醫療保健中整合人工智能會增加成本
AI integration in healthcare increases costs
Updated at: September 27, 2026 at 03:00 AM
將人工智慧融入醫療保健創造了一個具有挑戰性的經濟悖論。
The integration of AI into healthcare creates a challenging economic paradox.
雖然人工智慧常被承諾作為長期降低成本的一種方式，但目前的採用率卻經常增加醫療保健支出。
While often promised as a way to lower costs long-term, current adoption is frequently increasing healthcare expenditures.
這一趨勢是由多種因素推動的，包括基礎設施、資料清理以及與現有電子健康記錄進行複雜整合的高昂成本。
This trend is driven by several factors, including the high costs of infrastructure, data cleaning, and complex integration with existing electronic health records.
此外，人工智慧驅動的計費工具可能導致「升碼」(upcoding)，即人工智慧識別出額外的診斷以將就診歸入更高報銷額的類別，這在近期導致系統損失近 10 億美元。
Furthermore, AI-powered billing tools can lead to 'upcoding,' where AI identifies additional diagnoses to shift visits into higher-reimbursement categories, costing the system nearly $1 billion recently.
此外，目前的支付模式往往獎勵服務量而非效率，鼓勵醫療提供者優先考慮收入增加。
Additionally, current payment models often reward volume over efficiency, encouraging providers to prioritize revenue generation.
然而，專家認為人工智慧對於長期永續發展仍然至關重要。
However, experts argue that AI remains essential for long-term sustainability.
潛在的好處包括自動化行政任務、實現早期疾病檢測以及改善個人化治療。
Potential benefits include automating administrative tasks, enabling early disease detection, and improving personalized treatments.
許多觀察家指出我們正在經歷一種「J曲線」(J-Curve)效應，即成本在初期投資期間顯著上升，隨後隨著系統成熟且變得更有效率而最終下降。
Many observers suggest we are seeing a 'J-Curve' effect, where costs rise significantly due to initial investments before eventually declining as systems mature and become more efficient.
最終，儘管人工智慧的實施帶來了沉重的預付資本負擔和衡量挑戰，但它被視為應對現代醫療保健交付成本上升的必要演變，前提是未來的政策能成功將激勵措施與品質成果而非單純的服務量對齊。
Ultimately, while AI implementation presents significant upfront capital burdens and measurement challenges, it is viewed as a necessary evolution to handle the rising costs of modern healthcare delivery, provided that future policies successfully align incentives toward quality outcomes rather than simple volume.
