How to Build a Doctor Assistant App like Nabla Copilot

Healthcare professionals are constantly under pressure to deliver high-quality care while managing time-consuming administrative tasks. One of the biggest challenges they face is documenting patient interactions accurately without disrupting the flow of conversation. This is where AI-powered doctor assistant apps like Nabla Copilot are making a real difference. By listening, summarizing, and organizing clinical notes automatically, these tools help doctors focus more on patients and less on paperwork.

In this blog, we will talk about how such an app is developed, what technologies power it, and how it can be turned into a scalable product. If you are exploring opportunities in the AI healthcare space or planning to invest in smart clinical solutions, this guide will walk you through every essential aspect of building a doctor assistant app like Nabla Copilot. As we have developed and helped many companies launch their healthcare platforms, IdeaUsher has the expertise to support you in building a compliant, AI-powered doctor assistant app tailored to real-world clinical workflows.

What is Doctor Assistant App: Nabla Copilot?

Nabla Copilot is an ambient AI scribe or AI-powered doctor assistant app that records patient-clinician conversations and turns them into structured SOAP notes, summaries, and follow-up instructions in real time. It works through a Chrome extension, web, or mobile app and integrates seamlessly with EHR platforms. Privacy is a core feature, as no audio, transcripts, or clinical data are stored. With support for English and Spanish, specialty-specific templates, and the Magic Edit tool helps doctors to reduce documentation time and focus more on patient care.

Business & Revenue Model of Doctor Assistant App

Understanding the business and revenue model is key to building a sustainable AI doctor assistant platform. Monetization depends on the target audience, platform scale, and value delivered to clinicians or healthcare providers.

Business Model:

Nabla Copilot is an ambient AI platform for clinical workflows, using speech-to-text and large-language models to convert physician-patient encounters into EHR-ready notes. Accessible via browser, mobile, or API, it integrates with major EHR systems like Epic and NextGen without onboarding. It emphasizes physician-first design, bottom-up deployment (free tier leads to enterprise deals), and complies with HIPAA, GDPR, SOC 2, and ISO 27001.

Revenue Model: 

Nabla employs a freemium‑to‑subscription pricing model:

• Free tier: up to 30 consultations per month at no cost (unlimited for interns/residents)
• Pro tier: Around $120 per clinician per month for unlimited usage and full EHR integration
• Enterprise: volume‑based/custom pricing for hospitals or health systems, offering tailored ML models, automation, SSO, and dedicated support

How Nabla Copilot Works?

Nabla Copilot enhances clinical documentation by silently functioning in the background, allowing doctors to focus on patients. Its real-time features use ambient audio and advanced language models, making it highly efficient for healthcare workers.

1. Ambient Audio Capture

Nabla Copilot listens to doctor-patient conversations through a secure ambient audio stream that complies with HIPAA standards. It uses a mix of fine-tuned Whisper AI and Microsoft’s speech-to-text engine to transcribe conversations instantly. Personally identifiable details are excluded during processing and only reinserted if approved by the clinician, ensuring control over sensitive data from the start.

2. LLM-Powered Note Generation

Once the conversation is transcribed, Nabla uses a proprietary large language model trained on medical dialogue to convert it into structured SOAP notes. It filters out casual or irrelevant talk while preserving clinical meaning. With accuracy exceeding 90 percent, the notes maintain a high level of reliability, making them ready for immediate review without heavy edits.

3. Magic Edit and Custom Templates

Clinicians can fine-tune generated notes using Magic Edit, which allows changes in tone, detail level, or patient specifics with minimal effort. Nabla also supports custom instructions and specialty-based templates, making it adaptable for pediatricians, general practitioners, and multilingual practices, including those serving Spanish-speaking families.

4. Local Processing and Privacy-First Architecture

All audio processing and summarization are performed locally on the doctor’s device, not in the cloud. Nabla does not store audio, transcripts, or clinical content unless given explicit permission. This privacy-first architecture offers added reassurance for clinicians working in data-sensitive environments like hospitals or private practices.

5. EHR Integration and Final Review

Once reviewed and approved, the structured notes can be sent directly into EHR systems like NextGen, Cerner, or Oracle Health. This direct EHR integration eliminates manual entry, reduces clerical errors, and frees up significant time, helping clinicians reclaim up to two hours each day for patient care.

Why You Should Invest in Developing an AI Doctor Assistant App?

