auto-fix: strip 32 broken wiki links
Pipeline auto-fixer: removed [[ ]] brackets from links that don't resolve to existing claims in the knowledge base.
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Teleo Agents
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@ -27,25 +27,25 @@ The contrast is instructive: since [[medical LLM benchmark performance does not
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### Additional Evidence (extend)
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*Source: [[2025-06-01-abridge-valuation-growth-ai-scribe-metrics]] | Added: 2026-03-16*
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*Source: 2025-06-01-abridge-valuation-growth-ai-scribe-metrics | Added: 2026-03-16*
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Abridge's clinical outcomes data shows 73% reduction in after-hours documentation time, 61% reduction in cognitive burden, and 81% improvement in workflow satisfaction. The company won top ambient AI slot in 2025 KLAS annual report and deployed across 150+ health systems including Kaiser (24,600 physicians), Mayo Clinic (2,000+ physicians enterprise-wide), Johns Hopkins, Duke, UPMC, and Yale New Haven. This represents the transition from pilot adoption to enterprise-wide deployment at scale.
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### Additional Evidence (challenge)
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*Source: [[2025-06-01-abridge-valuation-growth-ai-scribe-metrics]] | Added: 2026-03-16*
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*Source: 2025-06-01-abridge-valuation-growth-ai-scribe-metrics | Added: 2026-03-16*
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Epic launched AI Charting in February 2026, creating an immediate commoditization threat to standalone ambient AI platforms. Abridge's response - pivoting to 'more than a scribe' positioning with coding, prior auth automation, and clinical decision support - suggests leadership recognized the documentation beachhead may not be defensible against EHR-native solutions. The timing of this strategic pivot (2025-2026) indicates the scribe adoption success may have a shorter durability window than the 92% adoption figure suggests.
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### Additional Evidence (challenge)
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*Source: [[2026-01-01-bvp-state-of-health-ai-2026]] | Added: 2026-03-16*
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*Source: 2026-01-01-bvp-state-of-health-ai-2026 | Added: 2026-03-16*
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The 92% figure applies to 'deploying, implementing, or piloting' ambient AI as of March 2025, not active deployment. This includes very early-stage pilots. The scope distinction between pilot programs and daily clinical workflow integration is significant — the claim may overstate actual adoption if interpreted as active use rather than organizational commitment to explore the technology.
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### Additional Evidence (extend)
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*Source: [[2026-03-11-wvu-abridge-rural-health-systems-expansion]] | Added: 2026-03-16*
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*Source: 2026-03-11-wvu-abridge-rural-health-systems-expansion | Added: 2026-03-16*
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WVU Medicine expanded Abridge ambient AI across 25 hospitals including rural facilities in March 2026, one month after Epic AI Charting launch. This rural expansion suggests ambient AI has passed from pilot phase to broad deployment phase, as enterprise technology typically enters academic medical centers first, then regional health systems, then rural/critical access hospitals last. The fact that a state academic health system serving one of the most rural and medically underserved states chose to expand Abridge post-Epic launch provides implicit market validation of Abridge's competitive position.
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@ -28,13 +28,13 @@ Since [[healthcares defensible layer is where atoms become bits because physical
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### Additional Evidence (confirm)
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*Source: [[2025-06-01-abridge-valuation-growth-ai-scribe-metrics]] | Added: 2026-03-16*
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*Source: 2025-06-01-abridge-valuation-growth-ai-scribe-metrics | Added: 2026-03-16*
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Abridge reached $100M ARR with 150+ health system customers by May 2025, achieving $5.3B valuation. This represents the clearest real-world validation of AI-native productivity claims in healthcare - a documentation platform scaling to 9-figure revenue without the linear headcount scaling that would be required for traditional medical transcription or documentation services.
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### Additional Evidence (confirm)
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*Source: [[2026-01-01-bvp-state-of-health-ai-2026]] | Added: 2026-03-16*
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*Source: 2026-01-01-bvp-state-of-health-ai-2026 | Added: 2026-03-16*
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BVP reports AI-native healthcare companies achieve $500K-$1M+ ARR per FTE with 70-80%+ software-like margins, compared to $100-200K for traditional healthcare services and $200-400K for pre-AI healthcare SaaS. This is the primary source for the productivity claim, providing the specific ranges that support the 3-5x multiplier.
