A telemedicine support for diabetes management: the T-IDDM project

Case-Based Decision Support for Patients with Type 1 Diabetes on Insulin Pump Therapy

“…The Telematic Management of Insulin-Dependent Diabetes Mellitus (T-IDDM) project was first to explore CBR for diabetes management [2,16,17]. The goals of T-IDDM were to: (a) support physicians in providing appropriate treatment for maintaining blood glucose control; (b) provide remote patients with telemonitoring and tele-consultation services; (c) provide cost-effective monitoring of large numbers of patients; (d) support patient education; and (e) allow insulin therapy customization [2].…”

“…The goals of T-IDDM were to: (a) support physicians in providing appropriate treatment for maintaining blood glucose control; (b) provide remote patients with telemonitoring and tele-consultation services; (c) provide cost-effective monitoring of large numbers of patients; (d) support patient education; and (e) allow insulin therapy customization [2]. T-IDDM integrated CBR with rule-based reasoning and a probabilistic model of the effects of insulin on blood glucose over time.…”

“…To provide individualized decision support that can help each patient maintain good blood glucose control, we propose a case-based approach. The use of CBR to enhance rule-based and model-based reasoning for diabetes management was first introduced by the T-IDDM project [2]. Our work differs from this project in three important ways: (1) it uses CBR as the primary reasoning modality, rather than as an adjunct to other reasoning approaches; (2) it adds consideration of life event data, which may influence blood glucose fluctuations; and (3) it focuses on patients on insulin pump therapy, a more advanced and flexible treatment regime than that used by T-IDDM patients.…”

Smart Telemedicine Support for Continuous Glucose Monitoring: The Embryo of a Future Global Agent for Diabetes Care

“…The next system developed by this group was called DIABTel 12 and included a function of glucose data download from the glucometer, with subsequent versions that bring us to the present. Other TM experiences took further steps toward the model most of us recognize; the following ones are good examples: the TIDDM project, 13 using Internet and including a smart analysis for insulin dose counseling; the Computer-Assisted Meal-Related Insulin Therapy project, 14 one of the first to demonstrate a significant change in hemoglobin A1c; the Multiaccess Services for Diabetes Management project, allowing "multi-access" from many different devices; the Informatics for Diabetes Education and Telemedicine study, 15 which included more than 2000 patients in two cohorts from socially underserved areas of New York, obtaining a reduction in hemoglobin A1c; and finally the Intelligent Control Assistant for Diabetes project, 16 integrating, for the first time, CGM into a smart TM platform with positive results in terms of hemoglobin A1c and glucose variability. 6 In general, new advances produced in communications technology have been incorporated progressively into different prototypes (Internet, email, video conferencing, mobile communications) but always with the restriction derived from the lack of interoperability.…”

Applying Multi-agent Systems Coordination to the Diabetic Healthcare Collaboration

“…Some basic properties well discussed in the MAS community such as autonomy and coordination [15], are very amenable to the above mentioned problems. MAS has been applied to diabetic healthcare by various researchers in fields such as glucose monitoring and alarm [4,9,10], biomedical control and management in diabetes [1,2,8,11,14].…”