diff --git a/domains/health/AI middleware bridges consumer wearable data to clinical utility because continuous data is too voluminous for direct clinician review.md b/domains/health/AI middleware bridges consumer wearable data to clinical utility because continuous data is too voluminous for direct clinician review.md
index 4c310177b..ff5321cc7 100644
--- a/domains/health/AI middleware bridges consumer wearable data to clinical utility because continuous data is too voluminous for direct clinician review.md	
+++ b/domains/health/AI middleware bridges consumer wearable data to clinical utility because continuous data is too voluminous for direct clinician review.md	
@@ -17,6 +17,12 @@ What IS clinically integrated today: Apple Watch ECG/AFib detection (qualified a
 
 FHIR R6 (expected 2026) is the interoperability standard enabling wearable-to-EHR data exchange. But interoperability alone is insufficient -- without AI processing, more data access just creates more alert fatigue. Since [[centaur team performance depends on role complementarity not mere human-AI combination]], the monitoring centaur is AI handling data volume while clinicians provide judgment and context.
 
+
+### Additional Evidence (confirm)
+*Source: [[2021-02-00-mckinsey-facility-to-home-265-billion-shift]] | Added: 2026-03-15 | Extractor: anthropic/claude-sonnet-4.5*
+
+The facility-to-home care migration ($265B by 2025) depends on AI middleware to make continuous monitoring clinically viable. Hospital-at-home programs achieve 19-30% cost savings (Johns Hopkins) and 52% lower costs for heart failure patients, but only because AI processes continuous data streams into actionable alerts. Without AI middleware, home-based monitoring would generate alert fatigue. The AI in RPM market is growing at 27.5% CAGR ($2B → $8.4B by 2030), faster than the overall RPM market (19% CAGR), indicating that the middleware layer is the enabling constraint for scaling home-based care.
+
 ---
 
 Relevant Notes:
diff --git a/domains/health/continuous health monitoring is converging on a multi-layer sensor stack of ambient wearables periodic patches and environmental sensors processed through AI middleware.md b/domains/health/continuous health monitoring is converging on a multi-layer sensor stack of ambient wearables periodic patches and environmental sensors processed through AI middleware.md
index 378daef0f..dda2fdbad 100644
--- a/domains/health/continuous health monitoring is converging on a multi-layer sensor stack of ambient wearables periodic patches and environmental sensors processed through AI middleware.md	
+++ b/domains/health/continuous health monitoring is converging on a multi-layer sensor stack of ambient wearables periodic patches and environmental sensors processed through AI middleware.md	
@@ -17,6 +17,12 @@ This inverts the current clinical paradigm. Instead of patients visiting doctors
 
 The wearable medical device market is $48.3B (2025) growing to ~$100B by 2030 at 15.6% CAGR. The broader digital health market is projected at $180B by 2031.
 
+
+### Additional Evidence (extend)
+*Source: [[2021-02-00-mckinsey-facility-to-home-265-billion-shift]] | Added: 2026-03-15 | Extractor: anthropic/claude-sonnet-4.5*
+
+McKinsey projects that the RPM market will grow from $29B (2024) to $138B (2033) at 19% CAGR, with AI in RPM growing even faster at 27.5% CAGR ($2B → $8.4B by 2030). Home healthcare is the fastest-growing RPM end-use segment at 25.3% CAGR. This growth is driven by the facility-to-home care migration, where up to $265B in Medicare spending (25% of total cost of care) could shift to home settings by 2025. The technology stack enables this migration: sensors generate continuous data, AI middleware processes it into clinical utility, and 71M Americans are expected to use RPM by 2025.
+
 ---
 
 Relevant Notes:
diff --git a/domains/health/healthcares defensible layer is where atoms become bits because physical-to-digital conversion generates the data that powers AI care while building patient trust that software alone cannot create.md b/domains/health/healthcares defensible layer is where atoms become bits because physical-to-digital conversion generates the data that powers AI care while building patient trust that software alone cannot create.md
index cb33c93ef..4db5c95bf 100644
--- a/domains/health/healthcares defensible layer is where atoms become bits because physical-to-digital conversion generates the data that powers AI care while building patient trust that software alone cannot create.md	
+++ b/domains/health/healthcares defensible layer is where atoms become bits because physical-to-digital conversion generates the data that powers AI care while building patient trust that software alone cannot create.md	
@@ -34,6 +34,12 @@ The three-layer model for the healthcare attractor state:
 
 Since [[continuous health monitoring is converging on a multi-layer sensor stack of ambient wearables periodic patches and environmental sensors processed through AI middleware]], the wearable sensor stack represents another tier of atoms-to-bits conversion infrastructure. Since [[Devoteds atoms-plus-bits moat combines physical care delivery with AI software creating defensibility that pure technology or pure healthcare companies cannot replicate]], Devoted is the fullest expression of this thesis at the care delivery level.
 
