How Our AI Medical Assistant Transformed Patient Care Efficiency

Discover how our AI medical assistant and AI nurse solutions transformed patient care efficiency in a pediatric office, saving time and enhancing satisfaction.

The AI agent healthcare solution streamlined patient care processes in a pediatric office.
Staff saved an average of 2.5 hours per day, enhancing operational efficiency.
A dynamic system was created for patients to easily update their information.
The AI agent reduced administrative work by 30 to 60 hours weekly for front desk and nursing staff.
Improved patient data management led to better patient satisfaction and engagement.
The project showcased the power of AI in transforming healthcare operations effectively.

Business Type

Pediatric Office

Live On

12/20/2024

Location

Orlando, FL

Work

React JS, Laravel ,PHP , MySQL , OpenAI

Client Objectives & Issues

Introduction
Our case study focuses on how an AI agent healthcare solution transformed patient care efficiency in a pediatric office. The goal was to improve processes and reduce the administrative burden on staff. With an innovative approach, the AI agent has streamlined operations, leading to better patient experiences and satisfaction.

Client Overview
The client is a pediatric office with two providers and five staff members. They see between 40 to 60 patients daily. High patient turnover means efficiency is crucial. The office aims to provide excellent care while managing tasks effectively.

Client Objectives & Issues
The office faced several challenges that slowed down operations. Staff spent 5 to 9 minutes reviewing each patient’s chart. This time was spent on:

Demographics: Checking if all patient information is accurate.
Insurance: Verifying the correct insurance details like PCP, copay, and deductibles.
Vaccine Status: Ensuring VFC (vaccine for children) status was correct to manage vaccine inventory.
Certification Needs: Making sure fields needed for PCMH certification were fulfilled.

In addition to front office needs, the nursing staff had their issues. They also spent 5 to 9 minutes figuring out patient clinical status. This included:

Physicals: Checking if yearly physicals were updated based on the Bright Futures development path.
Vaccines Due: Determining which vaccines were needed based on the Bright Futures schedule.
Care Plans: Identifying if patients with ADHD, asthma, or high BMI needed to be seen more often.

Primary Challenges

Data Management: Patient data was not centralized, making it hard to access.
HIPAA Compliance: Strict HIPAA guidelines were in place to protect patient data, complicating information sharing.
EHR Connection: Connecting and extracting information from the EHR was difficult.
Bright Futures Complexity: The Bright Futures development schedule had various milestones based on different ages, complicating data extraction.
Vaccine Schedule: The complexity of the Bright Futures vaccine schedule required individual tracking for each child.

Project Solution

Automation Middleware: A tool that helps automate tasks without compromising patient information.
Custom Tool: Data is extracted without PHI and stored securely.
AI Prompts: Specific prompts were created to gather important information for each child.
Data Scrubbing: Specific fields were cleansed to ensure clean data from the EHR.
AI Agent Training: The AI agent learned to identify missing demographics and check for well visit needs, vaccine status, and care plans.
Patient Communication: The AI agent reached out to patients with verification checks.
Information Updates: It provided front desk and nursing staff with updated information when patients arrived.

Technologies Used

React JS: For building user interfaces.
Laravel: A PHP framework for backend development.
PHP: For server-side scripting.
MySQL: For managing databases.
OpenAI: To power the AI components.

Development Cycle

The entire project cycle was completed in three weeks. This quick turnaround time demonstrated the effectiveness of the AI agent in addressing the office’s needs.

Project Impact

The implementation of the AI agent brought significant benefits:

Each of the five staff members saved 2.5 hours daily with a 40-60 patient load.
A dynamic system was established for patients to update their information easily.
The AI agent helped save 30 to 60 hours weekly in work for both front desk and nursing teams.
Conclusion
The AI agent healthcare solution led to dramatic improvements in patient care efficiency for the pediatric office. This technology not only saved time but also enhanced patient satisfaction and engagement. These changes have made a positive real-world impact on the healthcare industry.