AI-driven Optimization in Healthcare: the Diagnostic Process

Abstract: Purpose: Process optimization in healthcare using artificial intelligence (AI) is still in its infancy. In this study, we address the research question "To what extent can an AI-driven chatbot help to optimize the diagnostic process?" Design/ Method/ Approach: First, we developed a mathematical model for the utility (i.e., total satisfaction received from consuming a good or service) resulting from the diagnostic process in primary healthcare. We calculated this model using MS Excel. Second, after identifying the main pain points for optimization (e.g., waiting time in the queue), we ran a small experiment (n=25) in which we looked at time to diagnosis, average waiting time, and their standard deviations. In addition, we used a questionnaire to examine patient perceptions of the interaction with an AI-driven chatbot. Findings: Our results show that scheduling is the main factor causing issues in a physician's work. An AI-driven chatbot may help to optimize waiting time as well as p

Angaben zum Objekt

• Weitere Titel: Искусственный интеллект и оптимизация в области здравоохранения: процесс постановки диагноза
• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch
• Anmerkungen: Veröffentlichungsversion ; begutachtet (peer reviewed) ; In: European Journal of Management Issues ; 29 (2021) 4 ; 218-231

Beteiligte, Orts- und Zeitangaben

• Ereignis: Veröffentlichung
• (wo): Mannheim
• (wer): SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
• (wann): 2021
• Urheber: Lyon, Jérôme Yves ; Bogodistov, Yevgen ; Moormann, Jürgen

Weitere Informationen

• DOI: 10.15421/192121
• URN: urn:nbn:de:101:1-2022102511263838082637
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 25.03.2025, 13:54 MEZ