Layered Data Pipeline for Context-Aware Electronic Health Records and Healthcare Services

Recent advances in healthcare have provided a wide range of medical devices and biosensors that collect biomedical data from patients during different monitoring phases. At the same time, data repositories and data-oriented frameworks are needed to store, manage, analyze, and act upon the collected data to enable a wide range of healthcare services. However, storing raw medical data (e.g., hospital visits, vital signs) with no context offers incomplete information to the medical staff and facilities to efficiently offer services. Along with the raw data challenge, given the heterogeneity of the collected medical data and the medical services to be offered, utilizing one form of data repository can be challenging. In this paper, we propose layered data pipeline that targets these challenges through two main aspects. The first aspect is to build Context-aware Electronic Health Records (EHRs) that link the collected medical data with the context for a more concrete and profound image of the patient's medical status. The second aspect is to build a data pipeline that utilizes a set of databases to store, manage, and represent the EHRs and specific sub-portions to enable both patient-oriented and collective-oriented healthcare services. Patient-oriented services focus on individual EHRs for customized and tailored healthcare assistance, while collective-oriented services consider an anonymous collection of EHRs linked together to build a knowledge graph. The proposed system comprises three layers, where each layer builds parts of the context-aware EHRs and hosts certain stages of the data pipeline. The system is a work in progress, and in this paper, we discuss the main components of the proposed system, define the high-level structure of context-aware EHR, and present the proposed data pipeline through a proof-of-concept implementation for certain features.