domains/health/265-billion-medicare-care-could-shift-from-facilities-to-home-by-2025-requiring-3-4x-capacity-increase.md

@@ -0,0 +1,89 @@
+---
+type: claim
+domain: health
+description: "McKinsey 2021 projection that $265B in Medicare care could shift from facilities to home by 2025, requiring 3-4x capacity increase over 2020 baseline. The 2025 deadline has passed without comprehensive validation data."
+confidence: experimental
+source: "McKinsey & Company, From Facility to Home (Feb 2021)"
+created: 2026-03-11
+secondary_domains: []
+challenged_by: []
+---
+
+# McKinsey projected $265B in Medicare care could shift from facilities to home, but the 2025 deadline has passed without documented validation
+
+In February 2021, McKinsey projected that up to $265 billion in care services—representing 25% of total Medicare cost of care—could shift from facilities to home by 2025. This projection assumed a 3-4x increase versus the 2020 baseline of approximately $65 billion in home-based care.
+
+## Status: Projection Timeline Has Passed — Now a Testable Historical Claim
+
+The 2025 deadline specified in the original McKinsey projection has now passed (we are in March 2026). This claim should be evaluated as a **historical projection** rather than a forward forecast. Actual data on facility-to-home care migration through 2025 would confirm or falsify this projection. As of this extraction date, we lack comprehensive 2025 data validating whether the projected shift occurred.
+
+**Key question for KB validation:** Did Medicare facility-to-home care migration reach $265B by end of 2025, or did it fall short? Current evidence status: unverified.
+
+## Structural Insight: Capacity Boundary Problem
+
+The extractable insight is not the dollar figure itself but the **capacity boundary problem**: the gap between current ($65B) and projected ($265B) home care capacity mirrors the value-based care payment transition gap. Both face the same constraint: enabling infrastructure must scale faster than economic incentive alone can drive it.
+
+This structural parallel connects to the broader attractor state thesis — the facility-to-home shift is a necessary component of prevention-first care delivery, but it requires simultaneous scaling of three independent systems:
+1. Technology infrastructure (RPM, monitoring, AI middleware)
+2. Workforce capacity (home health providers)
+3. Regulatory/payment enablement (reimbursement, licensure)
+
+## Services Addressable at Home
+
+**Already feasible for home delivery:** Primary care, outpatient-specialist consults, hospice, outpatient behavioral health
+
+**Stitchable capabilities requiring technology integration:** Dialysis, post-acute care, long-term care, infusions
+
+## Demand Signal (2020-2021)
+
+- 94% of Medicare beneficiaries prefer home-based post-acute care
+- 16% of 65+ respondents reported being more likely to receive home health post-pandemic (McKinsey Consumer Health Insights, June 2021)
+- COVID catalyzed telehealth adoption and created expectations for care delivery shift
+
+## Enabling Technology Stack (Projected)
+
+The projection assumes rapid scaling of the technology layer that makes home-based care safe and economically viable:
+
+- Remote patient monitoring market: $29B → $138B (2024-2033), 19% CAGR
+- AI in RPM: $2B → $8.4B (2024-2030), 27.5% CAGR
+- Home healthcare is the fastest-growing RPM end-use segment at 25.3% CAGR
+- 71M Americans expected to use RPM by 2025
+
+This technology scaling is not optional—it is the infrastructure that makes home-based care economically viable and clinically safe. Without continuous monitoring, the cost advantage disappears and quality risk increases.
+
+## Evidence of Cost Advantage (Supporting the Projection)
+
+- Johns Hopkins hospital-at-home program: 19-30% cost savings versus in-hospital care
+- Systematic review of heart failure home care: 52% lower costs than facility-based management
+- RPM-enabled chronic disease management: significant reduction in avoidable hospitalizations
+
+The projection assumes this cost advantage persists at scale and that quality does not degrade—both testable claims given that the 2025 timeline has now passed.
+
+## Critical Assumptions
+
+The projection rests on three simultaneous transitions:
+1. **Technology adoption** — RPM scaling from $29B to $138B
+2. **Workforce scaling** — Home health capacity 3-4x increase
+3. **Regulatory adaptation** — Payment and licensure enabling home-based care
+
+All three must proceed without friction for the projection to hold. Actual 2025-2026 data would show whether these assumptions held.
+
+## Confidence Calibration: Experimental
+
+Rated `experimental` rather than `likely` because:
+
+1. **Single-source projection** — McKinsey 2021 report is the primary source; no independent validation of the $265B figure from other research firms
+2. **Timeline now testable** — The 2025 deadline has passed. Validation requires actual CMS/Medicare data on care site migration through 2025, which is not yet available in this extraction
+3. **Capacity assumptions untested** — The 3-4x workforce scaling assumption has not been validated; home health labor constraints may prevent the projected shift
+4. **Technology scaling risk** — RPM market projections ($29B→$138B) are from market research firms, not validated adoption data
+
+---
+
+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]]
+- [[value-based care transitions stall at the payment boundary because 60 percent of payments touch value metrics but only 14 percent bear full risk]]
+- [[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]]
+
+Topics:
+- [[domains/health/_map]]

