How to Build an EHR Summary App like Notable AI

It’s not easy being a healthcare worker these days. Between the endless paperwork and long hours, it’s no surprise that burnout is so common. But there’s hope on the horizon with AI. By taking over tasks like managing patient data, AI is freeing up more time for clinicians to focus on what they do best, caring for patients. 

By 2025, AI tools like EHR summarization could be a game-changer for reducing stress and improving efficiency in healthcare.

Physician burnout is a growing problem, and AI-powered EHR summary apps are a powerful tool in alleviating some of that burden. We understand how these apps can automate the summarization of patient data, making it easier for doctors to quickly access key information, prioritize care, and make informed decisions. With extensive experience in healthcare technology, IdeaUsher knows exactly what it takes to develop these intelligent systems. That’s why we’ve put together this blog to help you build an AI EHR summary app that streamlines clinical workflows, reduces clinician burnout, and improves patient care and security.

Key Market Takeaways for EHR Summary Apps

According to GrandViewResearch, the global market for EHR apps is expected to grow rapidly, with a projected annual growth rate of 8.87% from 2024 to 2030. This surge is driven by the growing digitalization of healthcare, wider adoption of EHR systems, and the increasing use of mobile devices by both clinicians and patients.

Source: GrandViewResearch

EHR summary apps are becoming more popular as healthcare providers seek tools to streamline data access and meet regulatory digital health requirements. These apps enable clinicians to quickly generate patient summaries, track health trends, and reduce administrative tasks. Patients also benefit from better access to their records, which fosters greater engagement and empowers them to manage their health.

Apps like Epic’s “Cheers” and Cerner’s “Virtual Agent” highlight the value of AI in this space. They automate administrative work, create real-time patient summaries, and help clinicians focus on what matters most, providing high-quality care while closing care gaps.

What is the Notable AI App and How Does It Work?

Notable AI is a cutting-edge automation platform designed to optimize healthcare workflows by using artificial intelligence to handle repetitive administrative tasks. It integrates seamlessly with EHR systems like Epic, Cerner, and Athenahealth, simplifying processes for clinicians and staff. Here’s a breakdown of how Notable AI functions and what sets it apart from traditional EHR systems:

Here are some of the core functionalities of Notable,

Automates Data Entry & Summarization:

Notable AI streamlines the management of clinical notes by automatically extracting relevant data from unstructured information. This reduces the time spent on manual data entry and helps organize medical records more efficiently. Additionally, it generates real-time summaries of doctor-patient conversations, so clinicians don’t have to document every detail after each visit.

AI Agents Execute Tasks

Rather than relying on clinicians to trigger tasks, Notable AI’s intelligent agents take action automatically. These agents handle important tasks like scheduling follow-up appointments or sending referrals, all based on up-to-date patient information. This approach reduces administrative burdens and helps improve overall workflow efficiency.

Seamless EHR Integration

Notable AI easily integrates with major EHR systems such as Epic and Cerner. It syncs data bidirectionally, ensuring that patient records remain accurate and secure. The platform adheres to FHIR standards, allowing for smooth data exchange without disrupting existing workflows.

How AI Agents Work in Notable AI?

Notable AI’s agents go beyond being simple chatbots. These agents actively complete tasks autonomously based on the latest data in the EHR, continuously improving through feedback and workflow patterns.

Key Capabilities of AI Agents:

• Patient Intake Automation: The agents automatically pre-fill EHR fields using historical data and new patient forms, saving time during intake.
• Smart Scheduling & Referrals: The system automatically schedules follow-up appointments and sends referrals based on visit documentation and insurance details.
• Continuous Self-Learning: Notable AI’s agents learn from clinician corrections and ongoing usage to improve task execution and predict future needs.

Key Components of Notable AI

1. EHR Summarization Engine:

Notable AI’s EHR Summarization Engine uses advanced NLP models to parse medical records. It extracts critical data such as diagnoses, medications, and treatment plans, making it easier for clinicians to access and understand patient information without sifting through lengthy notes.

2. Voice-to-Text Conversion:

The system also converts doctor-patient conversations into structured EHR notes. This voice-to-text conversion captures the details of the conversation accurately, reducing the need for manual documentation and ensuring that all important information is recorded in real time.

3. Task Automation Layer:

Notable AI’s task automation goes beyond simple notifications. For example, when a physician documents a referral, the system automatically finds an in-network specialist, sends the referral, and updates the EHR, ensuring tasks are completed promptly and accurately without clinician intervention.

