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Survey: 57% of home and community-based care providers now evaluating AI tools
A Healthcare IT Today survey published August 9 found that 57% of home- and community-based service providers are actively using, testing, or evaluating AI tools — a significant jump from prior-year figures. The top use cases are documentation assistance, scheduling optimization, and remote patient monitoring data summarization. Barriers to adoption include connectivity limitations in home settings, caregiver training burden, and concerns about AI performance with elderly and cognitively impaired patients.
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- community-care
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- AI-adoption
- remote-monitoring
- caregivers
The 57% figure is striking because home and community-based care has historically lagged acute-care settings in technology adoption — for reasons that are structural rather than cultural. Home health runs on thin margins, employs a large proportion of non-clinical and paraprofessional staff, operates in environments with inconsistent internet connectivity, and serves a patient population that skews elderly, cognitively impaired, and non-English-speaking.
Against that backdrop, the adoption rate reflects two forces. One is that documentation AI — ambient scribes and note-drafting tools — is directly applicable to home health visits, which generate substantial documentation burden for visiting nurses and home health aides without the institutional infrastructure support clinicians have in hospital settings. The per-visit documentation time savings are meaningful in a visit-based payment model. The other is that remote patient monitoring is generating data volumes that clinicians can’t review manually, and AI summarization tools that surface actionable signal from RPM data streams are getting traction.
The barriers are real and worth unpacking. Connectivity limitations mean that tools requiring continuous streaming to a cloud backend are not viable for home visits in many geographies — the AI has to work offline or with intermittent connectivity. Caregiver training burden matters a lot in a high-turnover workforce where the average tenure of a home health aide is measured in months. And AI performance with elderly patients with hearing impairment, accent variation, and cognitive impairment is meaningfully worse than with the demographic that most ambient-AI training sets overrepresent: younger, English-speaking, cognitively typical patients.
The data will improve as vendors prioritize this segment, but health systems and HCBS agencies adopting AI tools should verify performance benchmarks in their specific population rather than assuming transfer from acute-care evidence.
Primary source: Read the full original on Healthcare IT Today ↗