Merge branch 'main' into extract/2025-01-01-gimm-hoffman-chw-rct-scoping-review

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Leo 2026-03-18 09:23:24 +00:00
commit d6c3a31a27
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@ -7,11 +7,15 @@ date: 2025-01-01
domain: health domain: health
secondary_domains: [] secondary_domains: []
format: report format: report
status: unprocessed status: null-result
priority: medium priority: medium
triage_tag: claim triage_tag: claim
tags: [behavioral-economics, nudges, default-effects, medication-adherence, health-disparities, EHR] tags: [behavioral-economics, nudges, default-effects, medication-adherence, health-disparities, EHR]
flagged_for_rio: ["Behavioral economics mechanisms (commitment devices, default effects) are directly relevant to mechanism design in health contexts"] flagged_for_rio: ["Behavioral economics mechanisms (commitment devices, default effects) are directly relevant to mechanism design in health contexts"]
processed_by: vida
processed_date: 2026-03-18
extraction_model: "anthropic/claude-sonnet-4.5"
extraction_notes: "LLM returned 2 claims, 2 rejected by validator"
--- ---
## Content ## Content
@ -58,3 +62,11 @@ Additional context:
## Curator Notes ## Curator Notes
PRIMARY CONNECTION: healthcare is a complex adaptive system requiring simple enabling rules not complicated management because standardized processes erode the clinical autonomy needed for value creation PRIMARY CONNECTION: healthcare is a complex adaptive system requiring simple enabling rules not complicated management because standardized processes erode the clinical autonomy needed for value creation
WHY ARCHIVED: Default effects are the "simple enabling rules" the complex adaptive system claim describes. The CHIBE evidence makes this concrete: change the EHR default → change prescribing behavior → reduce disparities. This is the behavioral economics bridge between the KB's structural claims and operational interventions. WHY ARCHIVED: Default effects are the "simple enabling rules" the complex adaptive system claim describes. The CHIBE evidence makes this concrete: change the EHR default → change prescribing behavior → reduce disparities. This is the behavioral economics bridge between the KB's structural claims and operational interventions.
## Key Facts
- CHIBE had $49M in total grant activity in FY2025
- Penn Medicine Healthy Heart trial enrolled 2,000 patients in West/Southwest Philadelphia and Lancaster County (2024-2025)
- Opioid prescribing guidelines adherence increased from 57.2% to 71.8% with peer comparison + patient-reported outcomes feedback
- Game-based physical activity intervention increased step counts by 1,700 steps/day (equivalent to 70+ miles over intervention period)
- Penn Medicine is now funding scaled implementation of automated pharmacy referral program that increased statin prescribing

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@ -7,11 +7,15 @@ date: 2025-01-01
domain: ai-alignment domain: ai-alignment
secondary_domains: [internet-finance] secondary_domains: [internet-finance]
format: essay format: essay
status: unprocessed status: null-result
priority: high priority: high
triage_tag: claim triage_tag: claim
tags: [verification-tax, hallucination-costs, productivity-paradox, human-oversight, economic-incentives] tags: [verification-tax, hallucination-costs, productivity-paradox, human-oversight, economic-incentives]
flagged_for_rio: ["$67.4B in global hallucination losses — economic data on AI reliability costs"] flagged_for_rio: ["$67.4B in global hallucination losses — economic data on AI reliability costs"]
processed_by: theseus
processed_date: 2026-03-18
extraction_model: "anthropic/claude-sonnet-4.5"
extraction_notes: "LLM returned 2 claims, 2 rejected by validator"
--- ---
## Content ## Content
@ -50,3 +54,15 @@ Synthesis of multiple data points on the economic cost of verifying AI outputs:
## Curator Notes ## Curator Notes
PRIMARY CONNECTION: scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps PRIMARY CONNECTION: scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps
WHY ARCHIVED: Provides ECONOMIC data on oversight costs that complement the theoretical oversight degradation claim. The verification tax concept bridges the technical finding (oversight degrades) to economic consequences (verification costs compound). WHY ARCHIVED: Provides ECONOMIC data on oversight costs that complement the theoretical oversight degradation claim. The verification tax concept bridges the technical finding (oversight degrades) to economic consequences (verification costs compound).
## Key Facts
- Forrester Research (2025): $14,200/employee/year in hallucination mitigation costs
- Microsoft (2025): 4.3 hours/week average verification time for knowledge workers
- Forbes (2024): 77% of employees report AI increased workloads and hampered productivity
- $67.4 billion in global losses from AI hallucinations in 2024
- Hallucination detection tools market grew 318% between 2023-2025
- 76% of enterprises run human-in-the-loop processes to catch hallucinations
- 47% of enterprise AI users made major decisions based on potentially inaccurate AI content
- Forrester estimates 22% decrease in productivity due to manual verification overhead
- 95% of organizations see no measurable returns on AI investments (MIT Media Lab)

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@ -7,10 +7,14 @@ date: 2025-02-01
domain: ai-alignment domain: ai-alignment
secondary_domains: [collective-intelligence, cultural-dynamics] secondary_domains: [collective-intelligence, cultural-dynamics]
format: paper format: paper
status: unprocessed status: null-result
priority: high priority: high
triage_tag: claim triage_tag: claim
tags: [collective-creativity, human-ai-networks, diversity, homogenization, inverted-u, temporal-dynamics] tags: [collective-creativity, human-ai-networks, diversity, homogenization, inverted-u, temporal-dynamics]
processed_by: theseus
processed_date: 2026-03-18
extraction_model: "anthropic/claude-sonnet-4.5"
extraction_notes: "LLM returned 1 claims, 1 rejected by validator"
--- ---
## Content ## Content
@ -42,3 +46,14 @@ Experimental study: 879 human participants + 996 API calls to GPT-4o. Three cond
## Curator Notes ## Curator Notes
PRIMARY CONNECTION: collective intelligence requires diversity as a structural precondition not a moral preference PRIMARY CONNECTION: collective intelligence requires diversity as a structural precondition not a moral preference
WHY ARCHIVED: Provides empirical evidence for the temporal dynamics of AI integration — initial AI superiority degrades while hybrid systems improve. The 50-50 finding is the closest empirical data we have on "optimal integration level." WHY ARCHIVED: Provides empirical evidence for the temporal dynamics of AI integration — initial AI superiority degrades while hybrid systems improve. The 50-50 finding is the closest empirical data we have on "optimal integration level."
## Key Facts
- Study used 879 human participants and 996 GPT-4o API calls
- Networks organized in 5×5 grids over 25 iterations
- 100-person validation group rated creativity blind to source
- AI-only networks started at M = 3.571 creativity rating
- AI-only networks declined at M = -0.034 per iteration (SD = 0.17)
- Hybrid networks increased at M = 0.098 per iteration (SD = 0.039)
- GPT-4o exhibited thematic convergence toward space-related narratives ('universe,' 'cosmic')
- Humans anchored narratives to original elements like character names ('John') and objects ('keys')