A significant portion of provider time is spent piecing together the patient’s clinical narrative before each visit. While essential to safe and effective care, this process is time-consuming, inconsistent across providers, and often complicated by fragmented EHR interfaces. AI-powered pre-charting solves this problem by creating a unified, curated summary of a patient’s history—structured specifically for the upcoming encounter.
AI doesn’t just retrieve data; it understands context, relevance, and clinical priorities. The result is a pre-visit summary that gives the provider everything they need—without noise, distraction, or manual review.
AI-Powered Pre-Charting Includes All Essential Clinical Components
To build a complete picture of the patient, AI pulls together the components most relevant to clinical decision-making:
1. Recent and Relevant Clinical Notes
AI reviews prior visits, consult notes, ER discharges, hospital summaries, and specialty evaluations.
Rather than dumping raw text, it identifies:
- Key diagnoses
- Treatment changes
- Recommendations
- Abnormal findings
- Unresolved issues
The provider sees a distilled version of the timeline, not pages of prior notes.
2. Labs and Imaging Trends
AI extracts the most clinically meaningful values, such as:
- A1C trends for diabetics
- Lipid panel changes
- eGFR progression
- Radiology impressions
- Vital pattern shifts
Providers receive insights in trend form—not just data points—so they instantly understand disease trajectories.
3. Active and Historical Diagnoses
The system categorizes:
- Chronic conditions
- Acute issues
- Problems requiring follow-up
- Diagnoses relevant to risk adjustment or quality measures
This ensures no clinically significant condition is overlooked.
4. Medication List + Adherence Clues
AI reviews prescriptions, refills, changes, and potential issues:
- Medication discrepancies
- Non-adherence flags
- Drug interactions
- Needed chronic medication refills
- Payer formulary changes
What used to require multiple clicks is now summarized in one place.
5. Open Care Gaps and Preventive Needs
Quality programs depend on consistent, timely documentation.
AI flags:
- Missing screenings
- Vaccination needs
- Chronic disease monitoring requirements
- Unmet specialty follow-up
This benefits both patient outcomes and organizational performance.
6. Outstanding Referrals, Prior Authorizations, or Diagnostic Orders
AI identifies:
- Pending specialist consults
- Expired or incomplete authorizations
- Overdue imaging
- Never-scheduled referrals
Providers walk into the room knowing what operational tasks may block care.
7. Social, Behavioral, or Environmental Risk Factors
AI identifies risk indicators that influence clinical care:
- Housing instability
- Food insecurity
- Transportation barriers
- Behavioral health concerns
These factors often determine outcomes as much as clinical variables.
How AI Summarizes the Patient History for the Encounter
AI doesn’t treat every visit the same. It tailors the summary to the reason for visit and patterns relevant to that specialty or provider.
Visit-Type Awareness
For example:
- Diabetes follow-up: A1C, foot exams, microalbuminuria, retinal exams, medication adherence
- Orthopedic pain: Imaging, injections, physical therapy notes, functional status
- Behavioral health check: Symptoms, scales, medication stability, therapy notes
AI chooses what matters most—not everything it can find.
Provider Preference Modeling
Some clinicians want a SOAP-like structure.
Others want a bulleted problem list or chronological narrative.
AI adapts to match their clinical reasoning style.
The result?
A chart that feels like it was prepped by someone who knows the provider personally.
The Impact: Faster Understanding, Better Decisions, and Smoother Visits
When the provider opens the chart, they see:
- A clean clinical snapshot
- A simplified timeline
- Key issues front and center
- Outstanding tasks clearly flagged
- No need to dig through clutter
The visit becomes a clinical conversation—not a search for information.
AI-powered pre-charting turns chaotic data into an organized, personalized, clinically relevant summary that supports better care and reduces provider workload.