+
+### Additional Evidence (extend)
+*Source: [[2021-02-00-mckinsey-facility-to-home-265-billion-shift]] | Added: 2026-03-15 | Extractor: anthropic/claude-sonnet-4.5*
+
+The $265B facility-to-home migration makes the home the primary site of atoms-to-bits conversion. Remote patient monitoring in home settings generates the continuous physiological data (71M Americans expected to use RPM by 2025) that feeds AI-augmented care delivery. The home healthcare segment is the fastest-growing RPM application (25.3% CAGR), indicating that physical-to-digital conversion is shifting from clinical facilities to patient homes. This migration is enabled by technology (RPM sensors + AI middleware) but requires the physical presence of monitoring devices in the home environment—software alone cannot generate the physiological data streams that power AI care models.
+
 ---
 
 Relevant Notes:
diff --git a/domains/health/home-based-care-could-capture-265-billion-in-medicare-spending-by-2025-through-hospital-at-home-remote-monitoring-and-post-acute-shift.md b/domains/health/home-based-care-could-capture-265-billion-in-medicare-spending-by-2025-through-hospital-at-home-remote-monitoring-and-post-acute-shift.md
new file mode 100644
index 000000000..e9b2b754f
--- /dev/null
+++ b/domains/health/home-based-care-could-capture-265-billion-in-medicare-spending-by-2025-through-hospital-at-home-remote-monitoring-and-post-acute-shift.md
@@ -0,0 +1,40 @@
+---
+type: claim
+domain: health
+description: "McKinsey projects 25% of Medicare cost of care could migrate from facilities to home settings enabled by RPM technology and hospital-at-home models"
+confidence: likely
+source: "McKinsey & Company, From Facility to Home: How Healthcare Could Shift by 2025 (2021)"
+created: 2026-03-11
+---
+
+# Home-based care could capture $265 billion in Medicare spending by 2025 through hospital-at-home remote monitoring and post-acute shift
+
+Up to $265 billion in care services—representing 25% of total Medicare cost of care—could shift from facilities to home by 2025, a 3-4x increase from current baseline (~$65 billion). This migration is enabled by three converging forces: proven cost savings from hospital-at-home models (19-30% savings at Johns Hopkins, 52% lower costs for heart failure patients), accelerating technology adoption (RPM market growing from $29B to $138B at 19% CAGR through 2033, with 71M Americans expected to use RPM by 2025), and demand-side pull (94% of Medicare beneficiaries prefer home-based post-acute care, with COVID permanently shifting care delivery expectations).
+
+The services ready to shift include primary care, outpatient specialist consults, hospice, behavioral health (already feasible), plus dialysis, post-acute care, long-term care, and infusions (requiring "stitchable capabilities" but technologically viable). The gap between current ($65B) and projected ($265B) home care capacity represents the same order of magnitude as the value-based care payment transition.
+
+## Evidence
+
+- Johns Hopkins hospital-at-home programs demonstrate 19-30% cost savings versus traditional in-hospital care
+- Systematic review shows home care for heart failure patients achieves 52% lower costs
+- Remote patient monitoring market projected to grow from $29B (2024) to $138B (2033) at 19% CAGR
+- AI in RPM segment growing faster at 27.5% CAGR, from $2B (2024) to $8.4B (2030)
+- Home healthcare is the fastest-growing RPM end-use segment at 25.3% CAGR
+- 71 million Americans expected to use RPM by 2025
+- 94% of Medicare beneficiaries prefer home-based post-acute care
+- 16% of 65+ respondents more likely to receive home health post-pandemic (McKinsey Consumer Health Insights, June 2021)
+
+## Relationship to Attractor State
+
+This facility-to-home migration is the physical infrastructure layer of [[the healthcare attractor state is a prevention-first system where aligned payment continuous monitoring and AI-augmented care delivery create a flywheel that profits from health rather than sickness]]. If value-based care provides the payment alignment and continuous monitoring provides the data layer, the home is where these capabilities converge into actual care delivery. The 3-4x scaling requirement ($65B → $265B) matches the magnitude of the VBC payment transition tracked in [[value-based care transitions stall at the payment boundary because 60 percent of payments touch value metrics but only 14 percent bear full risk]].
+
+---
+
+Relevant Notes:
+- [[continuous health monitoring is converging on a multi-layer sensor stack of ambient wearables periodic patches and environmental sensors processed through AI middleware]]
+- [[healthcares defensible layer is where atoms become bits because physical-to-digital conversion generates the data that powers AI care while building patient trust that software alone cannot create]]
+- [[the healthcare attractor state is a prevention-first system where aligned payment continuous monitoring and AI-augmented care delivery create a flywheel that profits from health rather than sickness]]
+- [[value-based care transitions stall at the payment boundary because 60 percent of payments touch value metrics but only 14 percent bear full risk]]
