Research Projects
CAIS.ME - Context-Aware Information System for Medical Environments
Clinical data is often provided centrally in paper or digital form, resulting in limited accessibility for medical staff at the point of patient care. With so much complex and heterogeneous data, analog-digital barriers, and time pressure, important information can easily be overlooked to the detriment of the patient. Recent advances in areas such as wearables, Artificial Intelligence, Internet of Things, and high-speed networks have made it possible to develop an Adaptive and Mobile Information Provision (AMIP) system dedicated for wearables that could be applied in many different settings, including hospitals.
Context-Aware Information System for Medical Environments (CAIS.ME) aims to reduce the strain faced by healthcare professionals by providing them in their daily work with relevant information at the right time, right place and in the right format, for example, displaying patient vital signs during a ward round or alerting staff in case of an emergency.
An integral part of CAIS.ME are lightweight smart glasses equipped with various sensors, a microphone, speakers, and a semi-transparent display that enables users to maintain awareness of their surroundings while in usage. They are designed to be the main input and output device, allowing users to use both hands for work tasks and reducing the transmission of germs, an issue of great importance in a hospital environment. While the hands-free aspect of the system offers many opportunities, it also introduces new challenges in terms of human-machine interaction that need to be considered to maximize usability.
A fundamental aspect of the system revolves around providing an excellent user experience and increasing user acceptance. Through collaboration with potential users from various hospital departments, the most important objectives were identified, like AI supported personalization of the information flow and presentation, tailored to the user's needs and routines. Nevertheless, the possibilities for facilitating AI potential within the system are not limited to these examples only. Other future areas of application include among others context recognition, automatic documentation creation, wound type identification and the detection of concerning development of the patient's condition.
Besides adapting the system to the users’ needs, the adaptation should also manifest on the global system level through self-healing and self-optimization to meet non-functional requirements such as performance and availability. Therefore, questions about software architecture design, appropriate patterns and technologies need to be thoroughly examined in the scope of the project. Furthermore, the possibility of generalization and re-use of the proposed architecture for AMIP systems in other domains should be explored in more detail.
Vision Video:
Major research areas are:
- Architecture for a Self-Adaptive Systems
- AI-enhanced features (early warning system, text processing, image processing, data trends detection and adaptation)
- Process Mining
- Human-Computer-Interaction and UI design tailored for wearables
- Investigating suitable hardware solutions for smart glasses and indoor positioning
- Funded by: Else Kröner Fresenius Center for Digital Health - Pilot Project
- Contact person: Karsten Wendt
- Project Website: link
- Funding period: 01/2021 - 12/2024