IntelliClinic is an AI-Powered Healthcare Platform (AHP) is a cutting-edge solution designed to revolutionize the healthcare industry by integrating artificial intelligence into patient care workflows. AHP streamlines patient intake, automates the creation of focused HPIs, and provides support for differential diagnosis, significantly enhancing the efficiency of medical documentation and patient management. With its advanced NLP capabilities, the platform translates complex medical notes into actionable data, thereby improving the quality of care and facilitating better health outcomes. Emphasizing security and compliance, AHP ensures the protection and confidentiality of sensitive health information throughout the patient care process.
- Introduction
- Overall Description
- System Features
- External Interface Requirements
- System Features
- Other Nonfunctional Requirements
- Other Requirements
- Appendix A: Glossary
- Appendix B: Analysis Models
- Appendix C: To Be Determined List
This document specifies the technical requirements and architectural design for the development of an AI-Powered Healthcare Platform, hereinafter referred to as "AHP". AHP is designed to enhance the efficiency and effectiveness of healthcare services through the application of artificial intelligence.
The AHP will be a comprehensive solution capable of optimizing patient intake, generating focused HPI, aiding in differential assessment and planning, and automating patient note creation.
- HPI: History of Present Illness
- MVP: Minimum Viable Product
- EHR: Electronic Health Record
- NLP: Natural Language Processing
- API: Application Programming Interface
- SSL: Secure Sockets Layer
- HIPAA Compliance Documentation
- FHIR (Fast Healthcare Interoperability Resources) Specification
- Python Documentation
The document details the product functions, user interfaces, system features, and security requirements.
AI-Healthcare-Platform-MVP/
│
├── app/ # Application code
│ ├── __init__.py # Initializes the Python app as a module
│ ├── views.py # Handles the routing and views for the web application
│ ├── models.py # Defines data models for the application
│ ├── forms.py # Defines web-forms to interact with user input
│ ├── utils/ # Utility scripts and helper functions
│ │ ├── __init__.py
│ │ ├── ai_helpers.py # Helper functions for AI operations
│ │ └── db_helpers.py # Helper functions for database interactions
│ └── templates/ # HTML templates for the web interface
│ ├── layout.html # Base layout for the web pages
│ ├── index.html # Main page template
│ └── report.html # Template for displaying the patient report
│
├── ai/ # AI model code
│ ├── __init__.py
│ ├── intake_model.py # AI model for patient intake
│ └── note_generation_model.py# AI model for generating patient notes
│
├── static/ # Static files for the web interface
│ ├── css/ # CSS stylesheets
│ │ └── main.css # Main stylesheet for the application
│ ├── js/ # JavaScript files
│ │ └── main.js # Main JavaScript file for dynamic behaviors
│ └── img/ # Image files
│ └── logo.png # Logo image
│
├── tests/ # Automated tests
│ ├── __init__.py
│ ├── test_views.py # Tests for view functions
│ └── test_models.py # Tests for data models
│
├── config/ # Configuration files
│ ├── __init__.py
│ └── settings.py # Contains settings for the Django project
│
├── scripts/ # Deployment and utility scripts
│ ├── init_db.py # Script to initialize the database
│ └── start_server.sh # Script to start the web server
│
├── .gitignore # Specifies intentionally untracked files to ignore
├── requirements.txt # Lists all Python dependencies for the project
├── manage.py # Command-line utility for administrative tasks
├── Dockerfile # Instructions for Docker to build the application
├── docker-compose.yml # Defines and runs multi-container Docker applications
└── README.md # Project overview and instructions
- app/: This directory contains the core application logic, including view functions, data models, and form definitions. The
templates/sub-directory contains HTML templates to render the web pages. - ai/: This directory is dedicated to the AI aspects of the project, including the machine learning models that handle patient intake and note generation.
- static/: Contains all the static assets used by the web application like CSS, JavaScript, and images. These files contribute to the frontend part of the application.
- tests/: Includes tests for different parts of the application ensuring code reliability and ease of maintenance.
- config/: Holds configuration files for the application, such as Django settings.
- scripts/: Contains utility scripts that help in setting up the database or starting the server, which are especially useful for deployment.
- .gitignore: A Git configuration file that tells Git which files or directories to ignore in a project.
- requirements.txt: A plaintext file listing all the dependencies which can be installed using
pip install -r requirements.txt. - manage.py: A command-line utility that lets you interact with this Django project in various ways.
- Dockerfile and docker-compose.yml: Used for containerizing the application and defining multi-container Docker applications, respectively.
- README.md: The markdown file providing a description of the project, how to set it up, and how to use it.