4. No-Code Workflow Builder

The Flow Studio feature allows healthcare providers to create custom automation rules without needing to write code. This flexibility enables hospitals to tailor workflows to specific needs, such as flagging patients for follow-up or generating post-op instructions automatically based on set conditions.

5. Deep EHR Integrations

Notable AI integrates deeply with EHR systems like Epic, Cerner, and Athenahealth using FHIR/HL7 APIs. These secure and accurate integrations ensure smooth data exchange between systems, keeping patient records up to date without disrupting existing healthcare processes.

Why Notable AI Stands Out?

When compared to traditional EHR systems, Notable AI offers several advantages:

Why Healthcare Businesses Are Embracing EHR Apps Like Notable?

Healthcare businesses are embracing EHR apps like Notable AI to streamline operations, reduce administrative burdens, and improve patient care through real-time data and automation. This boosts staff productivity while supporting value-based care models.

Streamlining Operations

Healthcare organizations are moving away from manual processes that slow down operations. Notable AI helps eliminate time-consuming tasks like data entry and document management, allowing staff to focus on more critical patient care activities. This leads to smoother workflows and fewer delays, benefiting both providers and patients.

Enhancing Staff Efficiency

With automation, healthcare staff can work more efficiently. By reducing the time spent on administrative duties, staff can focus on meaningful interactions with patients, improving care quality. Notable AI’s technology helps reduce burnout by taking care of repetitive tasks, leading to happier and more engaged staff.

Improving Patient Engagement

Healthcare is becoming more patient-centered, and Notable AI makes it easier to engage patients throughout their care journey. The app’s real-time data capabilities ensure that healthcare providers have up-to-date patient information, allowing them to communicate better and make more informed decisions that benefit the patient experience.

Supporting Value-Based Care

As the healthcare industry shifts toward value-based care, Notable AI supports the transition by providing tools that allow healthcare businesses to make smarter, evidence-based decisions. With better data management and real-time analytics, organizations can align with value-based care models that prioritize patient outcomes and cost-efficiency

Benefits of Building an EHR Summary App Like Notable

Building an EHR summary app like Notable can save healthcare providers tons of time by automating data summarization. It makes patient onboarding faster, streamlines billing, and boosts efficiency in every step of the process.

Technical Benefits

1. Summarizing Structured & Unstructured Data

An EHR summary app can process and summarize both structured (e.g., lab results) and unstructured data (e.g., clinical notes) in real time. This allows healthcare providers to quickly access critical patient information, improving decision-making and reducing time spent searching for data.

2. Scalable AI for Healthcare Workflows

Scalable AI pipelines ensure the system can handle growing amounts of patient data without losing performance. This flexibility makes it ideal for both small practices and large healthcare networks, adapting to the needs of various environments.

3. HIPAA & HL7 Compliant Secure Architecture

To ensure patient privacy and data protection, the app must meet HIPAA and HL7 standards. Secure architecture with encryption safeguards sensitive healthcare data, making sure it’s compliant with necessary regulations.

Business Benefits

1. Reduced Administrative Burden

Automating data summarization frees clinicians from time-consuming administrative tasks. This lets them focus more on patient care, reducing burnout and improving workflow efficiency.

2. Faster Patient Onboarding & Throughput

With quick access to patient history and current health status, providers can speed up onboarding and appointment processes. This leads to reduced wait times and increased patient throughput.

3. Improved Billing & Revenue Cycle

Clear and accurate EHR summaries minimize errors in billing and coding, reducing claim denials. This helps optimize the revenue cycle and ensures faster, more accurate payments.

4. Higher Satisfaction for Patients & Providers

A streamlined, efficient system increases patient satisfaction by reducing wait times and improving care quality. Providers also benefit from reduced administrative burden, leading to greater job satisfaction.

Features to Include in an EHR Summary App like Notable AI

After developing numerous EHR summary apps for healthcare providers, we’ve learned what works best for users. Through trial and feedback, we’ve identified key features that truly make a difference in improving workflow efficiency, enhancing clinician decision-making, and streamlining administrative tasks. Here are the features we’ve found to be a hit with users in this type of app:

1. Role-Based Summaries

One feature that always resonates with users is the ability to toggle between summary views tailored to specific roles. For example, a cardiologist might prefer a summary that highlights cardiac history, while a primary care physician gets a broader, more general overview. 

2. “Drill-Down” and Source Traceability

Clinicians love the ability to drill down into any data point within the summary and instantly trace it back to the original source note in the EHR. This feature builds confidence in the AI-generated summaries and makes it easy for users to verify the information when needed, ensuring they can trust the data they’re working with.