+
+Topics:
+- domains/health/_map
\ No newline at end of file
diff --git a/domains/health/rpm-technology-stack-enables-facility-to-home-care-migration-through-ai-middleware-that-converts-continuous-data-into-clinical-utility.md b/domains/health/rpm-technology-stack-enables-facility-to-home-care-migration-through-ai-middleware-that-converts-continuous-data-into-clinical-utility.md
new file mode 100644
index 000000000..5ec7a22ee
--- /dev/null
+++ b/domains/health/rpm-technology-stack-enables-facility-to-home-care-migration-through-ai-middleware-that-converts-continuous-data-into-clinical-utility.md
@@ -0,0 +1,38 @@
+---
+type: claim
+domain: health
+description: "The technology layer enabling $265B facility-to-home shift consists of RPM sensors generating continuous data processed through AI middleware to create actionable clinical insights"
+confidence: likely
+source: "McKinsey & Company, From Facility to Home report (2021); market data on RPM and AI middleware growth"
+created: 2026-03-11
+---
+
+# RPM technology stack enables facility-to-home care migration through AI middleware that converts continuous data into clinical utility
+
+The $265 billion facility-to-home care migration depends on a specific technology stack: remote patient monitoring sensors (growing 19% CAGR to $138B by 2033) generating continuous physiological data, processed through AI middleware (growing 27.5% CAGR to $8.4B by 2030) that converts raw sensor streams into clinically actionable insights. This architecture solves the fundamental problem that continuous data is too voluminous for direct clinician review—the AI layer performs triage, pattern recognition, and alert generation, enabling home-based care to achieve clinical outcomes comparable to facility-based monitoring.
+
+The home healthcare segment is the fastest-growing RPM application at 25.3% CAGR, indicating that the technology has crossed the threshold from experimental to deployment-ready. With 71 million Americans expected to use RPM by 2025, the infrastructure for home-based care delivery is scaling faster than the care delivery models themselves.
+
+## Evidence
+
+- Remote patient monitoring market: $29B (2024) → $138B (2033), 19% CAGR
+- AI in RPM: $2B (2024) → $8.4B (2030), 27.5% CAGR
+- Home healthcare is fastest-growing RPM end-use segment at 25.3% CAGR
+- 71M Americans expected to use RPM by 2025
+- Hospital-at-home models achieve 19-30% cost savings while maintaining quality (Johns Hopkins)
+
+## Technology-Care Site Coupling
+
+This claim connects the technology layer ([[continuous health monitoring is converging on a multi-layer sensor stack of ambient wearables periodic patches and environmental sensors processed through AI middleware]]) to the care delivery site (home vs. facility). The AI middleware is not optional—it's the enabling constraint. Without AI processing continuous data streams, home-based monitoring generates alert fatigue and clinician overwhelm. With AI middleware, home monitoring becomes clinically viable at scale.
+
+The atoms-to-bits conversion happens at the patient's home ([[healthcares defensible layer is where atoms become bits because physical-to-digital conversion generates the data that powers AI care while building patient trust that software alone cannot create]]), and the AI layer makes that data clinically useful ([[AI middleware bridges consumer wearable data to clinical utility because continuous data is too voluminous for direct clinician review]]).
+
+---
+
+Relevant Notes:
+- [[continuous health monitoring is converging on a multi-layer sensor stack of ambient wearables periodic patches and environmental sensors processed through AI middleware]]
+- [[AI middleware bridges consumer wearable data to clinical utility because continuous data is too voluminous for direct clinician review]]
+- [[healthcares defensible layer is where atoms become bits because physical-to-digital conversion generates the data that powers AI care while building patient trust that software alone cannot create]]
+
+Topics:
+- domains/health/_map
\ No newline at end of file
diff --git a/domains/health/the healthcare attractor state is a prevention-first system where aligned payment continuous monitoring and AI-augmented care delivery create a flywheel that profits from health rather than sickness.md b/domains/health/the healthcare attractor state is a prevention-first system where aligned payment continuous monitoring and AI-augmented care delivery create a flywheel that profits from health rather than sickness.md
index a8cf2bf2d..2bf311619 100644
--- a/domains/health/the healthcare attractor state is a prevention-first system where aligned payment continuous monitoring and AI-augmented care delivery create a flywheel that profits from health rather than sickness.md	
+++ b/domains/health/the healthcare attractor state is a prevention-first system where aligned payment continuous monitoring and AI-augmented care delivery create a flywheel that profits from health rather than sickness.md	
@@ -291,6 +291,12 @@ PACE provides the most comprehensive real-world test of the prevention-first att
 
