Innovators from the Department of Clinical Neurosciences at the University of Cambridge had developed a machine learning algorithm to predict the speed of progression of Alzheimer’s disease. This decision support tool would enable physicians to better manage their patients by assigning them to the most appropriate treatment pathway. The innovators wanted to get a better grasp of the health economic benefits provided by their technology and needed a health economic assessment to support further funding rounds.
The Technology
Dementia is a chronic and progressive condition that causes significant difficulties for both patient and caregiver. Alzheimer’s Disease (AD) is the most common type, believed to account for 60 – 70% of cases . As Alzheimer’s Disease progresses, it precludes participation in normal activities and social interactions, often requiring social care provision in the form of at-home care, or care provided in an institution, such as a care- or nursing home.
Professor Zoe Kourtzi and Dr Timothy Rittman, an Honorary Consultant Neurologist, working in the Memory Clinic at Cambridge University Hospitals , (together “ the innovators”) have developed a machine learning algorithm which uses multimodal biological data sets to predict progression of Alzheimer’s Disease (AD) in patients. The intervention, called Predictive and Prognostic Model (PPM), combines results from MRI scans with results from cognitive tests and electronic health records to model the likely disease trajectory for each AD patient.
It is a clinical decision support tool which can help healthcare professionals predict the speed of progression of AD in their patients and thus assign them to the most appropriate treatment pathway.
Health Tech Enterprise (HTE) conducted a literature review and built a fully interactive model to simulate the budgetary impact of the technology on the addressable patient population over a time period of 5 years, as well as over the lifetime of an average patient in the UK.
Outcomes
The Budget Impact Analysis forecast a substantial potential saving cumulated over a period of 5 years. The sensitivity analysis revealed which of the benefits has the biggest potential budgetary impact, and recommendations were formulated as to what data would need to be verified and validated in future studies.
“The budget impact analysis has given fresh insights into our market penetration plans, as well as helping to secure additional funding for the project. HTE has been a pleasure to work with as a partner in this project – they have taken the time to make sure the assessment is tailored to our needs and as accurate as possible for the project.” Dr Timothy Rittman, Co-founder
