How to Make an AI Healthcare App like Sully AI

AI is transforming healthcare by improving diagnostic accuracy, personalizing treatment plans, and enhancing patient engagement. Platforms like Sully.ai are excellent examples of how AI can be used to revolutionize healthcare, providing intelligent solutions that analyze health data and offer actionable insights. These advancements are helping healthcare professionals make more informed decisions and provide better care to their patients.

In this blog, we will talk about how to make an AI healthcare app like Sully.ai. We will explore the role of AI in this app, the essential features to include, the steps necessary to develop a platform, and the potential challenges our developers might face, and how they solve those, because our developers have developed numerous AI healthcare apps for various enterprises. IdeaUsher has the expertise to help you launch your AI-powered healthcare app that can effectively meet the needs of both healthcare providers and patients.

What is Sully AI?

Sully.AI is an AI-powered healthcare automation platform designed to streamline clinical workflows and reduce administrative burdens for healthcare professionals. By integrating seamlessly with Electronic Health Records (EHR) systems, Sully.AI offers a suite of AI agents that assist throughout the patient journey from pre-visit screening to post-visit follow-up.

Business Model & Revenue Model: 

Sully.AI operates on a subscription-based business model, offering tiered pricing plans to healthcare organizations. Their offerings include:

• Pro Plan: Priced at $79 per provider per month.
• Premium Plan: Priced at $99 per provider per month.
• Enterprise Plan: Custom pricing tailored to the specific needs and scale of the healthcare institution.

Additionally, Sully.AI provides a free trial with 10 free visits per month, allowing potential customers to evaluate the platform’s capabilities before committing.

Why Sully AI Stands Out:

• Advanced AI Integration: Sully.AI leverages cutting-edge Artificial Intelligence technologies to automate various healthcare tasks that traditionally require manual input, such as medical scribing, dictation, coding, and patient communication. This helps healthcare providers focus on patient care rather than administrative tasks.
• Time Efficiency: By automating repetitive and time-consuming tasks, Sully.AI saves clinicians up to 2.8 hours a day on average. This boosts productivity, allowing providers to see more patients and provide higher-quality care.
• Clinician and Patient Focused: The platform alleviates clinician burnout by automating administrative tasks, giving providers more time to focus on patient care. This leads to a better overall experience for both healthcare professionals and patients.
• Seamless EHR Integration: Sully.AI integrates easily with a variety of EHR (Electronic Health Record) systems, which means healthcare organizations don’t have to overhaul their existing infrastructure to adopt the technology. It works within existing workflows for a smoother transition.
• Significant Impact on Efficiency: Healthcare organizations using Sully.AI have reported up to a 50% increase in efficiency, improving the throughput of patient visits and reducing delays in healthcare delivery.
• HIPAA Compliance and Data Security: Sully.AI operates within a HIPAA-compliant framework, ensuring the privacy and security of patient data, which is essential for healthcare providers in maintaining regulatory compliance and safeguarding sensitive health information.
• Proven Results: Over 300 healthcare organizations trust Sully.AI, showcasing its effectiveness in improving healthcare operations. Its proven track record in reducing clinician burnout by up to 80% speaks to the significant value it brings to practices and healthcare systems.
• User-Friendly and Customizable: The platform is designed to be intuitive and adaptable to different healthcare settings, with customizable features that meet the specific needs of individual clinicians, specialties, and organizations.

Why You Should Invest In Developing an AI Healthcare App like Sully AI?

The global agentic AI in healthcare market was estimated at USD 538.51 million in 2024 and is projected to reach USD 4.96 billion by 2030, growing at a CAGR of 45.56% from 2025 to 2030. This growth is driven by the increasing adoption of AI technologies in healthcare, enabling better decision-making, improved efficiency, and reduced clinical burnout.

Sully.ai, an AI healthcare app that automates documentation, raised $32.1 million in funding, bringing its total valuation to $5.3 billion. This highlights the strong investor interest in AI-driven healthcare solutions that can significantly reduce administrative workloads and improve clinical outcomes.

K Health, another AI-powered healthcare platform, raised $380 million in funding. The platform integrates AI to provide personalized healthcare services, and with $52 million in annual revenue, it demonstrates the growing demand for AI in healthcare.

Babylon Health, a leading AI healthcare provider, raised $635 million and was valued at $4.2 billion. By combining AI and telemedicine, Babylon Health is revolutionizing healthcare delivery, making it more accessible and efficient.