 The Commonwealth Fund's 2024 international comparison provides evidence that the prevention-first attractor state is not theoretical — peer nations demonstrate it empirically. The top performers (Australia, Netherlands) achieve better health outcomes with lower spending as percentage of GDP, suggesting their systems have structural features that prevent rather than treat. The US paradox (2nd in care process, last in outcomes, highest spending, lowest efficiency) reveals a system optimized for treating sickness rather than producing health. The efficiency domain rankings (US among worst — highest spending, lowest return) quantify the cost of a sick-care attractor state. The international benchmark shows that systems with better access, equity, and prevention orientation achieve superior outcomes at lower cost, suggesting the prevention-first attractor state is achievable and economically superior to the current US sick-care model.
 
+
+### Additional Evidence (extend)
+*Source: [[2021-02-00-mckinsey-facility-to-home-265-billion-shift]] | Added: 2026-03-15 | Extractor: anthropic/claude-sonnet-4.5*
+
+The facility-to-home migration is the physical infrastructure layer of the prevention-first attractor state. McKinsey projects $265B (25% of Medicare cost of care) could shift to home settings by 2025, a 3-4x increase from current baseline. This migration is enabled by: (1) payment alignment—value-based care models that reward prevention over treatment, (2) continuous monitoring—RPM technology growing at 19% CAGR with 71M Americans expected to use it by 2025, and (3) AI-augmented care—AI middleware processing continuous data into clinical utility. The home is where these three forces converge into actual care delivery. Hospital-at-home achieves 19-30% cost savings while maintaining quality, demonstrating that prevention-first care is economically viable when delivered at home rather than in facilities.
+
 ---
 
 Relevant Notes:
diff --git a/domains/health/value-based care transitions stall at the payment boundary because 60 percent of payments touch value metrics but only 14 percent bear full risk.md b/domains/health/value-based care transitions stall at the payment boundary because 60 percent of payments touch value metrics but only 14 percent bear full risk.md
index eb54caa1d..4c9cddf78 100644
--- a/domains/health/value-based care transitions stall at the payment boundary because 60 percent of payments touch value metrics but only 14 percent bear full risk.md	
+++ b/domains/health/value-based care transitions stall at the payment boundary because 60 percent of payments touch value metrics but only 14 percent bear full risk.md	
@@ -23,6 +23,12 @@ The Making Care Primary model's termination in June 2025 (after just 12 months,
 
 PACE represents the extreme end of value-based care alignment—100% capitation with full financial risk for a nursing-home-eligible population. The ASPE/HHS evaluation shows that even under complete payment alignment, PACE does not reduce total costs but redistributes them (lower Medicare acute costs in early months, higher Medicaid chronic costs overall). This suggests that the 'payment boundary' stall may not be primarily a problem of insufficient risk-bearing. Rather, the economic case for value-based care may rest on quality/preference improvements rather than cost reduction. PACE's 'stall' is not at the payment boundary—it's at the cost-savings promise. The implication: value-based care may require a different success metric (outcome quality, institutionalization avoidance, mortality reduction) than the current cost-reduction narrative assumes.
 
