Start with a clear question, plan a protocol, search the right databases, screen and appraise studies, synthesize findings, and report with PRISMA.
Good medical reviews guide care, shape trials, and reduce waste. The work pays off when the method is tight, transparent, and repeatable. This guide moves from scoping a topic to a publishable review, with field tested steps, sample wording, and pitfalls to avoid.
Know Your Review Type Before You Start
Pick the format that fits your aim and resources. A tight question about effects may call for a systematic review with or without meta-analysis. Broad mapping across concepts may suit a scoping review. Narrative overviews can work for quick orientation, but they carry bias unless handled with care.
| Review type | When to use | Output |
|---|---|---|
| Systematic review | Focused clinical or policy question with clear outcomes; plan to search, screen, and appraise in a structured way | Flow diagram, risk-of-bias table, evidence synthesis; meta-analysis if data align |
| Scoping review | Broad or emerging area; want to map concepts, sources, and gaps without study-level critical appraisal | Descriptive mapping of topics, study designs, and gaps |
| Narrative review | Orientation to a topic or theory; time is short; narrower search and flexible inclusion | Structured overview with transparent search notes and limits |
Doing A Literature Review In Medical Research: Step-By-Step
1) Define a precise question
Clarity here saves weeks later. Frame the question with PICO (Population, Intervention, Comparator, Outcome) for trials, or PEO (Population, Exposure, Outcome) for exposures. State the setting, time frame, and study designs you will include. Write it in one sentence that a colleague can test against an abstract.
2) Draft a protocol
Write the aims, eligibility criteria, databases, search strings, screening plan, data items, and analysis approach. If you plan a systematic review, pre-register the protocol at a public register such as PROSPERO, and add a version date. Pre-specification reduces bias and makes peer review smoother.
3) Build a reproducible search
List the databases that match your topic and region. PubMed covers biomedicine; Embase adds strong drug and device coverage; CENTRAL captures trials; CINAHL helps for nursing and allied health. Use both keywords and controlled terms. For medical topics, the MeSH Browser shows preferred headings and scope notes. Combine terms with AND/OR, apply field tags where needed, and avoid tight limits early on.
Draft one master string in PubMed first, then translate it for each platform. Save every full string, date, and database name in your notes. Capture grey sources when relevant: clinical trial registers, preprint servers, theses, or key society sites. If you run updates later, state the dates and any changes to strings.
4) Manage records and deduplicate
Export from each source in a standard format (RIS, EndNote XML, or CSV). Merge into a reference manager and remove duplicates with both exact and fuzzy matching. Keep a log of counts by source and the total after deduplication. That log feeds the PRISMA flow later.
5) Calibrate screening
Turn the inclusion criteria into yes/no rules. Train with a pilot of 50–100 titles and abstracts screened in duplicate, then tweak rules for clarity. Move to main screening in pairs with a third reviewer to resolve ties. Document reasons for exclusion at full-text stage, using short, consistent codes.
6) Extract data with a tested form
Create a form that captures study identifiers, design, setting, participants, exposure or intervention details, comparators, outcomes, time points, analysis model, and effect estimates. Pilot the form on a few diverse studies and refine ambiguous fields. Extract in duplicate when the stakes are high or the data are complex.
7) Appraise study quality and bias
Pick tools that match the design. For randomized trials, use RoB 2. For non-randomized studies of interventions, use ROBINS-I. For diagnostic accuracy, use QUADAS-2. Score domains rather than creating a single summary number. Record the rationale for each judgment and keep it linked to the extracted data.
8) Plan the synthesis
Decide early whether a meta-analysis is plausible. Check that populations, exposures, and outcomes align. If pooling makes sense, pick the effect measure (risk ratio, odds ratio, mean difference, or standardized mean difference) and model. Anticipate heterogeneity and plan subgroup or sensitivity checks. If pooling is not sound, write a narrative synthesis that groups studies by design, setting, dose, or outcome timing and explains patterns in plain terms.
9) Report with a standard checklist
Editors and readers expect clarity on what you did and why. Use the PRISMA 2020 checklist for systematic reviews and meta-analyses. Include a flow diagram, show every search string in an appendix, and share any code used for analyses. For effect reviews, the Cochrane Handbook gives step-wise methods from question design through analysis and write-up, with clear guidance on risk-of-bias tools and synthesis choices.
Search strategy essentials that save time
Use both structure and synonyms
Break the concept into blocks that mirror the question: population, exposure or intervention, and outcome. Within each block, list synonyms, acronyms, and spelling variants. Map each concept to controlled vocabulary terms and add free-text terms for recency.
Write clean Boolean
Use OR within blocks and AND between blocks. Truncate with care and test for false hits. Use proximity operators on platforms that support them. Keep parentheses tidy and run line-by-line tests so that each block returns what you expect.
Limit late, not early
Language or date filters can cut recall. Apply them only if they match your protocol and you report them. When limits are needed, state the reason, the exact filter used, and how many records were removed.
How To Write A Medical Research Literature Review For Publication
Align structure with the aim
For systematic reviews, follow IMRaD: Introduction, Methods, Results, Discussion. For scoping work, use a structure that mirrors the mapping aim and still signs your search and selection steps. Keep the abstract tight and informative with aims, data sources, eligibility, core results, and a clear takeaway.
