10 Ways to Use OpenEvidence You Haven’t Thought of Yet
A working clinician’s field guide to getting more from the most-used medical AI in American medicine
Watching how most clinicians use OpenEvidence I’ve noticed the most common use cases tend to revolve around the simple question and answer interface:
What diagnosis could this be?
Help me come up with a medication regimen for this problem.
What does the evidence say about choosing X over Y treatment for this condition?
OpenEvidence sits in a narrow category of clinical AI that pairs three things: peer-reviewed grounding, HIPAA-compliant deployment, and a conversational interface.
Used the obvious way, it answers questions. Used well, it behaves more like a senior colleague (and admin assistant) who has read everything, never sleeps, and is happy to argue with you at 5 a.m. before your first case.
Here are ten ways I use it that go past the typical Q & A.
1. Settle the clinical debate that’s eating your service
Every department has them. Should we use foleys in new hip fracture admissions? Should all level 1 traumas get a head CT? Does this patient need a pre-op echo? Is tranexamic acid worth the theoretical clotting risk in this population?
OpenEvidence is great at the “find me the evidence on X” loop. It gets useful when you ask it to adjudicate.
Prompt: “Summarize the highest-quality evidence on routine preoperative transthoracic echocardiography in patients over 65 undergoing hip fracture surgery without active cardiac symptoms. Stratify by study design and synthesize a recommendation a hospitalist and an orthopedic surgeon could both agree on.”
You’re asking the model in this case to take a defensible position , and to write it in language that positions you to have an informed discussion with a peer.
2. Crush the administrative work that’s burning you out
Prior auth letters! Disability and FMLA paperwork. Work-restriction notes. Appeals. Peer-to-peer prep.
This is the use case that surprises people, because it doesn’t feel like what a clinical AI tool is for. But OpenEvidence drafts cleanly, sources its claims, and runs on a HIPAA-compliant deployment when you paste in patient context.
Prompt: “Draft a prior authorization appeal letter for a 58-year-old patient with isolated medial compartment knee osteoarthritis (Kellgren-Lawrence grade 4) who failed 12 weeks of conservative management and is being denied a unicompartmental knee arthroplasty. Cite peer-reviewed evidence supporting UKA over TKA in this clinical scenario.”
The output is usually nearly a finished letter. It turns a 30-minute task into a 5-minute review, and the citations are already in the body, where the reviewer on the other end can’t pretend they aren’t there. You can use OpenEvidence for a veritable cornucopia of administrative use cases that you face as a clinician.
3. Build the economic argument for your health-tech idea
This one is for the clinician-founders. Investors and health-system buyers ask the same question in different costumes: prove this generates ROI.
OpenEvidence is not only a clinical repository. It surfaces the published peer-reviewed economic analyses, cost-effectiveness studies, and implementation outcomes living in journals most founders never search. I’ve used it to build the evidence base for why SMS, as a care-coordination modality, outperforms portal messaging on adherence and access.
Prompt: “Compile the peer-reviewed evidence on SMS-based patient outreach compared to patient portal messaging for medication adherence, appointment attendance, and post-discharge follow-up. Prioritize studies with cost-effectiveness data or quantified ROI.”
You won’t get a pitch deck (though you can get one if you make it to the bottom of this article ;-) ), but the output is a fantastic evidence based basis to make your proposal or research one.
Bonus tip for this one: Sometimes the hardest part isn’t searching the evidence — it’s knowing which questions the evidence has to answer. So make Claude do that work first. Paste in your business background and have it surface the ROI objections you’ll face in a diligence call or a health-system buying committee, then turn each objection into an OpenEvidence query. You walk into the room already holding the citations that close the gap. I have a prompt below for you to use for this use case:
Prompt:
I’m a clinician-founder building [one- to two-sentence description of your company, product, and buyer]. My business model and core claims are: [paste your background — revenue model, who pays, what you claim to improve clinically or operationally].
Act as a skeptical healthcare investor and a health-system economic buyer (CFO / VP of value-based care). Generate the 5–7 most incisive ROI challenges I’ll face — the questions that expose weak assumptions, unproven savings, or soft attribution. Be specific to my model, not generic.
Then, for the 2–3 challenges where published evidence could strengthen my case, write me copy-pastable OpenEvidence search prompts. Each should target peer-reviewed studies with cost-effectiveness data, quantified ROI, or implementation outcomes I can cite to answer that specific objection.
You can also use this flow to challenge and research outside ideas (Doximity Ask or Consensus can be substituted for OpenEvidence in this case).
4. Use the clinical-trials map you didn’t know existed
OpenEvidence will render an interactive map of active clinical trials matching a query. Most people never trigger it, because they don’t ask in a way that surfaces it.
Prompt: “Show me clinical trials for osteoporosis involving romosozumab or teriparatide”
or
“Find recruiting trials for fracture prevention in osteoporosis”
Then follow up with:
“Are any of these phase 2 or 3, and which ones are enrolling in the U.S.?”
In general, the best results come from:
Simple, focused disease terms (e.g., “osteoporosis” rather than “osteoporotic hip fracture”)
One concept per search rather than combining population + intervention + outcome
Drug-specific searches when a particular therapy is of interest
Iterative refinement — start broad, then ask follow-up questions to narrow
Useful for referrals. Useful for the patient who asks what’s out there. Useful, quietly, for reading where your specialty’s research dollars are actually flowing — which tells you where the field thinks it’s going.
5. Draft the introduction or discussion section of your paper
The intro and discussion are where academic manuscripts hemorrhage time. Because OpenEvidence is grounded in primary literature, it can scaffold these sections without the fabrication problem that makes generic LLMs unusable for academic work — the single failure mode that ends a manuscript’s credibility before peer review begins.
Prompt: “Draft the introduction section of a manuscript examining socioeconomic disparities in 90-day outcomes after total hip arthroplasty. Frame the gap in the literature, cite the foundational epidemiologic work, and end with a clear statement of the study question. Target 350 words.”
Enter whatever the topic may be and you will find this to be a great adjunct to your research workflows and grant-writing.
The next five uses expand beyond a better search box.
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