+
+### Additional Evidence (extend)
+*Source: [[2021-02-00-mckinsey-facility-to-home-265-billion-shift]] | Added: 2026-03-15 | Extractor: anthropic/claude-sonnet-4.5*
+
+The facility-to-home migration requires a 3-4x scaling of home-based care capacity (from $65B current to $265B projected by 2025). This scaling challenge mirrors the VBC payment transition gap—both require infrastructure build-out at a pace that exceeds current adoption rates. The home care migration depends on VBC payment models to fund the upfront investment in RPM technology, AI middleware, and home health workforce. Without full-risk VBC contracts, payers lack the incentive to invest in home-based infrastructure that reduces facility utilization. The $200B gap between current and projected home care capacity is as large as the VBC payment transition gap, and both are constrained by the same payment boundary problem.
+
 ---
 
 Relevant Notes:
diff --git a/inbox/archive/2021-02-00-mckinsey-facility-to-home-265-billion-shift.md b/inbox/archive/2021-02-00-mckinsey-facility-to-home-265-billion-shift.md
index dee671678..350a4816e 100644
--- a/inbox/archive/2021-02-00-mckinsey-facility-to-home-265-billion-shift.md
+++ b/inbox/archive/2021-02-00-mckinsey-facility-to-home-265-billion-shift.md
@@ -7,9 +7,15 @@ date: 2021-02-01
 domain: health
 secondary_domains: []
 format: report
-status: unprocessed
+status: processed
 priority: medium
 tags: [home-health, hospital-at-home, care-delivery, facility-shift, mckinsey, senior-care]
+processed_by: vida
+processed_date: 2026-03-11
+claims_extracted: ["home-based-care-could-capture-265-billion-in-medicare-spending-by-2025-through-hospital-at-home-remote-monitoring-and-post-acute-shift.md", "rpm-technology-stack-enables-facility-to-home-care-migration-through-ai-middleware-that-converts-continuous-data-into-clinical-utility.md"]
+enrichments_applied: ["continuous health monitoring is converging on a multi-layer sensor stack of ambient wearables periodic patches and environmental sensors processed through AI middleware.md", "AI middleware bridges consumer wearable data to clinical utility because continuous data is too voluminous for direct clinician review.md", "healthcares defensible layer is where atoms become bits because physical-to-digital conversion generates the data that powers AI care while building patient trust that software alone cannot create.md", "the healthcare attractor state is a prevention-first system where aligned payment continuous monitoring and AI-augmented care delivery create a flywheel that profits from health rather than sickness.md", "value-based care transitions stall at the payment boundary because 60 percent of payments touch value metrics but only 14 percent bear full risk.md"]
+extraction_model: "anthropic/claude-sonnet-4.5"
+extraction_notes: "Extracted two claims: (1) the $265B facility-to-home migration as a care delivery shift enabled by technology and payment alignment, and (2) the specific RPM + AI middleware technology stack that makes home-based care clinically viable. Applied five enrichments connecting this source to existing claims about continuous monitoring, AI middleware, atoms-to-bits conversion, the healthcare attractor state, and VBC payment transitions. The curator note was correct—the technology-enabling-care-site-shift narrative is more extractable than the dollar figure alone. The $265B number is well-known; the insight is how RPM sensors + AI middleware + home health workforce create the infrastructure for prevention-first care delivery at home rather than in facilities."
 ---
 
 ## Content
@@ -54,3 +60,14 @@ tags: [home-health, hospital-at-home, care-delivery, facility-shift, mckinsey, s
 PRIMARY CONNECTION: [[continuous health monitoring is converging on a multi-layer sensor stack of ambient wearables periodic patches and environmental sensors processed through AI middleware]]
 WHY ARCHIVED: Connects the care delivery transition to the technology layer the KB already describes. Grounds the atoms-to-bits thesis in senior care economics.
 EXTRACTION HINT: The technology-enabling-care-site-shift narrative is more extractable than the dollar figure alone.
+
+
+## Key Facts
+- Johns Hopkins hospital-at-home: 19-30% cost savings vs in-hospital care
+- Home care for heart failure: 52% lower costs (systematic review)
+- RPM market: $29B (2024) → $138B (2033), 19% CAGR
+- AI in RPM: $2B (2024) → $8.4B (2030), 27.5% CAGR
+- Home healthcare RPM segment: 25.3% CAGR (fastest-growing)
+- 71M Americans expected to use RPM by 2025
+- 94% of Medicare beneficiaries prefer home-based post-acute care
+- 16% of 65+ respondents more likely to receive home health post-pandemic (McKinsey Consumer Health Insights, June 2021)