Dynamic Value Trade-offs in Run-time to Provide Good, Customised Patient Care with Robots

The present study was designed to investigate the ability for robots to provide good care. Good care is determinative in practice and customised to each patient. That is, moral acts in care are consciously determined by a carer during the act of caring for someone. The antithesis is that good care is determinative in theory. During the act of care, codes of conduct, as well as healthcare laws and regulations set ethical principles for carers to uphold. Care practices and processes inform what actions carers can take towards patients to apply those principles so as to ensure appropriate care, but what is lacking is customised patient care. Between normative ethical theory principles, practices and processes there is a gap. Which practice is the most suitable, what kind of personal approach does a carer take in the present context, and how much care does a particular patient need? These are a few of the decisions which cannot be prescriptively predetermined by principles, but need to be determined during practice. Principles do not determine the act which is best for patients in situ of their changing contexts. Instead, conscious carers make this determination in practice. Unfortunately, in geriatric care, human carer numbers are dwindling and the population of elderly in care is rising. Not only do the number of carers need to be increased, but they need to be filled by conscious carers so as to ensure good care; one way to do this is with conscious carebots. Through its usability, user acceptability, and value sensitivity testing research phases, the present study found that conscious carebots can fill growing care shortage, whilst simultaneously providing good care. To make the determination of good care a new carebot model, the attento model, was inspired by, and designed according to CCVSD. Employing CCVSD to inform design and computational consciousness as a method for a carebot enabled the determination by uniquely providing extrinsic care value ordering, which in practice would be dynamic value trade-offs in run-time. The CCVSD-inspired attento model was presented in two research phases to test the hypotheses. The results of the research suggest that the attento model provides good, customised patient care in run-time. The present study contributes to literature on carebots, computational consciousness, and VSD through end-user perspectives currently lacking in the CCVSD literature.