According to Precedence Research, the global healthcare virtual assistants market is projected to grow from USD 969.1 million in 2023 to USD 14,419.2 million by 2032, expanding at a CAGR of 35.0%. This rapid growth is being driven by the urgent need for physician support tools that reduce administrative burden and enhance clinical workflows.

Nabla Copilot, one of the leading AI doctor assistant apps, has raised over $120 million, including a $70 million Series C round in 2025 and the platform now supports 85,000 clinicians across 130+ healthcare organizations, helping them automate clinical documentation, reduce administrative burden, and improve patient interaction time.

Abridge, another major player in this space, secured $250 million in Series D funding in early 2025, followed by an additional $300 million in mid-2025, bringing its valuation to $5.3 billion. The platform handles over 50 million medical conversations per year and is deployed across 150+ health systems.

Heidi Health, an AI-powered assistant used by primary care providers, has raised USD 26 million to date and the app now facilitates over 2 million patient interactions per week, proving that AI assistants are not just scalable but also applicable across different care settings.

The AI doctor assistant space is a promising digital health opportunity, supported by funding, rapid clinical adoption, and scalable use cases. These platforms are vital for modern healthcare, and investing in them fosters a shift that tackles clinical challenges, offers measurable ROI, and aligns with AI-integrated healthcare’s future.

Why the Healthcare Industry Needs AI Doctor Assistants?

The rising documentation burden, staff shortages, and growing demand for faster care have accelerated the adoption of AI doctor assistant apps. Below are real, measurable reasons why the healthcare sector needs them now more than ever.

1. Combat Clinician Burnout

Physicians spend nearly half their workday documenting patient notes, often outside clinic hours. AI doctor assistants ease this load by auto-generating structured notes during visits. In a pilot study, Mass General Brigham saw a 40% burnout reduction in just six weeks, proving how AI can bring immediate relief.

2. Enhance Patient-Provider Interaction

AI ambient scribes silently document while doctors focus on patients. This shift allows clinicians to make better eye contact, listen deeply, and build empathy. Real-time note-taking boosts patient satisfaction and strengthens the therapeutic alliance, especially in mental health and primary care settings.

3. Increase Accuracy and Completeness

Unlike rushed human inputs, AI-generated clinical notes capture subtle but important details that may be overlooked in busy encounters. With ~80% of AI notes accepted with minimal edits, clinicians get more reliable records that improve both compliance and continuity of care.

4. Operational Efficiency Gains

Beyond note transcription, AI assistants suggest billing codes, generate patient summaries, and organize tasks. This automation can cut administrative time by up to 20%, saving billions industry-wide. For overworked providers, every task AI handles means more time for clinical care.

5. Accelerated Clinical Innovation

Ambient AI is opening new possibilities in preventive care and diagnostics. For example, AI-assisted mammograms improved early cancer detection by 29%, and voice-based assessments now flag Alzheimer’s risk up to six years earlier, offering a new layer of proactive patient management.

6. Scalability and Provider Shortage Mitigation

As healthcare faces ongoing workforce gaps, AI doctor assistants serve as scalable digital partners. They reduce dependency on manual workflows, free up physician time, and help systems manage rising patient volumes without compromising care quality or speed.

Key Features to Include in a Doctor Assistant App

Developing an AI doctor assistant app requires attention to clinical accuracy, seamless integration, and usability. Each feature must enhance documentation workflows while supporting diverse care settings across specialties.

1. Ambient Clinical Capture

The real-time ambient AI feature present in apps like Nabla Copilot captures clinician-patient conversations automatically, without requiring manual input. This includes both in-person and virtual consultations. Multi-speaker environments are supported with high transcription accuracy, approximately 95 percent, ensuring that critical dialogue involving guardians or caregivers is accurately transcribed and documented.

2. Automatic Structured Notes

Verbal consultations are transformed into structured notes such as SOAP documentation, referral letters, or custom styles within seconds. Large language models trained on medical dialogue ensure more than 90 percent accuracy, with built-in noise reduction to exclude irrelevant dialogue and preserve only clinically significant details.

3. Magic Edit and Template Customization

The Magic Edit feature refines notes by adjusting tone, rephrasing key sections, or modifying verbosity through minimal input. Persistent template custom instructions ensure consistency across visits by adhering to specialty-specific language preferences, such as pediatric-focused phrasing or note formats required by specialized fields.