3. Timeline View

The interactive timeline is another feature that gets great feedback. It organizes important health events—such as diagnoses, surgeries, and medications—into a visual, chronological format. Clinicians can quickly see the patient’s health journey, making it easier to spot trends and make informed decisions at a glance.

4. Guided Workflow Prompts

For administrative tasks, interactive prompts work wonders. After summarizing a referral request, for example, a simple “Click to Initiate Prior Authorization” button pops up, auto-filling most of the required info. This helps save valuable time by reducing manual data entry and streamlining the process.

5. AI-Powered Patient Outreach

Automating patient outreach is a game changer. With AI, clinicians can send personalized SMS or voice messages to patients for things like appointment reminders or follow-up surveys. This feature is an easy win for improving patient engagement and ensuring no one misses important care-related communications.

6. Intuitive Workflow Builder 

The ability to build and customize workflows without coding is another hit. Non-technical users can use a drag-and-drop interface to create AI-driven automation, like sending automatic reschedule prompts when a patient misses an appointment. This allows healthcare teams to quickly adapt the app to their unique needs without needing developer assistance.

7. Intelligent Documentation Generation

Ambient AI-powered scribe functionality has been a favorite among clinicians. The AI listens in real-time, transcribes the conversation, and drafts clinical documentation such as SOAP notes or progress reports. This feature significantly cuts down on charting time, freeing up clinicians to focus more on patient care.

8. Conversational Clinical Query

Being able to ask natural language questions about a patient’s chart has been a major plus. Clinicians simply type or speak a question, like “What’s the full medication history?” and the AI quickly delivers a summarized, precise answer. This saves clinicians time by providing quick access to important patient data.

9. Evidence-Based Support at the Bedside

When summarizing a rare condition, the AI pulls in the latest clinical guidelines or research papers for clinicians to review. Having this evidence-based support right at the bedside helps clinicians make better-informed decisions and ensures they have the most up-to-date information available.

10. Adaptive Summary Preferences

Finally, AI that adapts to a clinician’s preferences over time is a feature that users appreciate. Whether it’s prioritizing the latest vitals or highlighting allergy alerts, the app learns what each clinician values most and automatically customizes future summaries to align with those preferences.

How to Build an EHR Summary App Like Notable AI?

We’ve years of experience in creating custom EHR summary apps designed to streamline healthcare workflows. We focus on practical solutions that help clinicians, nurses, and administrative staff work more efficiently, improving both patient care and operational effectiveness. Here’s a step-by-step breakdown of how we build an EHR summary app like Notable AI.

1. Defining Use Case & Roles

We begin by understanding the healthcare setting and the specific goals of the app. Whether it’s primary care, emergency, or billing, we define clear use cases. Then, we map out workflows for each user role, physicians, nurses, and billing agents, to make sure the app meets their specific needs and enhances productivity.

2. Setting Up Data Architecture

We integrate the app with existing EHR systems using FHIR/SMART APIs for smooth data flow. Both structured (lab results) and unstructured data (clinical notes) are made accessible, while middleware ensures the data is normalized and easily integrated across the system for consistency and ease of use.

3. Building AI Summarization Engine

To create accurate summaries, we use powerful NLP models like BioBERT, ClinicalBERT, or MedSpaCy. Depending on the requirements, we choose between extractive or abstractive summarization techniques. The goal is to distill complex medical data into clear, actionable insights that clinicians can easily read and use.

4. Integrating AI Agents

We add AI-powered agents that automate routine tasks like scheduling, authorizations, and closing care gaps. This reduces the administrative burden on staff and speeds up operations. Customizable workflows, based on AI and rule-based logic, allow each department to automate tasks with a simple drag-and-drop interface.

5. Designing Clinician Interface

We design an intuitive interface that gives clinicians quick access to patient summaries, task lists, and alerts. The dashboard is simple to navigate, and drill-down options allow clinicians to view source documentation when needed. The app is responsive, supporting both desktop and mobile platforms.

6. Ensuring Compliance & Monitoring

To meet HIPAA and other compliance standards, we implement encryption, multi-factor authentication, and audit logs. We also build in explainability tools like confidence scores and logic trails to build trust in the AI. Monitoring tools track app performance, allowing us to continually optimize the system and ensure it meets clinical needs.

Key Challenges in Building an EHR Summary App

After working with many clients, we’ve experienced the common challenges of AI integration in healthcare. The good news? We’ve found ways to overcome them, and we’re happy to pass along what works.

Challenge 1: Handling Unstructured EHR Data

Around 80% of clinical data is unstructured—think doctor’s notes, PDFs, and scanned documents. Traditional NLP can struggle with medical jargon and abbreviations. It’s messy, and it can slow things down.

