Building the Business Case: Transforming Patient Scheduling with AI Voice Agents

TIP: Take the time to educate your stakeholders on how AI Agents Function

Business Problem: Patient scheduling is a critical yet time-consuming task, with staff handling upwards of 500 appointment calls per week, leading to long wait times and inefficiencies. With four full-time staff dedicated to scheduling, follow-ups, and rescheduling, inefficiencies can lead to delays, patient dissatisfaction, and high operational costs - the costs for the 4 staff members exceed $200K annually.

This blog series will take you through the end-to-end process of implementing AI voice agents to streamline scheduling, improve efficiency, and reduce staffing costs - all while maintaining high quality patient care. The solution will also be adding an additional service to patients - providing a follow-up call to let them know "everything came back clear" - this is something that doesn't happen in the practice today as only calls that require follow-up procedures are being made. This new service is expected to add an additional 15% of total calls to the practice.

Management Team Vision: The initial vision from the dermatology practice leadership was to automate 90% of scheduling calls and reduce staffing to a single person. BAM! That sounds like huge costs savings, but does it really make sense to go with such a big-bang approach? Based on my experience with implementing AI-driven systems, I advised against this approach. Not only had they not considered the more complex or serious outcome calls (such as a Melanoma) but the management team hadn't considered the impact to practice operations interanlly. They also hadn't taken ito consideration that 50% of their clients are senior citizens and might not be receptive to the soltuion.

Considering these factors, I proposed a phased implementation over 12 months, allowing for gradual adaptation, issue resolution, and fine-tuning of the AI agent’s capabilities. This would allow time for patient education and understanding of the benefits the AI agent model brings. Specifically, patients who agreed to particpate in the program would be guaranteed to get their lab resutls within 24 hours of receipt instead of the traditional 7 - 10 business day backlog.

At the end of our discussions, we agreed on a more practical and incremental deployment strategy that balances efficiency gains while maintaining patient trust and operational stability.

Understanding the Business Problem: The goal of this AI-driven initiative is to optimize scheduling operations by transitioning 60% of scheduling calls to an AI voice agent over a 12-month period, leading to a reduction of two full-time staff positions while enhancing overall efficiency and patient experience.

Key challenges in the current system include:

• Backlogged scheduling and follow-ups, with some patients experiencing delays of up to three weeks before securing an appointment.

• High call volume, requiring excessive manual effort.

• Staff workload strain, reducing their ability to focus on complex cases.

• Limited scalability, as patient demand grows.

• Practice capacity issues - the practice had run out of office and parking lot space to accommodate more staff needed to support practice growth plans.

Defining Project Goals & Governance: I follow what could be considered an "old school" governance model - this ensures we have clearly outlined the business objectives, project outcomes, policies, controls, and things like HIPPA compliance requirements. I find this model ensures all key stakeholders have a clear understanding of project delivereables and how things like "scope changes" could impact the overall project goals.

Key Project Goals:

• Phase 1 (0 - 3 months): Implement AI Agents with a 30% call volume target.

• Phase 2 (3 - 6 months): Ramp up call volume to a 60% calll volume target.

• Phase 3 (6 - 9 months): Continue ramping up AI Agent call volumes based on adoption successes.

• Improved patient experience, with faster appointment scheduling and reduced wait times.

• Reduction in manual scheduling effort, freeing up staff for higher-value tasks.

• Significant cost savings, with a streamlined staffing model.

Key Stakeholders:

• Practice Management Team: Oversee scheduling operations and work with implemenation team on resolving any implemenation issues.

• iHussl Consultants: Delivery a customized AI Voice solution. Manage integration with existing processes / systems and ensure HIPPA compliance.

• Scheduling Staff: Provide feedback into any implemenation challenges and overall system management.

In the next blog post, we’ll explore the Solution Design & Platform Selection process, diving into the AI capabilities required to achieve these goals. Stay tuned!