Healthcare improvement based on learning from adverse outcomes

Quality and safety improvement is a relatively novel discipline in healthcare practice and research that solidified in the early 21st century. Since then, various systems have been installed to collect information on various types of adverse outcomes, such as adverse events, incidents and patient complaints. Data from these systems can be used to evaluate care delivered to individual cases as well as to study aggregated data for patterns, trends and other insights. More research is warranted to assess whether these systems actually meet the objective of continuous, systemwide learning and improvement. It was expected that existing practices could benefit from individual optimization as well as better integration, because most of this intelligence is currently stored and used in isolation. The research in this PhD thesis focused on how we can learn most effectively from various types of adverse outcomes in healthcare, in order to continuously improve the care delivered to patients. Specific research questions included how we can learn from: i) case discussions at morbidity and mortality conferences ; ii) integrating available information sources (e.g., incidents, patient experiences); iii) the context of everyday practice that produces both adverse and desired outcomes.