How to Develop an AI SOAP Note App like DeepScribe

Clinical documentation is one of the most time-consuming tasks for healthcare professionals. Physicians often spend hours transcribing or summarizing patient encounters, which can lead to burnout and reduced face-to-face interaction with patients. AI-powered SOAP note automation tools like DeepScribe are changing that by streamlining the process of note creation during consultations. These solutions help doctors focus more on care delivery while ensuring accurate, structured records are generated efficiently.

In this blog, we will understand the working process of the AI SOAP note app DeepScribe and the need for such an app in the healthcare industry. Also, we will talk about the key features to include, the development steps, estimated cost, and some potential challenges our developers might face, and how they are going to solve those, as we have worked with multiple enterprises to develop their healthcare products. IdeaUsher has the experience to deliver reliable, compliant, and scalable AI solutions tailored to the specific workflow needs of modern clinical environments. 

What Is an AI SOAP Note App DeepScribe?

DeepScribe is an AI SOAP note app that automates clinical documentation by listening to patient-provider conversations in real time. It uses ambient AI and medical-specific NLP models to generate structured SOAP notes directly into the EHR, reducing manual typing and cognitive load. Unlike basic transcription tools, this app understands medical context, extracts relevant symptoms, and creates compliant notes tailored to each specialty. It is designed to save physicians hours weekly while improving accuracy and consistency across patient records.

Business Model

DeepScribe is a SaaS platform offering ambient AI documentation for clinicians. It captures passive voice during patient visits to auto-generate SOAP notes and coding-ready docs. Its Customization Studio provides specialty templates, voice macros, real-time insights, and AI trained by clinicians for easy use. Tools like Clinical Moments and note analytics improve confidence and governance. The model targets mid-size to large practices and healthcare systems.

Revenue Model

An AI SOAP note app like DeepScribe serves individual clinicians and large healthcare organizations. Its revenue model emphasizes recurring value, flexible pricing, and scalable add-ons for different customer segments.

• Subscription-Based Pricing: Pricing begins at about $400 per user monthly for the basic plan, rising to around $500 for plans with EHR integration. Annual contracts save up to 20%, lowering long-term costs.
• Integration Fees: Clients pay a one-time integration fee for connecting the app to their existing EHR or IT systems. These fees are scaled based on system complexity and organization size.
• Premium or Enterprise Support: Optional support packages include dedicated account management, onboarding assistance, and priority customer service, ideal for clinics requiring hands-on implementation.
• Value-Added Services: Advanced modules such as automated ICD-10 coding, E/M code suggestions, and audit-compliance tools are offered as premium add-ons under higher-tier plans.

How AI SOAP Note App DeepScribe Works?

To understand what powers an app like DeepScribe, it’s important to look at its structured workflow. From capturing real-world interactions to transforming them into accurate SOAP notes, DeepScribe follows these steps to scribe the patient-consultant conversation.

1. Ambient Conversation Capture

The process starts with non-intrusive audio capture, where DeepScribe listens passively during patient visits. There are no wake words or manual triggers involved. Clinicians can conduct appointments naturally, and once the session ends, the system automatically begins converting the recorded conversation into draft documentation.

2. HealAI Medical Language Model

The captured audio is processed through HealAI, DeepScribe’s proprietary medical LLM trained on over 3 million labeled clinical conversations. It understands real-world dialogue and outperforms GPT-4 in clinical accuracy, making it capable of handling both structured and unstructured medical language with minimal loss in context.

3. Real-Time Note Generation

DeepScribe generates SOAP notes in real time during the appointment, not after. Its system runs hundreds of live inferences, helping providers review and refine notes instantly. This live approach reduces delays and increases accuracy while the clinical details are still top of mind.

4. Clinician Customization Options

To suit individual workflows, it includes a Customization Studio with over 50 configurable options. Clinicians can tailor everything from section structure to exam templates and writing style, making the output adaptable to different specialties and personal preferences without compromising documentation standards.

