Research Areas at TTL

In TTL, we will study how to integrate information from electronic health records, computer aided diagnosis and early warning system, medical knowledge databases and epidemiology in a medical probabilistic platform. Different health care providers and medical departments will test the systems: general practitioners, specialists in different departments, emergency department, follow-ups in post-surgery.

Workflow Management

Workflow management can be defined as a proactive system that manages and coordinates the flow of work between participants, according to a procedure consisting of a number of tasks. In this research area, we will address the development of nursing plans in accordance to the needs of TTL’s target patient group. 

Nursing plans are assumed to ensure a well-functioning communication between caregivers for chronic patients. They are also expected to ensure standardization/-predictability of the care process. There are different needs for experienced/-inexperienced staff, psychiatric/somatic departments, different patient groups, different professionals, and hospital/nursing homes. This makes the tailoring of nursing plans extremely demanding.

Currently, each hospital department has their own plans (often based on classification systems NIC and NANDA). Nursing homes have plans based on IPLOS. In addition, many chronic patients have an individual plan (a legal right).

Another research task is regional integration and reorganization practice. New IT systems and interactive services are supposed to improve collaboration between physicians in primary- and secondary care. Unfortunately, establishing such systems are risky (based on partial discouraging results from implementing teledermatology, telepathology services and electronic booking systems) due to how they induce a large change in current work practice. We will do research on how work should be (re)organized and how these systems should be designed in order to establish sustainable use.

Computer Aided Diagnoses

Computer-aided diagnostics (CAD) systems are already widely used in practical clinical situations for tasks such as the detection of breast and lung cancer and melanoma.

Most of these systems are detection systems designed to aid physicians when looking for abnormalities in diagnostic data. Others, known as differential systems, are designed to make diagnosis that is more complete rather than only look for abnormalities. Within the CAD research area, our focus will be on the detection of malign melanoma based on lesion images. This includes development of algorithms for image segmentation and quantification of visual lesion evaluation.

A third area will be study of patient compliance. In this context compliance refers to: 1) The degree to which the patient follows the directions the health professional has given him or her, including medication, diet or exercise, and 2) The degree to which the patient is able to follow his or her own resolve when it comes to life style related behavior.An interesting question is how sensor data best can be integrated in sustaining appropriate self-management behaviors for those with chronic and lifestyle related diseases, with tailored feedback into a mobile tool that will support the individual in sustaining life-style changes?

 Another area that will be addressed is correlation between pathological conditions and biometric data. This includes proof of the medical concept and development of the time series analysis algorithms for detection of deviations, blood glucose level, and infections. 

Health Intelligence

In general, we may define Health Intelligence as use and development of knowledge to improve the health of the population.

In TTL, we will focus on the disease surveillance system and how such systems can be used for the benefit of TTL’s targeted patient groups.

In this research area, we will address the development of methods for detection of deviations in spatio-temporal patterns of syndromic data, and develop models for spreads of infectious and non-infectious diseases for the purpose of prediction.

Health Terminals for Personalized Health Care

In recent years, we have experienced a shift from traditional telemedicine services towards personalized health care

The current focus of both the European Commission and others is on how to escalate the efforts on providing the patients and people generally with tools that aid in taking care of their own health. The background is of course the elderly-boom, the population growth generally, the increase in chronic diseases due to change in lifestyle, and finally the possibilities that new technology, especially mobile terminals and wearable sensors, provides.

Integrated Medical Sensors

The use of medical sensors and sensor-based systems constitutes a major part of Tromsø Telemedicine Laboratory.

In this research area, sensors monitoring vital health parameters (e.g. INR, ECG, blood glucose) and sensors for motivational health purposes (e.g. physical activity and eating habits) will be developed and/or utilized. The sensors and sensor-based systems will use both wearable and stationary sensors. In addition, interpretation of sensor data will be addressed.

This includes the use of body area networks (BAN). In TTL, we will focus on secure and stable networks supporting communication between the medical sensors and the patient-centric health terminals.