An exploration of prescribing and administration practices in care homes using big data

The care home population is recognised as being a high-risk group with respect to potential harm from medicines. However, as medicine administration records (MAR) have traditionally been recorded on paper, examination of medicines use in care homes has been challenging, time-consuming and resource intensive. This thesis explored the potential for using a database of secondary, pseudonymised electronic MAR (eMAR) data for furthering research and enhancing clinical practice in England, using exploratory analysis of dopaminergic medicines as a case study. The source database was interrogated, and data processed using SQL code. Statistical testing and figure creation was conducted using R code and Microsoft Excel. Analysis included assessment of the prevalence of dose omissions and a comparison of the standard approach for assessing the timeliness of levodopa administration by the time difference between the required and administered times (dosing accuracy) to a novel approach comparing the actual and expected time gaps between doses (dosing precision), using Bland-Altman quantile regression plots. A large sample of over 9,000 individuals across 310 care homes were identified following data pre-processing, and comparisons of dosing accuracy and dosing precision for assessing the timeliness of levodopa administration found discordance may occur between these measures. Therefore, this thesis concludes that harnessing eMAR data may facilitate large-scale research, strengthen clinical monitoring procedures and enable the development of complex clinical interventions. The development and use of data assets should be prioritised, alongside continual monitoring and improvement of data quality