Adelaide Health Services, SA
Abstract
Clinical Pharmacology is concerned with the safe, effective and rational use of medicines. Drug-drug interactions (DDIs) are a major cause of adverse drug events. The possibility of DDIs should be considered whenever multiple drugs are co-administered (polypharmacy). The traditional ‘drug-based’ evaluation of potential DDIs investigates all possible 2-way drug combinations in a prescription. However, this approach has both poor specificity and high time cost, which limit the assessment of potential DDIs in clinical practice. Matt and his colleague Tom Polasek are collaborating with Frank Lin to develop a a process to simplify the clinical evaluation of potential DDIs based on clinically relevant criteria.
This seminar will outline principles of clinical pharmacology and drug-drug interactions. Our work to date will be outlined and future directions will be proposed for discussion.
Speaker Profile
Matt Doogue is a physician passionate about applying Clinical Pharmacology principles for effective, safe and rational care of patients. He is a member of the written examination committee of the RACP and co-author of a successful online learning resource for Clinical Pharmacology www.icp.org.nz. He represents the Clinical members of ASCEPT (Australian Society of Clinical and Experimental Pharmacologists and Toxicologists).
Matt’s special interests include pharmacokinetic drug-drug interactions, therapeutic drug monitoring, quality use of medicines, and application of clinical pharmacology principles to endocrine research and practice.
Born and bred in NZ, Matt worked ski patrolling and avalanche forecasting prior to studying medicine. Nowadays his feet are more likely to be wearing young children than skis.
Diffusive uptake and cessation of multiple competing practices: potential implications for the equity of care
Date: Thursday 17th December 2009
Time: 3:00 - 4:00pm
Venue: AIHI Board Room, Level 1 AGSM.
Speaker: Dr. Adam Dunn
Abstract
There exists time lags between the discovery, testing, review, dissemination and diffusion of clinical practices through healthcare.
Understanding the dynamics that govern adoption and spread of recommended care across a healthcare system is important since recent evidence suggests that the provision of recommended care is heterogeneous and can be unacceptably low. One of the factors reported to influence the diffusion of information in healthcare is the shape of professional networks of individuals and groups. I present a model that extends typical network models of diffusion of innovation to include multiple competing practices, which allows for the explicit modelling of the ecology of healthcare practices. I demonstrate the construction of a semi-empirical professional network of individuals using information about hospital staffing across NSW and describe the individial decision-making mechanism that gives rise to the dynamics of diffusion in the model. Results show that high levels of clustering can significantly affect the time of uptake of practices along the dimension of clustering and our research suggests that the effects of diffusion in healthcare may contribute to the both the inequity and low levels of recommended care.
Speaker Profile
Adam Dunn is a Research Fellow at the Centre for Health Informatics
(CHI) and Australian Institute of Health Innovation (AIHI). His background is in computer science and the computational modelling of spatial complex systems (but please don't hold that against him). His research spans domains including computational formalisms, landscape ecology, social networks, organisation science and healthcare policy.
Adam’s focus in the CHI is on agent-based methods for communication system design in complex organisations. With a study of computational models of agent interaction, he is producing simulations of communication systems and providing support for the design of interventions aimed at increasing patient safety. Under the auspices of the Patient Safety Program Grant at the AIHI, Adam is constructing simulation-based models under the broad heading of health system safety.
He is currently developing agent-based models for patient safety and social network models describing the diffusion of recommended care.