Key Takeaways from New Research in Home Health Care
As AI becomes increasingly common in the home and workplace, research on its effects is still relatively rare. Specifically, there is limited research examining AI’s impact on low-wage workers in the context of home health care. New research from the Initiative on Home Care Work at Cornell University’s Center for Applied Research on Work (CAROW) sheds light on how AI can be used more fairly in home health care and low-wage work settings.
Who Are Home Care Workers (HCWs)?
Home Care Workers (HCWs) are formal, paid caregivers who provide at-home care for patients with serious illnesses, such as heart disease, dementia, Alzheimer’s disease, and more. HCWs include personal care aides, home health aides, and certified nursing assistants.
In general, HCWs are a marginalized workforce. 85% of HCWs are women, with many being immigrants and/or from racial and ethnic minorities. HCWs are paid low wages for challenging, high-stakes work. Many home care patients have dementia and other serious conditions that require constant care and monitoring. HCWs often work long hours, isolated within patients’ homes, where there is an unequal power dynamic between the workers, their patients, and the patients’ families. As a result, the home care industry experiences high turnover and burnout among HCWs, staff, and management.
In this unique work setting, home care agencies and workers could benefit from AI. However, the benefits and burdens of AI are not shared evenly between the agencies, workers, and tech companies that develop AI.
The Challenge of AI in the Home Health Care Setting
There are both pros and cons to AI in the home health care setting. AI can offer significant efficiencies in pairing HCWs with patients and scheduling, for instance. It can also benefit individual HCWs by providing useful medical information and reminders on the job.
However, technology doesn’t always benefit workers. For example, one AI technology commonly used in home health care tracks HCWs’ movements via GPS. In a study titled “Who is running it? Towards Equitable AI Deployment in Home Care Work,” extensive interviews with HCWs, management, and senior leadership at home health care agencies, as well as worker advocates, uncovered several challenges:
- Invisible Labor: AI can create additional, invisible labor for HCWs, like troubleshooting and regularly inputting data.
- AI Mistakes: AI can make errors, and when it does, those mistakes can negatively impact both the patient and the worker responsible for their care.
- Isolation of Workers: AI can further isolate HCWs—already working alone at a patient’s house—by reducing contact with supervisors and peers.
- Lack of Emotional Intelligence: AI may not factor in an HCW’s emotional intelligence and other soft skills that might make them well-suited to a particular patient. As a result, HCWs may miss out on potential pairings and lose income.
- Unawareness of AI: HCWs are often unaware of how AI is used, how it works, or how data from their interactions with patients is stored, even after leaving an employer.
- Blame for AI Errors: HCWs worry they will be blamed for AI errors. One HCW said, “I think it will be my fault because [the AI system] is only giving me suggestions, and I am the person who decides to do that suggestion or not. So, I will blame myself.”
These findings highlight that AI in home health care has reinforced power imbalances. It strengthens the tech companies that design it and the agencies that deploy it, while disempowering the surveilled and assessed HCWs, and, in turn, threatening patient outcomes.
There are three main channels to level the playing field: policy, unions and advocacy organizations, and home care agencies.
Recommendations for Policymakers
- Opt-Out Options: Ensure HCWs and their clients can opt out of AI use in home care settings.
- Transparency in AI Decisions: Establish a transparent process for HCWs to contest AI decisions.
- Regular Audits: Implement regular, transparent audits to assess the benefits and risks of AI, and how these are distributed among stakeholders.
- Collaboration with Unions: Policymakers should work closely with home health care unions and advocacy organizations to provide resources for democratic governance structures.
- Practical Guidance: Policies should provide practical guidance for implementing AI regulations beyond broad governance principles.
- Research Funding: Fund large-scale studies examining AI in different contexts (e.g., rural, international) and involving a broader range of stakeholders.
Recommendations for Home Health Care Unions and Advocates
- Data Literacy Training: Provide HCWs with training on how to use AI on the job, contest its outcomes, and contribute to AI governance structures.
- Democratic Governance Structures: Establish data cooperatives that bring stakeholders, including frontline workers, together to discuss AI use in home care.
- Inclusive Education: Educate and empower marginalized/vulnerable workers to be active participants in governance structures. The burden of gaining AI expertise should not fall solely on an overworked and underpaid workforce.
- Inclusion Efforts: Ensure genuine inclusion of frontline workers’ ideas and avoid tokenizing marginalized stakeholders.
Recommendations for Home Care Agencies
- Assess AI Tools: Before purchasing AI tools, home care agencies should assess their safety, reliability, and fairness.
- Bias Audits: Regularly audit AI tools for bias and other issues.
- Ongoing Monitoring: Implement mechanisms for ongoing monitoring of AI tool implementation and the use of data.
Conclusion
AI has the potential to enhance efficiency and improve the quality of care in home health care settings, but its implementation must be done fairly and transparently. To ensure that AI benefits all stakeholders—caregivers, patients, and families—policymakers, unions, advocates, and home care agencies must work together to create an environment where AI supports the people who need it most, without reinforcing power imbalances or exacerbating existing inequities. By adopting these recommendations, we can ensure that AI contributes to a more equitable, empowering, and effective home health care system.