Definition of information technology architectures for continuous data management and medical device integration in diabetes

Remote Blood Glucose Monitoring in mHealth Scenarios: A Review

Glucose concentration in the blood stream is a critical vital parameter and an effective monitoring of this quantity is crucial for diabetes treatment and intensive care management. Effective bio-sensing technology and advanced signal processing are therefore of unquestioned importance for blood glucose monitoring. Nevertheless, collecting measurements only represents part of the process as another critical task involves delivering the collected measures to the treating specialists and caregivers. These include the clinical staff, the patient's significant other, his/her family members, and many other actors helping with the patient treatment that may be located far away from him/her. In all of these cases, a remote monitoring system, in charge of delivering the relevant information to the right player, becomes an important part of the sensing architecture. In this paper, we review how the remote monitoring architectures have evolved over time, paralleling the progress in the Information and Communication Technologies, and describe our experiences with the design of telemedicine systems for blood glucose monitoring in three medical applications. The paper ends summarizing the lessons learned through the experiences of the authors and discussing the challenges arising from a large-scale integration of sensors and actuators.

Utilizing information technologies for lifelong monitoring in diabetes patients

BACKGROUND

Information and communication technologies have long been acknowledged to support information sharing along the whole chain of care, from the clinic to the homes of patients and their relatives. Thus they are increasingly being considered for improving the delivery of health care services also in light of clinical and technological achievements that propose new treatments requiring a tighter interaction among patients and physicians.

METHODS

The multiagent paradigm has been utilized within an architecture for delivering telemedicine services to chronic outpatients at their domiciles and enforcing cooperation among patients, caregivers, and different members of the health care staff. The architecture sees each communication device such as a palmtop, smart phone, or personal digital assistant as a separate agent upon which different services are deployed, including telemetry, reminders, notifications, and alarms. Decoupling services from agents account for a highly configurable environment applicable to almost any context that can be customized as needed.

RESULTS

The architecture has been used for designing and implementing a prototypical software infrastructure, called LifePhone, that runs on several communication devices. A basic set of services has been devised with which we were able to configure two different applications that address long-term and short-term monitoring scenarios for diabetes patients. The long-term scenario encompasses telemetry and reminder services for patients undergoing peritoneal dialysis, which is a treatment for chronic renal failure, a diabetes complication. The short-term scenario incorporates telemetry and remote alarms and is applicable for training patients to use an artificial pancreas.

CONCLUSIONS

Our experiments proved that an infrastructure such as LifePhone can be used successfully for bridging the interaction gap that exists among all the components of a health care delivery process, improving the quality of service and possibly reducing the overall costs of health care. Furthermore, the modularity of services allows for more complex scenarios encompassing data analysis or even involving actors at multiple institutions in order to better support the overall health care organization.

How continuous monitoring changes the interaction of patients with a mobile telemedicine system

BACKGROUND

The combination of telemedicine systems integrating mobile technologies with the use of continuous glucose monitors improves patients' glycemic control but demands a higher interaction with information technology tools that must be assessed. In this article, we analyze patients' behavior from the use-of-the-system point of view, identifying how continuous monitoring may change the interaction of patients with the mobile telemedicine system.

METHODS

Patients' behavior were evaluated in a clinical experiment consisting of a 2-month crossover randomized study with 10 type 1 diabetes patients. During the entire experiment, patients used the DIABTel telemedicine system, and during the intervention phase, they wore a continuous glucose monitor. Throughout the experiment, all user actions were automatically registered. This article analyzes the occurrence of events and the behavior patterns in blood glucose (BG) self-monitoring and insulin adjustments. A subjective evaluation was also performed based on the answers of the patients to a questionnaire delivered at the end of the study.

RESULTS

The number of sessions established with the mobile Smart Assistant was considerably higher during the intervention period than in the control period (29.0 versus 18.8, p < .05), and it was also higher than the number of Web sessions (29.0 versus 22.2, p < .01). The number of daily boluses was higher during the intervention period than in the control period (5.27 versus 4.40, p < .01). The number of daily BG measurements was also higher during the intervention period (4.68 versus 4.05, p < .05) and, in percentage, patients increased the BG measurements not associated to meals while decreasing the percentage of preprandial measurements. The subjective evaluation shows that patients would recommend the use of DIABTel in routine care.

CONCLUSIONS

The use of a continuous glucose monitor changes the way patients manage their diabetes, as observed in the increased number of daily insulin bolus, the increased number of daily BG measurements, and the differences in the distribution of BG measurements throughout the day. Continuous monitoring also increases the interaction of patients with the information system and modifies their patterns of use. We can conclude that mobile technologies are especially useful in scenarios of tight monitoring in diabetes, and they are well accepted by patients.

Automatic data processing to achieve a safe telemedical artificial pancreas

BACKGROUND

The use of telemedicine for diabetes care has evolved over time, proving that it contributes to patient self-monitoring, improves glycemic control, and provides analysis tools for decision support. The timely development of a safe and robust ambulatory artificial pancreas should rely on a telemedicine architecture complemented with automatic data analysis tools able to manage all the possible high-risk situations and to guarantee the patient's safety.

METHODS

The Intelligent Control Assistant system (INCA) telemedical artificial pancreas architecture is based on a mobile personal assistant integrated into a telemedicine system. The INCA supports four control strategies and implements an automatic data processing system for risk management (ADP-RM) providing short-term and medium-term risk analyses. The system validation comprises data from 10 type 1 pump-treated diabetic patients who participated in two randomized crossover studies, and it also includes in silico simulation and retrospective data analysis.

RESULTS

The ADP-RM short-term risk analysis prevents hypoglycemic events by interrupting insulin infusion. The pump interruption has been implemented in silico and tested for a closed-loop simulation over 30 hours. For medium-term risk management, analysis of capillary blood glucose notified the physician with a total of 62 alarms during a clinical experiment (56% for hyperglycemic events). The ADP-RM system is able to filter anomalous continuous glucose records and to detect abnormal administration of insulin doses with the pump.

CONCLUSIONS

Automatic data analysis procedures have been tested as an essential tool to achieve a safe ambulatory telemedical artificial pancreas, showing their ability to manage short-term and medium-term risk situations.