NOVEL SENSOR TECHNOLOGY INTEGRATION FOR OUTCOME-BASED RISK ANALYSIS IN DIABETES
Mahesh Subramanian, Edward C. Conley, OMER F. RANA, Alex Hardisty, Ali Shaikh, Stephen D. Luzio, David R. Owens, Steve M. Wright, Tim J. Donovan, Bharat Bedi, Dave Conway-Jones, David Vyvyan, Gillian Arnold, Chris Creasey, Adrian M. Horgan, Tristram Cox, Rhys Waite · 2008
Novel sensor-based continuous biomedical monitoring technologies have a major role in chronic disease management for early detection and prevention of known adverse trends. In the future, a diversity of physiological, biochemical and mechanical sensing principles will be available through sensor device 'ecosystems'. In anticipation of these sensor-based ecosystems, we have developed Healthcare@Home (HH) - a research-phase generic intervention-outcome monitoring framework. HH incorporates a closed-loop intervention effect analysis engine to evaluate the relevance of measured (sensor) input variables to system-defined outcomes. HH offers real-world sensor type validation by evaluating the degree to which sensor-derived variables are relevant to the predicted outcome. This 'index of relevance' is essential where clinical decision support applications depend on sensor inputs. HH can help determine system-integrated cost-utility ratios of bespoke sensor families within defined applications - taking into account critical factors like device robustness / reliability / reproducibility, mobility / interoperability, authentication / security and scalability / usability. Through examples of hardware / software technologies incorporated in the HH end-to-end monitoring system, this paper discusses aspects of novel sensor technology integration for outcome-based risk analysis in diabetes.