193. On Surgery Ward Resilience: Quantitative Concepts, Evaluation, and Analysis
Invited abstract in session WC-8: Patient Flow Optimization, stream Health Care Management.
Wednesday, 13:30-15:00Room: H8
Authors (first author is the speaker)
| 1. | Gabriela Ciolacu
|
| Karlsruhe Institute of Technology | |
| 2. | Siamak Khayyati
|
| HEC Liege, University of Liege | |
| 3. | Emilia Grass
|
Abstract
Effective surgical ward management in the context of adverse events is especially challenging. It requires balancing meeting uncertain and increasing patient demand with limited and costly resources for prolonged periods. To evaluate if the ward can meet sudden demand for surgical care without straining medical resources during an event, hospital decision-makers introduced resilience to dynamically assess the event’s impact, considering that the ward is the costliest medical unit. Resilience is the surgical ward’s capacity to prepare (1), withstand (2), absorb (3), and recover (4) from adverse events such as natural disasters, implying multiple phases.
This study examines quantitative resilience indicators and their application to surgical wards following adverse events. While reviewing 30 healthcare resilience publications, we noticed that quantitative evaluation heavily depends on the definition of the performance indicator. To the best of our knowledge, the current state-of-the-art healthcare excludes surgical care, focuses on performance assumptions derived from engineering systems (e.g., energy) that significantly differ from healthcare, and relies on cumulative resilience indicators, neglecting the four aforementioned phases. To account for such shortcomings and guided by literature, this study proposes a quantitative evaluation that provides a granular resilience indicator, including all phases. The study also tailors the indicator and its interpretation to the surgical ward. We illustrate the usefulness of the proposed evaluation in a simulation that models the surgical ward and adjacent units in the context of an adverse event as a queueing network.
Keywords
- Disaster and Crisis Management
- Business Analytics
- Health Care
Status: accepted
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