Investing in developing an AI-powered healthcare apps like Sully.ai positions you at the forefront of a rapidly expanding market. The impressive funding and growth of companies like Sully.ai, K Health, and Babylon Health show a clear trend towards the automation of healthcare workflows through AI. As the market continues to grow, these apps are likely to deliver significant returns, making it the perfect time to invest in AI-powered healthcare platforms.

Role of AI, EHR, FHIR, and Other Technologies in Sully.AI

This app is integrated with AI, EHRs, FHIR, and other technologies to automate administrative tasks and improve patient care. Below is a breakdown of how these key technologies play a role in Sully AI:

1. How Artificial Intelligence works in Sully.AI

• Medical Scribing and Documentation: AI is at the core of Sully.AI, automating real-time transcription and converting clinician-patient interactions into structured, accurate medical notes. The AI-powered medical scribe helps reduce the time spent on manual documentation, improving efficiency and reducing clinician burnout.
• Medical Dictation: AI-driven speech-to-text technology transcribes the clinician’s spoken words into text, making it faster and easier to document patient information without the need for manual typing.
• Medical Coding: AI automates the coding process by analyzing clinical data and assigning appropriate CPT (Current Procedural Terminology) or ICD (International Classification of Diseases) codes for billing and insurance purposes, which reduces the risk of errors and increases the speed of claims processing.
• Clinical Decision Support: AI assists healthcare providers by analyzing patient data and suggesting potential clinical actions or identifying patterns that might not be immediately apparent, helping clinicians make data-driven decisions.

2. EHR Integration

• Data Access and Automation: Sully.AI integrates with EHR systems that store patient data like medical history, labs, medications, and allergies. It uses this data to automate and improve documentation, data entry, and administrative tasks, boosting efficiency for clinicians managing patient records.
• Real-Time Updates: Sully.AI keeps EHRs up-to-date in real-time, ensuring clinicians have current, accurate information on patients’ condition, medications, and treatment plans. This improves care coordination and reduces errors.
• Workflow Optimization: By automating routine tasks like scheduling, patient communication, and documentation, Sully.AI reduces the administrative burden on clinicians and streamlines healthcare workflows within the EHR system, leading to improved clinical productivity.

3. FHIR (Fast Healthcare Interoperability Resources)

• Interoperability: FHIR is a standard for electronic health data exchange. Sully.AI uses FHIR to ensure seamless interoperability with other healthcare systems, including EHRs. By using FHIR APIs, Sully.AI can access and share patient data in real-time, enhancing communication between healthcare providers and institutions.
• Data Sharing: FHIR enables Sully.AI to securely exchange structured data like patient demographics, medications, lab results, and other clinical info with FHIR-compliant systems. This promotes sharing vital data among healthcare providers and third-party apps, enhancing care coordination.
• FHIR-based Apps: Sully.AI supports the integration of SMART on FHIR applications, which allows developers to build custom apps that interact with EHRs using FHIR, enabling further functionality and enhancing Sully.AI’s capabilities.

4. Other Key Technologies in Sully.AI

• Natural Language Processing: NLP is a key component of Sully.AI’s AI models, allowing the system to process and interpret free-text clinical notes. It enables the system to extract meaningful data from unstructured text and transform it into structured, actionable information.
• Speech Recognition: For medical dictation and transcription, Sully.AI uses speech recognition technology to transcribe verbal communication between clinicians and patients accurately, which is then structured into EHR records.
• Machine Learning: Sully.AI’s machine learning algorithms continuously learn from clinical data to improve their accuracy and adapt to specific workflows. This leads to more precise medical coding, better decision support, and improved scribing and documentation accuracy over time.

Designing a User-Centric AI Healthcare Platform

User experience is not just about visuals; it defines how patients and clinicians interact with the system. Every design choice must support trust, accuracy, and usability across roles.

1. Why UX Matters in AI Healthcare Platforms

Trust in healthcare starts with the interface. Clean, accessible designs help patients feel safe while guiding clinicians through fast, efficient workflows. Poor UX increases friction, reduces app usage, and delays trial timelines. Design must account for different needs between patients and clinical staff at every step of the journey.

2. Designing for Clinical Workflow Compatibility

Interfaces should match how clinical teams work, not force change. Reducing clicks, simplifying access, and aligning with decision flows improve adoption. Support across desktop, tablet, and mobile ensures usability in different hospital or research sites without affecting function or compliance.