5. Trust and Safety Suite

Before finalization, this app applies additional trust-building tools such as Clinical Moments, which link note content to source dialogue. Features like Note Insights and optional human audits offer transparency, helping organizations track accuracy metrics and maintain compliance across clinical documentation efforts.

6. Automated Integration and Coding

The final notes are seamlessly uploaded into major EHR systems and mapped to the correct patient records. DeepScribe also provides AI-supported coding suggestions for ICD-10, HCC, and E/M levels, helping improve billing efficiency and ensuring better alignment with payer documentation requirements.

Why You Should Invest In Launching an AI SOAP Note App?

According to IMARC Group, the market was valued at USD 79.35 billion in 2024 and is projected to reach USD 128.47 billion by 2033, growing at a CAGR of 5.22% from 2025 to 2033. This growth reflects the increasing demand for faster and more accurate clinical documentation through AI-powered tools, such as SOAP note apps.

DeepScribe, an AI SOAP note app, raised $61.2 million, including a $30M Series A and $24M Series B and it makes about $33.9M annually. Its deployments at providers like Ochsner Health show how ambient AI cuts documentation time and boosts physician satisfaction.

Suki, another voice-based documentation platform, secured $55 million in 2024 to expand its reach. These figures reflect a broader wave of venture capital confidence in AI healthcare tools aimed at reducing physician burnout and streamlining documentation.

AI SOAP note apps are solving a deeply rooted inefficiency in healthcare. With strong revenue momentum, growing adoption among providers, and increasing VC interest, investing in this space offers a strategic entry point into a market undergoing digital transformation.

Why Healthcare Needs AI-Powered SOAP Note Apps?

Clinical documentation is a major bottleneck in healthcare. AI SOAP note automation directly tackles issues like physician burnout and reimbursement delays, providing a smarter, safer way to document care.

1. Physician Burnout from Documentation Load

Over 60% of clinicians report burnout linked to documentation stress, often spending hours completing notes outside patient hours. AI SOAP note apps minimize after-hours work by generating structured notes from live conversations, helping restore physician wellbeing and preventing emotional exhaustion from admin overload.

2. Time Wasted on Manual Charting

Doctors lose nearly 16 minutes per patient to manual documentation, cutting into both patient volume and clinic efficiency. By automating note-taking in real time, AI SOAP note automation restores those lost minutes, allowing practices to see more patients or deliver higher-quality care without sacrificing time.

3. Reimbursement Risks from Inaccurate or Delayed Notes

Inconsistent or delayed SOAP notes often lead to coding errors, missed billing, and payer rejections. With automated documentation tools, notes are generated in-session and cross-checked for ICD/CPT compliance, reducing risk and increasing financial reliability across specialties and clinic sizes.

4. Need for More Face Time Between Doctors and Patients

EHRs have pushed clinicians behind keyboards, often at the cost of patient trust and engagement. AI SOAP note apps work passively in the background, capturing structured notes while allowing clinicians to maintain natural, eye-level interactions, boosting satisfaction on both sides of the conversation.

Key Features to Include in Your AI SOAP Note App

An effective AI SOAP note automation system is not built on isolated features but on a clinical pipeline that reflects how providers work in real time. The following capabilities are essential to delivering a system that integrates seamlessly into modern care settings while ensuring safety, efficiency, and adaptability.

1. Ambient Voice Capture

The foundation of any AI SOAP note app like DeepScribe uses ambient voice capture, allowing providers to speak naturally during consultations without pressing record. The system passively listens and securely records conversations, ensuring context-aware input for downstream processing. This non-intrusive design preserves the clinician-patient dynamic while capturing accurate clinical data.

2. Medical Entity Recognition

After capturing audio, the system leverages medical-grade NLP like HealAI to extract clinically relevant data. It recognizes symptoms, diagnoses, medications, and vital signs with contextual accuracy. Unlike generic speech recognition, this feature uses trained clinical models to isolate what matters and feeds structured data into the processing engine for real-time documentation.

