Why Home Care Workers?
Home Care Workers (HCW) are an important group of frontline healthcare workers that deliver at-home care to older adults to enable them to age in place. Research has shown that HCWs, primarily consisting of Black and Latina women, are often overlooked and undervalued. They work in isolated conditions, are paid low wages and often experience stress and burnout.
With the aging population, the demand of home care workers is increasing. There is a pressing need to support these workers.
With the aging population, the demand of home care workers is increasing. There is a pressing need to support these workers.
Safe and Trustworthy AI
A quick TLDR of this work! Read on below for more details.
Story behind the AI work.
AI systems are rapidly transforming the lives of workers in a wide range of domains, healthcare included. With the rising demand for home care services following an ageing population, AI poses a potential to improve efficiency and reduce costs, helping to ameliorate the caregiving crisis.
Conceptual Model based on the Social Ecological Model by Dr. Sterling (source)
To better understand the implications of current and near-future AIĀ deployments in home care work, we conducted a qualitative study to understand how AI affects various stakeholders. Guided by a conceptual model published by Dr. Sterling, we investigated the implications of AI in home care for workers, agencies, and advocacy groups.
Deductive and Inductive Coding Tree
Affinity Mapped Themes
We synthesized qualitative findings from 22 interviews via structural coding of high-level topics, inductive generation of sub-codes within categories, and finally affinity mapping to get key themes.
We found that HCWs do not understand how AI works, how their data can be used or why AI systems may retain their information. Considering risks that workers will be held responsible for AI mistakes, we first acknowledge the challenges to equitable AI development and deployment in home care work, and advocate for stakeholder-first literacy and education.
Pending review for second author paper submission to an ACM conference in 2025.
We found that HCWs do not understand how AI works, how their data can be used or why AI systems may retain their information. Considering risks that workers will be held responsible for AI mistakes, we first acknowledge the challenges to equitable AI development and deployment in home care work, and advocate for stakeholder-first literacy and education.
Pending review for second author paper submission to an ACM conference in 2025.
Wearable Technology
A quick TLDR of this work! Read on below for more details.
Story behind the wearables work.
In face of the challenges HCW face at work, research suggests they are motivated to be healthy, driven by their desire to care for others and their work with sick patients.
We investigated the feasibility and utility of using activity tracking devices to provide HCWs with fine-grained awareness and insight into their daily activities that affect their health and wellbeing.
We investigated the feasibility and utility of using activity tracking devices to provide HCWs with fine-grained awareness and insight into their daily activities that affect their health and wellbeing.
Study Process
Visualization of HCWs Realizations on Their Work-Life Balance.
We conducted mixed method research including in-person focus groups, interviews (remote and in-person), and a four-week field study with 17 participants, investigating feasibility and acceptability of deploying personal devices with home care workers to increase awareness and well-being.
Focus Group
Participant Trying the Watch-based Wearable Device
We found that HCWs often feel that they need to choose between sleep, work, and exercise. Approaches that center HCWs' perspectives and experiences worked better than simply showing health metrics. Referencing collective data helped them contextualize their experiences while fostering togetherness and solidarity. Future work could explore data cooperatives and peer support programs that support HCW advocacy via data sharing.
Pending review for second author paper submission to an ACM conference in 2025.
Pending review for second author paper submission to an ACM conference in 2025.