3. Patient-Facing Design Considerations

Design should prioritize readability, accessibility, and visual clarity. Features must support multilingual interfaces, low-literacy users, and diverse cultural norms. Visual aids and simplified summaries help patients understand next steps post-visit. Clear navigation and adaptable content improve trust and usability across different regions and patient demographics.

4. Building Trust Through Transparency and Control

Transparent AI design reduces confusion. Features such as editable responses, visible confidence scores, and manual review options allow users to follow how AI decisions are made. Interface cues like tooltips and alerts help eliminate the sense of a “black box” and foster ongoing trust in automation.

Key Features to Include in Your AI Healthcare App

When developing an AI healthcare app, adopt a system-level approach rather than focusing on isolated tasks. Effective platforms support the entire patient journey, from intake to follow-up. The following features are critical for building a scalable, compliant, and clinically valuable AI solution.

1. Real-Time AI Scribe with Clinical Understanding

An AI scribe should be capable of more than basic transcription. It must generate structured EHR-ready notes in real time while understanding clinical context such as patient symptoms, diagnoses, and treatment plans. A robust scribing system should also support decision-making by providing evidence-informed suggestions without interrupting physician workflows.

2. AI Receptionist and Intake Agent

To streamline front-desk operations, an AI intake agent should manage appointment scheduling, check-ins, and digital intake forms with minimal human intervention. The feature should integrate with the clinic’s systems, handle data capture accurately, and reduce administrative overhead while improving the pre-visit experience for patients.

3. AI Medical Coder and Billing Assistant

Automating the medical coding process is critical for improving billing accuracy and reducing reimbursement delays. This feature should convert consultation data into precise CPT and ICD codes, identify missing documentation, and support real-time claim submission. Reliable coding automation contributes directly to revenue cycle optimization.

4. AI Medical Interpreter and Multilingual Support

Multilingual capabilities are essential for equitable care delivery and ensuring compliance with language access regulations. The interpreter module should support real-time, two-way communication in multiple languages during consultations and documentation. This improves provider-patient understanding and helps remove communication barriers in diverse clinical settings.

5. Post-Visit Summary and Follow-Up Agent

An effective follow-up agent should automatically generate patient-friendly summaries, medication guidance, and care instructions. These outputs must be delivered through secure and accessible channels such as email, SMS, or mobile apps. This feature plays a key role in improving patient adherence and reducing post-care communication gaps.

6. Virtual Nurse Agent for Between-Visit Support

A virtual nurse agent can handle routine patient inquiries and assist with low-risk triage. It should provide accurate self-care recommendations, escalate urgent concerns when necessary, and help reduce the communication burden on clinical staff, particularly in high-volume care environments.

7. Personalized AI Behavior Through Physician-Centric Training

To ensure strong adoption, the AI platform must be able to adapt to individual physician preferences. This requires training on real clinical workflows, documentation patterns, and user behavior. Allowing clinicians to fine-tune the AI’s behavior using natural input can significantly enhance usability and trust.

8. Seamless EHR and EMR Integration

Integration with existing EHR systems is essential. The platform should support FHIR or HL7 standards and offer compatibility with leading vendors such as Epic, Cerner, and Athenahealth. The goal is to ensure smooth data exchange without requiring major workflow changes or additional training for staff.

9. Built-In Compliance, Security, and Data Privacy

Healthcare AI platforms must meet the highest data protection standards. The system should include end-to-end encryption, follow HIPAA-compliant architecture, and support certifications like HITRUST or ISO 27001. Additionally, it is important that customer data is not used for training, and clients maintain full control over encryption keys.

Training the AI: Clinical Data, Accuracy, and Validation

For AI in healthcare, success depends on how it’s trained and validated. Accuracy, ethics, and reliability must be embedded in the process from dataset selection to live testing.

1. What Data Sources Are Used for Training

Clinical AI platforms rely on de-identified transcripts, SOAP notes, structured workflows, and where needed, synthetic datasets that mimic real clinical behavior. General-purpose models fall short without this adaptation. Using clinical-grade datasets ensures safety, contextual accuracy, and alignment with how actual care is delivered inside regulated environments.

2. Customizing the Model for Physician Behavior

Models are adapted based on how physicians interact with digital tools, including documentation habits, specialty-specific phrasing, and workflow patterns. Tuning the AI to match these behaviors supports higher adoption and allows systems to feel more intuitive without requiring physicians to change how they already work.