4. Multi-Language and Patient Instructions

App like Nabla Copilot also use multilingual functionality that enables the generation of clinical notes and patient-facing instructions in preferred languages. For example, notes can be documented in English while instructions are issued in Spanish. This supports effective communication across language barriers and improves care engagement in multilingual patient populations.

5. Ambient Dot-Phrase Triggers

Clinic-specific voice commands automatically insert predefined phrases or medical content into structured notes. Statements like “nine-month physical exam is normal” trigger full standardized text blocks, streamlining documentation tasks and promoting uniform language across repetitive procedures or assessments.

6. EHR Integration and Coding Support

Seamless EHR integration enables direct note transfer into platforms such as Epic, NextGen, Cerner, Athenahealth, and Greenway following clinician approval. Built-in support for ICD and CPT code suggestions, along with eligibility checks, simplifies billing workflows and ensures that documentation supports administrative efficiency.

7. Privacy-First and Compliance

All audio processing and transcription are performed locally on the device. No transcripts or audio data are stored on external servers unless explicitly permitted. Full compliance with HIPAA, GDPR, and certifications like SOC 2 Type 2 and ISO 27001 ensures strong data protection standards are met throughout the AI doctor assistant app development process.

8. Cross-Platform Accessibility

The app must be compatible across platforms, including Chrome extensions, web portals, and mobile applications. This allows clinicians to operate within telehealth or in-person settings without shifting attention away from the patient, preserving both focus and workflow continuity.

9. Clinician Well-being and Efficiency Gains

Automation of documentation tasks reduces administrative workload by up to two hours per day. This supports lower burnout levels and greater time availability for direct patient interaction, reinforcing the role of an AI doctor assistant app in promoting long-term provider efficiency and satisfaction.

Development Process of an AI Doctor Assistant App like Nabla Copilot

Building an app like Nabla Copilot requires more than technical skills. Each phase must reflect a deep understanding of clinical workflows, compliance standards, and real-world expectations in AI doctor assistant app development.

1. Consultation and Requirement Discovery

Our team will begin by consulting with you to understand the vision, goals, and key features planned for the AI doctor assistant app. This includes defining the target specialties, preferred note formats like SOAP, BIRP or CHLA pediatric templates, required languages, EHR integration points, and any unique workflow expectations. Early clarity allows tailored planning from both technical and clinical standpoints.

2. Market and Compliance Research

Our compliance experts will assess regulatory frameworks including HIPAA, GDPR, SOC 2, and FDA SaMD. Competitor analysis will cover ambient AI scribes like Nabla, Nuance, and Heidi. This research ensures that your AI doctor assistant app like Nabla Copilot, follows secure consent mechanisms, data encryption policies, and includes measurable KPIs such as documentation time saved and clinician satisfaction scores.

3. AI Model Planning

Our AI developers will architect a pipeline combining Whisper or Microsoft’s speech API for transcription, masked for PII, followed by a proprietary LLM for SOAP note generation. The pipeline will support multilingual transcription, hallucination detection, and ≥ 90 percent accuracy, making the core of the AI doctor assistant app both clinically reliable and adaptable to diverse medical conversations.

4. UI/UX Design

Our design team will create clinician-focused interfaces with minimal clicks and clean overlays that function as Chrome extensions or embedded views within EHRs. These designs will support Magic Edit and template customization directly in the UI. The goal is to maintain clinician-patient eye contact without screen switching, essential for telehealth and in-person care experiences.

5. Backend and API Development

Developers will build both lightweight front-end export tools and deep back-end APIs to push structured notes directly into Epic, NextGen, or Cerner. Ambient dot-phrase triggers and secure, role-based access points will also be implemented. All API calls will be encrypted and auditable to meet healthcare-grade infrastructure standards during AI doctor assistant app development.

6. AI and NLP Integration

Our NLP engineers will connect local ambient listening systems to a secure cloud inference layer. This hybrid model supports real-time transcription, Magic Edit, ICD/CPT code suggestions, and multilingual instructions. Clinical ontologies like SNOMED, ICD, and CPT will guide LLM outputs, ensuring structured documentation aligns with both clinical logic and billing requirements.

7. Testing with Real Doctors

Pilot testing will be conducted across varied clinical settings to validate real-world usability, note accuracy, and workflow alignment. Clinicians will provide feedback on editing effort, output quality, and interface experience. Based on this input, the development team will refine templates, adjust model behavior, and optimize user interactions of your AI doctor assistant app to ensure consistent performance in diverse care environments.