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@ -128,17 +128,17 @@ If GLP-1 + exercise combination creates durable weight maintenance (3.5 kg regai
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---
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### Additional Evidence (challenge)
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*Source: [[2026-03-21-natco-semaglutide-india-day1-launch-1290]] | Added: 2026-03-21*
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*Source: 2026-03-21-natco-semaglutide-india-day1-launch-1290 | Added: 2026-03-21*
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Natco Pharma launched generic semaglutide in India at ₹1,290/month ($15.50) on March 20, 2026, the day the patent expired. This is 90% below innovator pricing and 2-3x lower than analyst projections made days earlier ($40-77/month within a year). 50+ manufacturers from 40+ companies are entering the market, with Sun Pharma, Zydus, Dr. Reddy's, and Eris launching on Day 1. The 'inflationary through 2035' timeline is empirically wrong for international markets—price compression is happening in 2026, not 2030+.
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### Additional Evidence (extend)
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*Source: [[2026-03-21-semaglutide-us-import-wall-gray-market-pressure]] | Added: 2026-03-21*
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*Source: 2026-03-21-semaglutide-us-import-wall-gray-market-pressure | Added: 2026-03-21*
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US patent protection extends to 2031-2033 for Ozempic and Wegovy, creating a legal wall that prevents approved generic competition until then. The compounding pharmacy channel that provided affordable access during 2023-2025 closed in February 2025 when FDA removed semaglutide from the shortage list. This means the US will remain 'inflationary' through legal channels through 2031-2033, but gray market pressure from $15/month Indian generics versus $1,200/month Wegovy will create illegal importation at scale.
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### Additional Evidence (challenge)
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*Source: [[2026-03-22-health-canada-rejects-dr-reddys-semaglutide]] | Added: 2026-03-22*
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*Source: 2026-03-22-health-canada-rejects-dr-reddys-semaglutide | Added: 2026-03-22*
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Health Canada rejected Dr. Reddy's generic semaglutide application in October 2025, delaying Canada launch to 2027 at earliest (8-12 month review cycle after resubmission). This contradicts the Session 9 projection of May 2026 Canada launch and reveals regulatory friction as a significant barrier to generic GLP-1 market entry. Canada's patents expired January 2026, but regulatory approval does not automatically follow patent expiration. The delay removes the primary high-income market data point for 2026, leaving only India's $15-55/month pricing as the sole confirmed generic market reference. Canada was expected to establish pricing floors for high-income markets with US-comparable health infrastructure, but that calibration point is now delayed 12+ months beyond patent cliff.
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@ -32,22 +32,22 @@ OpenEvidence reached 20M clinical consultations/month by January 2026 (up from 8
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---
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### Additional Evidence (extend)
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*Source: [[2026-03-21-openevidence-12b-valuation-nct07199231-outcomes-gap]] | Added: 2026-03-21*
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*Source: 2026-03-21-openevidence-12b-valuation-nct07199231-outcomes-gap | Added: 2026-03-21*
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OpenEvidence reached 30M+ monthly consultations by March 2026, including a historic milestone of 1 million consultations in a single day on March 10, 2026. The company projects 'more than 100 million Americans will be treated by a clinician using OpenEvidence this year.' This represents continued exponential growth from the 18M monthly consultations reported in December 2025.
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### Additional Evidence (challenge)
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*Source: [[2026-03-22-arise-state-of-clinical-ai-2026]] | Added: 2026-03-22*
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*Source: 2026-03-22-arise-state-of-clinical-ai-2026 | Added: 2026-03-22*
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ARISE report reframes OpenEvidence adoption as shadow-IT workaround behavior rather than validation of clinical value. Clinicians use OE to 'bypass slow internal IT systems' because institutional tools are too slow for clinical workflows. This suggests rapid adoption reflects institutional system failure, not OE's clinical superiority.
