A Privacy-Preserving National Clinical Data Warehouse: Architecture and Analysis

Authors

Md Raihan Mia, Bangladesh University of Engineering and Technology
Abu Sayed Md Latiful Hoque, Bangladesh University of Engineering and Technology
Shahidul Islam Khan, International Islamic University Chittagong (IIUC)
Sheikh Iqbal Ahamed, Marquette UniversityFollow

Document Type

Article

Publication Date

3-2022

Publisher

Elsevier

Source Publication

Smart Health

Source ISSN

2352-6483

Original Item ID

DOI: 10.1016/j.smhl.2021.100238

Abstract

A centralized clinical data repository is essential for inspecting patients’ medical history, disease analysis, population-wide disease research, treatment decision support, and improving existing healthcare policies and services. Bangladesh, a rapidly developing country, poses several unusual challenges for developing such a centralized clinical data repository as the existing Electronic Health Records (EHR) are stored in unconnected, heterogeneous sources with no unique patient identifier and consistency. Data integration with secure record linkage, privacy preservation, quality control, and data standardization are the main challenges for developing a consistent and interoperable centralized clinical data repository. Based on the findings from our previous researches, we have designed an anonymous National Clinical Data Warehouse (NCDW) framework to reinforce research and analysis. The architecture of NCDW is divided into five stages to overcome the challenges: (1) Wrapper-based anonymous data acquisition; (2) Data loading and staging; (3) Transformation, standardization, and uploading to the data warehouse; (4) Management and monitoring; (5) Data Mart design, OLAP server, data mining, and applications. A prototype of NCDW has been developed with a complete pipeline from data collection to analytics by integrating three data sources. The proposed NCDW model facilitates regional and national decision support, intelligent disease analysis, knowledge discovery, and data-driven research. We have inspected the analytical efficacy of the framework by qualitative evaluation of the national decision support from two derived disease data marts. The experimental result based on the analysis is satisfactory to extend the NCDW on a large scale.

Comments

Accepted version. Smart Health, Vol. 23 (March 2022): 100238. DOI. © Elsevier. Used with permission.

Recommended Citation

Mia, Md Raihan; Latiful Hoque, Abu Sayed Md; Khan, Shahidul Islam; and Ahamed, Sheikh Iqbal, "A Privacy-Preserving National Clinical Data Warehouse: Architecture and Analysis" (2022). Computer Science Faculty Research and Publications. 73.
https://epublications.marquette.edu/comp_fac/73