首份实证研究揭示医疗中计算机感知技术的各方关切与人文落地路径。
Stakeholder Perspectives on Humanistic Implementation of Computer Perception in Healthcare: A Qualitative Study
- 通过深度访谈102名利益相关者，提炼出7大核心关切领域。
- 提出'个性化路线图'机制，明确监测指标与临床干预阈值。
- 适合开发者、临床医生及政策制定者参考，保障技术人性化应用。
计算机感知（CP）技术（如数字表型、情感计算和被动传感）为个性化医疗带来前所未有的机遇，但也引发隐私、偏见及共情式关系照护被削弱的担忧。本研究首次基于证据，系统呈现设计者、部署者与使用者在真实场景中对CP技术整合到患者照护中的关系、技术与治理挑战的看法。我们对102名利益相关者（青少年患者及其照料者、一线医护人员、技术开发者、伦理法律政策或哲学学者）进行了深入半结构化访谈，由跨学科团队进行主题分析，通过双人编码与共识裁定确保可靠性。受访者提出了七个相互关联的关切领域：(1) 可信度与数据完整性；(2) 患者个体相关性；(3) 实用性与工作流融合；(4) 监管与治理；(5) 隐私与数据保护；(6) 对患者的直接与间接伤害；(7) 对还原论的哲学批判。为实现人文保障，我们提出‘个性化路线图’：由多方共同设计的计划，预先确定监测指标、反馈方式与时机、临床行动阈值，以及算法推断与实际体验冲突的处理流程。该框架为开发者、临床医生与政策制定者提供实用指导，助力在利用持续行为数据的同时，维系照护的人文核心。
原文摘要 · Abstract (English)
Computer perception (CP) technologies (digital phenotyping, affective computing and related passive sensing approaches) offer unprecedented opportunities to personalize healthcare, but provoke concerns about privacy, bias and the erosion of empathic, relationship-centered practice. A comprehensive understanding of perceived risks, benefits, and implementation challenges from those who design, deploy and experience these tools in real-world settings remains elusive. This study provides the first evidence-based account of key stakeholder perspectives on the relational, technical, and governance challenges raised by the integration of CP technologies into patient care. We conducted in-depth, semi-structured interviews with 102 stakeholders: adolescent patients and their caregivers, frontline clinicians, technology developers, and ethics, legal, policy or philosophy scholars. Transcripts underwent thematic analysis by a multidisciplinary team; reliability was enhanced through double coding and consensus adjudication. Stakeholders articulated seven interlocking concern domains: (1) trustworthiness and data integrity; (2) patient-specific relevance; (3) utility and workflow integration; (4) regulation and governance; (5) privacy and data protection; (6) direct and indirect patient harms; and (7) philosophical critiques of reductionism. To operationalize humanistic safeguards, we propose "personalized roadmaps": co-designed plans that predetermine which metrics will be monitored, how and when feedback is shared, thresholds for clinical action, and procedures for reconciling discrepancies between algorithmic inferences and lived experience. By translating these insights into personalized roadmaps, we offer a practical framework for developers, clinicians and policymakers seeking to harness continuous behavioral data while preserving the humanistic core of care.
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