TY - GEN
T1 - iFair
T2 - 10th IEEE International Conference on Smart Computing, SMARTCOMP 2024
AU - Kenne, Modeste Mefenya
AU - Date, Prasanna
AU - Eguchi, Ronald T.
AU - Hu, Zheng Hui
AU - Rousseau, Julie
AU - Venkatasubramanian, Nalini
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Efficient resource allocation is crucial in many domains, particularly in senior care, where assigning resources to older adults must consider uncertainties associated with vulnerable populations. In collaboration with Senior Health Facilities (SHFs) and domain experts, this paper presents iFair, a novel framework designed to assist decision-makers in equitably allocating scarce resources to older adults. iFair was prototyped in the context of ongoing work on a data exchange platform, CAREDEX, used for enhancing older adults' resilience during disasters. A key novelty of iFair focuses on aligning resident preferences with resources in urgent situations, expediting care, and enhancing task efficiency. We integrate static and dynamic environmental data, including facility layouts and sensor data, with detailed resident profiles to cater to the individual needs and preferences of residents. While our framework primarily focuses on allocation within facilities, it also extends to a regional scale to support the planning and transfer of seniors to mutual aid facilities. Our experiments adapt data from a real SHF to emulate resource allocation in an emergency fire evacuation setting and highlight the delicate balance that decision-makers can achieve between efficiency and fairness.
AB - Efficient resource allocation is crucial in many domains, particularly in senior care, where assigning resources to older adults must consider uncertainties associated with vulnerable populations. In collaboration with Senior Health Facilities (SHFs) and domain experts, this paper presents iFair, a novel framework designed to assist decision-makers in equitably allocating scarce resources to older adults. iFair was prototyped in the context of ongoing work on a data exchange platform, CAREDEX, used for enhancing older adults' resilience during disasters. A key novelty of iFair focuses on aligning resident preferences with resources in urgent situations, expediting care, and enhancing task efficiency. We integrate static and dynamic environmental data, including facility layouts and sensor data, with detailed resident profiles to cater to the individual needs and preferences of residents. While our framework primarily focuses on allocation within facilities, it also extends to a regional scale to support the planning and transfer of seniors to mutual aid facilities. Our experiments adapt data from a real SHF to emulate resource allocation in an emergency fire evacuation setting and highlight the delicate balance that decision-makers can achieve between efficiency and fairness.
KW - decision-making
KW - fairness
KW - resource allocation
KW - senior health care
UR - http://www.scopus.com/inward/record.url?scp=85200720480&partnerID=8YFLogxK
U2 - 10.1109/SMARTCOMP61445.2024.00025
DO - 10.1109/SMARTCOMP61445.2024.00025
M3 - Conference contribution
AN - SCOPUS:85200720480
T3 - Proceedings - 2024 IEEE International Conference on Smart Computing, SMARTCOMP 2024
SP - 22
EP - 30
BT - Proceedings - 2024 IEEE International Conference on Smart Computing, SMARTCOMP 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 June 2024 through 2 July 2024
ER -