Reducing medication-related harm (MRH) is a World Health Organisation patient safety campaign, with transitions of care a focus. Medication-related harm includes adverse drug reactions, and harm from poor adherence and medication errors. The aim of my research was to investigate the epidemiology of MRH in older adults (≥65 years) following hospital discharge, and examine if this harm can be predicted. The thesis consists of six data chapters based on three studies. The first was a systematic review of studies internationally that investigated the incidence of MRH in older patients following hospital discharge. No studies had been conducted in the UK, and methodological heterogeneity of the included studies limited the scope to draw conclusions on the burden of MRH post-discharge. The second study was qualitative, involving semi-structured interviews and focus groups with a purposive sample of independent and housebound older adults. This research explored the lived experience and impact of MRH on patients, and risk factors from the patient perspective. Four predominant themes around patient experience of the healthcare system, practicalities of using medicines, management of medication problems and personal beliefs were identified. The third study was a multicentre, prospective cohort study of 1280 older patients discharged from five hospitals in England. This cohort was followed up for eight weeks to identify MRH and associated health service utilisation, using three data sources; hospital readmissions, patient interviews and primary care records. Out of 1116 patients that completed follow-up, 37% experienced MRH and half the cases were potentially preventable. High-risk medicine groups were opiates, antibiotics, and benzodiazepines. The cost to the National Health Service of healthcare use due to MRH was estimated at £396 million annually. An investigation of whether discharging doctors in hospitals could predict the occurrence of MRH showed no association between the prediction and outcome, irrespective of clinical experience. Using data from the PRIME study, a risk prediction tool was developed through a multivariable stepwise regression using Akaike’s Information Criterion. The tool was internally validated using a bootstrap resampling method and has a discrimination C-statistic of 0.66 with good calibration. The tool now requires external validation prior to clinical implementation.
|Date of Award||Oct 2018|
|Supervisor||Khalid Ali (Supervisor), Chakravarthi Rajkumar (Supervisor), Kevin A. Davies (Supervisor), Stephen Bremner (Supervisor) & Lizzie Ward (Supervisor)|