3. Automatic SOAP Note Structuring

This feature auto-generates structured SOAP notes (Subjective, Objective, Assessment, Plan) directly from the captured consultation. The app applies clinical logic and templates to organize notes with precision, drastically reducing manual entry. Providers can later review or adjust, but the core framework is created instantly from the AI-processed interaction.

4. Real-Time EHR Sync

With secure real-time integration, the app synchronizes notes, observations, and codes directly into the EHR system. It supports standards like FHIR and HL7, ensuring compatibility across major hospital IT infrastructures. This capability minimizes admin time, reduces copy-paste errors, and improves chart completion rates without needing after-hours documentation.

5. Voice Commands for Workflow Control

Voice control allows clinicians to navigate or update the app during consultations without touching the device. Commands like “insert diagnosis” or “move to assessment” let users guide the AI mid-session. This functionality keeps workflows smooth while maintaining hands-free operation in environments like operating rooms or urgent care.

6. Multi-language & Accent Adaptability

The AI SOAP note automation app should support multiple languages and diverse accents, which are essential for international deployment and equitable care. Advanced models trained on healthcare-specific speech datasets ensure accurate recognition across varied speech patterns, helping eliminate disparities in note quality due to linguistic or accent-related misinterpretations.

7. HIPAA-Compliant Audio Storage

Audio recordings must follow HIPAA regulations, with encryption in transit and at rest. Providers should have control over data retention, deletion, and audit logs. This ensures that sensitive recordings are handled with full compliance and supports trust from both clinicians and patients in using the app routinely.

8. Editable Notes with Provider Review

Even with AI assistance, clinicians should retain control. The platform must offer easy in-app editing with version history, allowing providers to revise notes before finalizing. This ensures safety and personalization while the AI handles the groundwork. It also supports audits, billing, and downstream use in analytics or referrals.

9. Offline Recording Mode

To support remote visits and low-connectivity environments, the app should include an offline mode that records and stores sessions locally. Once connectivity is restored, the data is uploaded for processing. This ensures no clinical detail is lost, whether in rural areas, mobile units, or during home health visits.

Step-by-Step Process to Develop an AI SOAP Note App like DeepScribe

Building a high-performance AI SOAP note app like DeepScribe requires a staged, precision-driven approach. Here’s how our AI developers design, develop, and deploy a compliant and user-ready product, tailored to real clinical workflows and healthcare standards.

1. Consultation

In this stage, our team will consult directly with you to understand your vision, target users, clinical focus, and the features you want in your AI SOAP note automation app. We define goals, clarify expectations, and document your ideal workflow. This discovery process helps us design a solution tailored to your practice, regulatory environment, and long-term roadmap.

2. UI/UX Design and Voice Interaction Prototyping

Our UI/UX team designs a voice-first interface optimized for minimal distraction, including overlays for transcription, recording controls, and edit review toggles. We simulate real-world workflows for both in-person and telehealth visits, ensuring zero-click usability. This phase ensures that the AI SOAP note automation works naturally within the clinical environment.

3. Voice AI and NLP Integration

We integrate clinical-tuned ASR models like Whisper or Deepgram, fine-tuned on diverse medical conversations. Our NLP engineers develop pipelines that extract entities and map them into a SOAP structure in real time. We train the system to transform input like “knee pain x2 weeks” into meaningful Subjective entries within the AI SOAP note automation engine.

4. Backend and EHR Integration

Our developers build a modular backend using microservices for transcription, NLP processing, and formatting. We implement FHIR and HL7 APIs for secure, real-time EHR syncing. This allows our app to function both live during consults and asynchronously after visits, making AI SOAP note automation seamlessly integrated into provider systems.

5. Security, Testing, and HIPAA Compliance

We conduct penetration testing, audit trail validations, and simulate failure cases like dropped internet or transcription bursts. Our security team ensures encryption at rest and in transit, with granular access control across environments. This builds a fully HIPAA-ready AI SOAP note automation system that performs securely in all clinical settings.

