Background: People undergoing systemic cancer therapy frequently experience multiple interconnected symptoms that shape day-to-day functioning and quality of life (QoL). The Symptom Science Model 2.0 positions symptom clusters as complex phenotypes that can support anticipatory, person-centred care when integrated with organisational processes and clinical decision-making. However, current practices rarely link symptom-cluster insights to care pathways or governance structures in routine oncology services. Objectives: This three-phase funded programme aims to: (i) synthesise evidence on symptom-cluster patterns during active cancer treatment; (ii) develop and validate a short-term predictive model of QoL decrease using longitudinal electronic patient-reported outcomes (ePROs) and cluster-based features; and (iii) co-design and prepare the organisational integration of multidisciplinary, cluster-focused needs assessment and management. Methods: Phase I is a scoping review following PRISMA-ScR. Phase II uses retrospective, de-identified electronic patient-reported outcomes and electronic health record data from adults receiving outpatient cancer treatment. These include sociodemographic and clinical characteristics, treatment information, and repeated reports of symptoms and quality of life. Symptom clusters will be identified by examining how symptoms co-occur over time, and early changes in these patterns will be used to predict short-term quality-of-life decline within a treatment cycle (≈7–28 days). A silent-mode pilot will assess how the predictive model fits routine practice without influencing care. Phase III consists of co-design workshops with nurses, oncologists, and people undergoing treatment to translate the model into cluster-focused assessment and care processes aligned with existing outpatient workflows and organisational structures. Discussion: Digital Person bridges practice, organisation, and governance by linking lived illness experience to interpretable digital phenotypes and embedding these into service-level decision processes. The project advances precision health within a person-centred paradigm by supporting anticipatory, nurse-led multidisciplinary responses to evolving symptom burden. Outcomes will contribute to integrated oncology pathways that align technological innovation with ethical, organisational, and experiential dimensions of care.