One of the biggest challenges in pre-charting is deciding what actually matters for the upcoming visit. A patient’s record may include dozens of labs, hundreds of notes, and a long list of diagnoses, visits, messages, and referrals—but only a fraction of that is relevant for the encounter at hand.
AI-powered pre-charting solves this challenge by analyzing the purpose of the visit, the patient’s medical history, recent clinical events, and the provider’s documentation patterns. Instead of offering a generic data dump, the system intelligently filters and prioritizes what the clinician needs to know right now.
AI Begins by Understanding the Context of the Visit
Visit type is the anchor that determines clinical relevance.
For example:
Primary Care
- Annual exam: screenings, vaccines, preventive needs, chronic condition updates
- Diabetes follow-up: A1C trends, foot exam history, medication adherence
- Acute concern: recent messages, new symptoms, related diagnostics
Cardiology
- Hypertension check: BP trends, medication adherence, renal labs
- Heart failure management: weight changes, BNP, medication titration
Orthopedics
- Knee pain: imaging; PT progress; gait, ROM, and functional notes
Psychiatry
- Medication management: symptom scales, refill adherence, mood stability
The AI structures the summary around the clinical intent of the visit.
AI Filters the Record for Problems Requiring Attention Today
The system identifies:
- New or worsening symptoms
- Unresolved diagnostic findings
- Abnormal labs needing action
- Chronic conditions overdue for monitoring
- Follow-ups that should occur today
- Medication concerns (adherence, interactions, formulary changes)
This prevents the provider from missing important updates buried in the record.
AI Surfaces Data That Influences Today’s Decision-Making
For each active condition, AI extracts only the most impactful clinical inputs, including:
- Recent labs relevant to the condition
- Pertinent imaging or diagnostic trends
- Specialist recommendations
- Medication changes impacting treatment
- Clinical red flags
Rather than presenting every detail, AI highlights what will influence the plan.
AI Reconstructs the Clinical Story Without Noise
Clinical relevance often depends on pattern recognition, not raw data.
AI identifies:
- Trends (e.g., rising A1C, worsening kidney function, frequent ER visits)
- Recurrent complaints or symptom clusters
- Missed chronic care components
- Gaps that could affect insurance coverage or quality metrics
Providers don’t have to sort through multiple visits—AI summarizes the story.
AI Tailors Relevance to Each Provider’s Personal Preferences
Clinicians define relevance differently.
Some want:
- Deep clinical detail
- Prior specialist input
- Long-term chronic condition trends
Others want:
- A tight summary focused only on the visit reason
- Minimal background unless needed
- Highlighted operational blockers rather than clinical nuance
AI learns these patterns and adapts accordingly.
Over time, the system stops showing what you ignore and emphasizes what you always use.
AI Also Identifies Missing Data That Impacts the Visit
If a patient comes in for diabetes management and:
- No A1C is on file
- Eye exam is overdue
- Medication fills indicate possible non-adherence
AI flags these gaps automatically—helping providers address issues proactively and avoid incomplete documentation.
The Result: Providers Walk Into the Room Fully Prepared
The AI-generated pre-chart summary answers the core question:
“What does the provider need to know to deliver the best care in this visit?”
Clinicians start each encounter with:
- Clarity
- Focus
- Efficiency
- Confidence
No searching.
No scrolling.
No mentally reconstructing the patient story.
Just the right information—at the right time—prepared automatically.
