---
type: musing
domain: health
session: 24
date: 2026-04-21
status: active
---

# Research Session 24 — Clinical AI Deskilling Divergence + Digital Mental Health Access Expansion

## Research Question

**Primary:** Is there counter-evidence for AI-induced clinical deskilling — specifically, prospective studies showing AI calibrates or up-skills clinicians durably (not just while AI is present) — and does this evidence create a genuine divergence that changes the existing deskilling claim's confidence level?

**Secondary:** Is digital mental health actually scaling to underserved populations in 2025-2026, or does the existing KB claim (technology "primarily serves the already-served") still hold?

**Why this question now:**
Session 23 closed the loop on GLP-1 behavioral adherence. Two claims are READY TO EXTRACT from the extractor (GLP-1 access inversion, USPSTF gap). The most productive research direction for this session is the open structural question from Session 23:

- The clinical AI deskilling body of evidence has grown substantially (1 → 5+ quantitative findings, Natali 2025 synthesis). But Session 23 flagged a potential divergence: AI IMPROVES performance while present AND reduces performance when absent. These aren't contradictory — they're two halves of the same dependency mechanism. But the divergence file hasn't been created yet.
- If counter-evidence exists showing AI durably improves skills (calibration studies, error-reduction RCTs), the divergence is genuine. If not, the deskilling pattern is one-directional.
- The mental health thread is flagged as a KB thin area: "what DOES work for scalable mental health delivery." Zero evidence archived on whether digital therapeutics are expanding access vs. serving already-served.

## Keystone Belief

**Belief 1: Healthspan is civilization's binding constraint, and we are systematically failing at it in ways that compound.**

**Disconfirmation target:**
The specific grounding chain to challenge: the mental health supply gap is widening, not closing. If digital mental health is genuinely expanding access to previously underserved populations (Medicaid, rural, uninsured, non-English speaking), that would mean ONE layer of the compounding failure is being addressed. This wouldn't disconfirm Belief 1 wholesale, but it would complicate the "systematically failing" framing and require belief revision.

**Belief 5 disconfirmation target:**
If there are prospective studies showing AI PREVENTS clinical errors durably (not just while present), that would weaken the "novel safety risks" framing. The existing claim human-in-the-loop clinical AI degrades to worse-than-AI-alone... has confidence: likely. Evidence of durable up-skilling would challenge this.

**What I expected to find:**
- No prospective studies showing durable AI up-skilling; the calibration evidence probably exists for narrow tasks but not generalized to clinical skill development
- Digital mental health access expansion: mixed — some promising evidence for specific modalities (text-based, app-based) reaching underserved populations, but structural barriers (internet access, digital literacy) limiting reach
- The deskilling divergence is real but lopsided: strong evidence for AI dependency/deskilling; weak or absent evidence for durable calibration/up-skilling

## What I Searched For

- Clinical AI up-skilling calibration prospective studies 2025-2026 (durable skill improvement with AI)
- Clinical AI error reduction RCT evidence beyond diagnostic accuracy (does AI prevent wrong decisions that humans make?)
- Digital mental health Medicaid rural underserved access expansion 2025-2026
- Digital mental health scale access equity evidence
- USPSTF weight loss pharmacotherapy update 2026 (quick check — Session 23 said dead end but worth one re-check)
- GLP-1 biosimilar timeline FDA approval 2025-2026 (whether US generic access is moving faster than 2032 estimate)

## Key Findings

### 1. DISCONFIRMATION TEST RESULT — Clinical AI Up-Skilling: NULL (Belief 5 strengthened)

**The disconfirmation question:** Is there peer-reviewed evidence that AI exposure durably improves physician clinical skills?

**Answer: No — zero papers found.** PubMed search for "AI clinical decision support physician performance up-skilling calibration" (2024-2026) returned zero results. After 5+ years of large-scale clinical AI deployment (92% scribe adoption, 40% of physicians daily on OpenEvidence), no prospective study documents durable physician skill improvement from AI exposure.

**The complement:** The deskilling literature is growing in the same period:
- Heudel et al. 2026 (ESMO, PMID 41890350): scoping review through August 2025. Evidence "consistent across specialties." Four specialties documented: colonoscopy (ADR 28.4% → 22.4%), radiology (12% false-positive increase), pathology (30%+ reversal of correct diagnoses), cytology (80-85% volume reduction → training pipeline destruction).
- The cytology finding is new to this session: lab consolidation from 45 to 8 centers reduces training case volumes by 80-85%. This is never-skilling via structural destruction of apprenticeship infrastructure — not cognitive dependency, but pipeline elimination.
- The null result on up-skilling is itself the finding: the deskilling literature has no peer-reviewed counterweight.

**Belief 5 status:** SIGNIFICANTLY STRENGTHENED. The deskilling case is now one-directional: consistent cross-specialty empirical evidence of deskilling + never-skilling, zero peer-reviewed evidence of durable up-skilling, confirmed by a formal scoping review (Heudel 2026) that found no counter-evidence.

### 2. Digital Mental Health Access: NOT CLOSING THE GAP (Belief 1 not disconfirmed)

**The disconfirmation question:** Is digital mental health technology expanding access to underserved populations, complicating the "systematically failing" framing?

**Answer: No — multiple convergent findings confirm the technology-primarily-serves-already-served thesis.**

**Finding A — Jorem et al. 2026, JAMA Network Open (PMID 41784959):** 17,742 mental health specialists, 2018-2023 Medicare claims. Mental health telemedicine expansion associated with only 0.88 percentage points more rural visits. **Highest telemedicine providers see 3.55 percentage points FEWER new patients** than low-telemedicine providers — telemedicine is used for existing relationship retention, not new patient acquisition from underserved areas. Conclusion: "additional policy interventions may be required to achieve telemedicine's potential."

