Evidence map›Paper›PMID 36873583›Full record

ReviewInformatics in medicine unlocked2023

Identifying resilience strategies for disruption management in the healthcare supply chain during COVID-19 by digital innovations: A systematic literature review.

Goli Arji, Hossein Ahmadi, Pejman Avazpoor, Morteza Hemmat

Open access · goldAbstract readReview
In one paragraph

Review in Informatics in medicine unlocked, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
21.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 78 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Goli ArjiHealth Information Management, School of Nursing and Midwifery, Saveh University of Medical Sciences, Iran.
Hossein AhmadiCentre for Health Technology, Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK.
Pejman AvazpoorDepartment of Agriculture Economics, Ferdowsi University of Mashhad, Iran.
Morteza HemmatHealth Information Management, School of Nursing and Midwifery, Saveh University of Medical Sciences, Iran.
Saveh University of Medical Sciences · IRFerdowsi University of Mashhad · IRUniversity of Plymouth · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The worldwide spread of the COVID-19 disease has had a catastrophic effect on healthcare supply chains. The current manuscript systematically analyzes existing studies mitigating strategies for disruption management in the healthcare supply chain during COVID-19. Using a systematic approach, we recognized 35 related papers. Artificial intelligence (AI), block chain, big data analytics, and simulation are the most important technologies employed in supply chain management in healthcare. The findings reveal that the published research has concentrated mainly on generating resilience plans for the management of COVID-19 impacts. Furthermore, the vulnerability of healthcare supply chains and the necessity of establishing better resilience methods are emphasized in most of the research. However, the practical application of these emerging tools for managing disturbance and warranting resilience in the supply chain has been examined only rarely. This article provides directions for additional research, which can guide researchers to develop and conduct impressive studies related to the healthcare supply chain for different disasters.

Indexed as

COVID-19Healthcare supply chainLiterature reviewPandemicsSupply chain management

Identifiers

PMID36873583
PMCPMC9957975
OpenAlexW4322011969

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.