3. Ensuring Accuracy and Clinical Safety

Accuracy is enforced using real-world validation, peer-reviewed reference sets, and medical ontologies like ICD, SNOMED, and LOINC. Output is reviewed for clinical soundness, reducing the risk of hallucinations or misguidance. Benchmark testing helps define acceptable thresholds before clinical deployment in sensitive care environments.

4. Ethical Training and Data Governance

Ethical training begins with clear data consent policies. Only datasets with approved usage rights are used. Every stage includes bias checks, audit logs, and adherence to frameworks like HIPAA and GDPR. Training pipelines maintain full transparency to protect patient identity and support long-term accountability.

Development Steps for Building an AI Healthcare App Like Sully.ai

To develop a platform like Sully.ai, we follow a structured and domain-specific process. Each step focuses on delivering clinical accuracy, modular AI design, and strong compliance aligned with real healthcare challenges.

1. Define Patient Workflow and AI Agent Roles

We start by mapping the entire patient journey to identify where automation adds clinical value. Our team defines modular AI agents like scribe, intake, billing, nursing, and communication assistants to reflect real-world healthcare operations. Each agent is designed to operate independently but integrate seamlessly into a coordinated, end-to-end workflow that mirrors actual clinical environments.

2. Prioritize High-Impact Use Cases

Our team works with healthcare providers to identify high-friction, high-impact tasks. We prioritize features that directly reduce physician burden, such as real-time documentation and medical coding. Every use case is selected based on measurable outcomes like hours saved, throughput increased, or errors reduced, ensuring the platform delivers immediate operational and clinical benefits.

3. Build or Fine-Tune a Clinical AI Model

Our experienced AI developers either fine-tune an existing model or build a domain-specific LLM trained on real physician interactions, workflows, and task patterns. This allows us to create a personalized engine similar to Doctor-LM that understands clinical context, adapts to each doctor’s preferences, and ensures hyper-relevant, real-time responses in patient care scenarios.

4. Design Agent-Based System Architecture

Our engineers build a modular system architecture, where each AI agent functions as a microservice with defined tasks. This design allows for parallel development, scalability, and clean handoffs between agents. The architecture also supports future upgrades and custom workflows tailored to specific specialties or clinic operations.

5. Develop Real-Time Voice and NLU Engine

We implement a real-time voice interface that listens ambiently and captures clinical conversations across multiple accents. Our NLU engine extracts context-aware data like symptoms, assessments, and plans, then structures them into EHR-ready formats. This is foundational for building a responsive and compliant AI Scribe agent.

6. Enable EHR Integration with FHIR/HL7

Our integration team ensures seamless data exchange between the AI platform and systems like Epic, Athenahealth, or CharmHealth. Using FHIR, HL7, and vendor-specific APIs, we sync notes, appointments, codes, and patient records without disrupting existing workflows. This allows for easy adoption and minimal clinical downtime.

7. Add Multilingual Communication Support

Our software developers will integrate multilingual capabilities to support 20+ spoken and written languages within the platform. This allows providers to interact with patients more effectively and ensures compliance with language access regulations. Our team designs interpreter workflows that support real-time, bidirectional translation within scribe, nurse, and patient-facing agents.

8. Implement Compliance and Data Security

We build all systems to meet HIPAA, HITRUST, and ISO 27001 standards. Our developers enforce end-to-end encryption, access control, and data masking from the start. Importantly, we ensure that no customer data is used for AI training, and clients maintain full control of their encryption keys and data privacy policies.

9. Launch Pilot and Optimize for Scale

We deploy an MVP in a controlled clinical environment with selected agents and real users. Our team monitors feedback on agent usability, note accuracy, and workflow alignment. Based on real-time results, we iterate, refine, and prepare the platform to scale across departments, ensuring clinical fit and measurable performance improvements.

10. Post-Launch Strategy for AI Healthcare Apps

After the initial rollout, long-term success depends on ongoing optimization. We continue monitoring AI agent behavior, clinical alignment, and system uptime. Our developers provide model fine-tuning, EHR updates, security reviews, and feature scaling based on new use cases. This ensures your platform remains compliant, relevant, and high-performing in real-world healthcare environments over time.

Cost To Build an AI Healthcare App like Sully AI

Building an AI healthcare app like Sully.ai involves several factors that influence the overall cost, such as app complexity, AI features, and integration with existing healthcare systems. Understanding these elements is crucial for accurate budget estimation.

