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The article is focused on a new operation model for the management of ICU human resources, designed through a multi-layer architecture grouped into two stages with continuous interaction between them. On one hand the first stage is based on four layers (defined by as many calculation tools, including a neural net) designed to prognosticate the risk associated with the individual patient admitted to the ICU. On the other hand, the output of the first stage is used as input for the second. Thanks to this interaction it is possible to associate the riskiness of the individual patient calculated with the first model, with the riskiness of the entire ICU. The primary objective of ICU hospitalization is to stabilize patients’ vital functions. The provision of healthcare services in the ICU involves both planned tasks and sudden emergency interventions. To address this challenge, the authors propose an ANN-based model that optimizes the utilization of specialized medical personnel. The model considers various factors such as patient acuity, staff availability and workload distribution to make informed decisions on personnel allocation. The proposed model aims to improve the efficiency and effectiveness of healthcare delivery in the ICU by optimizing the use of specialized medical personnel, by ensuring that the right medical resource is available at the right time, and minimizing the waiting times during emergencies.

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A 4.0-Based Dual-Stage Model for Human Resource Optimization in ICU. Theoretical Design and Experimental Investigation of the Boston B.I.D. Database

  • 会议论文

  • 发表日期：

    2025年

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  • Teresa Murino,
  • Lorenza Paulone,
  • Emma Salatiello,
  • Silvestro Vespoli

摘要

The article is focused on a new operation model for the management of ICU human resources, designed through a multi-layer architecture grouped into two stages with continuous interaction between them. On one hand the first stage is based on four layers (defined by as many calculation tools, including a neural net) designed to prognosticate the risk associated with the individual patient admitted to the ICU. On the other hand, the output of the first stage is used as input for the second. Thanks to this interaction it is possible to associate the riskiness of the individual patient calculated with the first model, with the riskiness of the entire ICU. The primary objective of ICU hospitalization is to stabilize patients’ vital functions. The provision of healthcare services in the ICU involves both planned tasks and sudden emergency interventions. To address this challenge, the authors propose an ANN-based model that optimizes the utilization of specialized medical personnel. The model considers various factors such as patient acuity, staff availability and workload distribution to make informed decisions on personnel allocation. The proposed model aims to improve the efficiency and effectiveness of healthcare delivery in the ICU by optimizing the use of specialized medical personnel, by ensuring that the right medical resource is available at the right time, and minimizing the waiting times during emergencies.