AI 中文总结
本文提出数据医院，一种基于医院隐喻的人机协同工作流模型，通过十阶段流程实现可解释的研究数据质量辅助，贡献在于使可评估性、不确定性和过程可复现性可见。
AI 中文摘要
研究数据质量是多维且依赖于用途的：它产生于数据、预期用途、上下文知识、文档、干预决策和可追溯性的相互作用。本概念论文提出了数据医院，一种用于研究数据质量的人机协同控制与交互模型。采用医院隐喻，数据集被接收、情境化、评估、在专门站点审查、仅通过批准的干预进行修改、验证、文档化，并在干预充分指定时使其可重放。该概念结合了确定性分析和可检查证据，以及由数据博士提供的可选证据约束解释和明确的用户决策。保留原始数据和控制的工作状态将观察与干预分离。贡献并非新的清洗或插补算法，而是一个十阶段工作流，使可评估性、不确定性、干预权限、来源和过程可复现性可见。原型是一个实现支持的演示器，而非发布的研究工件，通过表示标准化、插补、患者档案文档和重放来说明概念的选定部分。论文最后提出了后续技术和用户中心评估的分阶段议程。
英文摘要
Research data quality is multidimensional and purpose-dependent: it emerges from the interplay of data, intended use, contextual knowledge, documentation, intervention decisions, and traceability. This concept paper presents the Data Hospital, a human-in-the-loop control and interaction model for research data quality. Using a hospital metaphor, datasets are admitted, contextualized, assessed, reviewed in specialized stations, modified only through approved interventions, validated, documented, and made replayable where interventions are sufficiently specified. The concept combines deterministic profiling and inspectable evidence with optional evidence-bound explanation by Dr. Data and explicit user decisions. Preserved Raw Data and controlled working states separate observation from intervention. The contribution is not a new cleaning or imputation algorithm, but a ten-stage workflow that makes assessability, uncertainty, intervention authority, provenance, and process reproducibility visible. The prototype is an implementation-backed demonstrator rather than a released research artifact and illustrates selected parts of the concept through representational standardization, imputation, Patient File documentation, and replay. The paper concludes with a staged agenda for subsequent technical and user-centered evaluation.
CommentsConcept paper, 16 pages, 13 figures, 6 tables
