How To Do A Systematic Review Step-By-Step In Medicine | Clinician Ready Playbook

Medical decisions deserve reliable summaries, not guesswork. A well run systematic review gives clinicians and policy makers a clear map of the current evidence. The steps below show a workable path you can follow from a blank page to a publishable review without wasted effort or messy detours.

Everything here fits standard practice in evidence synthesis. You will see where planning saves time, which tasks benefit from two reviewers, and how to present results in ways a busy reader can use. Links to trusted resources are included so you can check rules and templates while you work.

What A Systematic Review Truly Requires

In plain terms, a systematic review is a structured plan to find, select, and summarize studies that answer a tight clinical question. The plan should be transparent, repeatable, and anchored to a protocol made public ahead of screening. Searches span multiple databases and registries. Screening and data extraction happen in duplicate to lower errors. Study quality is appraised with fit for purpose tools. When studies align, a meta analysis combines their results; when they do not, a narrative synthesis shows the pattern in a consistent way.

For methods details and model choices, the Cochrane Handbook is the gold standard. For reporting, the PRISMA 2020 checklist sets clear expectations. To register your protocol, use PROSPERO.

Step Map For Medical Systematic Reviews

Step-By-Step Systematic Review In Medicine: Planning

Planning is the best time saver. A tight question and a lean protocol cut down on midstream changes, which can drain time and create avoidable doubts later. Keep the scope narrow enough that the final set of studies will hang together clinically and statistically.

Define A Tightly Scoped Question (PICO Variants)

Translate the clinical need into a precise PICO: population, intervention, comparator, and outcomes. Add setting and study design if those are non negotiable. Write exact inclusion and exclusion rules that match the PICO and stick to plain language so another team could apply the same rules and reach the same pile of studies.

Write A Protocol You Can Keep

State aims, eligibility, outcomes, search sources, screening plan, data items, risk of bias tools, and synthesis methods. Include a short note on how you will handle missing data, unit of analysis issues, and subgroup plans. When the outline is set, register it on PROSPERO to timestamp your intent and raise transparency.

Pick Outcomes That Matter Clinically

Name primary and secondary outcomes up front. Use accepted definitions and time points. Where possible align with core outcome sets used in the field so readers can line up your results with parallel reviews and guidelines.

Predefine Subgroups And Sensitivity Checks

List a small number of sensible subgroups tied to biology or care pathways. Plan sensitivity checks that probe main assumptions, such as excluding high risk studies or using alternate statistical models. Limit the list to questions your audience cares about.

Plan Resources And Timelines

Agree on roles for searching, screening, extraction, bias appraisal, and statistics. Book regular meetings. Choose software for screening and data capture, and set file naming rules so the record stays tidy.

Build A Reproducible Search Strategy

A strong search balances reach and precision. Work with an information specialist when you can. Build database strategies around controlled vocabulary and free text, then translate across platforms without losing meaning. Record the exact strings, dates, and limits so another reviewer can repeat the work.

Select Databases And Registries

Use at least two major bibliographic databases such as MEDLINE and Embase, plus CENTRAL for trials when relevant. Add subject databases, preprint servers where appropriate, and trial registries to catch ongoing or unpublished studies.

Design Boolean Strings And Filters

Combine concept blocks with OR within blocks and AND between blocks. Keep filters to a minimum unless they are validated for study design. Avoid language limits unless justified by team skills or resources.

Record Everything

Export results with fields needed for de duplication. Keep a search log that lists platform, interface, date, and hits per string. Save strategies to an appendix and prepare a PRISMA flow figure to document counts through each stage.

Screen Studies Without Bias

Run a calibration round so screeners apply the same rules. Then screen titles and abstracts in pairs with at least one independent vote per record. Advance any record with one include vote to full text. Resolve conflicts with a third reviewer or through consensus meetings. Record reasons briefly.

Title And Abstract Screening

Use short include and exclude labels that mirror the protocol rules. Keep notes on common reasons for exclusion. When in doubt, move to full text instead of risking a miss.

Full Text Screening

Reviewers work in pairs again. Record the exclusion reason for every full text not included. Keep PDFs and notes organized so any decision can be traced later.

Manage Disagreements

Set a simple rule: talk first, then seek a third vote when needed. Document final decisions in the screening log. This habit saves time when peer reviewers ask about edge cases.

Extract Data That You Can Trust

Design a form that captures study identifiers, design, setting, participants, interventions, comparators, outcomes, follow up, and analysis features. Pilot on a small sample and refine before full rollout. Two reviewers extract independently, then reconcile. Keep copies of all decisions and any queries to authors.

Design A Piloted Form

Start with a compact list of fields mapped to your planned analyses. Group items by domain so extraction flows quickly. Use data types that prevent input errors and pre code common responses where possible.