How to Fix It:

• Specialized NLP & Pretrained Models: We use models like GPT-4 and Med-PaLM, which we fine-tune on medical datasets for better accuracy. That way, the AI gets the context right.
• NLP Tools: Amazon Comprehend Medical and Google Healthcare NLP help with entity recognition and mapping medical concepts. They make life easier.
• Post-Processing for Abbreviations: We create rules to handle abbreviations like “HTN” → “Hypertension,” so nothing gets lost in translation.

Notable AI uses BERT-based models trained on tons of de-identified clinical notes to improve understanding of context. It works.

Challenge 2: Building Clinician Trust

Doctors often don’t trust AI because it’s a “black box” to them. They don’t always understand how the AI came to its conclusions, and there’s the fear of hallucinations (incorrect or fabricated data).

How to Fix It:

• Transparency Features: We make sure clinicians can click and see the original text behind AI-generated notes with a “Show Source” feature. No more mystery.
• Accuracy Tracking: We implement dashboards that show error rates by specialty. This helps clinicians see where things might go wrong.
• Human-in-the-Loop: For critical summaries (like discharge instructions), we make sure there’s an MD review before anything goes live.
  

Challenge 3: EHR Integration Complexity

EHRs use different standards (FHIR, HL7, proprietary APIs), and systems like Epic and Cerner often require custom interfaces to sync data. It’s a headache.

How to Fix It:

• Standardized Access with FHIR: We lean on SMART on FHIR for smooth, standardized EHR integration.
• Middleware Solutions: Redox Engine helps normalize data, so we’re not reinventing the wheel with each system.
• Work with Integration Experts: Collaborating with specialists like IdeaUsher makes the process much smoother, especially with legacy systems and real-time triggers.

Challenge 4: Scaling Across Departments

AI models that work well in primary care often fail when applied to specialized departments like oncology or orthopedics. Plus, each department has unique workflow needs.

How to Fix It:

• Specialty-Specific AI Agents: We deploy separate AI models for different specialties, so the solution is always tailored to the department.
• Modular, No-Code Workflow Builders: Let departments build their own custom workflows. For example, in post-op care, we can automatically generate PT referrals based on rules they set up.
• Kubernetes Scaling: For high-demand departments like ER or urgent care, we use Kubernetes to spin up resources as needed. No bottlenecks.

Essential Tools & APIs for Building an EHR Summary App

When developing an AI-powered EHR summarization app like Notable AI, it’s crucial to have the right tech stack to support real-time NLP processing, EHR integration, and scalable healthcare workflows. Here’s a breakdown of the key technologies to use for each stage of development.

1. Frontend & User Interface

Web Applications:

React.js / Angular: These are perfect for building dynamic EHR-like interfaces with complex data visualizations. React is especially beneficial for real-time note editing, like what you see in Notable AI’s provider dashboard.

Mobile Apps:

React Native / Flutter: These cross-platform frameworks are ideal for developing clinician-facing mobile apps, enabling features like voice-to-text for note-taking during rounds, allowing flexibility for healthcare professionals on the go.

2. Backend & Development Frameworks

Core Backend

Node.js (Express) is great for real-time event processing, like live transcription with WebSockets. Django and Flask are ideal for handling data-heavy APIs, with built-in security to meet healthcare regulations like HIPAA.

Databases

PostgreSQL is perfect for storing structured data like patient metadata, user roles, and audit logs, ensuring data integrity. MongoDB, on the other hand, excels at managing unstructured clinical notes and storing NLP training data, making it ideal for flexible, scalable storage.

Caching & Performance

Redis enhances performance by caching frequently accessed EHR data, significantly speeding up the real-time summarization process and reducing database load.

3. Cloud Infrastructure

Pro Tip: Notable AI uses AWS GovCloud for HIPAA-covered entities to ensure security and compliance.

4. AI/NLP Frameworks

Pretrained Clinical Language Models

Hugging Face Transformers, like BioBERT and ClinicalBERT, are fine-tuned to efficiently extract medical concepts from clinical data. Meanwhile, TensorFlow and PyTorch are essential for building custom summarization models, especially transformer-based models that condense and simplify clinical notes.

Specialized Medical NLP Libraries

MedSpaCy / ScispaCy: These are rule-based libraries that assist with clinical entity recognition, like identifying drug names and lab values.

