| 1. |
- Ali Fareedi, Abid, 1978-, et al.
(författare)
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Artificial Intelligence Agents and Knowledge Acquisition in Health Information System
- 2022
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Ingår i: MCIS 2022 Proceedings.
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Konferensbidrag (refereegranskat)abstract
- This research work highlights the need for AI-powered applications and their usages for the optimization of information flow processes in the medical sector, from the perspective of how AI-agents can impact human-machine interaction (HCI) for acquiring relevant and necessary information in emergency department (ED). This study investigates how AI-agents can be applied to manage situations of patient related unexpected experiences, such as long waiting times, overcrowding issues, and high number of patients leaving without being diagnosed. For knowledge acquisition, we incorporated modelling workshop techniques for gathering domain information from the domain experts in the context of emergency department in Karolinska Hospital, Solna, Stockholm, Sweden, and for designing the AI-agent utilizing NLP techniques. We discuss how the proposed solution can be used as an assistant to healthcare practitioners and workers to improve medical assistance in various medical procedures to increase flow and to reduce workloads and anxiety levels. The implementation part of this work is based on the natural language processing (NLP) techniques that help to develop the intelligent behavior for information acquisition and its retrieval in a natural way to support patients/relatives’ communication with the healthcare organization efficiently and in a natural way.
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| 2. |
- Ali Fareedi, Abid, 1978-, et al.
(författare)
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Conversational Artificial Intelligence (AI) in the Healthcare Industry
- 2023
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Ingår i: MCIS and MENACIS 2023: The 15Th Mediterranean Conference On Information Systems And The 6Th Middle East & North Africa Conference On Digital Information Systems.
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Konferensbidrag (refereegranskat)abstract
- The study presents an innovative approach to incorporating AI-driven conversational agents (CAs) or social robots technologies into healthcare information systems (HISs) and revolutionizing healthcare delivery systems. The study aims to improve accessibility and personalization, and minimize adverse risks, especially in the emergency departments (EDs). The study investigates patient-related experiences, long waiting times, and overcrowding issues during peak hours in EDs. Design science research methodology (DSRM) principles were tailored with modelling workshop method to capture domain contextual knowledge and include practitioners' cognitive-tacit knowledge-ability into HIS to address the above-mentioned issues. The developed social robot artifact incorporates an artificial intelligence markup language (AIML) technique as a model to restore domain knowledge of EDs, which serves as a foundation for developing goal-oriented interactive conversational system artifact between humans and machines. As a result, the study contributes that CAs, considered value-added AI-driven applications such as CAs or social robots, serve as a coworker to facilitate healthcare practitioners and patients, catering to patients' needs and communication to enhance care delivery experience and improve information flow processes using interactive services within EDs. The research presents a promising solution to improve patient outcomes, reduce waiting times, and enhance communication between patients and practitioners in EDs.
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| 3. |
- Ali Fareedi, Abid, 1978-, et al.
(författare)
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The Utilization of Artificial Intelligence for Developing Autonomous Social Robots within Health Information Systems
- 2023
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Ingår i: CEUR Workshop Proceedings. - : CEUR-WS. ; , s. 34-50
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Konferensbidrag (refereegranskat)abstract
- This study focuses on using AI systems, specifically conversational agents (CAs), to improve information flow during peak hours in healthcare emergency departments (EDs). We customized a Cross Industry Standard Process for Data Mining CRISP-DM approach to a CRISP-Knowledge graph (CRISP-KG) for overall design research. We use a knowledge graph approach to create an intelligent knowledge base (KBs) for CAs, which can enhance their reasoning, knowledge management, and context awareness abilities. We employ a collaborative methodology and ontology design patterns to develop a formal ontological model. Our goal is to build intelligent KBs for CAs that can interact with end-users and improve care quality in EDs, using Semantic Web Rule Language (SWRL) for inference. The KG approach can assist healthcare practitioners and patients in managing information flow more efficiently in EDs, ultimately improving care outcomes. © 2023 CEUR-WS. All rights reserved.
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| 4. |
- Ali Fareedi, Abid, 1978-, et al.
(författare)
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Value Added Conversational AI and Digital Health : An Ontology-Driven Approach
- 2024
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Ingår i: Proceedings of the 57th Hawaii International Conference on System Sciences. - Honolulu : University of Hawai'i at Manoa. - 9780998133171 ; , s. 4010-4019
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Konferensbidrag (refereegranskat)abstract
- AI-based assistants, such as conversational agents (CAs) and social robots, are becoming increasingly important in healthcare organizations. CAs provide a scalable and cost-effective platform for organizations supporting their employees by retrieving, structuring, and analyzing information to assist work processes. This study targets how knowledge graphs as ontological models manage CA that help improve the patient flow processes and reduce patients’ waiting time in the emergency departments (EDs). We tailored the design thinking (DT) method with modelling workshops employing conceptual modelling (CM) techniques to address these issues. We incorporated a hybrid formal approach of Methontology and Tove methodologies to build design artifacts, develop a goal-oriented interactive conversational system between humans and machines, and support information systems (IS). As a result, this ontology-driven approach contribution helps developers build value-added CAs to facilitate healthcare practitioners and patients. It is helpful for quality care delivery experience and improves bottlenecks in information flow within Eds.
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