title: Enhancing Clinical Decision Support Systems through Hospital Information System Integration and Machine Learning in a Context of the Emergency Department

authors: Jiang, Hongwei

abstract: This study conducts design science research (DSR) aimed at addressing the challenge of improving clinical decision support (CDS) in Emergency Departments (EDs). Making timely decisions by physicians to meet the diverse demands within hospital EDs is a challenge as the inadequacies of current Hospital Information Systems (HIS) can host fragmented Electronic Health Record (EHR) data across multiple sub-systems. The advancement of Information Technology (IT), notably machine learning (ML), brings new initiatives for addressing this issue. To tackle this complexity, this research deploys DSR to design a novel system integration architecture. This architecture provides a comprehensive framework that orchestrates HIS integration across multiple strata, encompassing the business, application, and technology layers, to facilitate the integration of ML-based Clinical Decision Support Systems (CDSS) within the ED setting. Two design artefacts are developed as outcomes of DSR: firstly, the development of the system integration architecture, and secondly, the creation of the ML-based CDSS. The former serves as a meticulously designed blueprint for HIS integration, ensuring the effective functioning of ML-based CDSS at the point of care. The latter represents a pioneering CDSS system, harnessing the power of ML algorithms to furnish real-time, context-aware decision support. The impact of the ML-based CDSS on ED efficiency is subjected to rigorous evaluation. This evaluation is conducted by leveraging historical Electronic Health Record (EHR) data within a simulated ED environment. The simulation, calibrated with parameters drawn from a real-world hospital ED setting, yields promising results. These findings underscore the feasibility and the manifold benefits of integrating ML-based CDSS to augment ED efficiency. Furthermore, this research makes a noteworthy contribution to the theoretical underpinnings of information system design. It achieves this by pioneering the development of a novel system integration architecture. This architecture serves as a bridge, alleviating the knowledge gap that traditionally separates HIS integration from ML-based CDSS. The study advances the understanding of strategic design and integration of hospital information systems to support ML and CDSS. Ultimately, this advancement holds the potential to catalyse substantial improvements in patient care and outcomes, especially within the intricate and high-stakes environment of the Emergency Department.

text: Enhancing Clinical Decision Support Systems through Hospital Information System Integration and Machine Learning in a Context of the Emergency Department This study conducts design science research (DSR) aimed at addressing the challenge of improving clinical decision support (CDS) in Emergency Departments (EDs). Making timely decisions by physicians to meet the diverse demands within hospital EDs is a challenge as the inadequacies of current Hospital Information Systems (HIS) can host fragmented Electronic Health Record (EHR) data across multiple sub-systems. The advancement of Information Technology (IT), notably machine learning (ML), brings new initiatives for addressing this issue. To tackle this complexity, this research deploys DSR to design a novel system integration architecture. This architecture provides a comprehensive framework that orchestrates HIS integration across multiple strata, encompassing the business, application, and technology layers, to facilitate the integration of ML-based Clinical Decision Support Systems (CDSS) within the ED setting. Two design artefacts are developed as outcomes of DSR: firstly, the development of the system integration architecture, and secondly, the creation of the ML-based CDSS. The former serves as a meticulously designed blueprint for HIS integration, ensuring the effective functioning of ML-based CDSS at the point of care. The latter represents a pioneering CDSS system, harnessing the power of ML algorithms to furnish real-time, context-aware decision support. The impact of the ML-based CDSS on ED efficiency is subjected to rigorous evaluation. This evaluation is conducted by leveraging historical Electronic Health Record (EHR) data within a simulated ED environment. The simulation, calibrated with parameters drawn from a real-world hospital ED setting, yields promising results. These findings underscore the feasibility and the manifold benefits of integrating ML-based CDSS to augment ED efficiency. Furthermore, this research makes a noteworthy contribution to the theoretical underpinnings of information system design. It achieves this by pioneering the development of a novel system integration architecture. This architecture serves as a bridge, alleviating the knowledge gap that traditionally separates HIS integration from ML-based CDSS. The study advances the understanding of strategic design and integration of hospital information systems to support ML and CDSS. Ultimately, this advancement holds the potential to catalyse substantial improvements in patient care and outcomes, especially within the intricate and high-stakes environment of the Emergency Department.

year: 2024

title: Ethnic and religious discrimination? A multicultural analysis of muslim minorities in the west

authors: Foroutan, Yaghoob

abstract: Using a discrimination approach, this paper analyzes the consequence of religious affiliation on the status of female migrants in the labor market. Available literature emphasizes the fact that the combination of a set of major sources of discrimination such as gender, migration, ethnicity, and religion makes more severe the existing discrimination. In order to examine this relationship, the paper utilizes the statistical method of logistic regression to determine the effect of religion while other competing determinants such as ethnic and migration characteristics as well as human capital and family characteristics are held constant in the analysis. Accordingly, it is proposed that the analysis undertaken in this paper has a good potential to advance the existing knowledge on the role of religion and ethnicity from a discrimination perspective. Key findings of this empirical investigation suggest that employment differentials tend to be mainly the consequence of achieved characteristics such as human capital and length of residence in the country of destination rather than the act of discrimination in the labor market per se, arising from characteristics such as migration status, religion, and ethnicity. However, further collaborations especially qualitative studies are required to shed further light on the complicated issue of ethnic and religious discrimination.

text: Ethnic and religious discrimination? A multicultural analysis of muslim minorities in the west Using a discrimination approach, this paper analyzes the consequence of religious affiliation on the status of female migrants in the labor market. Available literature emphasizes the fact that the combination of a set of major sources of discrimination such as gender, migration, ethnicity, and religion makes more severe the existing discrimination. In order to examine this relationship, the paper utilizes the statistical method of logistic regression to determine the effect of religion while other competing determinants such as ethnic and migration characteristics as well as human capital and family characteristics are held constant in the analysis. Accordingly, it is proposed that the analysis undertaken in this paper has a good potential to advance the existing knowledge on the role of religion and ethnicity from a discrimination perspective. Key findings of this empirical investigation suggest that employment differentials tend to be mainly the consequence of achieved characteristics such as human capital and length of residence in the country of destination rather than the act of discrimination in the labor market per se, arising from characteristics such as migration status, religion, and ethnicity. However, further collaborations especially qualitative studies are required to shed further light on the complicated issue of ethnic and religious discrimination.

year: 2011

title: Indigenous psychologies globally – a perspective from Aotearoa/New Zealand

authors: Nikora, Linda Waimarie

subjects: Maori

abstract: Discusses indigenous psychology from a global perspective and then from the perspective of Aotearoa/New Zealand.

text: Indigenous psychologies globally – a perspective from Aotearoa/New Zealand Discusses indigenous psychology from a global perspective and then from the perspective of Aotearoa/New Zealand.

year: 2004-06