technique and competence as a nurse) and “relational skills” (social skills allowing
conversation with others and both accepting and being accepted by others) are
important in the field of nursing (Matsushita, 2017, p. 63), the care delivery process
in caregiving settings requires the utilization of skills.
This leads to attempts to determine whether things using innovative knowledge
such as AI, IT, or robot technology can be applied for providing better psychological
care to CHRs/DUs. Utilizing things that assist humans in the care delivery process is
one type of service innovation. Resolving the inconsistency between customization
(providing service matching a client’s needs) and standardization (increasing the
efficiency of service provision) is one challenge in service innovation that may be
solvable through the adoption of an open innovation perspective in which clients are
involved (Chesbrough, 2011). The sharing of knowledge, including tacit knowledge,
between service receivers and providers is necessary if we are to realize service
innovation (Chesbrough, 2011). Sharing knowledge is similarly thought to be
essential in care settings. In this chapter, these activities are regarded as healthcare
innovation, the process of creating new value by combining things and human
activities with the goal of mental healthcare for CHRs/DUs. Healthcare innovation
is achieved when new products or technologies, such as IT, AI, and robots, are
introduced from external sources based on the needs and evaluations of CHRs/DUs
and used, an appropriate service delivery method is found, and this is shared with
CHWs, leading to the creation of value for CHRs/DUs.
As the aim of this chapter was generating value for CHRs/DUs, we did not
address perspectives on generating value for CHWs. In this chapter, we presented
a case that introduced a method of use for the communication robot Pepper and
Facial expression analysis software as an example of healthcare innovation.
According to Tao (1995, p. 22), “the subjective assessment of client satisfaction is
an important indicator for the evaluation of productivity and efficiency” in serviceproviding organizations. However, in cases of CHRs/DUs with dementia, communication is often difficult and how CHWs recognize users’ psychological state, when
they are using services, is a challenge. In this sense, methods for verifying efficiency
in the use of communication robots also required a creative solution.
Considering this, it was demonstrated that it would be easier to understand the
psychological states of CHRs/DUs with dementia if Facial expression analysis
software—a thing resulting from scientific and technological development—could
be used to acquire and utilize nonverbal information from facial expressions using
noncontact methods. With this, it would be possible to implement individualized
care appropriate for older adults with dementia from a comprehensive perspective
including both verbal and nonverbal information. There is also a high need for
technology that can grasp pathology using facial information in telemedicine
(Takahashi, Ito, & Kawaguchi, 2019). It seems that the application of noncontact
Facial expression analysis software will become increasingly more important in
settings of medicine and caregiving.
Figure 6 presents new perspectives of innovation in the field of health informatics
from the above arguments. These involve three processes: (1) translating emotion
Information Technology/Artificial Intelligence Innovations Needed for Better. . .
53
conversation with others and both accepting and being accepted by others) are
important in the field of nursing (Matsushita, 2017, p. 63), the care delivery process
in caregiving settings requires the utilization of skills.
This leads to attempts to determine whether things using innovative knowledge
such as AI, IT, or robot technology can be applied for providing better psychological
care to CHRs/DUs. Utilizing things that assist humans in the care delivery process is
one type of service innovation. Resolving the inconsistency between customization
(providing service matching a client’s needs) and standardization (increasing the
efficiency of service provision) is one challenge in service innovation that may be
solvable through the adoption of an open innovation perspective in which clients are
involved (Chesbrough, 2011). The sharing of knowledge, including tacit knowledge,
between service receivers and providers is necessary if we are to realize service
innovation (Chesbrough, 2011). Sharing knowledge is similarly thought to be
essential in care settings. In this chapter, these activities are regarded as healthcare
innovation, the process of creating new value by combining things and human
activities with the goal of mental healthcare for CHRs/DUs. Healthcare innovation
is achieved when new products or technologies, such as IT, AI, and robots, are
introduced from external sources based on the needs and evaluations of CHRs/DUs
and used, an appropriate service delivery method is found, and this is shared with
CHWs, leading to the creation of value for CHRs/DUs.
As the aim of this chapter was generating value for CHRs/DUs, we did not
address perspectives on generating value for CHWs. In this chapter, we presented
a case that introduced a method of use for the communication robot Pepper and
Facial expression analysis software as an example of healthcare innovation.
According to Tao (1995, p. 22), “the subjective assessment of client satisfaction is
an important indicator for the evaluation of productivity and efficiency” in serviceproviding organizations. However, in cases of CHRs/DUs with dementia, communication is often difficult and how CHWs recognize users’ psychological state, when
they are using services, is a challenge. In this sense, methods for verifying efficiency
in the use of communication robots also required a creative solution.
Considering this, it was demonstrated that it would be easier to understand the
psychological states of CHRs/DUs with dementia if Facial expression analysis
software—a thing resulting from scientific and technological development—could
be used to acquire and utilize nonverbal information from facial expressions using
noncontact methods. With this, it would be possible to implement individualized
care appropriate for older adults with dementia from a comprehensive perspective
including both verbal and nonverbal information. There is also a high need for
technology that can grasp pathology using facial information in telemedicine
(Takahashi, Ito, & Kawaguchi, 2019). It seems that the application of noncontact
Facial expression analysis software will become increasingly more important in
settings of medicine and caregiving.
Figure 6 presents new perspectives of innovation in the field of health informatics
from the above arguments. These involve three processes: (1) translating emotion
Information Technology/Artificial Intelligence Innovations Needed for Better. . .
53