Tell the reader how to use your findings
State what the body of evidence can support, what it cannot, and why. If the evidence is thin or inconsistent, say so. Point to the next study that would move the field, and be specific about design, population, and outcomes.
Use visuals that reduce cognitive load
Figures and tables should earn their space. Add a PRISMA flow, study selection summary, and risk-of-bias heat maps as needed. In meta-analyses, forest plots and funnel plots help readers judge weight and spread.
Common mistakes and quick fixes
- Vague question: Rewrite with PICO or PEO and define the setting.
- Too few sources: Add at least two major databases plus registers and grey sources where relevant.
- One-person screening: Use duplicate screening for main stages or add verification on a sample.
- No saved strings: Archive every search exactly as run with date and platform.
- Mixing designs in a single pool: Separate by design or use models that handle the mix.
- Hidden limits: Report every filter and its impact on counts.
- Thin reporting: Use PRISMA items as section subheads while drafting.
Risk of bias and certainty of evidence
Keep bias judgments close to outcomes
Bias can differ by outcome even within the same study. Judge at the outcome level when feasible and reflect those judgments in the synthesis. Weight judgments by domain rather than summing scores.
Explain heterogeneity
Clinical, method, and statistical differences all matter. Describe how populations, doses, timing, and measures vary. In meta-analysis, report I² with a confidence interval and comment on direction and size, not just the number.
Rate certainty
When the question fits, use a structured approach to rate certainty across outcomes. Report reasons for any rating change such as risk of bias, inconsistency, indirectness, imprecision, or publication bias. Present a summary of findings table when possible.
Second table: frequent bias patterns and remedies
| Bias pattern | What it looks like | What to do |
|---|---|---|
| Selection bias | Imbalance in baseline traits; unclear allocation method | Seek allocation concealment details; favor low-risk trials in primary analysis |
| Performance bias | Unequal co-interventions; lack of blinding where it matters | Check protocol-mandated care; run sensitivity checks by blinding status |
| Detection bias | Outcome assessors aware of group assignment | Prefer objective outcomes; analyze by assessor blinding |
| Attrition bias | High or uneven loss to follow-up | Use intention-to-treat when reported; test impact of missing data |
| Reporting bias | Selective outcomes or time points | Compare protocols, registers, and full texts; query authors when feasible |
Write methods so others can repeat them
Document every choice
State who built the search, who screened which records, how conflicts were handled, and which software was used. List all eligibility rules word-for-word. Link to a full search log and any code or data needed to rerun the analysis.
State deviations from the plan
If the team changed the protocol, explain the change, the timing, and the reason. Mark unplanned analyses and keep them separate from prespecified ones.
Ethics, funding, and disclosures
Declare funding and any roles of sponsors. Report conflicts for each author. If patient data were used, state approvals and data safeguards. If no ethics review was needed, explain why based on jurisdictional rules for secondary research.
Practical timeline and team roles
Right-size the team
At minimum, you need two independent screeners, a content lead, and a methods lead. Add a statistician when effect sizes will be pooled. Define roles early to avoid duplicated effort and bottlenecks.
Plan milestones
Set dates for protocol, search, screening, extraction, analysis, and drafting. Reserve time for calibration, pilot tests, and an external check of the search. Build in a gap for one update search near submission.
Polish the write-up
Keep claims proportional to evidence
Match language to the strength of the body of evidence. Avoid spin words. Report absolute effects with denominators where possible, not only relative changes. Flag any safety signals and note where data are sparse.
Make peer review easy
Label appendices clearly: search strings, selection forms, data extraction forms, risk-of-bias tables, and analysis code. Cross-reference each item from the main text. A tidy supplement speeds review and boosts trust.
Where to find the gold-standard methods
For effect reviews, the Cochrane Handbook gives detailed, field tested methods. For reporting, PRISMA 2020 supplies checklists and flow diagrams. For search terms and subject headings, the MeSH Browser helps you find the right vocabulary and scope notes.
Meta-analysis notes without heavy math
Pick a model that fits the question and the spread of effects. Fixed effect treats all studies as one shared effect; random effects allow true differences across studies. When event rates are low, use continuity corrections with care and test different choices. Favor confidence intervals and prediction intervals over single point estimates. Preplan how you will handle cluster trials, crossover designs, and multi-arm trials so weights stay valid.
Guard against small-study effects
Check funnel plot when you have at least ten studies, add Egger or Harbord tests where suitable. Report any asymmetry and test if results shift when the smallest studies are set aside.
Grey sources that reduce missed evidence
Trial registers, preprints, dissertations, and conference abstracts can reveal outcomes that never reached journals. Scan regulatory documents for safety signals and protocol details. When a study is only an abstract, contact authors for data or a full paper. State how you handled records that lacked full text and whether they were kept in sensitivity checks.
Citation management hygiene
Use stable folder names and versioned export files. Add tags for screening status and reasons for exclusion. Store PDFs with a clear naming scheme that includes first author, year, and short title. Back up the library to a shared drive so the whole team can recover from a machine crash.
You now have a clear path from question to manuscript. Work through the steps, keep notes as if a future you had to rerun every action, and write so a busy clinician can scan and use the findings with confidence.