A Coherent Healthcare System with RDBMS, NoSQL and GIS Databases

With new database system development and new data types emerging, many applications are no longer using a monolithic, simple client/server structure, but using more than one types of database systems to store heterogeneous data. In this project, we exploit the benefits of combing Relational Database Management System (RDBMS) and NoSQL systems in the development of better Electronic Health Records (EHRs) and Clinical Decision Support Systems (CDSS). Specifically, MySQL, MongoDB, and GIS databases are integrated to improve EHR systems and to provide better clinical decision supports. The ACID (atomicity, consistency, isolation, durability) properties of the RDBMS ensure data integrity, database security, efficient SQL queries, easy data access, and effective transaction processing. MongoDB provides the system with clear internal data structure, easy scaling-out, fine-Tuning, and convenient mapping of application objects to the database objects. The GIS database allows vivid visualization of the geographic locations of patients, physician offices, and medical facilities. The integrations of these database systems in healthcare help application systems to comply with the EHR HIPAA requirements without compromising on scalability and performance

Triangulum City Dashboard: An Interactive Data Analytic Platform for Visualizing Smart City Performance

Cities are becoming smarter by incorporating hardware technology, software systems, and network infrastructure that provide Information Technology (IT) systems with real-time awareness of the real world. What makes a “smart city” functional is the combined use of advanced infrastructure technologies to deliver its core services to the public in a remarkably efficient manner. City dashboards have drawn increasing interest from both city operators and citizens. Dashboards can gather, visualize, analyze, and inform regional performance to support the sustainable development of smart cities. They provide useful tools for evaluating and facilitating urban infrastructure components and services. This work proposes an interactive web-based data visualization and data analytics toolkit supported by big data aggregation tools. The system proposed is a cloud-based prototype that supports visualization and real-time monitoring of city trends while processing and displaying large data sets on a standard web browser. However, it is capable of supporting online analysis processing by answering analytical queries and producing graphics from multiple resources. The aim of this platform is to improve communication between users and urban service providers and to give citizens an overall view of the city’s state. The conceptual framework and architecture of the proposed platform are explored, highlighting design challenges and providing insight into the development of smart cities. Moreover, results and the potential statistical analysis of important city services offered by the system are introduced. Finally, we present some challenges and opportunities identified through the development of the city data platform.publishedVersio

Big Data Analytic Framework for Organizational Leverage

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User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Designing for Nurse-AI Collaboration in Triage

The Local Emergency Medical Communication Centers (LEMCs) play a crucial role in the Norwegian healthcare system by receiving calls for immediate medical assistance. Registered nurses operate the phone calls, and their task is to assess the situation and triage the caller into appropriate triage levels indicating when and how help should be provided. Telephone triage poses challenges due to the limitations of audio communication, time sensitivity, and complex decision-making. Additionally, nurses often face the burden of managing clinical tools across multiple interfaces. This thesis explored how to design a system to support nurses in telephone triage and how we can facilitate nurse-AI collaboration in the process. A Research through Design (RtD) methodology was employed, and an iterative design approach was utilized. The research investigated the design aspects of AI-based suggestions and the use of natural language when creating semi-structured documentation. Four prototype iterations were developed throughout the study, and researchers from RE-AIMED and telephone operators conducted evaluations of the prototypes. Designing a tool for telephone triage requires understanding the user's needs and workflow. It is, therefore, crucial to involve telephone operators in the design process. The prototype demonstrated how we could design for incorporating AI in the triage process, and this thesis explores the various considerations when designing for nurse-AI collaboration. One notable finding was the importance of enabling documentation in natural language, as relying solely on structured documentation may fail to capture the caller's specific situation. Additionally, it is important to design a system that facilitates documentation of patient-initiated information and questions initiated by the nurses or the system.Masteroppgave i informasjonsvitenskapINFO390MASV-INF

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Dashboard Confessional: Co-Designing a decision-making support tool to support Resident's test ordering

Master of DesignArt and DesignUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/156122/1/DeepBlue_Jesko_2020_MDes_Thesis.pd

Visualizing and Interacting With Social Determinants of Health

Purpose: The purpose of this study is to examine the use of information visualization to represent specific social determinants of health, and to examine the benefits of such representation for health policymaking. Methods: The study developed a prototype for a visualization tool (www.healthvisualization.ca), which represents the conceptual framework for the social determinants of health (CSDH) and new ways to represent related health equity indicators. This tool was used by study participants. The experience of these participants and the usability of the tool were evaluated using qualitative semi-structured interviews. Results: Visualizing the CSDH framework helps to present the social determinants of health more effectively, allowing better visualization of indicators. Communicating healthcare indicators to policymakers is a complex task because of the complexity of these indicators. Conclusions: The contribution of information visualization to policymaking could only be understood by taking into consideration the different factors that impact health decision-making and evidence uptake