Previously submitted to: Journal of Medical Internet Research (no longer under consideration since May 08, 2025)
Date Submitted: Jan 22, 2025
Open Peer Review Period: Jan 22, 2025 - Mar 19, 2025
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AI-Driven Resilience Strategies for Enhancing Healthcare Supply Chain Resilience: A Systematic Literature Review
ABSTRACT
Background:
The COVID-19 pandemic has exposed the vulnerabilities of global supply chains (SC), particularly within the healthcare sector, underscoring the need for advanced methods to enhance SC resilience and sustainability. Pandemics, such as COVID-19 and Influenza, pose considerable risks to healthcare supply chain (HSC) performance, demanding robust analytical tools to optimize system efficiency under uncertain conditions.
Objective:
In this paper, we map the current literature and synthesize insights on the role of leadership in driving Artificial Intelligence (AI)-driven resilience approaches for enhancing HSC within healthcare organizations. This systematic literature review aims to review the HSC-resilience (HSCR) and apply a novel network range directional measure model to evaluate the sustainability and resilience of HSC in response to the COVID-19 pandemic.
Methods:
The systematic literature review followed the PRISMA guidelines, encompassing multiple databases, including Business Source Premier, CINAHL, ACM Digital Library, MEDLINE, PsycINFO, Web of Science, PubMed, and ScienceDirect. The review targeted articles published from 2016 to 2024, focusing on empirical studies. A predetermined search strategy used keywords such as SC resilience, artificial intelligence, healthcare, and related terms. The analysis involved an inductive, thematic approach to qualitatively map the evidence. The screening and data extraction processes were independently carried out by two reviewers, with Cohen's kappa used to assess interrater agreement. Data synthesis was accomplished through a narrative approach.
Results:
A comprehensive case study demonstrates the practical application of this model, revealing its capability to assess SC resilience and sustainability under diverse data conditions. The findings highlight how decision-making unit efficiency varies with changing circumstances, showcasing the model’s robustness in evaluating HSC performance during disruptions. The final number of studies included in the systematic literature review was 39. These clinical decision-making units included quantitative and qualitative decision support models 16/39 (41%) and 25/39 (59%), respectively. The earliest article was published in 2018; the most recent was from 2022.
Conclusions:
This study is one of the first to compare AI and conventional human systematic review methods as real-time tools for gathering literature on AI-driven resilience strategies to strengthen HSC. While the model proves effective in assessing SC resilience and sustainability, a key limitation lies in the practical implementation of such advanced methodologies within HSC. Future research should focus on the real-world deployment of these models to strengthen SC resilience in the face of potential disruptions.
Citation
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