Data Extraction Tools for Systematic Reviews (Honest Comparison)

Data extraction is where a systematic review turns included studies into a table you can synthesize. This page is a fit guide for that step. Each tool is matched to the job its own site describes.

Disclosure: we make Evidence Table Builder, Study Screener and SystematicReviewTools.app. We include them where they fit and say where other tools are the better choice.

Feature statements were checked on each vendor's own site. Each block is marked verified Oct 2026. If a claim was not on the page we checked, it is not repeated here. Vendor marketing percentages are left out, because this article did not measure them.

If you are still deciding what the table should hold, start with What is an evidence table and Best practices for data extraction.

Evidence Table Builder does not screen titles and abstracts. For screening, use Study Screener. For separate tools across protocol, search, deduplication, a screening queue, extraction, and an outline, see SystematicReviewTools.app.

Best for / not ideal for are our editorial judgement, not vendor claims. That applies to the table and to the lines under each tool.

Where each tool fits

Tool	Best for	A better fit for some readers
Evidence Table Builder	Templates, quote-linked extraction, and quality-assessment profiles after screening	Screening: Study Screener. Two extractors in the software: Covidence, EPPI-Reviewer, Nested Knowledge, or Laser AI's quality-assurance pass
Excel or Google Sheets	A shared extraction grid the team already knows	A second person checking extraction in the software: Covidence Extraction 1 or 2, EPPI-Reviewer, Nested Knowledge, or Laser AI's quality-assurance pass
Covidence (extraction)	Extraction inside a full review, structured or custom	Only the evidence table after screening is done: Evidence Table Builder
DistillerSR	Configurable extraction with an audit trail, AI-assisted screening, and human-checked suggestions	A small shared grid: Excel or Google Sheets
EPPI-Reviewer	Coding tools, machine learning for screening, and double coding	A PICO form with RevMan export: Covidence Extraction 1
Elicit	Exploratory columns, including tables and figures, with a source behind each cell	Two independent extractors and a consensus form: Covidence Extraction 1 or 2
Nested Knowledge	Tagging, AI-assisted extraction, and arm- and timepoint-level quantitative data	A narrative form with no quantitative synthesis: Covidence Extraction 2 or EPPI-Reviewer
Laser AI	Screening and extraction together, with a later quality-assurance pass	PDFs you have already included and no screening to configure: Evidence Table Builder

Evidence Table Builder

Best for: The evidence table after screening is done. You reuse extraction templates, run AI-assisted extraction, and keep an audit trail of verbatim quotes and location pointers from the PDF. Quality-assessment profiles, including RoB 2 and ROBINS-I, draft checklist judgements you can check against the paper. The way that AI-assisted extraction is set up is aligned with RAISE guidance.

Not ideal for: Title and abstract screening. Use Study Screener, then bring the included PDFs here. For two people extracting inside the software, Covidence Extraction 1 or Extraction 2, EPPI-Reviewer comparison mode, Nested Knowledge's dual meta-analytical extraction, or Laser AI's quality-assurance pass is the better fit. An institution that wants one system from reference import through a PRISMA diagram will be better served by Covidence or DistillerSR.

One reviewer can open an answer, leave it unchanged, or edit it. The save stores that reviewer's name and whether the answer was verified or edited. It does not keep a second extractor's table beside the first, and it does not compare the two. The field-level adjudication ladder is a method for two people doing that comparison themselves. That post says the ladder is for human–human adjudication. It is not a screen in the product.

See the pricing page.

Study Screener's site describes finishing title and abstract screening there, then continuing in Evidence Table Builder for the PDFs (verified Oct 2026). The template, audit-trail, disagreement, and quality-assessment pages linked above are ours.

Excel and Google Sheets

Best for: A small review whose extraction form is a shared grid.

Not ideal for: A source quote stored with each cell, or two independent extractors reconciling inside the software. Covidence Extraction 1 or Extraction 2, EPPI-Reviewer comparison mode, Nested Knowledge's dual meta-analytical extraction, or Laser AI's quality-assurance pass is the better fit for that check. Evidence Table Builder is the better fit when the priority is a quote-linked table after screening is finished.

Excel's help center documents collaboration, formulas, and data analysis for Excel workbooks, and a built-in AI assistant for analyzing and cleaning data (verified Oct 2026). The Google Sheets product page describes a collaborative, AI-powered spreadsheet: several people can edit the same file at once, sharing settings control who can view or edit, revision history can restore an earlier version, and an assistant can build or edit a sheet from a written prompt and fill tables (verified Oct 2026). This article does not name those assistants. Covidence's extraction guidance also names Excel as a place to build a custom form when the data do not fit its templates (verified Oct 2026).

A spreadsheet remains a fair way to extract a review. Many teams should stay there.

Covidence (extraction module)

Best for: Teams who extract inside the same review they screen, either with a structured form for quantitative synthesis or a custom form for narrative and mapping work.