6. Deployment and Maintenance

We deploy using Docker containers across HIPAA-compliant infrastructure like AWS or Azure, ensuring scalability. CI/CD pipelines enable continuous delivery, while our ops team monitors transcription accuracy and model health. We gather clinician feedback for ongoing model tuning, ensuring the AI SOAP note automation improves with every real-world interaction.

Cost to Develop an AI SOAP Note App like DeepScribe

Building an AI SOAP note automation app like DeepScribe involves multiple phases, from consultation to deployment. The overall development cost depends on the scope of features, tech stack, and regulatory needs specific to clinical documentation environments.

Total Estimated Cost: $65,000 – $130,000 

Note: These estimates vary by feature complexity, EHR integration, and compliance. Enterprise solutions with AI SOAP automation and real-time sync may need extra investment during scaling or onboarding.

Consult with IdeaUsher to get a tailored quote and strategic guidance for your AI SOAP note automation platform. Our AI healthcare tech experts will help you scope, plan, and launch a solution that fits your clinical and business goals.

Technology Stack for AI SOAP Note Apps

To ensure smooth, real-time SOAP note automation, every component in the tech stack must support clinical-grade accuracy, compliance, and performance. Below is a carefully selected technology mix we use to build robust AI SOAP note apps like DeepScribe.

1. AI/NLP Models

To drive accurate SOAP note automation, our team integrates leading medical NLP engines tailored for healthcare documentation.

• OpenAI GPT: Enables natural language understanding to turn spoken inputs into readable, clinically structured notes.
• AWS Comprehend Medical: Extracts medical entities like symptoms, conditions, and dosage from unstructured voice data with high precision.
• Google Med-PaLM: Trained on peer-reviewed medical literature, useful for enhancing clinical relevance and maintaining documentation standards.

2. Voice Processing

Capturing speech accurately in real-world clinical environments requires specialized ASR systems optimized for medical terms and accents.

• Whisper API: OpenAI’s robust speech-to-text model capable of handling overlapping speech and low-quality audio.
• Deepgram: Offers real-time transcription with medical vocabulary tuning and accent adaptation.
• Google Speech-to-Text: Supports multi-language environments with noise cancellation tailored for clinical background sound.

3. Frontend

We design responsive, voice-first interfaces that allow providers to review, edit, or command notes in real time with zero-click usability.

• React Native: Enables smooth, cross-platform development for both Android and iOS with rich UI flexibility.
• Flutter: Ideal for fast prototyping and creating consistent interfaces across mobile and web platforms.

4. Backend

To support NLP pipelines, transcription engines, and real-time note processing, we use scalable, high-performance server technologies.

• Python (FastAPI): Powers our asynchronous NLP services and API endpoints with low latency.
•  Node.js: Used for lightweight services, such as real-time microphone triggers or provider feedback routing.

5. Database

Storing structured health data requires compliant and query-efficient database systems that align with healthcare interoperability standards.

• PostgreSQL: Handles structured note storage, user settings, and logging with strong relational integrity.
• MongoDB: Stores unstructured inputs and transcribed text prior to NLP processing.
• FHIR-compliant storage: Ensures data portability and compatibility across EHRs and external clinical systems.

6. Cloud & DevOps

The app should deploy using secure, HIPAA-ready cloud infrastructure and automate scaling, logging, and updates via containerized workflows.

• AWS HealthLake: Provides secure, FHIR-native data storage and analytics for clinical records.
• Azure HealthCloud: Supports voice model training and medical AI inference with compliance-first configurations.
• Docker & Kubernetes: Enable flexible, modular deployments with autoscaling for transcription and processing pipelines.

Challenges to Solve in Building an AI SOAP Note App

Before building an AI SOAP note automation platform, it’s important to address specific technical and clinical hurdles that can affect accuracy, compliance, and usability. Below are the most common development challenges and how our team solves each one in production environments.

1. Handling Ambient Noise and Overlapping Speech

Challenge: In real-world clinical settings, patient conversations often involve interruptions, side conversations, or background noise. Capturing clear and accurate speech under these conditions is critical to generating usable SOAP notes but remains a major technical challenge.

