How to Use OpenEvidence for a Research Question

To use OpenEvidence for medical research, open Ask, pose a specific literature question, and check the sources linked in the answer before drawing a conclusion. Its Ask guide↗ (opens in a new tab) covers evidence questions, literature exploration, guideline summaries, drug information, and medical education. Keep the question general and separate it from an individual encounter. Tools like fenbs can keep your public source links and research-process lessons available when you continue later with another AI.

This workflow is for professional learning and evidence review. OpenEvidence supports healthcare professionals, and its terms leave clinical judgment with the clinician. A referenced answer is a starting point for investigation, not permission to apply a recommendation to a patient without the appropriate review.

Where should you start your research?

1. Check that you can use OpenEvidence under its professional-account requirements and your employer’s policies.
2. Open Ask and choose one general question about published evidence. Do not start by copying an encounter history.
3. Name the topic, the population discussed in the literature, the question you want answered, and the kind of output you need.
4. Read the answer, then open its linked references and verify the claims you would use.
5. Write down the remaining research question before moving to a different topic.

The terms of use↗ (opens in a new tab) describe professional registration and possible verification of account information. They also restrict access from some regions, including the EU, UK, and EEA. Confirm your eligibility rather than assuming that a public guide means the service is available to everyone everywhere.

Use the documented Ask section rather than looking for a guessed research-mode button. The public instructions do not establish one universal submit-button label or an identical sequence for every phone. If access is missing, confirm account and organizational requirements before trying a different device.

How do you make the question specific enough?

OpenEvidence’s How to Prompt Effectively↗ (opens in a new tab) guidance recommends specificity, a clear output format and desired outcome, and a defined scope. A useful research question therefore says what you want to understand and where the answer should stop. Avoid a broad instruction such as “Tell me everything about screening.”

For a journal-club discussion, explain how randomized trials and observational studies answer different questions about screening programs. Separate study design, measured outcomes, and limitations. Link the sources supporting each explanation. Keep this educational and do not make patient-specific recommendations.

That example gives the answer a purpose and makes the review manageable. You can assess whether each source supports an explanation of study design without deciding anything about an individual’s care. If the result is too broad, narrow one dimension, such as one study design or one outcome, rather than adding a long collection of unrelated requests.

Ask a follow-up when a distinction remains unclear, but keep your original question visible in your own notes. Otherwise, a useful explanation of a side issue can feel like an answer to the question you first asked. For a separate comparison exercise, see OpenEvidence and ChatGPT research answers.

What should you check in the cited evidence?

Check the connection between the claim and the source, not just whether a reference exists. Open the source, find the relevant passage or result, and ask whether it supports the wording in the answer. A paper can discuss the same topic while supporting a narrower conclusion.

• Scope: does the publication address the population, setting, and outcome in your question?
• Type of evidence: is this original research, a review, a guideline, or another kind of publication?
• Support: which part of the source backs the particular statement you want to retain?
• Limitations: what uncertainty, exclusions, or qualifications would change how you describe the result?
• Access: did you read the relevant source text, or only an abstract or summary?

Mark inaccessible evidence as unchecked. Do not turn an abstract into a claim that you reviewed the complete study, and do not silently remove uncertainty to make a teaching slide simpler. When sources disagree, keep the disagreement explicit and identify the next check instead of choosing whichever answer reads most smoothly.

The broader guide to reading research papers with AI covers a different task: working through the paper itself. Here, the important habit is to leave each research question with a clear division between verified statements and open questions.

How can you revisit questions in Collections?

OpenEvidence’s Collections guide↗ (opens in a new tab) documents folders for saving questions and answers. Open Collections in the sidebar. After an answer, click the folder icon beneath the response, before the references, to add it to a collection.

For older questions, create a collection and select Manage Collection. A collection card’s three-dot menu offers title and description editing, sharing, public visibility, and search. These are the vendor’s documented instructions; they are not a promise that every mobile screen uses the same controls.

Keeping an answer does not verify it. Give the collection a topic name that helps you return to the right literature question, and review its contents before any sharing. The guide restricts public collections to content with no protected health information. Never save patient or confidential details. That rule also applies when making an external learning record in fenbs.

What if the answer is broad or hard to verify?

First separate a question problem from an evidence problem. If the response wanders, rewrite the scope and requested output. If a specific statement lacks support in the linked material, leave that statement out of your retained conclusion until you can verify it. Repeating the question alone is not evidence that the second answer is better.

For teaching or administration outside this workflow, see ChatGPT for clinicians. For planning a broader investigation, see AI assistants for research. Keep each tool’s role clear so a general research aid does not quietly become the source of a clinical decision.

A useful lesson might be “Ask for study design and limitations separately, then check both against the publication.” Save that general habit, not the patient-specific reason you originally became interested in the topic. fenbs keeps such lessons and the next research step together; how it works explains the shared record.

How to keep research lessons with fenbs

Keep the useful work so you can carry on from exactly where you left off, on any device and in another connected AI. Save a small, readable record of your public research question, checked sources, and next step.

1. Create a free account.
2. Write the general research question and add links to the public sources you actually checked. Never save patient or confidential details.
3. Add one short lesson about what helped you ask or verify the question, and say what still needs checking.
4. Choose a supported assistant from the connections page, and ask it to read your saved work before continuing.
5. Before you stop, save the next step so you or another assistant can pick up there.

fenbs is not a clinical record system or a replacement for reading the evidence. OpenEvidence’s public guides do not document a custom connection to fenbs, so add only your own safe learning summary. Browse more AI at work guides when you want another general workflow to test.