8. Deployment and Ongoing Optimization

The app will launch in waves, beginning with self-service clinicians and scaling across enterprise teams. Our developers will provide onboarding, live support, and documentation. KPIs such as note quality and saved time will be monitored. Updates to models, templates, and compliance frameworks will continue post-deployment to maintain long-term performance and user confidence.

Cost to Build an AI Doctor Assistant App

Estimating the cost of an AI doctor assistant app involves evaluating each development stage, from planning to deployment. Several factors like features, integrations, and compliance heavily influence the overall budget.

Development Phase	Description	Estimated Cost
Consultation & Requirement Discovery	Initial discussions to define app vision, features, user needs, and core functionality.	$5,000 – $8,000
Market & Compliance Research	Research of competitors, regulations (HIPAA, GDPR), and planning security protocols.	$6,000 – $10,000
AI Model Planning	Designing LLMs and speech models with hallucination control and clinical accuracy goals.	$12,000 – $18,000
UI/UX Design	Creating intuitive, doctor-friendly interfaces, voice interaction points, and layouts.	$8,000 – $14,000
Backend & API Development	Building secure backend systems and integrating with major EHR platforms via APIs.	$15,000 – $25,000
AI/NLP Integration	Implementing ambient AI features, transcription, multilingual support, and Magic Edit.	$18,000 – $30,000
Testing with Real Doctors	Running pilots, collecting feedback, improving note accuracy, and refining UX.	$7,000 – $12,000
Deployment & Optimization	Gradual rollout, performance tracking, continuous updates, and support documentation.	$6,000 – $10,000

Total Estimated Cost Range: $70,000 – $135,000

Note: The above estimates show average costs for a full-featured AI doctor app like Nabla Copilot. Actual costs vary with project scope, technology, and third-party integrations. For a lean MVP or quicker launch, budgets can be adjusted.

Consult with IdeaUsher to get a tailored cost and feature breakdown based on your specific goals, clinical requirements, and market strategy.

Technology Stack for Doctor Assistant Apps

Choosing the right technology stack is crucial for a doctor assistant app like Nabla Copilot. These apps process voice input, run NLP models, and comply with healthcare data laws while providing a fast, intuitive experience. Here are the core technologies used:

1. AI/NLP Models

These models serve as the brain of your app, enabling advanced language understanding tailored for healthcare contexts.

• OpenAI GPT-4: Useful for generating human-like consultation summaries and context-aware clinical notes.
• Google Med-PaLM: Trained specifically for medical use cases, enhancing diagnostic accuracy and reducing hallucinations.
• AWS Comprehend Medical: Ideal for extracting structured data (symptoms, medications, conditions) from unstructured doctor-patient dialogues.

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2. Voice Tech

Real-time, accurate voice recognition is foundational—poor transcription ruins downstream summaries.

• Whisper API (OpenAI): Open-source, multilingual speech-to-text engine that performs well in noisy environments.
• Google Speech-to-Text: Offers high-accuracy transcription with healthcare-specific language models.
• Deepgram: Optimized for low-latency streaming and custom vocabulary for medical terminology.

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3. Frontend Technologies

UI/UX matters to doctors. Fast, touch-friendly interfaces ensure adoption during busy clinical workflows.

• Flutter: Cross-platform development with high performance on iOS and Android; ideal for rapid prototyping.
• React Native: Great for apps needing web + mobile synergy; supports native components for faster rendering.

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4. Backend Technologies

The backend must handle both real-time voice input and AI-driven processing without delays or lags.

• Node.js: Lightweight and event-driven, ideal for handling asynchronous tasks like voice streaming or API requests.
• Python (FastAPI): Perfect for deploying AI/NLP models and handling backend logic efficiently.
• Firebase: Great for rapid MVPs and real-time syncing of user sessions, though often paired with a more scalable server for production.

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5. Database & Healthcare Integration

Medical apps must use healthcare-grade data models for interoperability and compliance.

• PostgreSQL / MongoDB: Flexible storage for structured and unstructured data (e.g., notes, logs, analytics).
• FHIR / HL7-Compliant Storage: Ensures compatibility with Electronic Health Record (EHR) systems like Epic, Cerner, or AthenaHealth.

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6. Cloud & DevOps

Scalability and security go hand-in-hand. A strong cloud architecture ensures uptime, regulatory compliance, and performance during high loads.