Not ideal for: Umbrella or prognosis data that do not fit the templates. Covidence tells those teams to consider a custom Extraction 2 form or another tool such as Excel. If screening is already finished and you only need the evidence table, Evidence Table Builder is the better fit.

Covidence documents Extraction 1 and Extraction 2 (verified Oct 2026). Extraction 1 collects structured data against comparison groups for quantitative synthesis or meta-analysis. Its sections are identification, methods, population, interventions, and outcomes. A template can be reused from another review. The quality-assessment template can start from the Cochrane Risk of Bias 1 tool or a custom tool. The PDF and the form sit side by side, and extracted data auto-saves. When a DOI is available, reviewers accept or reject each suggestion, and a supporting quote is shown. Export formats include RevMan, CSV, and Excel.

Extraction 2 is built from headings, text fields, single-choice fields, checkboxes, and tables. Quality assessment is optional and can start from a Cochrane risk-of-bias tool or from scratch. Export is CSV, either consensus data or each reviewer's data. You can switch between the two templates later, and the data stays in the template you leave.

The comparison table dated 16 August 2026 marks dual reviewer extraction, and single reviewer extraction, for both Extraction 1 and Extraction 2. Extraction 2's overview, dated 27 August 2026, describes assigning two reviewers or one, then flagging conflicts for a final decision. This article follows that later overview on suggestions: it says a suggestion from an accessible full-text PDF is kept only after a reviewer accepts it, with a supporting quote. The earlier comparison table marks automatic suggestions for Extraction 1 only. Sources below name both pages and which one this article follows.

Covidence's public site also presents screening, quality assessment, and a PRISMA flow diagram in the same review (verified Oct 2026).

DistillerSR

Best for: Organizations that want configurable extraction forms, an audit trail, AI-assisted screening, and AI-assisted suggestions a person accepts or rejects.

Not ideal for: A student team that only needs a shared grid. Excel or Google Sheets is the better fit. If screening is already done and the remaining job is the evidence table, Evidence Table Builder is the better fit.

DistillerSR describes configurable workflows you can standardize across reviews, an integrated audit trail with version control, and AI-assisted forms with reusable answer lists (verified Oct 2026). Previously extracted data can be reused to fill forms. For screening, it describes reordering of unscreened references from the team's include and exclude decisions, classifiers that label references, and checks that flag excluded references which resemble included ones. Extraction suggestions are linked back to the source document, can include risk-of-bias information, and reviewers accept or reject them. Reporting is described as customizable. The same site presents DistillerSR as literature-review software, so extraction sits inside a wider workflow than a table builder.

EPPI-Reviewer

Best for: Reviews organized as coding tools, including line-by-line coding of PDFs, machine learning to help with screening, and teams who double-code and then reconcile.

Not ideal for: A review whose next step is a RevMan export from a PICO-structured form. Covidence Extraction 1 is the better fit for that. EPPI-Reviewer can import standard codesets as well as codes you write yourself.

EPPI-Reviewer describes itself as software for systematic reviews, meta-analyses, narrative reviews, and meta-ethnographies. Version 6 runs in the browser. The project page records an update on 16 April 2026 and describes the software as part of Cochrane's review-production infrastructure (verified Oct 2026).

The codes guide explains extraction as a standard coding tool. Normal mode expects one reviewer. Comparison mode expects more than one reviewer, then a comparison before the final answer (verified Oct 2026). The EPPI-Reviewer 6 help index lists double coding and reconciliation, line-by-line PDF coding, configurable reports aimed at analysis in Excel, and a guide to importing standard codesets (verified Oct 2026). The same help index points to machine-learning facilities, priority screening, classifiers, and a clustering tool that uses text mining to group references (verified Oct 2026).

EPPI-Reviewer also documents AI-assisted coding for extraction. EPPI says that feature is for evaluation purposes only, and that they do not currently recommend it in a real review until its accuracy has been tested (verified Oct 2026).

Elicit (table-style extraction)

Best for: Exploratory extraction. You set the columns, run them across screened-in papers, and open a cell to read the supporting quote. The public page includes tables and figures in that extraction.

Not ideal for: A protocol-locked form completed by two extractors, with conflicts resolved in the software. Covidence Extraction 1 or Extraction 2 is the better fit. Elicit also gathers and screens papers. When that work is already finished and you want the evidence table, Evidence Table Builder is the better fit.

Elicit's systematic-review help center describes setup, gathering papers, optional title-and-abstract screening, optional full-text screening, extraction, and a research report (verified Oct 2026). For extraction, you define columns on a pilot subset. Suggested columns come from the research question; you edit, remove, or add them, then run extraction. Each cell in the finished table can be opened to view supporting quotes and to check the AI-assisted answer. The help center says that, when figure extraction is turned on, extraction can use figures, charts, and diagrams as well as text. The same article lists CSV export of results from any step. The public systematic-review page says extraction covers quantitative and qualitative data, including from tables and figures, with a quote or figure from the source (verified Oct 2026).