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### Additional Evidence (extend)
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*Source: [[2026-03-22-openevidence-sutter-health-epic-integration]] | Added: 2026-03-22*
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*Source: 2026-03-22-openevidence-sutter-health-epic-integration | Added: 2026-03-22*
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Sutter Health (3.3M patients, ~12,000 physicians) integrated OpenEvidence into Epic EHR workflows in February 2026, marking the first major health-system-wide EHR embedding. This shifts OpenEvidence from standalone app to in-workflow clinical tool, institutionalizing what ARISE identified as physicians bypassing institutional IT governance.
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### Additional Evidence (extend)
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*Source: [[2026-03-20-iatrox-openevidence-uk-dtac-nice-esf-governance-review]] | Added: 2026-03-24*
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*Source: 2026-03-20-iatrox-openevidence-uk-dtac-nice-esf-governance-review | Added: 2026-03-24*
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iatroX reports OE has 'signalled plans for global expansion as a key 2026 and beyond initiative' with UK, Canada, Australia identified as 'English-first markets with lower regulatory barriers.' However, iatroX notes this perception may be inaccurate for UK: NHS requires DTAC + MHRA Class 1 for formal deployment. OE's characterization of UK as having 'lower regulatory barriers' relative to US may be a strategic misjudgment—UK NHS has MORE formal digital health procurement governance than US (no federal equivalent to DTAC).
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@ -19,25 +19,25 @@ The near-term trajectory: mandatory outpatient screening by 2026, Z-code adoptio
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### Additional Evidence (extend)
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*Source: [[2024-09-19-commonwealth-fund-mirror-mirror-2024]] | Added: 2026-03-12 | Extractor: anthropic/claude-sonnet-4.5*
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*Source: 2024-09-19-commonwealth-fund-mirror-mirror-2024 | Added: 2026-03-12 | Extractor: anthropic/claude-sonnet-4.5*
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The Commonwealth Fund's 2024 international comparison provides quantified evidence of the population-level cost of not operationalizing SDOH interventions at scale. The US ranks second-worst on equity (9th of 10 countries) and last on health outcomes (10th of 10), with the highest healthcare spending (>16% of GDP). This outcome gap relative to peer nations with lower spending demonstrates the opportunity cost of the US healthcare system's failure to systematically address social determinants. Countries with better equity and access outcomes (Australia, Netherlands) achieve superior population health despite similar or lower clinical quality and lower spending ratios. The international comparison quantifies what the SDOH adoption gap costs: the US achieves worst population health outcomes among wealthy peer nations despite world-class clinical care, suggesting that the 3% Z-code documentation rate represents billions in foregone health gains.
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### Additional Evidence (challenge)
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*Source: [[2025-04-07-tufts-health-affairs-medically-tailored-meals-50-states]] | Added: 2026-03-18*
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*Source: 2025-04-07-tufts-health-affairs-medically-tailored-meals-50-states | Added: 2026-03-18*
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The JAMA Internal Medicine 2024 RCT testing intensive food-as-medicine intervention (10 meals/week + education + coaching for 1 year) found NO significant difference in HbA1c, hospitalization, ED use, or total claims between treatment and control groups. This challenges the assumption that SDOH interventions produce strong ROI—the RCT evidence shows null clinical outcomes despite addressing food insecurity directly.
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### Additional Evidence (extend)
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*Source: [[2025-09-01-lancet-public-health-social-prescribing-england-national-rollout]] | Added: 2026-03-18*
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*Source: 2025-09-01-lancet-public-health-social-prescribing-england-national-rollout | Added: 2026-03-18*
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England's social prescribing provides international counterpoint: 1.3M annual referrals with 3,300 link workers represents the operational infrastructure that US SDOH interventions lack. However, UK achieved scale without evidence quality - 15 of 17 economic studies were uncontrolled, 38% attrition, SROI ratios of £1.17-£7.08 but ROI only 0.11-0.43. This suggests infrastructure alone is insufficient without measurement systems.