• AWS or Azure HealthCloud: HIPAA-compliant cloud platforms with integrated AI services and scalable infrastructure.
• Docker & Kubernetes: Used for containerized deployments, scaling AI components, and rolling out updates with minimal downtime.

Why It Matters: Scalability, Security & Real-Time Responsiveness

• Real-time performance is critical: Doctors need instant transcription and summaries during live consultations without lag or delay.
• High-stakes environments demand reliability: A delayed or inaccurate output can affect diagnosis, patient satisfaction, and trust in the system.
• Secure data handling is non-negotiable: Medical data must be protected with end-to-end encryption, role-based access, and HIPAA/GDPR compliance.
• Scalability supports long-term growth: Whether it’s a solo clinic or a hospital network, your infrastructure must handle thousands of concurrent users and growing AI workloads.
• Smooth integration with EHR systems: Compatibility with platforms like Epic, Cerner, or AthenaHealth ensures the AI assistant fits into existing workflows without friction.
• Downtime or failure leads to workflow disruption: Doctors won’t tolerate tools that crash or fail during busy clinic hours. Real-time stability is essential for adoption.

Monetization Strategies for Doctor Assistant Apps

Turning a doctor assistant app into a sustainable business requires a monetization model aligned with the healthcare ecosystem. Below are proven strategies used by successful AI health platforms like Nabla Copilot, tailored to different customer segments and usage levels.

1. SaaS Subscription Model for Clinics

Charge clinics and solo practitioners a recurring monthly or annual fee based on the number of users, consultations, or features. This model ensures predictable revenue and encourages long-term relationships. Offer tiered plans with add-ons like advanced analytics or EMR integrations to support upselling.

2. Custom Integrations for Hospitals & EHR Vendors

Large healthcare institutions often demand tailored deployments. Offer custom API or SDK-based integrations with hospital systems or electronic health records (EHR) like Epic or Cerner. These enterprise deals often involve higher implementation fees and multi-year contracts.

3. Licensing AI/NLP APIs to Third Parties

If your app’s core value lies in its AI engine, consider licensing the NLP or transcription API to other digital health platforms or telehealth services. This creates a new B2B revenue stream while keeping your product at the center of healthcare innovation.

4. Analytics & Reporting as a Premium Add-On

Offer actionable insights to clinics, including time saved, documentation accuracy, and patient engagement trends. These data-driven dashboards can be sold as premium modules to leadership teams or health administrators.

5. Enterprise White-label Options

For platforms targeting global expansion or partnerships, white-labeling allows you to offer your app under other brand names. Hospitals, insurers, or EHR vendors can license your platform and rebrand it for their ecosystem.

Conclusion

Building a doctor assistant app like Nabla Copilot requires a thoughtful blend of AI technology, healthcare compliance, and user-centric design. From real-time transcription to accurate clinical summaries, each feature must be tailored to fit seamlessly into a doctor’s daily workflow. As the demand for intelligent healthcare tools continues to grow, creating a solution that improves efficiency and reduces burnout holds significant long-term value. With the right development approach and a clear understanding of the industry’s needs, it is possible to build a platform that not only supports medical professionals but also sets new standards in patient care.

Why Choose Idea Usher to Develop Your AI Doctor Assistant App?

Idea Usher empowers healthtech innovators to build AI-powered doctor assistant apps that simplify clinical documentation and reduce burnout. From real-time transcription to EHR integration and multilingual note generation, we help you bring your version of Nabla Copilot to life with enterprise-grade precision.

Why Work with Us?

• End-to-End AI Integration: We bring together speech recognition, LLM-based summarization, and real-time SOAP note generation in one seamless platform.
• Customizable Features: Whether it’s Magic Edit-style tone control or specialty-specific templates, we tailor every element to your clinical workflows.
• Trusted by Health Innovators: We’ve delivered AI health platforms for leaders like Allied Health, CosTech Dental, and Vezita, proving our ability to build for scale and compliance.
• Secure and Compliant: Every solution we build is HIPAA/GDPR-ready and designed with privacy-first architecture.
• Rapid Prototyping to Launch: Our agile process ensures fast iterations without compromising on reliability or performance.

Browse our portfolio to discover how we’ve partnered with healthtech innovators to develop AI-driven solutions that transform clinical workflows and reduce documentation time.

Book a discovery call today and let’s turn your idea into a powerful clinical solution.

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