Solution: Our team trains voice models on clinical acoustics and uses speaker diarization tuned for healthcare. We also apply noise reduction filters and custom ASR models optimized for AI SOAP note automation to accurately separate voices and clean overlapping segments during transcription.

2. Ensuring Clinical Accuracy and Compliance

Challenge: Medical documentation must meet strict regulatory and billing standards. If the AI captures incomplete, vague, or incorrect details, it could increase liability and reduce trust among clinicians using the app.

Solution: We embed medical NER models that tag and validate entities across SOAP structure. These are cross-referenced in real time with ICD-10 and CPT databases, enabling us to deliver AI SOAP note automation that meets billing and audit standards without manual correction.

3. EHR Integration Bottlenecks

Challenge: Every clinic or hospital uses a different EHR system, many of which have closed architectures or outdated APIs. This makes it difficult to sync SOAP notes seamlessly into the clinician’s existing workflow.

Solution: Our developers build middleware with FHIR-compliant APIs and HL7 connectors that act as a universal translator for EHR systems. This lets us map the output of the AI SOAP note automation engine directly into any EHR with minimal disruption or retraining.

Monetization Models for AI SOAP Note Automation App

Monetizing an AI SOAP note automation app involves more than just charging for access. You need flexible revenue models tailored to solo clinicians, health systems, EHR vendors, and third-party tools. Below are monetization strategies that balance scalability, usability, and recurring revenue.

1. Per-Provider SaaS Subscription

Offer a monthly or annual subscription model where individual clinicians or clinics pay to access the AI SOAP note automation platform. Scalable seat pricing allows flexibility for solo practices or larger care teams, with usage-based upgrades for advanced analytics and transcription quotas.

2. Custom Integrations for EHR Vendors

Monetize by providing tailored middleware and APIs for EHR platforms looking to integrate AI SOAP note automation natively. These integrations ensure seamless workflow within existing systems, offering deployment support, training, and long-term maintenance as part of enterprise contracts.

3. API Licensing for Documentation Tools

Package your platform’s SOAP structuring, medical NER, and note generation features into APIs that third-party medical software can license. This approach enables plug-and-play documentation enhancements, creating recurring revenue without needing to serve end-users directly.

4. Voice-to-Text Model as a Service

Offer the core ambient speech capture and medical transcription engine as a stand-alone service for developers. This model supports integration into telehealth platforms or scribe tools, charging by transcription minutes or character volume with options for real-time streaming.

5. White-Label Solutions for Hospitals

Sell fully branded versions of your AI SOAP note app to hospitals or health systems. These white-label deployments offer local customization, custom EHR integration, and data control while maintaining the core AI SOAP note automation features under the hood.

Conclusion

Building an AI SOAP note app like DeepScribe requires a thoughtful approach to both technology and clinical usability. From real-time speech recognition to accurate note structuring and secure EHR integration, every component must align with the needs of healthcare professionals. A well-designed solution not only reduces administrative burden but also enhances the quality of care by giving providers more time to engage with patients. With the right strategy, development process, and compliance measures in place, creating an AI-powered documentation tool can lead to meaningful improvements in clinical workflows and contribute to better patient outcomes across care settings.

Why Choose IdeaUsher for Your AI SOAP Note App Development?

Building an AI SOAP note tool like DeepScribe requires more than transcription capabilities. At IdeaUsher, we engineer clinical documentation systems that interpret patient-doctor conversations, automate structured note creation, and stay compliant with healthcare regulations.

Why Work with Us?

• Medical AI Precision: We develop NLP-powered platforms that ensure accurate SOAP formatting and clinical relevance.
• Tailored Development: From UI/UX to backend logic, we build apps that reflect your practice’s unique note-taking workflow.
• Reliable Track Record: We’ve delivered successful AI-powered platforms including Vezita, CosTech Dental App, Allied Health Platform, and Mediport.
• EHR and System Compatibility: Our apps are designed for easy integration with your practice’s existing systems.

See how we’ve helped healthcare providers reduce note fatigue and boost accuracy with custom AI tools. 

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