AI in Healthcare Right Now: Practical Gains from Heidi + OpenEvidence
The useful conversation about AI in medicine is no longer “is this coming?” It’s “what actually improves care quality and clinician performance today?”
Two tools that matter in real clinical workflows are:
- Heidi (ambient documentation): heidihealth.com
- OpenEvidence (point-of-care evidence retrieval): openevidence.com
This isn’t about replacing physicians. It’s about reducing workflow friction in places where friction hurts care.
1) Heidi: reducing documentation drag at the point of care
Documentation burden is one of the biggest sources of lost clinical focus. Ambient documentation tools like Heidi aim to reduce that by converting visit conversation into structured clinical notes faster.
Why this is clinically relevant
When documentation overhead drops, you get downstream effects:
- better continuity (more complete notes, less deferred charting)
- less memory reconstruction at end of day
- improved visit flow (fewer interruptions to type/format)
- reduced after-hours charting load
Where it helps most
- high-volume outpatient visits
- follow-up encounters where prior context matters
- workflows with repetitive structure (HPI/assessment/plan patterns)
What still requires physician judgment
- final medical decision-making
- differential diagnosis quality
- plan safety and nuance
- coding/compliance validation
Heidi can accelerate note production, but the physician remains accountable for accuracy, interpretation, and final sign-off.
2) OpenEvidence: faster evidence lookup during real-time decisions
Clinicians don’t struggle because information is unavailable; we struggle because retrieval is slow relative to clinic pace. OpenEvidence is useful because it compresses that retrieval time for focused clinical questions.
Where this matters operationally
- confirming best-practice options under time pressure
- sanity-checking management pathways
- quickly surfacing relevant evidence when patient factors create edge cases
The practical gain
The value is not “AI gives the answer.” The value is reducing time-to-context so the physician can make a better-informed decision faster.
Guardrails that still matter
- verify applicability to the patient in front of you
- cross-check high-stakes decisions against source-quality standards
- treat outputs as decision support, not autonomous recommendations
3) What this changes in day-to-day practice
Used correctly, these tools improve three things:
- Attention allocation: less clerical interruption, more cognitive bandwidth for patient interaction
- Decision velocity: faster retrieval of relevant evidence during care
- Workflow consistency: fewer dropped details across busy clinic days
4) What AI is (and is not) doing in 2026
What it is doing well:
- documentation acceleration
- evidence retrieval acceleration
- workflow compression
What it is not doing:
- replacing physician judgment
- removing accountability
- eliminating the need for clinical reasoning