Medical questions deserve answers that are careful, reproducible, and free from wishful reading. A proper medical literature review gives you that. The process looks like research, project management, and clear writing rolled into one. This guide shows you how to plan, search, screen, appraise, extract, synthesize, and report without losing your sanity or your audit trail.
This article draws on widely used methods and reporting standards. See the PRISMA 2020 guidance, the Cochrane Handbook, and the EQUATOR Network for full details and checklists.
Choose your review type
Different review types answer different needs. Pick one and stick to it, because your choice sets your scope, screening rules, and write-up format.
| Review type | Primary goal | When to choose |
|---|---|---|
| Systematic review (with or without meta-analysis) | Answer a focused question with predefined methods | You have clear outcomes and comparable designs |
| Scoping review | Map concepts, evidence types, and gaps | Topics with diverse designs or unclear outcomes |
| Rapid review | Deliver a time-bound summary with pragmatic shortcuts | Policy or clinical deadlines limit full methods |
| Narrative review | Explain a topic with expert synthesis | Broad subjects or theory building |
| Umbrella review | Synthesize findings across existing reviews | Many overlapping systematic reviews exist |
| Diagnostic test accuracy review | Estimate sensitivity, specificity, and related metrics | Questions about test performance |
Doing a proper medical literature review: step-by-step
Before you open a database, write a short protocol. Two pages is enough. State your question, scope, eligibility rules, outcomes, databases, screens, extraction plan, appraisal tools, and synthesis approach. Give each item a version number. Store the file in a shared folder with date-stamped names. If the work is a formal systematic review, register the protocol in PROSPERO when eligible.
Define a focused question
Turn the topic into a crisp question. Tools like PICO, PECO, or SPIDER keep you honest about population, exposure or intervention, comparison, and outcomes. Record synonyms for each concept and a plan for subgroup or sensitivity analyses. Write clear exclusion rules for language, setting, and study design. Avoid vague terms that invite bias during screening.
Build a search strategy
Plan your searches across at least two major databases. MEDLINE via PubMed, Embase, CENTRAL, CINAHL, and PsycINFO cover most clinical areas. Combine text words with controlled vocabulary such as MeSH and Emtree. Use Boolean operators, phrase searching, truncation, and field tags to reduce noise. Save each full query string, the platform used, the date run, and the number of hits. Keep a search log with any limits or filters and why you used them.
Broaden reach with trial registries, preprints, theses, conference abstracts, and reference lists. Scan landmark studies forward and backward with citation tracking. Note all sources in your log so the search can be repeated later. If possible, ask a health sciences librarian to test the strategy against known sentinel papers.
Set up tools and a file plan
Pick a reference manager for deduplication. EndNote, Zotero, and Papers all work. For screening and extraction, Rayyan, Covidence, or EPPI-Reviewer keep teams in sync. Create folders for raw exports, deduplicated libraries, screening decisions, extraction forms, risk-of-bias tables, and analysis files. A short README inside each folder saves hours later.
Pilot your screening rules
Train your team with a small set of records. Agree on fast cues for inclusion and exclusion. Document how to handle non-English texts, preprints, and overlapping populations. Two independent reviewers should screen titles and abstracts, then full texts. Record reasons for exclusion at full-text stage using a short list tied to your eligibility rules. Track agreement rates and settle disagreements with a third reviewer.
Document with a PRISMA flow
Log counts at each step: records identified, records after deduplication, records screened, reports assessed, and studies included. Add reasons for exclusion at full-text stage. When you write up, the PRISMA 2020 flow diagram makes this record clear, concise, and easy to read.
Design a data extraction form
Start with a pilot form. Capture study identifiers, design, setting, sample details, inclusion criteria, exposures or interventions, comparators, outcomes, time points, effect measures, analysis methods, funding, and conflicts. Add variables that explain differences across studies, such as dose, baseline risk, or follow-up length. Test the form on three to five studies, adjust, and lock the version. Extract in duplicate until error rates fall to a level you can accept, then single extract with verification on a random sample.
Appraise study quality
Match the appraisal tool to the design. Use RoB 2 for randomized trials, ROBINS-I for non-randomized studies, QUADAS-2 for diagnostic accuracy, QUIPS for prognostic factors, and AMSTAR 2 for reviews. Rate each domain, justify every judgment, and predefine rules for “overall” risk. Keep a log of queries to authors and how you handled missing data.
Plan your synthesis
Write down in advance when you will pool results and when you will stay narrative. Choose effect measures that fit your data: risk ratio, odds ratio, hazard ratio, mean difference, or standardized mean difference. Record the model choice and reasons. Heterogeneity matters; report I2, the between-study variance, and your clinical read of why studies differ. If you test subgroups or run sensitivity checks, limit them to a short list tied to the protocol.
How to conduct a medical literature review for clinicians
Clinical readers want answers they can trust and apply. That asks for sharp outcomes, clear effect sizes, and plain language on certainty. Plan your Summary of Findings early. GRADE tables help readers see what the numbers mean in practice and how much confidence to place in them.
Write for clarity and reuse
Structure the report so a busy reader can scan and then dig in. Lead with the question, the main finding, and any limits on certainty. Methods need enough detail to repeat the work. Place full search strings, extraction forms, and decision logs in a supplement or public repository. Keep tables tight and consistent so the story carries across pages.
