Education and Healthcare Suck for the Same Reasons
Frustration with both education and healthcare systems centers on how they prioritize standardized metrics, bureaucratic incentives, and cost efficiency over human outcomes like learning, trust, and compassionate care. Commenters argue that simply increasing funding hasn’t improved results in places like U.S. schools and hospitals; instead, misaligned incentives, depersonalization (e.g., doctors as data clerks), and overreliance on narrow measurements distort what institutions optimize for. Emerging tools such as AI “scribes” and alternative models like learning pods are seen as partial fixes, but many emphasize the need to rethink what should be measured, who systems are designed to serve, and how much autonomy practitioners are given.
Metrics, Management, and Goodhart’s Law
- Many criticize the mantra “if you can’t measure it, you can’t manage it” as reductive and harmful when overapplied.
- Others argue metrics are philosophically necessary: if something cannot in any way be detected, it cannot be managed.
- Several point out that complex work (software, teaching, medicine) resists simple metrics; attempts are easily gamed and can distort behavior.
- A recurring theme: metrics are useful prompts and proxies, but never sufficient on their own; “metrics-supremacy” is seen as dangerous.
- Some suggest involving frontline practitioners in choosing which metrics to optimize, rotating them regularly to reduce myopia.
Healthcare Practice, Documentation, and AI
- Multiple commenters note doctors spending more time typing than listening; record-keeping and billing workflows are seen as crowding out empathy.
- Some argue the core issue is underinvestment in people (scribes, admin support), not record-keeping itself.
- AI scribes are highlighted as one of the few current LLM uses clinicians actually like, reportedly improving visits by freeing attention for patients.
- There is disagreement on what to measure: patient-centric metrics (time to appointment, time with doctor, perceived adequacy of attention) vs. hard outcomes like mortality, which are noisy and lagging.
Education Funding, Outcomes, and Inequality
- Strong disagreement on whether “more funding” is the key fix.
- Several claim the U.S. already spends heavily per student, with flat test scores and poor outcomes in many districts, implying money is not the main constraint.
- Others push back, citing structural inequality, distribution of funds, curriculum quality, and student backgrounds; they reject framing “bad kids” as the core problem.
- Examples are given of wealthy districts with high spending but declining outcomes, and poor states with low spending but strong test performance.
Standardization, Scale, and Trust
- Some see standardization as an unavoidable response to scale; others blame deeper issues: loss of trust in professionals and fear of failure driving control systems.
- One view: both healthcare and education are distorted because payers and “customers” differ (insurers vs. patients; parents vs. children), leading institutions to optimize for third-party metrics and incentives.
Alternative Models and Role of AI
- Ideas floated: self-directed learning pods, community-funded clinics, income-linked school funding, lifelong satisfaction surveys.
- Skeptics doubt such models can scale beyond niches.
- Several note LLMs might make high-quality one-on-one tutoring widely accessible, pushing schools and doctors toward roles emphasizing character development and bedside manner rather than information delivery.