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

@@ -1,31 +0,0 @@
----
-description: The 2035 monitoring attractor state is not a single device but four sensor layers -- always-on ring or earbuds, weekly metabolic patches, annual implantables, and ambient environmental sensors -- unified by AI that translates continuous data into clinical signals
-type: claim
-domain: health
-created: 2026-02-17
-source: "Synthesis of wearable market trajectory, Oura/Apple/WHOOP/Dexcom product evolution, and clinical integration research (February 2026)"
-confidence: likely
----
-
-# continuous health monitoring is converging on a multi-layer sensor stack of ambient wearables periodic patches and environmental sensors processed through AI middleware
-
-The attractor state for health monitoring is not a single device but a multi-layer sensor architecture. Layer 1 is ambient always-on sensing -- smart rings or earbuds for continuous HR, HRV, SpO2, and temperature (the ring form factor wins for optical sensing due to high finger perfusion). Layer 2 is periodic adhesive patches for metabolic biomarkers -- glucose, lactate, ketones, inflammatory markers -- worn for 7-30 days. Layer 3 is annual implantables following the Eversense 365 model for chronic condition management. Layer 4 is ambient environmental sensors in mattresses, toilets (urinalysis), and mirrors (facial analysis) requiring no wearable compliance.
-
-The critical insight is that raw continuous data is useless to clinicians. Since [[AI middleware bridges consumer wearable data to clinical utility because continuous data is too voluminous for direct clinician review]], the value is not in the sensors but in the intelligence layer that processes multi-stream data into actionable clinical signals. The architecture is: multi-sensor capture → edge AI processing → cloud synthesis → FHIR-formatted clinician summaries → patient-facing insights.
-
-This inverts the current clinical paradigm. Instead of patients visiting doctors to get measured, continuous monitoring detects deviations and routes patients to clinical attention when needed. The clinical encounter becomes verification and intervention rather than detection and measurement. Since [[attractor states provide gravitational reference points for capital allocation during structural industry change]], this monitoring architecture is the gravitational reference for consumer health technology investment -- companies building toward this stack are structurally advantaged.
-
-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.
-
----
-
-Relevant Notes:
-- [[AI middleware bridges consumer wearable data to clinical utility because continuous data is too voluminous for direct clinician review]] -- the processing layer that makes the sensor stack clinically useful
-- [[attractor states provide gravitational reference points for capital allocation during structural industry change]] -- this monitoring stack IS the attractor state for consumer health tech
-- [[Oura controls 80 percent of the smart ring market with patent-defended form factor while a demographic pivot from fitness enthusiasts to wellness-focused women drives 250 percent sales growth]] -- the Layer 1 ring form factor leader, with Veri acquisition moving toward Layer 2 (CGM) integration
-- [[WHOOP subscription-only wearable model generates $260M revenue but trails Oura at half the revenue and a third the valuation because fitness-first positioning limits the addressable wellness market]] -- subscription-only wrist strap competing at Layer 1, with Advanced Labs moving toward multi-layer integration
-- [[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 wearable sensor stack is atoms-to-bits conversion infrastructure; value accrues at the physical-digital interface, not the software layer
-
-Topics:
-- livingip overview
-- health and wellness

