Cancer Clinic Redesign: Opportunities for Resource Optimization.

Area of Science:

• Healthcare Operations
• Oncology Management
• Health Systems Engineering

Background:

• Ambulatory cancer centers experience fluctuating patient demand and variable staff availability, leading to operational instability.
• Misalignment of resources causes overscheduled clinics, budget deficits, and prolonged patient wait times, exceeding targets.
• Current operational models often lack agility and data-informed decision-making capabilities.

Purpose of the Study:

• To deploy a Learning Health System (LHS) framework for enhancing operational performance in an ambulatory cancer center.
• To transition from physician-centric scheduling to a disease-site-based caseload management model.
• To optimize resource allocation and improve clinic efficiency and patient flow.

Main Methods:

• Applied value stream mapping, operations research, and statistical process control within the LHS framework.
• Transitioned from a fixed physician template to a quarterly realigned caseload management by disease site model.
• Adapted a block scheduling model to align regional demand with optimized human and physical resources.

Main Results:

• Demonstrated improved utilization of clinical space and increased weekly consistency in clinic activity.
• Achieved a better distribution of patient activity across the workweek.
• Increased the ratio of monthly patient encounters per nursing hours worked and the percentage of services delivered by full-time nurses.

Conclusions:

• The implementation of a Learning Health System framework significantly improved operational stability and efficiency in an ambulatory cancer center.
• A data-informed demand capacity model facilitates the use of predictive analytics for enhanced clinical responsiveness.
• The adapted caseload and block scheduling models effectively align regional demand with optimized resources, improving cancer care delivery.