照護轉型──在AI與數據驅動下，從被動走向主動與預測照護

Care Transformation: The AI- and Data-Driven Shift From Reactive to Proactive and Predictive Care

隨著全球高齡化與慢性疾病盛行，傳統以疾病發生後處置為核心的被動醫療模式，已逐漸無法回應當前健康照護體系的結構性挑戰。臺灣已邁入高齡社會，失能、失智與多重共病所造成的長期照護需求，進一步強調照護模式轉型的迫切性。本文以「AI與數據」驅動下照護轉型的必要性為出發，引出「主動照護」與「預測照護」的啟動、趨勢、及相關實證研究。同時，探討護理專業如何由被動應對角色，轉型為風險辨識、健康管理與預測決策的關鍵參與者，並強化以人為中心的連續性照護。最後提出，未來護理教育與制度設計應同步強化資料素養、倫理治理與跨專業合作，以促進預測照護在臺灣照護體系中的永續發展。

With global population aging and the increasing prevalence of chronic diseases, the traditional reactive medical model, which focuses on post-illness management, is increasingly inadequate for addressing the structural challenges currently facing healthcare systems. With Taiwan now an aging society, the long-term care needs arising from disability, dementia, and multiple comorbidities further underscore the urgent need to transform existing models of care. This article begins by examining the necessity of care transformation driven by artificial intelligence (AI) and data, and then examines the emergence of“proactive care”and“predictive care”and related trends and empirical research. This article also examines how the nursing profession may evolve away from its reactive role and into a key participant in risk identification, health management, and predictive decision-making to strengthen person-centered continuity of care. Finally, the authors propose that a goal of future nursing education and system design should be to enhance data literacy, ethical governance, and interdisciplinary collaboration concurrently to promote the sustainable development of predictive care within Taiwan’s healthcare system.