An Integrated Decentralised–Centralised Oncology Care Model to Improve Cancer Screening, Access, and Continuity of Care in Rural Eastern Cape, South Africa: Implementation Study at Nelson Mandela Academic Hospital

Background: Rural and resource-constrained settings face major barriers to timely cancer screening, diagnosis, and treatment due to limited specialist availability and centralised service-delivery models. In the Eastern Cape, a largely rural province with a constrained oncology workforce, a decentralised–centralised hybrid model was introduced to improve access to cancer care. Nelson Mandela Academic Hospital serves as the central referral hub within this model. This study evaluates the implementation process and impact of this decentralised cancer care model on service utilisation, access, and continuity of care. Methods: A quantitative quasi-experimental pre–post implementation and quality improvement evaluation was conducted using retrospectively collected routine service utilisation and programme data from April 2023 to February 2025. The study assessed the impact of a decentralised oncology care model on access, service integration, and utilisation outcomes. Data from facility registers and district health information systems were managed using Microsoft Excel and analysed using Stata and IBM SPSS Statistics. Descriptive statistics, correlation analysis, and linear regression were used to compare pre- and post-implementation changes in patient volumes, screening coverage, referral completion, workforce capacity, gender distribution, and service uptake. The intervention decentralised screening, diagnosis, follow-up, and patient navigation services to district and satellite facilities while centralising specialised oncology care at referral centres to improve accessibility, efficiency, and continuity of care. Results: Cancer patient attendance increased substantially over the study period, from 355 patients in April 2023 to a peak of 1039 in April 2024, with a sustained upward trend (B = 18.03, p = 0.005), reflecting an average monthly increase of 18 patients. Female patients accounted for most visits, while male attendance showed a significant increasing trend (B = 8.03, p < 0.001). Service integration improved, with strong positive correlations between new patient registrations, follow-up care, palliative services, and inpatient admissions, indicating an expanding continuum of care. Breast and cervical cancers contributed the highest service burden, while cervical and lung cancers showed significant upward trends. Seasonal variation in attendance was observed, particularly during festive periods. From an implementation perspective, screening coverage for priority cancers increased by 18%, while 732,349 individuals were reached through community awareness initiatives. Access improved substantially, evidenced by a reduction of 56,400 km in cumulative patient travel distance over one year. Workforce capacity was strengthened through the training of 517 healthcare workers, and 1943 patients received structured navigation support. Referral efficiency and continuity of care improved, although persistent bottlenecks were observed in diagnostic and referral pathways. Conclusions: The decentralised–centralised oncology care model demonstrated improved cancer service utilisation, access, and continuity of care in a rural, resource-limited setting. However, increasing patient volumes and interconnected service demands place additional pressure on health system capacity. Sustained investment in workforce development, screening—particularly for cervical cancer—and system efficiency is required. This model provides a scalable and context-appropriate framework for strengthening oncology services in similar low-resource settings.