Automated SOAP Notes
AI dynamically structures and generates notes based on diagnosis type, symptoms, and case severity.
Case Study
AI-Enabled Healthcare Practice Management System
Reinventing healthcare with Bonami
Client Profile
Multi-specialty healthcare network across inpatient, outpatient, and telehealth practices.
Engagement Focus
Create a self-learning PMS spanning documentation, billing, claims, imaging, and virtual care.
The Challenge
Healthcare professionals were spending more time filling forms than treating patients.
The client’s existing EHR system lacked interoperability and automation, resulting in:
01
Lengthy SOAP note creation (15–20 mins per visit)
02
Fragmented billing across departments and insurers
03
Manual code searches prone to claim denials
04
Disjointed data across imaging, forms, and teleconsultations
05
Limited predictive insight into claim aging and patient risk
These inefficiencies not only delayed reimbursements but also reduced provider throughput by nearly 40%.
The Solution
Bonami built a modular AI-powered EHRMS unifying NLP, OCR, and predictive intelligence
Natural Language Billing
Converts simple phrases like “Send John’s bill for today’s visit” into automated, coded invoices.
Semantic Code Search
Contextual retrieval across 100K+ ICD-10 & CPT codes ensures frictionless claim submission.
Smart OCR Automation
150+ forms (20–25 pages each) digitized with adaptive field detection and dynamic recognition.
Predictive Analytics Engine
Models claim aging, insurer patterns, and follow-up probabilities using historical data.
Imaging Comparison Engine
Detects anatomical variances across X-rays and reports for clinical review and next-step suggestions.
AI Navigation Agent
Context-aware assistant routes users directly to relevant modules like “claims”, “consults”, or “imaging”.
Telemedicine Auto-Summaries
AI-generated call briefs with follow-up recommendations and referral nudges.
Each layer of intelligence continuously learns from provider behavior to make every interaction faster, more accurate, and context-aware.
Experience Re-Imagined
From manual bottlenecks to guided, intelligent care journeys.
Before
- Manual intake notes, disconnected imaging uploads
- Staff triaged claims through email threads and spreadsheets
- Coding teams sifted through 100K+ records with search keywords
- No real-time visibility into claim aging or denials
After
- Voice-led SOAP capture converted to structured encounters
- Claims generated in natural language, auto validated against payer rules
- Predictive nudges for follow-ups and denial prevention
- Unified analytics exposing throughput, revenue, and clinical KPIs
The Impact
Bonami’s AI-driven transformation redefined efficiency benchmarks for the healthcare group.
0%
Charting Time
~20 min/visit → ~6 min/visit
0%
Claim Accuracy
60–65% → 85%+
0%
Claim Turnaround
5–7 days → 2–3 days
0×
Provider Throughput
1.2x baseline → 2.4x throughput
KPI
Before
After
Improvement
Charting Time
~20 min/visit
~6 min/visit
70% faster
Claim Accuracy
60–65%
85%+
+25% improvement
Claim Turnaround
5–7 days
2–3 days
60% faster cycle
Provider Throughput
1.2x baseline
2.4x throughput
2× productivity
“Our clinicians now finish documentation before the next patient walks in.” — Medical Director, Partner Clinic
Design Philosophy
Every design decision followed Bonami’s core UX principle — clarity drives care.
- Clean, white-space-rich dashboards
- High-contrast typography for clinical readability
- Teal-to-cyan gradient overlays (#00607A → #6EE7FF)
- Data visualizations that translate complexity into confidence
- Interactive animations that mirror real workflows (form-to-digital transitions, AI chat pulse, KPI counters)
Technology Stack
Django + DRF
Next.js
PostgreSQL
GPT-5
Deepgram
PyTesseract OCR
HL7/EDI Mapper
AWS Lambda
AWS S3
Tailwind
Ant Design
Key Takeaways
Bonami’s EHRMS solution empowered healthcare providers to:
- Automate 70% of repetitive documentation tasks
- Achieve near-real-time billing and compliance
- Unlock AI-driven insights for patient outcomes and financial performance
- Establish a scalable foundation for future AI integrations across departments