A plain language summary is a short, accessible account of research that helps non-specialists understand what was studied, what was found, how certain the findings are, and what they may mean.
A plain-language summary asks a medical writer to do more than replace technical terms with everyday words. The writer must understand the evidence, decide what a particular reader needs to know, and explain it without distorting the meaning of the findings.
As scientific and medical journals and hospitals work to make research more accessible, plain language summaries can connect new findings with the people who need to understand them.
In that sense, writing a plain language summary is a microcosm of the medical writer’s role: connecting research with its readers while keeping the message intact. This guide walks through that work in a practical process and a worked example using a fictional study.
What is a plain language summary?
A plain language summary is a short account of a study written in everyday language for people who aren’t experts in the field. It helps readers understand four things:
- what was studied and why
- what was found
- how certain the findings are
- what the findings may mean, future implications, and limitations
Readers may include patients and caregivers, media, policymakers, or healthcare professionals reading outside their specialism.
What formats should I expect to write a plain language summary in?
A text plain language summary may appear within or alongside a journal article. A plain language summary of publication (PLSP) is a distinct article type: the Open Pharma cross-publisher definition describes PLSPs as standalone, peer-reviewed articles with a unique DOI. PLSPs differ from text or graphical summaries that accompany the original article and from regulatory clinical trial results summaries, which have separate requirements.
Before you start, check the brief for the intended audience, format, required content, and word limit.
How to write an effective plain language summary
1. Start with the reader and the original evidence
Before you start writing, it’s important to get clarity on who you are writing for and what the study actually says.
For the reader: Ask who they are, why they are reading, what they already know, and what they most want to find out. A summary for people living with a condition looks quite different from one for a commissioning editor, even when the study is the same.
For the evidence: Read the full paper, including the methods and limitations, not just the abstract. You can’t properly represent a study if you’ve only read its highlights. As you read, identify the study design, comparator, any statistical analysis, and primary and secondary outcomes. Those details shape what the findings mean and which ones matter most to your reader. That understanding helps the writer keep each claim in proportion to the study.
It can help to become familiar with the IMRaD format. For a broader look at the role of medical writers in research, read my guide to medical writing in clinical research.
2. Choose the essential information
The craft lies in deciding what information belongs. The reader and the evidence each shape that decision.
For the reader: Decide what readers need to know to understand the finding. A summary for people living with a condition will differ from one for a commissioning editor. Ask whether study design, adverse events, or other details help readers follow the main point. If the summary sits beside the paper, some methodological details may already be available to curious readers.
For the evidence: Keep the study’s main finding and limitations central. If the primary endpoint was an increase in overall survival but you emphasise a secondary endpoint, such as quality of life, the study’s most important findings get lost in translation.
3. Translate the language and the numbers
For the reader: Define essential technical terms in context, using a few plain words. Give each number a denominator and time period so readers can tell what it describes. For comparisons, show each group’s absolute rates and include the relative effect when it helps readers understand the size of the difference.
For the evidence: Preserve distinctions that affect the finding’s meaning. A relative change can sound large without the starting and comparison rates. Keep correlation distinct from causation, represent uncertainty fairly, and don’t turn a study result into treatment advice.
Common obstacles to writing an effective plain language summary
These are the two most common roadblocks most writers hit when crafting plain language summaries:
- Adding too much: Extra detail can feel responsible, but it often buries the main point the reader needs.
- Dropping a key limitation: Removing a limitation may make a summary cleaner, but it can distort the finding. Without caveats, a result can sound stronger than it is.
Both roadblocks call for judgment: include enough detail to make the finding understandable, and retain the caveats that keep it honest.
The plain language process in action
(A note on this example: The study, numbers, and wording below are invented for teaching purposes. They are not based on a real paper and should not be cited or treated as evidence.)
Imagine a simple trial testing whether an hourly phone reminder to stand and move could help office workers avoid back pain. Here is how its finding might read in an excerpt from the original paper. The example shows how a writer can make the study accessible while keeping its evidence and limits intact.
The original paper
“In a 12-month, two-arm, parallel-group randomised controlled trial, 600 office workers without current low back pain were allocated 1:1 to an hourly activity-reminder application or a standard back-care leaflet. The cumulative incidence of a new low back pain episode was 18.0% in the intervention arm versus 27.0% in the control arm (relative risk 0.67, 95% CI; p=0.008). The intervention was unblinded, and outcomes were self-reported.”
The plain language version
Researchers wanted to see whether an hourly phone reminder to stand and move could help office workers avoid back pain. They followed 600 office workers who did not have back pain. Half received an app that reminded them to stand and move each hour; the other half received a leaflet about looking after their backs.