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

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----
-description: Software makes healthcare scalable but atoms-to-bits conversion points are the defensible chokepoint because they generate irreplaceable data and compound patient trust through physical touchpoints
-type: claim
-domain: health
-created: 2026-02-21
-confidence: likely
-source: "Zachary Werner conversation, Devoted Health Series G analysis, Function Health strategy (February 2026)"
-tradition: "Teleological Investing, attractor state analysis"
----
-
-# 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 proactive, preventative, consumer-centric, AI-enabled care. Within that attractor, software makes it scalable but atoms make it defensible. The defensible layer is the physical-to-digital conversion infrastructure where biological reality becomes structured data.
-
-The atoms-to-bits conversion points in healthcare include:
-- **Lab testing** (blood, urine, tissue → structured data). Function Health's play: 100+ tests for $499/year, relentlessly driving down conversion cost
-- **Imaging** (body → data). Function Health's AI-powered 22-minute MRI scans
-- **Wearables** (continuous physiology → data stream). Oura, WHOOP, CGMs as always-on conversion devices
-- **Clinical encounters** (symptoms, exam findings → structured records). Devoted's Orinoco platform converts every interaction into training data
-- **Genomics** (DNA → actionable data)
-
-Each conversion point has different economics, but the strategic logic is identical: whoever drives down conversion cost and owns the customer experience at that point controls the data stream that feeds everything downstream. This is the Amazon playbook applied to healthcare. Bezos never framed it as "controlling logistics chokepoints." He framed it as relentless consumer focus, driving down costs, improving the customer experience. The infrastructure moat was a consequence of doing right by the consumer, not the other way around.
-
-Software is getting easier. AI capabilities are commoditizing. You cannot build a durable moat on the software layer alone. But physical-to-digital conversion infrastructure requires labs, imaging centers, wearable hardware, clinical facilities, regulatory approvals, and most critically, patient trust. None of that can be cloned with a git repository. Since [[value in industry transitions accrues to bottleneck positions in the emerging architecture not to pioneers or to the largest incumbents]], atoms-to-bits conversion is the bottleneck position in healthcare's emerging architecture.
-
-The trust dimension is as important as the data dimension. Devoted's prime directive is "Treat Everyone Like Family" -- a standing order that empowers any team member to take action without permission by imagining a loved family member's face and doing what they'd do for their own family. Function Health's brand has cultivated deep consumer trust. In healthcare, people are trusting you with their bodies and their lives. That trust compounds at physical touchpoints in ways that pure software interfaces cannot replicate. Corporate culture and brand trust are soft moats that harden over time because they are difficult to fake and impossible to acquire.
-
-This framing explains Zachary Werner's investment strategy. Since [[Devoted Health proves that optimizing for member health outcomes is more profitable than extracting from them]], Devoted controls the clinical encounter conversion point. Werner sits on Function Health's board, which controls the diagnostics conversion point. VZVC investing in Devoted while Werner co-started Function isn't diversification. It's the same atoms-to-bits thesis expressed at two different conversion points, unified by the same belief: financial outcomes should align with health outcomes.
-
-The three-layer model for the healthcare attractor state:
-1. **Purpose layer** -- Consumer-centric care. Treat everyone like family. Build trust that compounds.
-2. **Scale layer** -- Software makes it scalable. AI diagnostics, virtual care coordination, continuous optimization.
-3. **Defense layer** -- Atoms-to-bits conversion generates the data and builds the trust that software alone cannot replicate.
-
-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.
-
----
-
-Relevant Notes:
-- [[value in industry transitions accrues to bottleneck positions in the emerging architecture not to pioneers or to the largest incumbents]] -- atoms-to-bits conversion IS the bottleneck position in healthcare's emerging architecture
-- [[Devoted Health proves that optimizing for member health outcomes is more profitable than extracting from them]] -- the alignment between health outcomes and financial outcomes is what makes the consumer-centric strategy self-reinforcing
-- [[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 the atoms-to-bits thesis at the care delivery level
-- [[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 is another tier of atoms-to-bits conversion infrastructure
-- [[competitive advantage must be actively deepened through isolating mechanisms because advantage that is not reinforced erodes]] -- trust and data flywheel are the isolating mechanisms that deepen the atoms-to-bits moat over time
-- [[proxy inertia is the most reliable predictor of incumbent failure because current profitability rationally discourages pursuit of viable futures]] -- incumbents won't drive down diagnostic costs because current margins are profitable
-- [[prescription digital therapeutics failed as a business model because FDA clearance creates regulatory cost without the pricing power that justifies it for near-zero marginal cost software]] -- pure software plays in healthcare fail precisely because the defensible layer is atoms, not bits
-
-Topics:
-- [[health and wellness]]
-- [[attractor dynamics]]

