The effect of machine learning tools for evidence synthesis on resource use and time-to-completion: protocol for a retrospective pilot study

Background Machine learning (ML) tools exist that can reduce or replace human activities in repetitive or complex tasks. Yet, ML is underutilized within evidence synthesis, despite the steadily growing rate of primary study publication and the need to periodically update reviews to reflect new evidence. Underutilization may be partially explained by a paucity of evidence on how ML tools can reduce resource use and time-to-completion of reviews. Methods This protocol describes how we will answer two research questions using a retrospective study design: Is there a difference in resources used to produce reviews using recommended ML versus not using ML, and is there a difference in time-to-completion? We will also compare recommended ML use to non-recommended ML use that merely adds ML use to existing procedures. We will retrospectively include all reviews conducted at our institute from 1 August 2020, corresponding to the commission of the first review in our institute that used ML. Conclusion The results of this study will allow us to quantitatively estimate the effect of ML adoption on resource use and time-to-completion, providing our organization and others with better information to make high-level organizational decisions about ML..

Highlights Machine learning (ML) tools for evidence synthesis now exist, but little is known about whether they lead to decreased resource use and time-to-completion of reviews. We propose a protocol to systematically measure any resource savings of using machine learning to produce evidence syntheses. Co-primary analyses will compare “recommended” ML use (in which ML replaces some human activities) and no ML use. We will additionally explore the differences between “recommended” ML use and “non-recommended” ML use (in which ML is over-used or under-used)..

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:12

Enthalten in:

Systematic Reviews - 12(2023), 1 vom: 17. Jan.

Sprache:

Englisch

Beteiligte Personen:

Muller, Ashley Elizabeth [VerfasserIn]
Berg, Rigmor C. [VerfasserIn]
Meneses-Echavez, Jose Francisco [VerfasserIn]
Ames, Heather M. R. [VerfasserIn]
Borge, Tiril C. [VerfasserIn]
Jardim, Patricia Sofia Jacobsen [VerfasserIn]
Cooper, Chris [VerfasserIn]
Rose, Christopher James [VerfasserIn]

Links:

Volltext [kostenfrei]

Themen:

Artificial intelligence
Business process management
Machine learning
Research waste
Systematic review

Anmerkungen:

© The Author(s) 2023

doi:

10.1186/s13643-023-02171-y

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

OLC2133425713

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