Closing the trust gap: Women and tech-enabled home care

Hospital at Home models have the potential to transform patient outcomes and reduce costs. Yet for women, who make up the majority of caregivers and healthcare decision-makers, distrust of digital diagnostics and AI-enabled care remains a major barrier to adoption. This barrier isn’t insurmountable, understanding the root causes of women’s tech concerns and building with Compassion IT best practices can make women more comfortable with the technologies that underpin Hospital at Home programs. 

Women and caregiving: The billion-dollar backbone

Women provide the bulk of informal care in the United States. The Family Caregiver Alliance estimates that 66% of caregivers are women. This informal unpaid caregiving ranges from $148 billion to $188 billion in economic value annually. This makes women the front-line Hospital at Home managers. For these programs to succeed, we need to understand the unique concerns of female caregivers and their expectations for trustworthy, empathic, and human-centered health technology. 

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The AI trust gap: Women’s digital skepticism

Despite being active health information seekers women are less likely than men to trust digital sources and AI-powered tools. A report from Pew Research Center found that “both experts and the public see men’s views as better represented in AI design than the views of women.” For example, 75% of experts say the people who design AI take men’s perspectives into account at least somewhat well – but only 44% say this about women’s views. A 2022 Pew Research Center report on women and AI shared that 43% of women were concerned with AI’s ability to diagnose health problems, compared with 27% of men. 

This skepticism is a barrier to trusting and therefore adopting things like remote patient monitoring tools that triage data with AI, AI generated care plans, and anomaly prediction software. This concern is not unfounded. Health AI, if not carefully designed, can inherit and amplify existing societal biases, including gender bias. There have been numerous studies exemplifying these biases, from downplaying women’s health issues, misdiagnosing mental health conditions among female patients, and underdiagnosing liver disease. 

The Hospital at Home model inherently shifts some aspects of care away from the traditional hospital setting to the patient’s home, relying heavily on remote monitoring and potentially AI-driven triage systems. Failing to address concerns about AI algorithm bias, data privacy, and transparency when introducing AI into Hospital at Home programs risks eroding women’s trust, making many caregivers less likely to embrace it.

Designing for trust: How to close the gap

Alongside building confidence that AI and algorithms are evidence-based and trustworthy, Hospital at Home programs need to also plan for a smooth user experience with digital tools. Poorly designed hardware and apps can actually increase anxiety instead of reducing it. Female caregivers are frustrated when technologies ignore patient agency or personal goals, fail to adapt to diverse family needs, and add administrative burden without freeing up time for genuine care. When done well though, technology can lead to “improvements in physical health, sleep quality, reduction in caregiver burden, decrease in stress and/or anxiety, and increased prioritization of self-care,” according to The impact of technology use for care by informal female caregivers on their well-being: a scoping review.

To earn women’s trust and unlock the power of tech-enabled care within Hospital at Home programs designers and health systems should:

• Prioritize digital literacy: Offer education that respects varying experience levels, learning styles, technological comfort, and age to accommodate caregivers’ unique needs. 
• Reduce administrative friction: Use technology for passive monitoring so caregivers can focus on the human elements of caregiving
• Include women in the development of AI models and user research: Avoid inadvertent bias and plan for end user feedback to increase the chance of acceptability and adoption 
• Be transparent: Disclose possible risks, limitations, and privacy considerations clearly with every technology that is introduced and how it will dovetail with clinical oversight. 

By understanding the unique concerns of women and building technology with best practices for compassion and user-centered design, health systems can make Hospital at Home models more accessible and genuinely supportive for female caregivers, ultimately closing the trust gap and improving the success of the programs. 

Katie D. McMillan, MPH is the CEO of Well Made Health, LLC, a business strategy consulting firm for health technology companies. She is also a curious researcher and writer focusing on digital health evidence, healthcare innovation, and women’s health. Katie can be reached at [email protected] or LinkedIn.