SIMCard: Toward better connected electronic health record visualization. (17th October 2022)

• Record Type: Journal Article
• Authors: Sassi, Salma; Chbeir, Richard
• Is Part Of: Concurrency and computation. Volume 34:Number 28(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-17
• Subjects: aggregation -- electronic health records -- health related data -- interoperability -- IoT -- IVT -- knowledge representation -- visualization; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7399
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Recently, several healthcare organizations use decision‐making systems based on electronic health record (EHR) data in order to guarantee patient's safety and improve the quality of healthcare. In essence, the evolutions of Internet of Things (IoT) technologies have been of great help for implementing an integrated and interoperable decision‐making system based on EHR and medical devices (MDs). Those IoT‐based systems allow Clinicians collecting real‐time health data and provide accurate patient's monitoring. Nevertheless, several studies have shown that it is hard to improve the quality of healthcare using the current EHR IoT‐based systems since they do not allow to easily express clinician needs. Interactive visualization tools have been proposed to improve the efficacy and utility of these EHR based systems. However, there is no framework that provides a visual summary of patient data to clinician for planning specific clinical tasks, subsequently evaluating clinician responses, visually exploring EHR data and MDs data, gaining insights, supporting dynamic coordination processes care, and forming and validating hypotheses and risks. This article addresses this problem and introduces SIMCard, an aggregation‐based connected EHR visualization framework for patient monitoring, interpreting and predicting with MDs. The proposed framework aims to synthesize patient's clinical data into a single aggregating model for both EHR and MD conforming to health standard and terminologies. It also allows to link the aggregating model to the relevant medical knowledge in order to provide a connected and dynamic care and preventive plan. Last but not least, it provides an aggregated visualization model capable of displaying graphically a patient's personal data from databases, healthcare devices and sensors to reduce cognitive barriers related to the complexity of medical information and interpretation of health data. To demonstrate the refinement and design of our system and to observe user's actual practice of visualizing and analyzing real‐world dataset, we evaluated our system and compare to existing ones.