Total Estimated Cost: $65,000 – $150,000

This cost breakdown includes estimates for development across various phases, but actual costs can vary based on the complexity of the app, development team rates, and regional pricing differences.

Consult with IdeaUsher for expert guidance and customized AI healthcare app development. We provide comprehensive solutions, ensuring your app, like Sully.AI, meets security, compliance, and functionality standards while smoothly handling integration and regulatory challenges. 

Challenges in Building a Healthcare App Like Sully.AI

Building a healthcare app like Sully.AI, which uses AI for personalized health insights, predictive analytics, and monitoring, faces unique challenges. These include ensuring data privacy, maintaining prediction accuracy, and navigating regulations. Here are key challenges and strategies for overcoming them.

1. Data Privacy and Security Challenges

Challenge: In the healthcare sector, data privacy is critical. Ensuring secure handling and compliance with regulations like HIPAA and GDPR is essential. AI-driven healthcare apps handle vast amounts of personal data, and breaches or mishandling can have severe consequences.

Solution: To address these challenges, we will implement end-to-end encryption for all data in transit and at rest, applying data anonymization during AI training to protect patient identities. Using OAuth 2.0, multi-factor authentication, and RBAC will ensure only authorized users access sensitive info. Regular security audits and penetration testing will identify and mitigate vulnerabilities.

2. AI and Accuracy Issues

Challenge: AI models in healthcare, such as those in Sully.AI, are only effective if trained on accurate and diverse data. Bias in training data and inaccurate AI models can result in incorrect diagnoses or treatment suggestions, affecting patient safety.

Solution: To improve accuracy, we will continuously train AI models with diverse real-world data and collaborate with healthcare professionals for review and oversight. We will use bias mitigation like stratified sampling to represent diverse populations and incorporate AI explainability to foster trust and understanding.

3. Regulatory Hurdles and Certifications

Challenge: Navigating complex healthcare regulations such as HIPAA, GDPR, and FDA medical device regulations is challenging for AI apps like Sully.AI. If the app provides medical recommendations or diagnostics, obtaining necessary certifications (e.g., FDA or CE) is crucial.

Solution: To overcome regulatory hurdles, we will engage legal experts early to ensure we understand and meet HIPAA, GDPR, and other standards. If needed, we’ll classify Sully.AI as a medical device and work with FDA or CE regulators for certifications. We will keep comprehensive documentation during development, testing, and compliance, ensuring traceability and adherence. Regular audits will help us stay compliant with evolving regulations.

Conclusion

Building an AI healthcare app like Sully.ai offers immense potential to improve patient care and streamline healthcare processes. By integrating advanced AI algorithms, healthcare providers can gain valuable insights, enhance decision-making, and personalize treatment plans. Developing such an app requires a solid understanding of healthcare data, machine learning, and regulatory standards to ensure both accuracy and compliance. With the right combination of technology and thoughtful design, an AI healthcare app can play a key role in transforming healthcare delivery, ultimately benefiting both patients and healthcare professionals by improving outcomes and efficiency.

Why Choose IdeaUsher for Your AI Healthcare App Development?

At IdeaUsher, we specialize in building AI healthcare apps like Sully.AI that streamline clinical workflows, reduce administrative burdens, and enhance patient care. Whether you’re developing an AI-powered clinical decision support system, automated documentation platform, or personalized health assistant, our team is here to help you create an impactful solution.

Why Work with Us?

• AI & Healthcare Expertise: We leverage the latest AI technologies to build intelligent healthcare apps that integrate seamlessly with existing healthcare systems, improving operational efficiency and patient outcomes.
• Custom Solutions: From concept to deployment, we create tailored AI healthcare solutions to meet the unique needs of your healthcare organization, ensuring high-quality care.
• Proven Success: We’ve worked with companies like Vezita, CosTech Dental App, Allied Health Platform, and Mediport, delivering AI-driven healthcare solutions that streamline workflows and enhance patient care.
• Scalable & Secure: Our solutions are scalable and secure, ensuring long-term success while maintaining compliance with healthcare standards and regulations.

Explore our portfolio to see how we’ve helped healthcare businesses integrate AI into their apps for improved operational efficiency and better patient care.

Reach out today for a free consultation, and let us help you develop an AI healthcare app that enhances your healthcare organization’s performance and outcomes!

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