Stochastic models to enable tailoring of medications to patients with multiple morbidities

Chronic non-communicable diseases (NCDs) constitute a major global burden and public health challenge. NCDs such as diabetes, cardiovascular disease, cancer, and chronic respiratory diseases are the leading cause of mortality worldwide. Clinical pathways and guidelines documented by the National Institute of Health and Care Excellence (NICE) in the UK constitute an evidence-based knowledge base with recommendations on the medications and alternatives available to treat chronic conditions. However, guidelines often do not address the needs of patients with complex cases of chronic conditions where medications need to be chosen with care. Patients with multimorbidity are subject to multiple treatment pathways and there is a risk of undetected adverse reactions to combinations of prescribed medications. In Scotland, over half of all people with chronic conditions have comorbidities. A recent survey indicates that medicines are implicated in 5-17% of hospital admissions, of which half are considered preventable. Furthermore, the majority of these medicine related occurrences are due to well-known adverse effects of commonly prescribed drugs. This project looks at how can we use data to better understand and tailor care planning and treatment cognisant of multimorbidity burden for an individual and their predicted life expectancy and ability to benefit from treatment (that outweigh the risks).