Planting Scheduling Decision Trees that Empower First Contact Resolution
- Kendra Ask Carlson, M.S., Operations Manager, Office of Access Management, Mayo Clinic
- Andy Johnson, M.B.A., Principal Access Advisor, Office of Access Management, Mayo Clinic
- Brynn Howard, M.H.A., Administrator, Office of Access Management, Mayo Clinic
Mayo Clinic, Rochester, processes over 1,100 new appointment requests across 40 specialties (subdivided into 135 subspecialties and more than 3,000 providers) daily. Our mission is to ensure all patients are scheduled in the right place, at the right time, and with the right provider. To achieve this feat, a scheduling tool must:
- Help the practice identify the reason for the request,
- Understand patient symptoms and specific concerns, and
- Leverage summary-level patient information to aid scheduling outcomes.
Answering these questions ensures patients are cared for in the most efficient way possible. Our tool is the algorithmic request decision trees - 469 in total - that were developed for each specialty when we implemented our EHR system five years ago. After years of asynchronous updates, practice modifications, and enhancements to trees that may (or may not) have aided the scheduling process, we recognized that a comprehensive review was in order.
The Office of Access Management leadership team decided to wholistically review decision trees with the objective of building a strong scheduling foundation that would allow for future agility and technology. To achieve this, we developed a process to ‘spruce up’ the trees to reflect the current needs of the practice and incorporate new and upcoming tools:
- Provide first contact resolution in our call centers,
- Determine decision tree best practices,
- Create a standard for evaluating each tree compared to the best practices,
- Develop a standard for evaluating each tree to ensure the accuracy in outcome, and
- Evaluate prevalence of indications that lead to multiple decision trees.
A project team was developed, led by our access analysts, focused on bridging practice operations, scheduling, and EHR system builders, to review and optimize all 469 trees. Due to volume, scaling the work was required, and we wanted a quantifiable tool to determine how to distribute the work in an 18-month timeframe. The decision tree assessment tool was weighed with the following criteria:
- Number of decision tree “hits”,
- Complexity of the decision tree build (based on a ranking of 1-5), and
- Number of nodes/rules.
In conclusion, engaging in the effort to ‘plant’ scheduling decision trees is allowing us to meet our primary objectives in an optimized manner via new learnings on best practices to upskill our staff and enhancing our ability to create agility in a constantly evolving practice.