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### Additional Evidence (extend)
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*Source: [[2025-01-01-nashp-chw-state-policies-2024-2025]] | Added: 2026-03-18*
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*Source: 2025-01-01-nashp-chw-state-policies-2024-2025 | Added: 2026-03-18*
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Community health worker programs demonstrate the same payment boundary stall: only 20 states have Medicaid State Plan Amendments for CHW reimbursement 17 years after Minnesota's 2008 approval, despite 39 RCTs showing $2.47 ROI. The billing infrastructure bottleneck is identical to Z-code documentation failure — SPAs typically use 9896x CPT codes but uptake remains slow because community-based organizations lack contracting infrastructure and Medicaid does not cover provider travel costs (the largest CHW overhead expense). 7 states have established dedicated CHW offices and 6 enacted new reimbursement legislation in 2024-2025, but the gap between evidence (strong) and operational infrastructure (absent) mirrors the SDOH screening-to-action gap.
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@ -115,7 +115,7 @@ Weight regain data shows GLP-1 alone (8.7 kg regain) performs no better than pla
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Novo Nordisk's response to India's generic launch reveals market expansion strategy: only 200,000 of 250 million obese Indians are currently on GLP-1s. The company is competing on 'market expansion over price war,' suggesting the primary barrier is access/awareness, not price sensitivity. This implies persistence challenges may be access-driven in international markets rather than purely adherence-driven.
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### Additional Evidence (extend)
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*Source: [[2025-04-01-jmir-glp1-digital-engagement-outcomes-retrospective]] | Added: 2026-03-24*
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*Source: 2025-04-01-jmir-glp1-digital-engagement-outcomes-retrospective | Added: 2026-03-24*
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US real-world data from JMIR 2025 shows digital engagement produces 11.53% weight loss vs. 8% for non-engaged participants at month 5 (3.5pp advantage). Study covers both semaglutide and tirzepatide, demonstrating the behavioral support effect generalizes across GLP-1/GIP receptor agonists. When supply and coverage issues are addressed, persistence improves to 63%, suggesting the adherence gap is partially addressable through digital platform integration (live coaching, monitoring, education).
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@ -21,7 +21,7 @@ The emerging consensus: healthcare AI is a platform shift, not a bubble, but the
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### Additional Evidence (confirm)
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*Source: [[2026-01-01-bvp-state-of-health-ai-2026]] | Added: 2026-03-16*
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*Source: 2026-01-01-bvp-state-of-health-ai-2026 | Added: 2026-03-16*
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Abridge raised $300M Series E at $5B valuation and Ambiance raised $243M Series C at $1.04B valuation by early 2026, demonstrating the capital concentration in category leaders. Function Health's $300M Series C at $2.2B valuation further confirms winner-take-most dynamics in health AI.
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@ -40,7 +40,7 @@ OpenEvidence valuation trajectory demonstrates extreme winner-take-most dynamics
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---
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### Additional Evidence (confirm)
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*Source: [[2026-03-21-openevidence-12b-valuation-nct07199231-outcomes-gap]] | Added: 2026-03-21*
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*Source: 2026-03-21-openevidence-12b-valuation-nct07199231-outcomes-gap | Added: 2026-03-21*
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OpenEvidence raised $250M at $12B valuation in January 2026, representing a 3.4x valuation increase in approximately 3 months (from $3.5B in October 2025). This is extraordinary velocity even by AI standards, with the company achieving $150M ARR (1,803% YoY growth from $7.9M in 2024) at ~90% gross margins. The winner-take-most pattern is evident as OE captures the clinical AI category.
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@ -32,22 +32,22 @@ OpenEvidence became the first AI in history to score 100% on all parts of the US
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### Additional Evidence (confirm)
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*Source: [[2026-03-21-openevidence-12b-valuation-nct07199231-outcomes-gap]] | Added: 2026-03-21*
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*Source: 2026-03-21-openevidence-12b-valuation-nct07199231-outcomes-gap | Added: 2026-03-21*
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OpenEvidence's medRxiv preprint (November 2025) showed 24% accuracy for relevant answers on complex open-ended clinical scenarios, despite achieving 100% on USMLE-type multiple choice questions. This 76-percentage-point gap between benchmark performance and open-ended clinical scenarios confirms that structured test performance does not predict real-world clinical utility.
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### Additional Evidence (extend)
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*Source: [[2026-03-22-arise-state-of-clinical-ai-2026]] | Added: 2026-03-22*
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*Source: 2026-03-22-arise-state-of-clinical-ai-2026 | Added: 2026-03-22*
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ARISE report identifies specific failure modes: real-world performance 'breaks down when systems must manage uncertainty, incomplete information, or multi-step workflows.' This provides mechanistic detail for why benchmark performance doesn't translate — benchmarks test pattern recognition on complete data while clinical care requires uncertainty management.
