Articles

Trust Scarcity in the Age of AI: Why American Community-Based Care Needs More Than Instant Information
Artificial intelligence is making information easier to produce, but trustworthy professional knowledge may become more valuable as a result. This flagship analysis examines how AI-generated content affects Medicaid, HCBS, disability services, behavioral health, aging services and provider governance across the United States, and why regulatory accuracy, professional judgment, transparent evidence and human accountability are essential to safe, person-centered decisions. Read more...
The Future of Remote Monitoring in HCBS: Independence, Safety or Surveillance?
Remote monitoring could help people receiving Home- and Community-Based Services remain independent, prevent avoidable emergencies, and strengthen continuity of support. Yet the same technologies can introduce intrusive observation, uncertain consent, inequitable access, and new forms of control. This article examines the federal-state framework, Medicaid funding, provider operations, workforce implications, privacy safeguards, governance, and evidence needed to distinguish genuinely person-centered monitoring from surveillance. Read more...
Ambient Intelligence in Community Care: Could Technology Detect Risk Without Constant Human Monitoring?
Ambient intelligence could help community-based care providers identify changes in movement, routines, environmental conditions and wellbeing without relying on continuous human observation. This USA flagship analysis examines the emerging technology, its limitations, Medicaid funding and state oversight, and the operational safeguards needed to protect autonomy, privacy and safety across HCBS, LTSS, disability services and aging in place. Read more...
Agentic AI in HCBS: Where Could Autonomous Digital Systems Safely Fit?
Agentic AI could move community-based care technology beyond passive alerts and administrative automation toward systems capable of coordinating tasks, responding to changing conditions and initiating defined actions. For Medicaid HCBS providers, health plans and state agencies, the opportunity is significant—but so are the questions of consent, clinical authority, accountability, privacy and safety. This article examines where bounded autonomy may be useful, where human decisions remain essential and how organizations can govern responsible adoption. Read more...
What Happens When AI Becomes Part of Everyday Community-Based Care?
Artificial intelligence is moving beyond administrative experimentation toward everyday decisions in community-based care. This flagship analysis examines how AI could reshape Medicaid-funded HCBS, LTSS, disability and behavioral health services, from workforce scheduling and documentation to risk identification and care coordination. It explores the implications for participant rights, provider governance, privacy, payment, workforce competence and the evidence needed to distinguish useful innovation from unsafe automation. Read more...
Digital Safeguarding Risk Scores in US Community-Based Care: Prevention, Rights, Bias and Accountability
Digital safeguarding risk scores promise earlier identification of abuse, neglect, exploitation and service deterioration, but their use could also introduce bias, unnecessary restrictions and false reassurance. This flagship analysis examines their potential across Medicaid HCBS, LTSS, IDD and behavioral health services, exploring state oversight, mandatory reporting, data quality, predictive models, provider governance and practical safeguards for protecting individual rights. Read more...
The Future of AI-Assisted Care Planning in Social Care Services
AI could reshape care planning across HCBS, LTSS, IDD and behavioral health by helping teams interpret complex information, identify changing needs and strengthen person-centered decisions. This flagship analysis examines where AI-assisted planning could add value, where human judgment must remain decisive, and how providers, Medicaid systems and health plans can govern emerging tools without allowing efficiency, prediction or automation to displace rights, relationships and individual choice. Read more...
Can AI Improve Mental Capacity Decision-Making Support Without Replacing Professional Judgment?
AI could help community-based care teams organize evidence, identify communication needs, surface inconsistencies and strengthen decision-specific capacity support. But in the United States, legal authority, informed consent, supported decision-making and guardianship vary by jurisdiction. This article examines how providers, health plans and state systems could use AI without allowing technology to displace autonomy, due process or accountable professional judgment. Read more...
AI-Powered Risk Detection in U.S. Community-Based Care: From Early Warning to Accountable Human Action
Artificial intelligence could help U.S. community-based care organizations detect emerging safety, continuity and quality risks earlier by connecting signals that conventional monitoring often examines separately. This flagship analysis explores how providers, Medicaid agencies and health plans can use AI-supported risk detection responsibly while protecting rights, strengthening data governance, retaining human judgment and ensuring that prediction leads to proportionate, accountable action. Read more...
Predictive Safeguarding in U.S. Community-Based Care: Can Data Help Prevent Harm Before It Happens?
Safeguarding systems traditionally respond once an incident, allegation or visible deterioration has occurred. This flagship analysis examines how U.S. providers, Medicaid agencies, health plans and protective-service partners could connect incident, workforce, service, quality and experience data to identify emerging risk earlier—while preserving individual rights, mandatory-reporting responsibilities, human judgment and accountable governance. Read more...
Could AI Become a Care Coordinator? The Future of Human and Digital Support in U.S. Community-Based Care
AI could increasingly support navigation, information synthesis, follow-up and coordination across U.S. community-based care, but replacing human care coordinators is a very different proposition. This flagship analysis examines where AI could strengthen Medicaid HCBS, LTSS, IDD and behavioral health coordination, where human judgment and relationships remain indispensable, and what governance, rights, workforce and data safeguards an augmented model would require. Read more...
Using AI to Identify Community Support Needs Earlier in U.S. Community-Based Care
Community support needs often change between formal assessments, annual reviews and authorization cycles. This flagship analysis examines how AI and predictive analytics could help U.S. providers, Medicaid programs and health plans identify emerging unmet need earlier, while preserving human judgment, person-centered planning, due process and state-specific accountability. It explores data, workforce, funding, equity, governance and the operational controls required for responsible implementation. Read more...