Over the past decade, caring for patients with multiple chronic diseases has become one of the most important and difficult tasks facing healthcare. Multimorbidity, defined as the presence of two or more long-term conditions, inevitably leads to the use of multiple drugs, known as polypharmacy, due to the singular disease model adopted in most healthcare services. Polypharmacy increases the risk of adverse drug reactions, drug–drug interactions, hospitalisations, poor medication adherence and mortality. Adverse drug events also have a significant impact on the efficiency of healthcare services to administer drugs, raise safety concerns for patients and result in a financial burden on the NHS. But most adverse events in healthcare are preventable.
Funded by Above and Beyond, the Higher Education Innovation Fund and the University of Plymouth’s Faculty of Health PhD studentship,
Professor Shang-Ming Zhou
and his team and partners used AI technologies, including machine learning, deep learning and statistics technologies, to extract relevant information from electronic patient records and other test data. They hoped this would enable the safe and effective use of medications for the best possible outcomes in patients. In particular, they were building evidence for the safe use of medications to assist practitioners in improving their medication-use systems to prevent medication errors and patient harm.
