Internationally, there is an emerging interest in the inadvertent harm caused to patients by the provision of healthcare services. Since the publication of the Institute of Medicine’s report, To Err is Human, in 1999, research and policy directives have predominantly focused on patient safety in hospital settings. More recently, the World Health Organization has highlighted 2-3% of primary care encounters result in a patient safety incident. Given around 330 million general practice consultations occur in the UK each year, unsafe primary care is a poorly understood, major threat to public health. In 2003, a major investment was made in the National Reporting and Learning System to better understand patient safety incidents occurring in England and Wales. Over 40,000 incident reports have arisen from general practice. These have never been systematically analysed, and a key challenge to exploiting these data has been to generate learning from the largely unstructured, free-text descriptions of incidents. My thesis describes the empirical development and application of methods to classify (structure) incident report data. This includes the development of coding frameworks specific to primary care, aligned to the WHO International Classification for Patient Safety, to describe the incident, contributory factors and incident outcomes. I have developed a mixed-methods approach which combines a structured process for coding reports and an exploratory data analysis with subsequent thematic analysis. Analyses of reports can generate hypotheses about priorities for systems improvement in primary care at a local and national level. Existing interventions or initiatives to minimise or mitigate patient safety risks can be identified through scoping reviews. Future research and quality improvement activities should deepen understanding about the risks to patients, and generate knowledge about how interventions made in practice can improve safety.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:723573 |
| Date | January 2017 |
| Creators | Carson-Stevens, Andrew |
| Publisher | Cardiff University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://orca.cf.ac.uk/104070/ |
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