AHP will function as a standalone module capable of seamless integration with existing healthcare IT ecosystems, enhancing their capabilities through AI-driven solutions.
- AI-driven patient intake
- Generation of focused HPI
- Differential diagnosis support
- Automated patient note creation
- Integration with EHR systems
- Physicians: Require efficiency in documentation and patient management.
- Patients: Seek medical advice and use AHP for preliminary assessments.
- Administrators: Manage AHP operation and oversee system settings.
AHP will be a cloud-based SaaS solution, accessible via web browsers on desktop and mobile devices.
- AHP will be developed using the Django web framework.
- Compliance with HIPAA is mandatory.
- The initial release will
support English language only.
- Online help within the application.
- User manual for end-users and administrators.
- API documentation for integrators.
- Access to cloud services provided by AWS.
- The initial AI model will be trained on a dataset provided by a partnering healthcare institution.
This feature will include an intelligent questionnaire that adapts to the patient's inputs in real-time. The AI will analyze responses to prioritize follow-up questions, minimize patient fatigue, and maximize the collection of relevant health data. The system will utilize a decision tree algorithm which will be trained on initial datasets to recognize patterns in symptom presentation and medical history.
The AI model will process the intake information to produce a concise HPI document. The model will be trained using supervised learning techniques on anonymized patient histories to identify and summarize key health information, which will be crucial for the differential diagnosis phase.
The NLP module will convert free-text patient notes into structured data. It will employ text analysis to extract and categorize medical entities, such as symptoms, diagnoses, and treatments, facilitating easy search and retrieval. This module will be essential for generating summaries and reports for both healthcare providers and patients.
- The system will have a responsive web interface.
- Dashboards for real-time data presentation.
The web interface will be built using a mobile-first design approach ensuring compatibility with a wide range of devices. The user interface will be clean and minimalistic, with an emphasis on usability and accessibility standards. It will include interactive elements such as modals, tooltips, and dropdown menus to provide a dynamic user experience without overwhelming the user with medical jargon.
No specific hardware interface is required.
- AWS for cloud hosting.
- FHIR API for EHR integration.
- Python libraries for NLP.
- HTTPS for secure data transmission.
The platform will use RESTful APIs for communication between the frontend and backend services. These APIs will be secured using JWT (JSON Web Tokens) to ensure that only authenticated and authorized users can access sensitive endpoints.
- OAuth 2.0 for authentication.
- SSL encryption for data in transit.
- Reliability: Cloud-hosting for high availability.
- Usability: Intuitive interface design.
- Maintainability: Well-documented codebase with unit tests.
- The system will support a minimum of 1,000 concurrent users.
- Response times will not exceed 2 seconds for any transaction.
The platform will be optimized for quick load times, with an aim for initial contentful paint to occur within 1 second, and interactive readiness within 2 seconds over a standard broadband connection. To achieve this, backend services will be stateless to enable horizontal scaling, and front-end assets will be minimized and served from a content delivery network (CDN).
- Error handling mechanisms to prevent system crashes.
- Data validation to prevent incorrect data entry.
The platform will feature robust input validation to prevent common security threats such as SQL injection, Cross-Site Scripting (XSS), and Cross-Site Request Forgery (CSRF). It will also implement rate limiting to protect against denial-of-service attacks.
- All data will be encrypted at rest using AES-256.
- Regular security audits and compliance checks.
- Scalability: Designed to easily add more resources.
- Flexibility: Modular design for easy updates and changes.
- The system will prioritize data privacy in accordance with HIPAA.
- Data backups will be performed daily.
- A disaster recovery plan will be established and tested quarterly.
Data retention policies will comply with medical data retention requirements, which typically dictate that patient records be preserved for a minimum number of years post-treatment. Archiving solutions will be implemented to ensure data is securely stored long-term and can be retrieved when necessary.
The platform will comply with all relevant healthcare regulations, including but not limited to HIPAA in the United States, GDPR in the European Union, and PIPEDA in Canada. Regular audits will be conducted to ensure ongoing compliance.
Refer to 1.3 Definitions, Acronyms, and Abbreviations.
- Included are ER diagrams, UML models, and data flow diagrams for the AHP.
This appendix will include UML diagrams to outline the system architecture, ER diagrams to map out the database schema, and sequence diagrams to describe the flow of operations within the platform. These models will provide a visual representation of the system's components and their interactions, facilitating a better understanding of the system's functionality.
- Selection of third-party services for email and SMS notifications.
- Choice of AI model training platforms.
- Finalize the choice of machine learning and data analytics tools.
- Determine the third-party service providers for auxiliary functionalities such as two-factor authentication and push notifications.