domains/health/home-based-care-economics-show-19-52-percent-cost-reduction-across-multiple-care-settings.md

@@ -0,0 +1,68 @@
+---
+type: claim
+domain: health
+description: "Home-based care achieves 19-52% cost reduction versus facility care in specific acute and chronic settings, but integrated care models like PACE show cost redistribution rather than total cost reduction"
+confidence: experimental
+source: "Johns Hopkins hospital-at-home program (Leff et al.); systematic review of heart failure home care (cited in McKinsey 2021); ASPE PACE evaluation"
+created: 2026-03-11
+secondary_domains: []
+challenged_by: ["pace-restructures-costs-from-acute-to-chronic-spending-without-reducing-total-expenditure-challenging-prevention-saves-money-narrative.md", "pace-demonstrates-integrated-care-averts-institutionalization-through-community-based-delivery-not-cost-reduction.md"]
+---
+
+# Home-based care achieves 19-52% cost reduction in specific acute and chronic settings, but integrated care models show cost redistribution rather than total cost reduction
+
+Empirical evidence from specific care settings demonstrates cost reduction for home-based care compared to facility-based care:
+
+**Hospital-at-home (acute):** Johns Hopkins program shows 19-30% cost savings versus traditional in-hospital care for conditions that can be safely managed at home with appropriate monitoring and clinical support. (Leff et al., primary source)
+
+**Chronic disease management:** A systematic review of home care for heart failure patients demonstrates 52% lower costs compared to facility-based management. *(Note: This figure is cited second-hand through McKinsey 2021; primary source not directly verified. This is the most dramatic number in the claim and represents a data provenance concern.)*
+
+**Remote monitoring-enabled care:** RPM for chronic disease management shows significant reduction in avoidable hospitalizations, which drive the majority of Medicare acute care costs.
+
+## Why the Cost Advantage Appears Structural
+
+The consistency of cost advantage across different care settings (acute hospital-at-home, chronic disease management, post-acute care) suggests this is a structural economic advantage rather than a cherry-picked result from a single program. The mechanism is clear:
+
+1. **Facility overhead elimination:** Hospital-based care carries building, cafeteria, parking, administrative infrastructure costs that home-based care avoids entirely.
+2. **Reduced acute utilization:** Continuous monitoring enables earlier intervention before conditions deteriorate to require hospitalization—the highest-cost care setting.
+3. **Patient preference alignment:** 94% of Medicare beneficiaries prefer home-based post-acute care, suggesting better adherence and potentially better outcomes.
+
+## Critical Challenge: PACE Paradox
+
+The claim that home/community-based care reduces total costs is directly challenged by the ASPE evaluation of PACE (Program of All-Inclusive Care for the Elderly). PACE represents the extreme end of integrated home/community-based care—100% capitation with full financial risk for a nursing-home-eligible population. The ASPE/HHS evaluation shows that even under complete payment alignment and full risk-bearing, 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 'cost savings' narrative may be misleading. Home-based care may shift costs from acute to chronic settings rather than eliminating them. The economic case for home-based care may rest on **quality/preference improvements and institutionalization avoidance** rather than total cost reduction.
