Related Posts
by Isaac Greszes, Eleos
Purpose-Built AI for Care at Home
Architecture, Scalability, and Security
In part one of this 4-part series, we discussed how care at home agencies can realize the full impact of AI software that goes beyond the testing period. The best way to do this is to find purpose-built tech and evaluate AI solutions for real-world outcomes.
Part two focuses on AI architecture, scalability and security.
Architecture and Scalability Across the Tech Ecosystem
AI does not operate in isolation. It sits within a broader ecosystem of EHRs, compliance programs, quality initiatives, and IT infrastructure.
For care-at-home organizations, long-term outcomes depend on whether an AI platform can:
- Adapt to evolving documentation and regulatory requirements
- Scale reliably during census fluctuations
- Integrate cleanly with existing systems
- Improve over time without creating operational drag
Health informatics research increasingly highlights risks such as model drift — where AI performance degrades as populations, workflows, or clinical practices change — reinforcing the need for continuous monitoring rather than one-time deployment.
Vendors with limited clinical depth or brittle configurations may show early promise in pilots, but often struggle to sustain efficiency and ROI at scale.
Security, Governance
and the Link to Long-Term Value
HIPAA compliance remains foundational, but AI introduces additional governance considerations related to transparency, accountability, fairness, and ongoing risk management.
Healthcare organizations increasingly evaluate AI vendors based on:
- Independent security and privacy assessments
- Clear contractual boundaries around data use
- Explicit retention and deletion policies
- Documented processes for monitoring AI behavior over time
Expectations
Recent federal regulation, including the ONC’s HTI-1 Final Rule, formalizes new transparency and risk-management expectations for AI-enabled clinical systems — extending well beyond traditional privacy frameworks.
Emerging standards such as ISO 42001, focused on AI management systems, reflect a broader shift toward formal governance of AI in high-risk domains like healthcare. While adoption is still evolving, these frameworks provide executives with a useful lens for assessing vendor maturity.
Strong governance is not only a risk-mitigation strategy — it is a prerequisite for sustaining outcomes, protecting organizational reputation, and maintaining provider trust.
A Practical Takeaway
AI has demonstrated the potential to reduce administrative burden, improve documentation quality, and deliver measurable ROI in healthcare — including regulated, care at home settings.
However, results are not guaranteed. They depend on evidence-backed design, workflow alignment, scalability, and governance discipline.
For care-at-home leaders, the most reliable path to value is not adopting AI quickly, but evaluating it rigorously — with a focus on how the technology is built, validated, and governed.
For organizations navigating pilot fatigue, the critical shift is not testing more tools, but selecting platforms designed for scale, governance, and long-term operational impact.
# # #
This is part 2 of a 4-part series. Read part 1 and come back next week for part 3, “From Evaluation to Execution.”
About Eleos
©2026 by The Rowan Report, Peoria, AZ. All rights reserved. This article originally appeared in The Rowan Report. One copy may be printed for personal use: further reproduction by permission only. editor@therowanreport.com