The dual-review steps in that help article are about screening decisions. The extraction section describes one table of columns.

Nested Knowledge

Best for: A living review that tags concepts, can use AI-assisted extraction, and then gathers continuous, dichotomous, or categorical data across arms and timepoints. Two people can extract the meta-analytical data so a third person can adjudicate.

Not ideal for: A narrative form that never becomes quantitative synthesis. Covidence Extraction 2 or EPPI-Reviewer is the better fit. Nested Knowledge also screens inside the same project. A team that has already screened elsewhere, and only wants the table, is better served by Evidence Table Builder.

Nested Knowledge describes AutoLit as the place to search, screen, extract, and complete critical appraisal, and Synthesis as the place to visualize and analyze (verified Oct 2026). Tagging builds a hierarchy of concepts. Meta-analytical extraction, optional and off until you enable it, can be done by hand from text and tables. The documentation notes that this manual path can be time-intensive. The same overview points to Smart Meta-Analytical Extraction. That page describes automatic extraction and harmonising of quantitative data from full-text tables, with each point linked to the source table and left editable (verified Oct 2026).

Nested Knowledge's AI documentation also describes AI-assisted screening, AI-assisted tagging that can generate a hierarchy and extract or recommend evidence, and first-pass answers to risk-of-bias and quality assessments that a reviewer then verifies (verified Oct 2026). Two people can still extract the same meta-analytical data so a third person can adjudicate (verified Oct 2026).

Laser AI

Best for: Teams who want screening and extraction in one workspace, reusable forms, AI-assisted suggestions a reviewer accepts, edits, or rejects, and a quality-assurance pass after the first extraction.

Not ideal for: A single evidence table from PDFs you have already included, with no screening stage to set up. Evidence Table Builder is the better fit. A simple shared grid is still a better fit in Excel or Google Sheets.

Laser AI's product page says extraction suggestions are linked to the PDF source, and that reviewers accept, edit, or reject them. A table-extraction module detects tables and pulls values from user-defined mappings. A history log records edits. Teams can reuse templates and controlled vocabularies. Screening and extraction share one workspace (verified Oct 2026). The extraction help article shows the form beside the PDF, and it tells reviewers to accept or reject suggestions in the field. The same help center documents a first extraction pass, a quality-assurance pass, and data-extraction export (verified Oct 2026).

How to choose

Use Evidence Table Builder when screening is finished elsewhere and the work left is a template-driven, quote-linked evidence table plus quality-assessment profiles, with one reviewer verifying or editing each answer.
Stay in Excel or Google Sheets when the form is short and the team already shares a workbook.
Use Covidence Extraction 1 when the destination is quantitative synthesis and a RevMan export. Both Extraction 1 and Extraction 2 are marked for two reviewers.
Use Covidence Extraction 1 or Extraction 2, EPPI-Reviewer comparison mode, Nested Knowledge dual meta-analytical extraction, or Laser AI's quality-assurance pass when a second person checks extraction in the software.
Use Elicit when you are still discovering which columns matter, including values in tables and figures, and you want a source behind each cell.
Use DistillerSR or Laser AI when the organization wants screening and extraction in one audited workspace.

Sources checked (verified Oct 2026)

Covidence extraction choice and the 16 August 2026 comparison table (dual reviewer extraction marked for Extraction 1 and Extraction 2; automatic suggestions marked for Extraction 1 only): which template to use. This article follows the later page where they disagree on Extraction 2 suggestions.
Extraction 1 overview: Extraction 1.
Extraction 2 overview, dated 27 August 2026, including suggestions from an accessible full-text PDF and two-reviewer consensus: Extraction 2.
Whole-review stages: covidence.org.
DistillerSR workflow, audit trail, AI-assisted screening, and extraction suggestions that can include risk-of-bias information: distillersr.com.
EPPI-Reviewer product page: eppi.ioe.ac.uk/cms/er. Coding modes: codes quick guide. Help index, including machine learning, priority screening, classifiers, clustering, and importing standard codesets: ER 6 help. AI-assisted coding, with the "for evaluation purposes only" caveat: automated coding.
Elicit extraction columns, quotes, and figure extraction when that option is on: systematic reviews in Elicit. Tables and figures on the public page: Elicit systematic review.
Nested Knowledge product: about.nested-knowledge.com. Manual and dual meta-analytical extraction, plus the pointer to Smart Meta-Analytical Extraction: meta-analytical extraction. Smart Meta-Analytical Extraction: smart MA extraction. AI-assisted screening, tagging, and risk-of-bias drafts: AI in Nested Knowledge.
Laser AI: product page and performing data extraction, including the quality-assurance pass.
Excel, including a built-in AI assistant: Excel's help center. Google Sheets, including a built-in AI assistant: Sheets product page.
Study Screener handoff into extraction: study-screener.com.