Reducing Patient No-Shows with CCD’s (A GeBBS Healthcare Company) Predictive Model

Reducing Patient No-Shows with CCD’S Predictive Model

1. 70% Reduction in Predicted Cancellations
2. +50,000 Patients Served Annually
3. 25% Increase in Resource Utilization

Overview

Our client faced a significant challenge with high patient no-shows, reaching a rate of 9.4%. This negatively impacted operational efficiency, increased costs, and overall patient care and satisfaction. Implementing our advanced predictive model, this Healthcare Organization substantially improved patient scheduling, resulting in a 70% reduction in predicted cancellations and over $300,000 in cost savings across seven locations.

Background

Our client, a leading healthcare network with 20 locations across the country, was dealing with:

1. An alarmingly high no-show rate of 9.4%
2. Significant uncaptured revenue
3. Inefficient resource utilization

These issues had a huge negative impact on financial outcomes and patient care quality.

Goals

1. Reduce the no-show rate by predicting patient cancellations.
2. Increase operational efficiency and optimize resource utilization.
3. Improve patient experience through more consistent appointment attendance.

Solution: CCD’s (A GeBBS Healthcare Company) Predictive No-Show Model Implementation

We implemented our proprietary no-show predictive model to tackle this challenge head-on:

1. Advanced patient risk assessment: Using machine learning to identify high-risk patients likely to miss appointments.
2. Data-driven optimization: Applied model outcomes to derive innovative scheduling tactics.
3. Targeted intervention strategies: Developed personalized approaches for high-risk patients.

Results

1. 70% reduction in predicted cancellations: Focusing on the patients identified by our model helped our client successfully reduce 70% of the potential no-shows.
2. Improved resource utilization: Staff scheduling, facility management, and overall operational efficiency improved, allowing the client to better allocate resources and reduce downtime.
   
3. $300,000+ in cost savings: No-shows reduction led to $300,000 in cost savings across just seven locations within the first six months of implementation.
4. Enhanced patient experience and appointment adherence: Through timely reminders and rescheduling options.

Key Takeaways

1. Data-driven decision making: Predictive modeling allows the client to make decisions based on data, leading to more effective scheduling strategies and interventions.
2. Scalable solution: Implemented across seven locations effectively demonstrates the solution’s scalability
3. Positive financial impact: The substantial cost savings highlight the financial benefits of addressing no-shows through advanced predictive analytics.

Conclusion

Our predictive no-show model provided a strategic advantage to this healthcare organization, helping them tackle one of the most persistent challenges in healthcare as shown in the substantial cost savings, improved operational efficiency and patient care.

Next Steps

Expansion to additional locations: To maximize impact of our ML-based solution.

Based on the success in the initial 7 locations, we can project:

1. Total annual cost savings: Approximately $857,000 across all 20 locations.
2. Improved patient care: Potential to serve an additional 50,000 patients annually.
3. Operational efficiency: 25% increase in overall resource utilization.

Integration with other operational processes: To increase operational efficiency across the entire organization.

Continuous improvement: Regularly update and refine the predictive model based on new data and evolving patient behavior trends.

Client Testimonial

“The implementation of this predictive model has been transformative. We’ve seen significant financial benefits and marked improvement in patient satisfaction and care delivery. It’s a game-changer for our entire network.”

Chief Operations Officer