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### Additional Evidence (extend)
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*Source: [[2025-11-01-jmir-knowledge-practice-gap-39-benchmarks-systematic-review]] | Added: 2026-03-24*
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*Source: 2025-11-01-jmir-knowledge-practice-gap-39-benchmarks-systematic-review | Added: 2026-03-24*
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JMIR systematic review of 761 studies provides methodological foundation: 95% of clinical LLM evaluation uses medical exam questions rather than real patient data, with only 5% assessing performance on actual patient care. Traditional benchmarks show saturation at 84-90% USMLE accuracy, but conversational frameworks reveal 19.3pp accuracy drop (82% → 62.7%) when moving from case vignettes to multi-turn dialogues. Review concludes: 'substantial disconnects from clinical reality and foundational gaps in construct validity, data integrity, and safety coverage.' This establishes that the Oxford/Nature Medicine RCT deployment gap (94.9% → 34.5%) is part of a systematic field-wide pattern, not an isolated finding.
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### Additional Evidence (extend)
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*Source: [[2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct]] | Added: 2026-03-24*
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*Source: 2026-02-10-oxford-nature-medicine-llm-public-medical-advice-rct | Added: 2026-03-24*
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Oxford Nature Medicine 2026 RCT (n=1,298) extends the benchmark-to-clinical-impact gap to public users: LLMs achieved 94.9% condition identification in isolation but users assisted by LLMs performed no better than control groups (<34.5%). The 60-point deployment gap held across GPT-4o, Llama 3, and Command R+, indicating the interaction mode—not the model—explains the failure. Root cause identified as 'two-way communication breakdown' where users couldn't extract correct guidance even when AI possessed the right answer.
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### Additional Evidence (confirm)
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*Source: [[2024-09-19-commonwealth-fund-mirror-mirror-2024]] | Added: 2026-03-12 | Extractor: anthropic/claude-sonnet-4.5*
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*Source: 2024-09-19-commonwealth-fund-mirror-mirror-2024 | Added: 2026-03-12 | Extractor: anthropic/claude-sonnet-4.5*
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The Commonwealth Fund's 2024 Mirror Mirror international comparison provides the strongest real-world proof of this claim. The US ranks **second in care process quality** (clinical excellence when care is accessed) but **last in health outcomes** (life expectancy, avoidable deaths) among 10 peer nations. This paradox proves that clinical quality alone cannot produce population health — the US has near-best clinical care AND worst outcomes, demonstrating that non-clinical factors (access, equity, social determinants) dominate outcome determination. The care process vs. outcomes decoupling across 70 measures and nearly 75% patient/physician-reported data is the international benchmark showing medical care's limited contribution to population health outcomes.
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### Additional Evidence (extend)
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*Source: [[2025-00-00-nhs-england-waiting-times-underfunding]] | Added: 2026-03-15*
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*Source: 2025-00-00-nhs-england-waiting-times-underfunding | Added: 2026-03-15*
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The NHS paradox—ranking 3rd overall while having catastrophic specialty access—provides supporting evidence that medical care's contribution to health outcomes is limited. A system can have multi-year waits for specialty procedures yet still rank highly in overall health system performance because primary care, equity, and universal coverage (which address behavioral and social factors) matter more than specialty delivery speed for population health outcomes.
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### Additional Evidence (confirm)
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*Source: [[2025-12-01-who-glp1-global-guidelines-obesity]] | Added: 2026-03-16*
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*Source: 2025-12-01-who-glp1-global-guidelines-obesity | Added: 2026-03-16*
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WHO's three-pillar framework for GLP-1 obesity treatment explicitly positions medication as one component within a comprehensive approach requiring healthy diets, physical activity, professional support, and population-level policies. WHO states obesity is a 'societal challenge requiring multisectoral action — not just individual medical treatment.' This institutional positioning from the global health authority confirms that pharmaceutical intervention alone cannot address health outcomes driven by behavioral and social factors.
