为资源有限的医疗系统构建可扩展的隐私保护病历仓库，支持多源数据整合与疫情预测。
Design and Implementation of a Scalable Clinical Data Warehouse for Resource-Constrained Healthcare Systems
- 采用封装层自动采集多源医疗数据，用Soundex算法解决无唯一标识时的患者匹配问题。
- 在孟加拉国登革热案例中实现临床、人口与环境数据融合，支持疫情预警与资源规划。
- 基于NoSQL的架构查询效率比SQL高40%-69%，可支撑每日1900万条记录处理。
集中式电子健康记录库对疾病监测、公共卫生研究和循证决策至关重要。然而，发展中国家因医疗数据分散、记录不统一及缺乏标准患者标识，难以实现可靠记录关联与数据互通，且受基础设施与隐私问题制约。本研究提出面向资源受限环境的可扩展、隐私保护临床数据仓库NCDW，整合116万条临床记录。框架包含封装式数据获取层，实现多源数据安全自动接入；引入Soundex算法解决无唯一ID下的患者身份错配。设计模块化数据集市，以孟加拉国登革热为例，融合临床、人口与环境数据，用于疫情预测与资源调度。定量评估显示该模型能强化国家决策支持系统，具备传染病管理适应性。数据库技术对比表明，NoSQL在复杂查询中性能优于SQL达40%-69%；系统负载估算显示，架构可支撑每日1900万条记录（5年共34TB）。通过调整接入层适配ICD-11与HL7 FHIR等标准，该框架可推广至全球发展中国家，助力新冠肺炎、结核病等传染病管理。
原文摘要 · Abstract (English)
Centralized electronic health record repositories are critical for advancing disease surveillance, public health research, and evidence-based policymaking. However, developing countries face persistent challenges in achieving this due to fragmented healthcare data sources, inconsistent record-keeping practices, and the absence of standardized patient identifiers, limiting reliable record linkage, compromise data interoperability, and limit scalability-obstacles exacerbated by infrastructural constraints and privacy concerns. To address these barriers, this study proposes a scalable, privacy-preserving clinical data warehouse, NCDW, designed for heterogeneous EHR integration in resource-limited settings and tested with 1.16 million clinical records. The framework incorporates a wrapper-based data acquisition layer for secure, automated ingestion of multisource health data and introduces a soundex algorithm to resolve patient identity mismatches in the absence of unique IDs. A modular data mart is designed for disease-specific analytics, demonstrated through a dengue fever case study in Bangladesh, integrating clinical, demographic, and environmental data for outbreak prediction and resource planning. Quantitative assessment of the data mart underscores its utility in strengthening national decision-support systems, highlighting the model's adaptability for infectious disease management. Comparative evaluation of database technologies reveals NoSQL outperforms relational SQL by 40-69% in complex query processing, while system load estimates validate the architecture's capacity to manage 19 million daily records (34TB over 5 years). The framework can be adapted to various healthcare settings across developing nations by modifying the ingestion layer to accommodate standards like ICD-11 and HL7 FHIR, facilitating interoperability for managing infectious diseases (i.e., COVID, tuberculosis).
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