Operationalizing patient flow in high-complexity service lines: a health system perspective using oncology care

Abstract

Background Health systems face increasing oncology demand amid workforce constraints and rising care complexity. Outpatient cancer volumes in the United States are projected to grow nearly 18% over the next decade, driven by earlier diagnoses, evolving screening guidelines, and continued site-of-care shifts — while inpatient volumes remain essentially flat but grow in case mix complexity. Many organizations attempt to improve access through isolated interventions — navigation programs, clinic expansion, or scheduling redesign — yet delays in treatment initiation and operational inefficiencies persist. Objective This article presents a system-level operational framework for managing oncology access as a continuous patient flow problem rather than a series of disconnected scheduling events. Approach This perspective is informed by iterative operational experience within a large academic oncology program and synthesizes challenges observed across referral intake, diagnostics, clinic scheduling, and treatment delivery. The framework integrates existing literature on patient flow, care coordination, and access management to organize access into three interdependent domains: entry, throughput, and transition. Key findings Applying this framework highlights fragmentation across traditional organizational boundaries and supports the establishment of unified operational ownership for access. Aligning performance measurement with patient progression — rather than isolated scheduling metrics — enables more consistent identification of bottlenecks and improved coordination across multidisciplinary teams. Conclusion Managing oncology access as an integrated patient flow system provides a scalable and practical approach for improving timeliness and operational efficiency. The framework does not require capital expenditure as a prerequisite; however, operational investment in Electronic Health Record (EHR) configuration, data infrastructure, and dedicated coordination staffing is typically required and should be planned accordingly. The framework is designed to optimize existing resources first and to inform capital decisions with operational evidence.