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### Additional Evidence (extend)
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*Source: [[2025-04-07-tufts-health-affairs-medically-tailored-meals-50-states]] | Added: 2026-03-18*
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*Source: 2025-04-07-tufts-health-affairs-medically-tailored-meals-50-states | Added: 2026-03-18*
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While social determinants predict health outcomes in observational studies, RCT evidence from food-as-medicine interventions shows that directly addressing social determinants (food insecurity) does not automatically improve clinical outcomes. The AHA 2025 systematic review of 14 US RCTs found Food Is Medicine programs improve diet quality and food security but "impact on clinical outcomes was inconsistent and often failed to reach statistical significance." This suggests the causal pathway from social determinants to health is more complex than simple resource provision.
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### Additional Evidence (confirm)
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*Source: [[2026-03-19-vida-ai-biology-acceleration-healthspan-constraint]] | Added: 2026-03-19*
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*Source: 2026-03-19-vida-ai-biology-acceleration-healthspan-constraint | Added: 2026-03-19*
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Amodei's complementary factors framework explicitly identifies 'human constraints' (behavior change, social systems, meaning-making) as a factor that bounds AI returns even in biological science. This provides theoretical grounding for why the 80-90% non-clinical determinants remain unaddressed by AI-accelerated biology—they fall into the 'human constraints' category that AI cannot optimize.
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### Additional Evidence (confirm)
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*Source: [[2026-03-10-abrams-bramajo-pnas-birth-cohort-mortality-us-life-expectancy]] | Added: 2026-03-24*
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*Source: 2026-03-10-abrams-bramajo-pnas-birth-cohort-mortality-us-life-expectancy | Added: 2026-03-24*
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PNAS 2026 attributes US life expectancy stagnation to 'a complex convergence of rising chronic disease, shifting behavioral risks, and increases in certain cancers among younger adults' — explicitly identifying behavioral and social factors as the drivers of cohort-level mortality deterioration, not medical care quality.
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**What surprised me:** The USMLE 100% score and the 1M consultations/day milestone suggest OpenEvidence is in a different category from early clinical AI tools. At 20M consultations/month with verified physicians, this is larger than any previously deployed clinical decision support system.
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**What I expected but didn't find:** No peer-reviewed outcomes data on whether OpenEvidence-assisted consultations produce better patient outcomes. The benchmark performance (USMLE 100%) doesn't necessarily translate to clinical impact — existing KB claim [[medical LLM benchmark performance does not translate to clinical impact]] is a direct challenge to this data.
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**What I expected but didn't find:** No peer-reviewed outcomes data on whether OpenEvidence-assisted consultations produce better patient outcomes. The benchmark performance (USMLE 100%) doesn't necessarily translate to clinical impact — existing KB claim medical LLM benchmark performance does not translate to clinical impact is a direct challenge to this data.
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**KB connections:**
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- Updates: [[OpenEvidence became the fastest-adopted clinical technology in history reaching 40 percent of US physicians daily within two years]] — the claim is still accurate but understates 2026 scale
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@ -61,7 +61,7 @@ Epic Systems announced its AI Charting feature on February 4, 2026 — a native
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**KB connections:**
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- Challenges the "beachhead" interpretation of: [[AI scribes reached 92 percent provider adoption in under 3 years because documentation is the rare healthcare workflow where AI value is immediate unambiguous and low-risk]]
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- The Epic threat parallels the "Big Tech risk" in Belief 4 (atoms-to-bits boundary) — but applied to documentation software, not hardware. The moat (clinical trust, regulatory expertise) may not apply to documentation where Epic already has the trust.
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- Connects to: [[AI-native health companies achieve 3-5x the revenue productivity of traditional health services]] — the question is whether that productivity premium survives platform commoditization
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- Connects to: AI-native health companies achieve 3-5x the revenue productivity of traditional health services — the question is whether that productivity premium survives platform commoditization
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**Extraction hints:**
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- CLAIM CANDIDATE: "Epic's native AI Charting threatens to commoditize ambient documentation, forcing standalone AI scribe companies to differentiate on clinical decision support and workflow automation rather than note quality"
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Loading…
Reference in a new issue