CLEAR-D: An Innovative Dual-Disease Early Detection System for Cancer and Glaucoma Among Diabetic Patients Toward Sustainable Healthcare Transformation
List of Authors
Izzatul Ussna Ridzwan, Maziah Mokhtar, Mohd Zulkeflee Abd Razak, Siti Fara Fadila Abd Razak, Zurina Ismail
Keyword
Dual-Disease Early Detection; Cancer and Glaucoma; Diabetes-Linked Diagnosis; Value Creation Model; Health Sustainability
Abstract
The Cancer and Glaucoma Early Automated Recognition – Diabetes-linked (CLEAR-D) system is an advanced dual-disease early detection platform tailored for diabetic individuals who face a heightened risk of both glaucoma and cancer. This study conceptualizes CLEAR-D as a value creation model that integrates patient-centred design principles to promote early diagnosis, enhance healthcare accessibility, and ensure long-term sustainability. The system is structured around three key modules: (i) a disease risk assessment engine, (ii) a fraud detection mechanism targeting cancer- and glaucoma-related donations, and (iii) the CLEAR-D Teal Card, which supports continuity of care and grants patients privileged access to healthcare services. Methodologically, the study employs a design science research approach combined with a value innovation framework, examining the intersection of public health outcomes, economic resilience, and technology-enabled inclusivity. Empirical results demonstrate that the CLEAR-D system effectively reduces treatment costs through proactive prevention, enhances healthcare efficiency, and empowers patients via data-driven health management. Moreover, it reinforces national health resilience by facilitating early interventions and optimizing healthcare resource allocation. In conclusion, the CLEAR-D framework illustrates how healthcare innovation can be systematically integrated into sustainable public health ecosystems. The model offers a scalable pathway for improving clinical outcomes, strengthening health system efficiency, and advancing socioeconomic sustainability within diabetic care contexts.