🟢 Interactive Demo · HCR-02⏱️ Try in ~2 min

Clinical Guideline Q&A

5-second answer with evidence grade + source citation.

👉 For
Resident / Attending physician
😖 Current pain
Flipping through 30+ pages of guidelines at 2 AM; can't recall renal-adjusted dosing.
✨ With AI
5-sec answer with evidence grade + source-document link.
📜 Customer story

Dr. Park, an ED resident, at 2 AM with a STEMI patient. Question: 'Anticoagulation for a 58yo male, creatinine 1.85?' — 5 seconds later: 'Enoxaparin 1 mg/kg q24h (renal-adjusted), Class IIa-B, ESC 2023 Guidelines p.42.'

🎮 Interactive demo

Real functional demo · auto-plays after case selection
💬 Live Clinical Query
Emergency Department · Cardiology Consult
STEMI with renal impairment · 02:14 AM
Dr
Dr. Sarah Patel · Cardiology Fellow

58yo male, acute STEMI, creatinine 1.85 mg/dL (eGFR 42), no prior anticoagulation. What anticoagulation strategy?

Age
58
Sex
Male
Presentation
STEMI (anterior)
Creatinine
1.85 mg/dL ↑
eGFR
42 mL/min
Weight
78 kg
Bleeding risk
Low (CRUSADE 22)
Prior anticoag
None
📚 Retrieved Sources
From 5 guideline corpora · 200+ flowcharts · 50+ dosing tables
[1]2023
2023 ESC STEMI Guidelines
Section 5.2.1 · Antithrombotic therapy
ESC · P. 42
[2]2023
2023 ESC Antithrombotic Therapy in NSTE-ACS
Section 7.3 · Renal impairment
ESC · P. 78
[3]2022
ACC/AHA 2022 STEMI Update
Recommendation 5.1.3
ACC/AHA · P. 142
[4]2024
Enoxaparin Renal Dosing — KDIGO
Renal-adjusted heparin protocol
KDIGO · P. 28

⚙️ AI processing pipeline

Tech stack, concrete operations and processing time at each step

1

Medical question understanding & rewrite

⏱ 0.4s
🛠️ Tech stack
BioMedBERT + GPT-4o + medical few-shot QA templates
📋 Operation

Converts colloquial question to structured query (disease / patient features / intervention / outcome); auto-completes patient characteristics as RAG conditions.

2

Multi-document semantic retrieval (RAG)

⏱ 0.3s
🛠️ Tech stack
BGE-M3 medical embedding + Milvus vector DB + cross-encoder rerank
📋 Operation

Searches ESC + ACC/AHA + KDIGO + 38 guideline PDFs (320,000 paragraphs) — Top 20 retrieved, reranked to Top 5.

3

Evidence-grade parsing

⏱ 0.2s
🛠️ Tech stack
Rule matching + LLM extraction
📋 Operation

Extracts recommendation class (I/IIa/IIb/III) and evidence level (A/B-R/B-NR/C-LD); supports both ESC and ACC/AHA grading.

4

Cross-guideline comparison

⏱ 0.5s
🛠️ Tech stack
GPT-4o + knowledge-conflict detection
📋 Operation

If ESC and ACC/AHA differ, auto-flags the divergence and surfaces clinical decision factors.

5

Personalized recommendation

⏱ 1.2s
🛠️ Tech stack
GPT-4o + medical KG (SNOMED-CT)
📋 Operation

Adjusts dose and regimen based on patient (age / renal / comorbidity / contraindications); flags contraindications.

6

Citation & explainability

⏱ 0.6s
🛠️ Tech stack
Source back-trace + PDF highlight + screenshot generation
📋 Operation

Each paragraph has a clickable source-PDF link with jump-to-page; explains the AI decision path.

💰 ROI across three dimensions

Itemized indicators across each dimension for a complete view

💰

Financial

Per-query physician time cost $7 → $0.07
⏱️

Efficiency

10min → 5s
ED decision speed +70%
🏢

Organizational

Residents on independent call 6 months earlier
medication error rate ↓40%

🧩 AI capabilities used

B1Information Extraction (Entity/Relation/Event)· UnderstandingC1Retrieval-Augmented Generation (RAG)· RetrievalE1Multi-step Reasoning Agent· ReasoningE4Decision Matrix / Scoring· ReasoningH1Multi-turn Dialogue (Context / Persona / Compliance)· Dialogue

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