PAC DB: The Adaptive Engine Behind ClearChart.AI
PAC DB™ (Patient Adaptive Communication Database) is the AI engine that transforms complex medical data into summaries patients can actually understand — and act on. It continuously adapts each summary to the way each patient reads, understands, and responds, ensuring communication is clear, personal, and effective.
This isn’t one-size-fits-all communication. PAC DB™ observes how patients engage, then adjusts future messages based on their feedback — making each interaction easier to understand and more relevant. The result? Better comprehension, stronger adherence, and more equitable care for every patient.
Step-by-Step - How PAC Works in Practice.
- New Data Is Released: Lab results, visit notes, or medication updates become available in Epic.
- ClearChart.AI Summary Is Generated: PAC DB selects tone, reading level, and explanation strategy based on patient needs.
- Patient Reviews: The patient reads their summary and may provide CPV feedback.
- Future Summaries Are Refined: Feedback helps improve clarity and relevance for similar future cases — without storing PHI.
It's Clear to You, Because it Learned From You.
From Complexity to Clarity for Every Patient
- Universal Personalization: Delivers customized summaries regardless of language, literacy level, or background.
- Equity Built In: Adapts to how the patient understands — not how the system assumes they should.
- Patient-Centered AI: We use AI to empower the people it was meant to serve, not to replace them.
What PAC DB Summaries Include.
- Lab Results & Explanations: What results mean, what’s normal, and next steps.
- Visit Summaries: Friendly, plain-language overviews of diagnoses, procedures, and notes.
- Medication Instructions: Why prescribed, how to take, and potential side effects.
- Next Steps & Follow-Up: Checklist-style actions for ongoing care.
- Expandable Sections: High-level overviews with optional deeper details.
Why PAC DB is Different
- Designed for Personalization: Adjusts tone, phrasing, and layout based on literacy standards, best practices, and CPV feedback.
- Feedback-Informed, Not Data-Trained: Learns from de-identified input — never from raw patient data.
- Evolves Over Time: Refines communication as more feedback is gathered from diverse populations.
- Supports Health Equity at Scale: Delivers culturally nuanced, literacy-appropriate communication to reduce disparities.
- No PHI Stored: Summaries are generated in real time with no PHI retained or used for AI training.
- Built on Trusted Sources: Uses SNOMED CT, MedlinePlus, NIH Plain Language standards.
Equity-Driven Communication at Scale.
PAC DB adapts tone, reading level, and clarity based on literacy signals, language preferences, and CPV feedback. It analyzes how often patients open summaries, which sections they explore, and their demonstrated health literacy from past interactions — all de-identified and privacy-protected.
By making each interaction with health data more clear, comfortable, and valuable, PAC DB helps ensure that every patient receives explanations that are technically accurate, emotionally supportive, and culturally appropriate.
If you’ve made it this far, you’re one of us.
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