Abstract
Comprehensive Geriatric Assessment (CGA) is an integrated clinical process to evaluate frail elderly people in order to create therapy plans that improve their quality and quantity of life. The whole process includes the completion of standardized questionnaires or specific movements, which are performed by the patient and do not necessarily require the presence of a medical expert. With the aim of automatizing these parts of the CGA, we have designed and developed CLARC (smart CLinic Assistant Robot for CGA), a mobile robot able to help the physician to capture and manage data during the CGA procedures, mainly by autonomously conducting a set of predefined evaluation tests. Using CLARC to conduct geriatric tests will reduce the time medical professionals have to spend on purely mechanical tasks, giving them more time to develop individualised care plans for their patients. In fact, ideally, CLARC will perform these tests on its own. In parallel with the effort to correctly address the functional aspects, i.e., the development of the robot tasks, the design of CLARC must also deal with non-functional properties such as the degree of interaction or the performance. We argue that satisfying user preferences can be a good way to improve the acceptance of the robot by the patients. This paper describes the integration into the software architecture of the CLARC robot of the modules that allow these properties to be monitored at run-time, providing information on the quality of its service. Experimental evaluation illustrates that the defined quality of service metrics correctly capture the evolution of the aspects of the robot’s activity and its interaction with the patient covered by the non-functional properties that have been considered.
Highlights
IntroductionThe Comprehensive Geriatric Assessment (CGA) is a clinical procedure designed to capture information on medical, psycho-social and functional resources and problems of elderly people
The Comprehensive Geriatric Assessment (CGA) is a clinical procedure designed to capture information on medical, psycho-social and functional resources and problems of elderly people.The collected data is subsequently used to personalize the treatment prescription, allowing the creation of an overall plan for follow-up
Experimental evaluation illustrates that the defined quality of service metrics correctly capture the evolution of the aspects of the robot’s activity and its interaction with the patient covered by the non-functional properties that have been considered
Summary
The Comprehensive Geriatric Assessment (CGA) is a clinical procedure designed to capture information on medical, psycho-social and functional resources and problems of elderly people. The collected data is subsequently used to personalize the treatment prescription, allowing the creation of an overall plan for follow-up. CGA is an interdisciplinary effort, requiring the coordination of several clinical professionals such as occupational therapists, physiotherapists, social workers, or medical doctors. A typical CGA session takes about three hours of clinicians’ time, and it should be repeated every six months to be really efficient [1]. The CGA comprises three different types of activities: a clinical interview, a multidimensional assessment and a customized care plan. A clinical interview allows the patient and their relatives to discuss the elder health problems. Multidimensional clinical tests are performed to evaluate the overall patient status
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