People who live with a number of medical conditions (multiple long-term conditions or MLTCs) are at high risk of poor health. They are often prescribed multiple medicines. When the number of medicines is greater than five, this is called polypharmacy. The relationship between MLTCs and polypharmacy is complex and not well understood. We know that some patients enter a downward spiral, developing an increasing number of conditions and being prescribed more and more medicines. This can cause health problems, as individual medicines may interact with one another or have side-effects. Other medicines may modify the downward spiral by preventing the development of conditions such as heart disease and cancer. All of this makes it difficult to design interventions to ensure medicines are prescribed in combinations that do more good than harm.
Our long-term goal is to better understand the dynamic relationship between MLTCs and polypharmacy, to optimise the medicines prescribed for individual patients. This research will also identify key points for intervention, to maintain the best possible health trajectory for people with MLTCs.
Our group has experience in applying new developments in computer technology, termed artificial intelligence (AI) and machine learning, to healthcare data. We will develop these methods to look for new patterns linking MLTCs and prescribed medicines within UK biobank data. The information and patterns generated will feed into the design of a larger collaborative project accessing electronic care records in the North East of England and East London.
PPI helped to shape the proposed research, from the title and questions being addressed, through to the dissemination strategy. Because of the complexity of MLTCs, the many risk factors for them, and the sensitivity of data in electronic healthcare records, PPI is embedded across all aspects of our work.
In the long-term, our research will lead to an alert system in medical records and strategies for prevention and improved management of multiple long-term conditions. With PPI, we will find the best way of sharing findings from our work with diverse audiences. This will include insights into effective ways of forming multidisciplinary research teams and PPI for MLTCs. As a minimum, we will use webinars and social media, conferences presentations and scientific publications.
