A systems approach to the use of routinely collected data for healthcare improvement

Healthcare is increasingly digitised and recorded, producing huge volumes of ‘routinely- collected’ data. This data is increasingly viewed as a valuable resource for the improvement of healthcare, although a truly unifying framework to translate data into knowledge and thence change is lacking. From an initial introduction to key concepts and a critique of competing methodologies this thesis identifies a ‘systems-approach’ as a potential framework to guide data analysis, design, implementation, and overarching project management. The thesis provides evidence for the potential of a systems-approach through two main avenues, a systematic review of the medical improvement literature and the reporting and reflection on two data-intensive improvement initiatives pertaining to the perioperative care of older surgical patients. Four key stages of these case studies are used to determine the role of a systems-approach in the use of routinely-collected healthcare data, with the selection of these phases of work informed by a detailed review of the improvement and epidemiological literature. Evidence for the benefits of a systems-approach is advanced from reflections on project set-up, database construction, development of a causal-model, the design of covariates to capture complex system events (such as integrated working), and the translation of statistical results towards real-world solutions. These results provide evidence that a systems-approach appears to benefit work conducted at a ‘project’ and ‘task’ level, can increase transparency in key phases of a data-science workflow, aid in the identification of risks that could lead to biased results, and that an under- standing of the data-generating system appears crucial to understand causal relationships that may be captured in statistical results. These conclusions, as well as the results of each key project phase, form the clear original contributions of this work. The thesis concludes by positing a model of data-intensive improvement as incorporating iterative phases of systems-supported data-science, data- informed design, and data-evidenced implementation which is ready for evaluation in future studies.
Wellcome Trust; Addenbrookes Charitable Trust