SHAKIR ANSARI
How We Enhanced Patient Care with an AI Nurse Solution
Discover how our AI medical assistant transformed patient care by reducing administrative burdens and enhancing efficiency, making healthcare smoother for providers and families alike.
- Enhanced Efficiency: Our AI agent reduced administrative tasks for front office and nursing staff, saving each member approximately 2.5 hours per day.
- Streamlined Patient Care: Implementation of an AI solution streamlined processes for handling patient demographics, insurance details, and vaccination statuses.
- Improved Patient Engagement: The AI agent proactively contacted patients for updates, ensuring timely communication and securing their involvement in their care plans.
- Compliance and Accuracy: The solution ensured adherence to HIPAA regulations while providing clean, accurate data for clinical decision-making.
- Significant Time Savings: Overall, the AI agent saved the pediatrics office between 30 to 60 hours of work weekly, leading to a more efficient and responsive healthcare environment.
- Advanced Data Management: Centralizing patient data improved operational efficiency and facilitated better healthcare delivery within the practice.
- Innovative Technology Stack: Leveraged technologies such as React JS, Laravel, PHP, MySQL, and OpenAI to create a robust solution tailored to client needs.
- Real-World Impact: Enhanced patient satisfaction and experience through reduced wait times and more accurate healthcare services, setting a new standard in the healthcare industry.
Business Type
Pediatric Clinic
Live On
12/20/24
Location
Orlando, FL
Work
React JS, Laravel ,PHP , MySQL , OpenAI
Categories
The pediatrics office had clear goals but faced issues that challenged their operations.
Key objectives included:
- Streamlining patient chart reviews: Front office staff spent 5 – 9 minutes checking demographics and insurance details.
- Ensuring accurate vaccine inventory: VFC status needed verification to supply the correct vaccines.
- Meeting PCMH certification requirements: Other essential data fields were crucial for compliance.
Additionally, nursing staff faced their own challenges:
- Assessing patient clinical status: This also required 5 – 9 minutes of review time.
- Maintaining physical exam schedules: Ensuring yearly checks aligned with Bright Futures development paths.
- Updating care plans as needed: Certain conditions like ADHD or high BMI required more frequent visits.
Several primary challenges slowed down the workflow in the pediatrics office.
- Lack of centralized data: Patient information was scattered across different sources.
- Compliance with HIPAA guidelines: Protecting patient data while accessing it was complex.
- Integration with EHR systems: Extracting and connecting data from EHR systems proved difficult.
- Complex Bright Futures guidelines: Different age milestones added layers of complexity in data retrieval.
- Intricate vaccine schedules: Tailoring vaccination data for each child was a challenge.
To address these challenges, we implemented a robust AI solution.
- Automation middleware: This was the backbone that streamlined processes.
- Custom data extraction tool: Designed to pull information without exposing PHI; all data was encrypted.
- AI prompts for individual children: This helped determine crucial details accurately.
- Data scrubbing: Specific fields were cleaned to ensure reliable information from EHR.
- AI agent training: The agent detected missing demographics and identified care plan needs.
- Proactive patient contact: The AI agent reached out for identity verification and to update patient information.
- Real-time information updates: Front desk and nursing staff received timely updates for patient visits.
To bring our solution to life, we utilized several technologies:
- React JS: For building the user interface.
- Laravel: To streamline backend processes.
- PHP: For server-side logic.
- MySQL: To manage patient and operational data.
- OpenAI: For enhancing AI capabilities.
The entire project unfolded over three weeks and followed a structured development cycle:
- Initial Consultation: We assessed workflows, pinpointed pain points, and set goals.
- Planning and Design: Our team designed the architecture, including essential integrations.
- Development Phase: We built the automation solution and tested workflows for efficiency.
- Testing and Quality Assurance: We ensured all functionalities performed reliably and met security standards.
- Deployment: The solution went live with team training for effective use.
- Ongoing Maintenance and Support: We monitored performance and offered troubleshooting and optimization services.
The results of implementing the AI agent in healthcare were impressive. Each staff member saved about 2.5 hours daily.
For a patient load of 40 to 60, this led to significant productivity gains.
- Dynamic patient system: The new model allowed easy patient data updates.
- AI agent’s role: The agent saved 30 to 60 hours weekly for front desk and nursing staff.