Prescriptive analytics for inventory management in health care

Galli, Leonardo; Levato, Tommaso; Schoen, Fabio; Tigli, Luca

doi:10.1080/01605682.2020.1776167

Cited by 22 publications

(5 citation statements)

References 17 publications

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“…The need for Business Intelligence systems in hospital management has been highlighted by different authors, but there are few examples in the context of drug management ( González-Pérez et al, 2022 ) and none of them considers both structured and unstructured data from different sources as the proposed architecture does. Existing studies focus on processing structured data obtained from the hospital ERP ( González-Pérez et al, 2022 ; Sajogo, Teoh & Lebedevs, 2023 ), sometimes using AI ( Galli et al, 2021 ; Xia, 2022 ; Raza et al, 2022 ), or unstructured data from social networks ( Li et al, 2019 ; Hajjami, Berrada & Harti, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

A computer architecture based on disruptive information technologies for drug management in hospitals

Chalmeta

Ruíz

Soriano-Irigaray

2023

PeerJ Computer Science

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The drug management currently carried out in hospitals is inadequate due to several factors, such as processes carried out manually, the lack of visibility of the hospital supply chain, the lack of standardized identification of medicines, inefficient stock management, an inability to follow the traceability of medicines, and poor data exploitation. Disruptive information technologies could be used to develop and implement a drug management system in hospitals that is innovative in all its phases and allows these problems to be overcome. However, there are no examples in the literature that show how these technologies can be used and combined for efficient drug management in hospitals. To help solve this research gap in the literature, this article proposes a computer architecture for the whole drug management process in hospitals that uses and combines different disruptive computer technologies such as blockchain, radio frequency identification (RFID), quick response code (QR), Internet of Things (IoT), artificial intelligence and big data, for data capture, data storage and data exploitation throughout the whole drug management process, from the moment the drug enters the hospital until it is dispensed and eliminated.

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Section: Discussionmentioning

confidence: 99%

A computer architecture based on disruptive information technologies for drug management in hospitals

Chalmeta

Ruíz

Soriano-Irigaray

2023

PeerJ Computer Science

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“…Inventory management: AI systems are being used to predictively manage inventory in hospitals [60,61]. By analyzing usage patterns, patient inflow, and other relevant data, AI can forecast the need for medical supplies, medications, and equipment.…”

Section: Ai For Hospital Logistics and Resource Managementmentioning

confidence: 99%

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

Maleki Varnosfaderani,

Forouzanfar

2024

Bioengineering

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As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI’s potential to mitigate these issues and aims to critically assess AI’s integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI’s transformative potential, this review equips researchers with a deeper understanding of AI’s current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.

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“… Sitompul et al (2008) studied the relationship between demand variability, capacity constraints, and safety stock and proposed an alternative model for setting up safety stock in a supply chain with a sufficient capacity. Galli et al (2021) proposed an inventory optimization model for the medical inventory domain to minimise inventory levels and the need for emergency replenishment. Duarte, Gonçalves & Santos (2021) developed a mixed integer linear programming model and solved it with a solver based on a branch-and-cut algorithm.…”

Section: Related Workmentioning

confidence: 99%

Impact of green technology innovation based on IoT and industrial supply chain on the promotion of enterprise digital economy

Song

2023

PeerJ Computer Science

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With the gradual deterioration of the natural environment, a green economy has become a competing goal for all countries. As a trend of green innovation development, the digital economy has become a research hotspot for scientists. In this article, we study the supply chain management of enterprises in green innovation and digital economy development and complete the identification and demand prediction of warehouse goods through the Internet of Things (IoT) and artificial intelligence (AI). As the stuff meets the goods detection and storage, we employ an intelligent method to detect and classify the goods. The demand prediction analysis is carried out based on historical data on goods demand in the enterprise. The absolute error between the prediction result and the actual demand within 1 week is less than 30 goods by the particle swarm optimization-support vector machine (PSO-SVM) method used in this article. First, the goods identification task is completed based on video surveillance data using YOLOv4, and the recognition rate is as high as 98.3%. This article realises enterprises’ intelligent supply chain management through the intelligent identification of goods and the demand forecasting analysis of goods in the warehouse, which provides new ideas for green innovation and digital economy development.

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Prescriptive analytics for inventory management in health care

Cited by 22 publications

References 17 publications

A computer architecture based on disruptive information technologies for drug management in hospitals

A computer architecture based on disruptive information technologies for drug management in hospitals

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

Impact of green technology innovation based on IoT and industrial supply chain on the promotion of enterprise digital economy

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