**Finding B — Journal of Telemedicine and Telecare 2025:** 2019-2020 Medicare claims. COVID telehealth expansion EXPANDED disparities. Rural patients were MORE likely to use telehealth in 2019 (early adopters), LESS likely in 2020 (crowded out by urban surge). "Many patients in greatest need of healthcare are least likely to utilize telehealth services."

**Finding C — Lancet Digital Health 2025 + npj Digital Medicine 2025:** Smartphone mental health apps have real efficacy (Hedges' g = 0.43) but 64% attrition in motivated, self-selected RCT participants. Real-world reach in underserved populations (lower digital literacy, privacy concerns, cultural/linguistic barriers) would be substantially lower. The populations with greatest treatment gap face highest engagement barriers.

**Finding D — KFF 2025:** Medicaid adults with mental illness receive treatment at HIGHER rates than commercially insured (59% vs. 55%) — the largest unmet need is among the uninsured (63% unmet need). The primary access failure is not Medicaid populations but the uninsured. This reframes the problem: coverage matters more than technology.

**Finding E — Mental health workforce shortage (JAPNA 2025, Nursing Clinics 2026):** 51-55 million Americans restricted by provider shortage. Shortage worsening. Telehealth proposed as mitigation but not resolving the structural gap.

**Belief 1 status:** NOT DISCONFIRMED. The "systematically failing" framing holds. Technology is not closing the access gap for underserved populations — it's serving existing patients more conveniently. The structural gap (51-55 million affected, shortage worsening, digital tools with 64% attrition in best-case conditions) is not being offset by technology deployment. Coverage (Medicaid) matters more than technology for actual treatment rates.

### 3. COUNTERINTUITIVE FINDING — Medicaid outperforms commercial insurance on mental health treatment rates

Medicaid adults with mental illness receive treatment at 59% vs. 55% for commercially insured — Medicaid is actually the better mental health coverage vehicle. The structural explanation: Medicaid has historically stronger behavioral health infrastructure (behavioral health carve-outs, FQHCs, community mental health centers) than commercial plans, which have narrow behavioral health networks despite parity requirements. The primary access gap is for the uninsured (37% treatment rate vs. 63% unmet need).

### 4. GLP-1 Biosimilars — Already in KB (no new archiving needed)

Background agent search found an existing KB claim: "Indian generic semaglutide exports enabled by evergreening rejection create a global access pathway before US patent expiry" (Delhi High Court ruling, March 2026). This thread is covered. The claim shows US patents remain active until 2031-2033, with Canadian high-income market launch in May 2026 as first test case. No new archiving needed.

## Follow-up Directions

### Active Threads (continue next session)

- **Clinical AI deskilling divergence file:** The evidence is now sufficient to create a divergence file between "AI deskilling (performance declines when AI removed)" and "AI up-skilling while present (performance improves with AI assistance)." These are both true simultaneously — the dependency mechanism. The null result on durable up-skilling makes this a lopsided divergence with strong deskilling evidence and zero up-skilling counter-evidence, but the divergence captures the important structural tension. **Next session: draft the divergence file.** Files to reference: human-in-the-loop clinical AI degrades to worse-than-AI-alone... + AI diagnostic triage achieves 97 percent sensitivity....

- **Cytology never-skilling claim:** The Heudel 2026 finding on 80-85% training volume reduction (45 → 8 labs) is a new structural pathway distinct from cognitive deskilling. This is extractable as a standalone claim: "AI-enabled screening consolidation eliminates the training case volumes that develop clinical judgment, creating never-skilling through structural destruction of apprenticeship pipelines." The cytology case is the cleanest example. **Next session: extract this claim from Heudel 2026.**

- **Medicaid mental health advantage:** The KFF finding (Medicaid 59% > commercial 55% treatment rate) is counterintuitive and extractable. The structural explanation (Medicaid behavioral health carve-outs + FQHC infrastructure) is more interesting than the raw number. **Next session: verify with additional KFF/SAMHSA data and extract if confirmed.**

- **Mental health app attrition claim:** The 64% attrition in motivated RCT samples (Lancet Digital Health 2025, npj Digital Medicine 2025) is extractable as evidence for why digital mental health doesn't close the population-level access gap even when efficacy is real. **Next session: extract the two-part finding (real efficacy + engagement failure).**

### Dead Ends (don't re-run these)

- **GLP-1 biosimilars/USPSTF status:** GLP-1 biosimilar thread already covered by existing KB claim (Indian generics, Delhi HC ruling). USPSTF GLP-1 update — confirmed dead end from Session 23, nothing new. Don't re-run these searches.

- **AI durable up-skilling literature search:** Confirmed null. Zero papers in PubMed. Don't search again for 6 months unless there's a specific trigger (RCT publication announced, medical school prospective study published).

- **Health Affairs/SAMHSA/APA direct website fetches:** These URLs consistently return 403 errors. Use PubMed searches and KFF instead for US health data.

### Branching Points (one finding opened multiple directions)

- **Jorem et al. "fewer new patients" finding:** Direction A — extract as standalone claim about telemedicine's retention vs. access-expansion mechanism; Direction B — frame as divergence between "telemedicine solves the access gap" (optimistic thesis) and "telemedicine serves existing relationships" (Jorem finding). Direction A first; the divergence can come later when there's a real competing claim.

- **Mental health treatment gap coverage reframe:** Direction A — extract the Medicaid > commercial finding as a structural claim about behavioral health carve-outs; Direction B — use this to challenge the "serving the already-served" framing (Medicaid IS the most-served by mental health systems, but that's because Medicaid was designed for vulnerable populations). These aren't contradictory — pursue both, but frame carefully to avoid false tension.
