When every minute matters : using predictive analytics of intervention durations to support hospital scheduling

Oberste, Luis ; Aydingül, Okan ; Jussupow, Ekaterina ; Heinzl, Armin

URL: https://aisel.aisnet.org/pacis2021/221/
Document Type: Conference or workshop publication
Year of publication: 2021
Book title: PACIS 2021 : Pacific Asia Conference on Information Systems, 12th to 14th July 2021, Dubai
The title of a journal, publication series: PACIS Proceedings
Volume: 2021, 221
Page range: 1-9
Conference title: PACIS 2021
Location of the conference venue: Online
Date of the conference: 12.-14.07.2021
Place of publication: Atlanta, GA
Publishing house: AISeL
Publication language: English
Institution: Business School > ABWL u. Wirtschaftsinformatik I (Heinzl 2002-)
Subject: 004 Computer science, internet
330 Economics
Keywords (English): Predictive analytics, treatment durations, hospital scheduling, healthcare
Abstract: Clinical interventions subordinate to medical pathways are characterized by patient-specific complications and variability of process durations. At the same time, estimating these durations is critical for developing accurate schedules. However, data of clinical information systems are recorded primarily for reporting, liability, and billing purposes; the systems do not fully capture detailed process information. Very little work has been done on predicting these types of durations for scheduling, other than using experts’ estimates or historical averages. We evaluate how predictive analytics based on patient-specific features can help develop estimates of otherwise unknown process durations, taking infusion chemotherapy as an example. We highlight the challenges of using clinical real-life data and discuss how we plan to address these challenges in the future.
Additional information: Online-Ressource

Dieser Eintrag ist Teil der Universitätsbibliographie.

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