The effect of machine learning tools for evidence synthesis on resource use and time-to-completion : protocol for a retrospective pilot study
© 2023. The Author(s)..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Systematic reviews - 12(2023), 1 vom: 17. Jan., Seite 7 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Muller, Ashley Elizabeth [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Completed 19.01.2023 Date Revised 14.02.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s13643-023-02171-y |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM351613854 |
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| 100 | 1 | |a Muller, Ashley Elizabeth |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The effect of machine learning tools for evidence synthesis on resource use and time-to-completion |b protocol for a retrospective pilot study |
| 264 | 1 | |c 2023 | |
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| 500 | |a Date Completed 19.01.2023 | ||
| 500 | |a Date Revised 14.02.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. The Author(s). | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Business process management | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Research waste | |
| 650 | 4 | |a Systematic review | |
| 700 | 1 | |a Berg, Rigmor C |e verfasserin |4 aut | |
| 700 | 1 | |a Meneses-Echavez, Jose Francisco |e verfasserin |4 aut | |
| 700 | 1 | |a Ames, Heather M R |e verfasserin |4 aut | |
| 700 | 1 | |a Borge, Tiril C |e verfasserin |4 aut | |
| 700 | 1 | |a Jardim, Patricia Sofia Jacobsen |e verfasserin |4 aut | |
| 700 | 1 | |a Cooper, Chris |e verfasserin |4 aut | |
| 700 | 1 | |a Rose, Christopher James |e verfasserin |4 aut | |
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