extract: 2025-01-01-gimm-hoffman-chw-rct-scoping-review #1200

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"date": "2026-03-18"
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@ -7,11 +7,15 @@ date: 2025-01-01
domain: health
secondary_domains: []
format: report
status: unprocessed
status: null-result
priority: medium
triage_tag: claim
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"]
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
@ -58,3 +62,11 @@ Additional context:
## 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
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
secondary_domains: [internet-finance]
format: essay
status: unprocessed
status: null-result
priority: high
triage_tag: claim
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"]
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
@ -50,3 +54,15 @@ Synthesis of multiple data points on the economic cost of verifying AI outputs:
## Curator Notes
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).
## 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
secondary_domains: [collective-intelligence, cultural-dynamics]
format: paper
status: unprocessed
status: null-result
priority: high
triage_tag: claim
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
@ -42,3 +46,14 @@ Experimental study: 879 human participants + 996 API calls to GPT-4o. Three cond
## Curator Notes
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."
## 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')