Over one year, about 18 of every 100 people in the app group reported a new episode of back pain, compared with about 27 of every 100 in the leaflet group. That is 9 fewer cases per 100 people in the app group in this trial. The findings suggest the reminders may lower the chance of new back pain, but the result should be interpreted cautiously: participants knew which group they were in and reported their own pain. The study also does not tell us whether reminders help people who already have back pain.
What changed, and why
- The rates use the same denominator and time period. The absolute difference is 9 percentage points (27% minus 18%); the relative reduction is about 33%. Reporting the group rates keeps the scale of the comparison visible.
- The comparator stays in – The plain version keeps what the other group received, the leaflet, because the result is a comparison between two groups, not a standalone fact about the app.
- The absolute and relative effects describe the result in different ways. The 9 percentage-point difference shows the gap between the groups; the relative reduction of about 33% expresses that gap in relation to the leaflet group. Neither measure is inherently wrong, but reporting the group rates helps readers see the absolute scale.
- The limitations are included – The facts that people knew their group and reported their own pain, and that the study says nothing about existing back pain, all stay in. Removing them could make the finding appear much stronger than it is.
- No treatment advice – The summary reports a research finding without recommending that people use the app. Whether an intervention is useful depends on further evidence and the reader’s circumstances.
Review for accuracy and understanding
A plain language summary needs two kinds of checking. The first is accuracy, which asks us to evaluate if the evidence is being fairly represented.
Can every statement be traced back to the original study? Is the uncertainty represented fairly, the terminology right, the numbers framed honestly? This is where scientific or editorial review earns its keep, ideally from someone who can compare your summary against the source.
The second is understanding, which is best judged by the people you are writing for. Ask intended readers to review the summary and tell you what they think it means. The CDC’s Clear Communication Index can help assess public-facing materials, but it is a diagnostic tool, not a substitute for reader testing. Readability scores can flag long sentences and words, but they cannot confirm that readers understand the meaning. Reader feedback checks whether the explanation reaches the people it was written for.
A plain language summary checklist
When you think a draft is done, run it through this short checklist:
- The single main point is clear within the first few lines.
- The reader knows why the study was done and who or what was studied.
- The design and the comparator are described in plain terms.
- Every number has a denominator and a time period.
- Benefit, risk and uncertainty can be told apart.
- At least one main limitation is stated plainly.
- No jargon is left undefined, and no treatment advice is implied.
- Every statement can be traced back to the original study.
- A non-specialist reader has confirmed that it actually makes sense.
Building the skills behind the summary
Writing a strong plain language summary means bridging two responsibilities: making research understandable while keeping its meaning intact. That judgment is central to medical writing, and it grows with practice, source checking, and feedback from the people you’re writing for. A well-crafted summary is also work worth showing in your portfolio.
If you’d like help strengthening how you read research and write for non-specialists, my mentoring and practical resources for writers are a great place to start. If your organisation is commissioning accessible research communications, you can find out more about my patient-facing work here.
Frequently Asked Questions
What should a plain language summary include?
Usually it will include why the study was done, who or what was studied, the design and comparator, the main findings with numbers in context, the primary limitations, and what the findings may mean. It should define essential terms, keep uncertainty visible, and avoid turning results into treatment advice.
How is a plain language summary different from an abstract?
An abstract is written for specialists and assumes technical knowledge, compressing methods and statistics into dense prose. A plain language summary is written for non-specialists, so it explains terms, carefully frames the numbers, keeps uncertainty in plain sight and focuses on meaning. The two often sit side by side, but they are written for different readers.
How long should a plain language summary be?
It depends on the commission or journal, so there is no single right answer. As one example, Taylor & Francis recommends a text plain language summary of around 250 words or less to support indexing on PubMed, while other formats, publishers and commissions set different limits. Let the length follow the audience and the specific requirement rather than using a universal rule.
Can a readability score tell me whether my summary is clear?
Only partly. Readability scores measure things like sentence and word length, which is useful, but they cannot confirm that a reader understood the meaning. A summary can score well and still mislead or confuse. The real test is whether your intended readers understand it and know what it means, which is why reader testing and expert review are so important.
Further resources
Open Pharma PLSP Playbook and checklist — guidance for standalone, peer-reviewed publication summaries.
NIHR Plain English summaries — practical research-summary guidance, including reader feedback and examples.
CDC Clear Communication Index — a research-based tool for assessing public-facing materials.
Cochrane Handbook, Chapter 15 — technical guidance on interpreting relative and absolute effects, natural frequencies, and uncertainty.