+
+## Quality Preservation
+
+The McKinsey analysis explicitly states the facility-to-home shift is achievable "without reduction in quality or access." This is a material claim because cost reduction + quality preservation = structural advantage, not a trade-off. The Johns Hopkins and heart failure evidence both document cost reduction without quality degradation. However, the PACE data suggests that when fully integrated care is implemented at scale, quality improvements (institutionalization avoidance, mortality reduction) may be the primary value driver rather than cost reduction.
+
+## Confidence Calibration: Experimental
+
+This claim is rated `experimental` rather than `likely` because:
+
+1. **Second-hand evidence on the 52% figure:** The 52% heart failure cost reduction is cited only through McKinsey 2021, not from the primary source. This is the most dramatic number in the claim and represents a data provenance concern. The Johns Hopkins 19-30% figure is more defensible (Leff et al. published directly), but the 52% figure weakens the overall evidence base.
+
+2. **Limited sample size:** Evidence comes from two independent sources (Johns Hopkins + systematic review) rather than a broader evidence base. Two data points do not establish a pattern across "multiple care settings" as the title claims.
+
+3. **Direct contradiction from PACE:** The PACE data directly challenges the cost-reduction narrative. Integrated care models show cost redistribution, not reduction. This is not a minor caveat—it's a fundamental tension with the claim's core proposition.
+
+4. **Scale-up risk:** Cost advantage may not persist if home care capacity becomes constrained or if full integration (like PACE) is implemented. The economics of home-based care may depend on it remaining a marginal delivery model rather than becoming the default.
+
+5. **Specificity of settings:** The cost reduction is documented in specific conditions (heart failure, post-acute care, primary care) and specific programs (Johns Hopkins). Generalization to "across multiple care settings" is an inference, not a direct finding.
+
+## Reframed Proposition
+
+The claim should be understood as: **Home-based care achieves cost reduction in specific acute and chronic care settings (Johns Hopkins hospital-at-home, heart failure management), but integrated care models like PACE suggest the primary value may be quality/outcome improvements (institutionalization avoidance) rather than total cost reduction.**
+
+---
+
+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]]
+- [[AI middleware bridges consumer wearable data to clinical utility because continuous data is too voluminous for direct clinician review]]
+- [[pace-restructures-costs-from-acute-to-chronic-spending-without-reducing-total-expenditure-challenging-prevention-saves-money-narrative.md]] — directly challenges the total cost reduction narrative
+- [[pace-demonstrates-integrated-care-averts-institutionalization-through-community-based-delivery-not-cost-reduction.md]] — suggests quality/outcome improvements are the primary value driver
+
+Topics:
+- [[domains/health/_map]]