Example Workflow:

Speech-to-text (Whisper API) → Clinical NLP (BioBERT) → Structured summary (custom Python post-processing)

5. Healthcare Integration Standards

EHR Connectivity

FHIR is the modern, RESTful API standard widely used by EHR systems like Epic, Cerner, and Athenahealth for seamless data exchange. While HL7 v2 is an older protocol, it remains crucial for connecting with legacy hospital systems that have not yet adopted newer standards.

Middleware Solutions:

SMART on FHIR: This enables secure embedding of applications within existing EHR workflows, such as launching apps from an Epic sidebar.

Redox Engine / Health Gorilla: These provide pre-built connectors for 500+ EHRs/PMS systems, making it easier to:

• Auto-pull patient demographics
• Push AI-generated summaries back to EHR systems
• Trigger billing and coding events

6. Compliance & Security Tools

• AWS KMS / Azure Key Vault: These tools ensure encryption of Protected Health Information (PHI) both at rest and in transit.
• OpenID Connect (OAuth 2.0): Implement role-based access control to differentiate between clinicians and admins.
• AuditDash: A tool to track every AI-generated action, ensuring HIPAA compliance during audits.

Use Case: Automating Triage in a Multi-Specialty Hospital

One of our clients, a 500-bed academic medical center, approached us with a pressing challenge that was slowing down care. Here’s how we solved it for them.

The Challenge: Inefficient Triage Slowing Down Care

The hospital faced several inefficiencies in its triage process: nurses were spending more than 8 minutes per patient manually reviewing Epic charts, leading to delays in care. Additionally, 40% of overdue preventive screenings were missed, and 25% of cases experienced billing delays due to incomplete documentation, affecting both patient outcomes and operational efficiency.

The Solution: EHR Summary App Integrated with Epic

To address these issues, we implemented IdeaUsher’s EHR Summary App, integrated directly with the hospital’s Epic EHR system, with the following features:

Smart Triage Summaries

Nurses were spending too much time reviewing patient charts. To solve this, we implemented AI-generated, one-page summaries delivered to nurses 15 minutes before intake. These summaries included essential information like active diagnoses, medications, recent lab results, and risk alerts (e.g., “Uncontrolled diabetes – A1C 9.2”), so nurses could be fully prepared for the patient visit.

Autonomous Care Coordination

Our client needed a more efficient way to manage preventive care and follow-up appointments. We introduced AI agents that auto-schedule preventive screenings (like mammograms and colonoscopies) and follow-ups (e.g., post-op checks). The system also prioritizes high-risk patients in the triage queue, ensuring the most urgent cases are handled first.

Clinician Decision Support

We developed a unified dashboard that shows AI-generated summaries and allows clinicians to easily access the original notes with a click. Additionally, it offers evidence-based suggestions, like “Consider statin for ASCVD risk,” to support informed decision-making.

Revenue Cycle Optimization

Billing issues were another hurdle. To address this, we introduced real-time coding assistance. ICD-10 codes are now auto-suggested based on visit notes, and the system alerts clinicians to any care gaps, improving documentation accuracy and reducing claim denials.

The Results: 6-Month Impact

Lessons for Implementation

• Start with high-impact areas: Triage and coding bring the fastest return on investment (ROI).
• Use hybrid AI-human workflows: Ensure that MDs approve all critical decisions for better accuracy and trust.
• Measure time savings: Reducing chart review time allows more patient capacity, improving throughput.

Conclusion

Building an EHR summary app like Notable AI goes beyond simple note summarization, it’s about creating seamless, intelligent automation that enhances every step of the care process. With the right data architecture, NLP models, and workflow tools in place, healthcare providers can deploy AI systems that truly add value. At IdeaUsher, we offer comprehensive development and integration support to help bring these platforms to life, from FHIR API implementation to custom AI model deployment and tailored UI dashboards.

Looking to Develop an EHR Summary App like Notable AI?

At IdeaUsher, we combine the latest in AI technology with deep healthcare expertise to create an EHR summarization platform tailored to your specific needs. Whether you’re looking to enhance provider workflows or improve patient care, we’ve got you covered.

Let IdeaUsher help you build a cutting-edge EHR summary app that:

• Automates clinical notes with 90%+ accuracy
• Integrates seamlessly with Epic, Cerner, and more
• Saves 6+ hours/week per provider

Why Choose Us?

With 500,000+ hours of coding expertise, our ex-FAANG/MAANG engineers specialize in:

• Healthcare AI/ML (NLP, predictive analytics)
• FHIR/HL7 integrations (SMART on FHIR certified)
• HIPAA-compliant deployments

Proven track record – We’ve transformed workflows for hospitals, clinics, and digital health platforms, delivering real-world results.

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