Eirik Årsand, Norway
University Hospital of North Norway Norwegian Center for E-health ResearchPresenter of 2 Presentations
HOW PEOPLE WITH TYPE 1 DIABETES CAN ASSIST IN DISEASE OUTBREAK DETECTION
Abstract
Background and Aims
After 9/11 and several global epidemics, syndromic surveillance has become an even more important part of a country’s national emergency preparedness. The challenge is to detect the spread of an infectious disease as early as possible. This project is based on a hypothesis that through analysis of health data from people with Type 1 Diabetes (T1D), it is possible to detect a disease close to the point of infection, i.e., long before the disease onset.
Methods
An in-house Electronic Disease Surveillance Network, EDMON system, has been developed to test our hypothesis. EDMON is a real-time early disease outbreak detection system that uses self-recorded health data from people with T1D. The models have been tested using 11 years of high precision continues data recorded from 3 individuals with T1D.
Results
By combining continuously recorded health data (blood glucose, insulin, carbohydrates, physical activity and geographical location) from people with T1D, we are able to detect abnormal changes in their BG, changes that indicate that they have been infected with a pathogen. The discrepancies are identified by the use of advanced statistical and machine learning models.
Conclusions
Through the EDMON system, we can receive real-time information about the spread of infectious diseases in our neighborhood, and thus, can take the necessary action in order to reduce the risk of becoming infected. EDMON might be a very useful tool for people with T1D as well as the rest of the population in disease prevention.
HEALTHCARE PERSONNEL’S EXPECTATIONS OF A SYSTEM FOR SHARING AND USING PATIENT-GATHERED DATA
Abstract
Background and Aims
Personal health-sensors and devices are quickly entering the marked, answering the needs of people with diabetes’ self-management. This has led to an increasing amount of patient-gathered health data, which we foresee will be important in meetings between healthcare personnel and patients. Building on the previous FI-STAR project, we address this issue in the current FullFlow project.
Methods
Prior to testing an in-house developed system that allows people with diabetes to share their self-gathered data during consultations, we queried healthcare personnel (n=17; 12 GPs, 4 nurses, and 1 nutritionist) about their perceptions of, and suggestions for, the proposed system.
Results
All the healthcare personnel informants reported that they expected the designed system to be useful during consultations. Ten of them gave specific suggestions about how they expected the system to function, including: 1) possibilities for remote consultations; 2) support for keeping track of types of carbohydrates, not only amount; 3) support for keeping track of lipid levels; 4) automatic data transfer from apps, e.g. Strava, and devices, e.g. glucose meters and insulin pens; 5) support for all kinds of mobile phones; 6) integration of this system’s functions with electronic health record systems; 7) highlighting changes since last consultation; 8) transfer of consultation notes and hospital system information into the patients’ app to provide them with tailored recommendations for follow-up at the next consultation.
Conclusions
Healthcare personnel are positive to a system for using patient-gathered data, and they contribute with creative and specific suggestions for how such systems should work.