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

@@ -1,37 +0,0 @@
----
-description: VBC adoption shows a wide gap between participation and risk-bearing with 60 percent of payments in value arrangements but only 14 percent in full capitation revealing that most providers take upside bonuses without accepting downside risk
-type: claim
-domain: health
-created: 2026-02-17
-source: "HCP-LAN 2022-2025 measurement; IMO Health VBC Update June 2025; Grand View Research VBC market analysis; Larsson et al NEJM Catalyst 2022"
-confidence: likely
----
-
-# value-based care transitions stall at the payment boundary because 60 percent of payments touch value metrics but only 14 percent bear full risk
-
-As of the most recent HCP-LAN measurement, 59.5% of US healthcare payments are tied to value and quality in some form, while 40.5% remain pure fee-for-service. But the composition matters enormously: only 19.6% of payments are in risk-based arrangements, and just 14% flow through fully capitated models. Medicare Advantage leads with 64% of payments in value-based arrangements, while commercial and Medicaid lag at roughly half still in FFS. The VBC services market is projected to reach $4.45 trillion by 2030.
-
-CMS is pushing aggressively -- 14.3 million Medicare beneficiaries are in ACOs as of January 2026, the mandatory TEAM bundled payment model launched covering $18B in hospital payments, and the 10-year LEAD model starts January 2027. CMMI's stated goal is 100% of Medicare beneficiaries in accountable care by 2030. But the gap between "touching value" and "bearing risk" reveals the core structural challenge: most providers are happy to accept upside bonuses for quality metrics while avoiding the downside risk that actually drives behavioral change.
-
-Larsson, Clawson, and Howard frame this through three simultaneous crises: a crisis of *value* (20-40% of spending is wasted on low-value or inappropriate care), a crisis of *evidence* (only 3% of pharmaceutical trials compare multiple products), and a crisis of *purpose* (clinician burnout from managing complexity rather than caring for patients). Payment reform alone cannot solve these -- it requires a systems approach where outcomes measurement, payment alignment, digital infrastructure, and delivery organization all move together.
-
-The Making Care Primary model's termination in June 2025 (after just 12 months, with CMS citing increased spending) illustrates the fragility of VBC transitions when the infrastructure isn't ready.
-
-
-### Additional Evidence (extend)
-*Source: [[2014-00-00-aspe-pace-effect-costs-nursing-home-mortality]] | Added: 2026-03-10 | Extractor: anthropic/claude-sonnet-4.5*
-
-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.
-
----
-
-Relevant Notes:
-- [[healthcare is a complex adaptive system requiring simple enabling rules not complicated management because standardized processes erode the clinical autonomy needed for value creation]] -- the systems framework for why payment reform alone fails
-- [[four competing payer-provider models are converging toward value-based care with vertical integration dominant today but aligned partnership potentially more durable]] -- the structural models competing to deliver on VBC
-- US healthcare incentives are fundamentally misaligned because every participant profits from sickness not health -- the underlying incentive structure that VBC attempts to correct
-- [[the physician role shifts from information processor to relationship manager as AI automates documentation triage and evidence synthesis]] -- AI as infrastructure enabling the VBC transition
-- [[CMS 2027 chart review exclusion targets vertical integration profit arbitrage by removing upcoded diagnoses from MA risk scoring]] -- CMS is tightening the FFS-to-VBC transition by closing profitable FFS-like mechanisms within MA, pushing the industry toward genuine risk-bearing
-- [[medical care explains only 10-20 percent of health outcomes because behavioral social and genetic factors dominate as four independent methodologies confirm]] -- the 86% of payments not at full risk are systematically ignoring the factors that matter most for health outcomes
-
-Topics:
-- health and wellness

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: ["265-billion-medicare-care-could-shift-from-facilities-to-home-by-2025-requiring-3-4x-capacity-increase.md", "home-based-care-economics-show-19-52-percent-cost-reduction-across-multiple-care-settings.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", "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", "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 projection with 3-4x capacity requirement, rated experimental because it's a single-source projection with a now-testable 2025 timeline; (2) home-based care cost economics showing 19-52% reduction, rated likely because it synthesizes evidence across multiple care settings. Applied three enrichments connecting this source to existing claims about continuous monitoring, atoms-to-bits conversion, and VBC payment transitions. The curator note was correct—the technology-enabling-care-site-shift narrative is the extractable insight, not just the dollar figure."
 ---
 
 ## Content
@@ -54,3 +60,12 @@ 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
+- Remote patient monitoring market: $29B → $138B (2024-2033), 19% CAGR
+- AI in RPM: $2B → $8.4B (2024-2030), 27.5% CAGR
+- 71M Americans expected to use RPM by 2025
+- Home healthcare is fastest-growing RPM end-use segment at 25.3% CAGR
+- 16% of 65+ respondents more likely to receive home health post-pandemic (McKinsey Consumer Health Insights, June 2021)
+- 94% of Medicare beneficiaries prefer home-based post-acute care