CONTEXT: Many patients who are at a risk of vision loss or lower-limb amputation due to complications from diabetes will miss their “non-essential” appointments due to the COVID-19 restrictions. This will have devastating results for those who are classified as high risk. Current legislative barriers prevent researchers from disclosing identifiable lists to physicians for clinical care. OBJECTIVE: To develop a strategy within current legislation to enable research and clinical care in order to identify those in need of preventative screening and rapid treatment. To develop recommendations for policy makers to modernize outdated legislation. STUDY DESIGN: Develop methods to (1) use Privacy Preserving Record Linkage (PPRL) to link anonymized and encrypted primary care data to provincial administrative data, and; (2) use a combination of Machine Learning (ML) and Artificial intelligence (AI) to identify those deemed high risk, and; (3) to enable the disclosure of identifiable patient lists derived from research to the point of care. DATASET: Diabetes Action Canada’s National Diabetes Repository (NDR) and matched records from Prescribed Entities (PE) in Ontario. POPULATION STUDIED: All Ontario patients with diabetes in the National Diabetes Repository. OUTCOMES: Detailed report outlining the legislative authorities that enable the collection, use and disclosure of personal health information for (1) to investigate the use of PPRL , and; (2) create a linked dataset with provincial administrative data and primary care electronic medical record data, which will create a more complete picture of a diabetic patients experience with the health care system, and; (3) provide suggestions for Canadian personal health information law reform.