Handle special designs and outcomes
Cluster trials need design effects or effective sample sizes. Cross-over trials carry period and carryover issues. Observational studies need attention to confounding and measurement. Diagnostic questions call for paired measures and a shared threshold or a method that respects threshold shifts. Time-to-event outcomes should align on hazard ratios or a common time point. If outcomes come in different scales, use standardized metrics and translate back to a scale that clinicians recognize.
Match study designs to appraisal tools
Use tools that are built for the biases you will face. This quick map keeps teams from mixing apples and oranges during appraisal.
| Study design | Appraisal tool | Core domains judged |
|---|---|---|
| Randomized trial | RoB 2 | Randomization, deviations, missing data, measurement, selection of the reported result |
| Non-randomized intervention study | ROBINS-I | Confounding, selection, classification, deviations, missing data, measurement, reporting |
| Diagnostic accuracy study | QUADAS-2 | Patient selection, index test, reference standard, flow and timing |
| Prognostic factor study | QUIPS | Participation, attrition, measurement of prognostic factor and outcomes, confounding, analysis |
| Systematic review | AMSTAR 2 | Protocol, search, selection, extraction, bias appraisal, synthesis, publication bias |
| Qualitative evidence | CASP or GRADE-CERQual | Method limits, relevance, coherence, adequacy of data |
Keep bias and missing evidence in check
Plan outreach for missing outcomes or unclear methods. Keep a log of emails and replies, with dates. Record any use of imputation or conversions. Watch for small-study effects and selective reporting. When you suspect missing evidence, say how it might tilt the result and show any sensitivity runs that probe the concern.
Ethics and registration basics
Most evidence syntheses use published information. Many institutions treat this as work outside human subjects oversight, while some require an administrative determination. Check local rules early and file whatever letter you receive with your protocol. If your question meets PROSPERO criteria, register. Registration sets a public time stamp and reduces scope drift. If PROSPERO is not a fit, share the protocol publicly and cite it in the manuscript.
Manage data like a research team
Name files with dates and descriptors. Freeze the dataset used for analysis in a read-only folder. Keep a changelog for corrections. If you write code for plots or meta-analysis, store it. A simple codebook for variable names avoids painful guesswork during revisions.
Timeline and teamwork tips
Set roles and touchpoints on day one. A lean team works well: one lead, one search specialist, two screeners, two extractors. Hold weekly check-ins and keep decisions in a running log. Batch tasks and set numeric targets per week so the pipeline flows. Track counts on a shared sheet: records retrieved, screened, full texts found, studies included, and outcomes extracted. When time is tight, narrow scope or add help where the bottleneck lives, such as full-text retrieval or data extraction.
Turn findings into a clean story
Readers remember contrasts, not file names. Use a one-line takeaway for each outcome. Connect effect sizes to absolute risks and patient counts. Note harms and costs beside benefits. If results differ across risk groups or settings, show that split in a small table or figure and explain the size and direction in plain words.
Report with the right checklists
Tie your write-up to the correct reporting guideline. PRISMA 2020 fits most intervention reviews. PRISMA-ScR fits scoping reviews. QUADAS reporting fits diagnostic accuracy. The EQUATOR decision tool points you to the right checklist for other designs. A filled checklist and a flow diagram save reviewers time and smooth peer review.
Common mistakes you can avoid
Vague scope or shifting outcomes
Write outcomes and subgroups before you search. Lock protocol versions. If a change is needed, state it and why. Hidden scope shifts breed doubt and weaken trust.
Over-restrictive filters
Heavy language or date limits can hide landmark studies. Run test sets to see what falls out when you apply each filter. Report every limit you used and give a reason that links back to the question.
Inconsistent screening decisions
Use calibration rounds. Keep a rulebook of tricky cases and the final call. Revisit a small sample mid-screen to see if the team is still aligned.
Mixing designs without a plan
Pooling randomized and observational evidence is not a default choice. If you join them, explain the logic and guard the interpretation with clear caveats. Keep separate analyses ready, because readers will ask for them.
Neglecting context and applicability
Clinical setting, baseline risk, and care pathways shape what a result means. Frame your message for the setting that matches your body of studies. Say when transfer to another setting would be risky.
Quality control checklist before submission
Methods and transparency
- Protocol available and dated; any changes stated and justified
- Full search strings, platforms, and dates shared
- PRISMA flow complete and consistent with logs
- Screening done in duplicate with reasons for exclusion recorded
- Extraction piloted; error rates checked; audit trail stored
- Appraisal matched to design with judgments and quotes recorded
Analysis and presentation
- Effect measures reported with units and time points
- Heterogeneity stated with numbers and a plain language read
- Sensitivity and subgroup checks tied to protocol, not fishing trips
- GRADE judgments feed the Summary of Findings
- All tables and figures carry clear titles and can stand alone
Writing and sharing
- Main messages up front, not buried in appendices
- Plain language statements for non-specialists
- Data, code, and forms shared in a stable location
- Conflicts of interest and funding described without hedging
Reviewer notes you can prepare
Reviewers often ask the same questions. Keep notes handy on these points: how you set the scope, how you handled non-English studies, how you classified multi-arm trials, why you chose a random or fixed effect model, how you handled cluster designs, and how you judged small-study effects. Short, direct answers speed the final round.
From plan to print without drama
The best reviews look calm because the messy parts live in the logs. Write your protocol, keep clean records, and treat your later self like a teammate. With a focused question and methods, your review will stand up to audits, feed guidelines, and help care teams make better choices for patients.