Capture Study Characteristics

Record sample sizes, baseline balance, dose and timing, co interventions, and outcome definitions. Note funding and conflicts of interest stated by the authors. These details inform bias judgements and later GRADE style certainty statements.

Handle Missing Data

Decide in advance how you will treat missing standard deviations, medians without spreads, or time to event data without hazards. State conversion rules and priority orders for multiple scale versions of the same outcome.

Appraise Risk Of Bias

Pick a tool that matches study design. Train the team on the tool’s domains and decision rules. Appraise in pairs with consensus. Report domain level and overall judgements for each study, and link those judgements to synthesis choices.

Make Judgements That Others Can Reproduce

Before the full round, hold a training session using two or three studies that reflect tricky features you expect to see. Walk through each domain and agree on what evidence triggers each judgement. Use the tool’s signalling questions as prompts, not as a tally. Keep brief notes that quote the line or table that drove the call. Notes help later when you need to explain why a study sat in a certain box.

Tie bias judgements to your synthesis plan. If randomization looks unclear or allocation concealment was weak, you may decide to down weight those studies or run a sensitivity check without them. When blinding cannot be done, pay attention to outcome type. Mortality often resists bias; pain scales can swing with expectations. Make these links explicit so readers see how study quality influenced every choice down the line.

Common Risk Of Bias Tools And When To Use Them

Doing A Systematic Review In Medicine: Methods Walkthrough

Now turn a tidy dataset into results that stakeholders can read and act on. Tighten inclusion once more if the body of evidence shows clear split populations or interventions. Then choose synthesis paths that suit the data and your protocol.

Meta-Analysis: When To Pool

Meta analysis needs comparable populations, interventions, and outcomes. When alignment is thin or designs vary wildly, use structured narrative synthesis with tables that line up effect directions and magnitudes without pooling.

Pick Effect Measures And Models

For dichotomous outcomes, use risk ratio or odds ratio; for time to event, use hazard ratio; for continuous outcomes, use mean difference or standardized mean difference. Choose fixed or random effects models based on clinical diversity and the aim of inference. Keep model choices linked to protocol text.

Assess Heterogeneity And Inconsistency

Report tau squared and I squared along with a quick scan of forest plots. If heterogeneity is large, inspect study features and outcome definitions. Run planned subgroup or sensitivity checks to see if findings hold across main splits.

Probe Small Study And Publication Bias

Create funnel plots when you have enough studies and apply simple tests with care. Cross check against trial registries and preprints to spot missing studies. Note any concerns in the certainty narrative so readers weigh results with the right caution.

Rate Certainty And Craft Takeaways

Summaries land better when readers see both effect sizes and certainty. Use accepted approaches such as GRADE style language to convey how much confidence to place in pooled results and why that rating was chosen.

Report With PRISMA

Match your write up to the PRISMA 2020 checklist. Include a clear title, an abstract that mirrors methods and results, a flow figure with counts through each stage, full search strategies, tables of study features and results, bias tables, and a balanced plain language section that spells out what the findings mean for care.

Flow Diagram And Checklist

Use the PRISMA flow template to display numbers from identification through inclusion. Check off each checklist item as you draft. Place the full search strings in an appendix so readers can repeat the work.

Plain Language And Clinical Takeaways

Write for the busy clinician. Open with the main message, then list main numbers and caveats. Avoid jargon. Say what patients and teams can do differently on Monday based on the evidence you found.

Common Pitfalls And Fixes

Scope creep: Resist adding extra questions once screening starts. Park them for a later review. Poor calibration: Run a small pilot for screening and extraction to align decisions. Messy records: Use a shared naming scheme and a versioned data file. One reviewer only: Use pairs for screening and extraction when stakes are high.

Vague outcomes: Lock definitions and time points early. Selective reporting: Use registries and protocols to chase outcomes not reported in the paper. Model shopping: Pick models from the protocol and stick with them unless data demand a change, then explain the reason.

Ethics, Registration, And Data Sharing

Most reviews do not need ethics board review, yet they still benefit from open practices. Register on PROSPERO, post search strings and extraction forms, and share cleaned datasets and code where licenses allow. These habits lift trust and make updates far easier later.

Quality Checks Before Submission

Proof the paper line by line against the protocol and the PRISMA list. Rerun the main analysis from the raw dataset to confirm numbers. Redraw main figures with consistent scales and labels. Invite a colleague not on the team to read the plain language section and tell you what they would do after reading it. If that action matches your message, you are ready to submit.

Run a final citation check to match text claims with tables and figures. Confirm that numbers in the abstract match the main results. Recreate one forest plot from scratch to test the workflow. Name files with dates and version tags so the archive tells the story of how the paper took shape. Keep a changelog in the dataset.