Title: The Causes and Consequences of Healthcare Incidents (CHI)
Funder: Economic and Social Research Council

Abstract: CHI will pursue objectives across four areas: academic, methodological, capacity building and policy as follows: Academic Objectives A1: Descriptive Analysis We will produce detailed descriptive analyses of the prevalence and distribution of Healthcare Incidents (HI), studying contributing factors including patient characteristics, time of day, clinical subspecialty, and setting. A2: Causes of Incidents Whilst previous research has established the causes of the most serious incidents, and studied particular contexts in detail, less is known about the overall distribution of causes of HIs, and what explains variation. The second phase of the project addresses this gap, building on the descriptive data analysis both quantitatively and qualitatively. A3: Consequences of Incidents HIs are common but little is known about their consequences beyond any immediate harm and "the true economic impact of medication errors has not been accurately estimated" (Walsh et al, 2017). Thus, a prescription-error in which a patient is given the wrong dose of a drug may cause no obvious immediate harm - but may have long-term repercussions. For example, paracetamol is the medicine most frequently prescribed and associated with a prescribing error, it is freely available, but "must be used cautiously in certain patient groups and dose adjusted in patients with lower weight" (Patel et al, 2014). This project will, for the first time, combine data on all HIs with medical records to allow us to produce evidence of how HIs contribute causally to adverse health outcomes, the costs of healthcare, and litigation. Methodological Objectives M1: Dataset development We will develop NLP techniques to allow the anonymisation of the free-text component of the HI records and their inclusion in the SAIL (Secure Anonymised Information Linkage ) databank. M2: Integrate qualitative, econometric, and ML techniques. To ensure that the results of our quantitative analysis are consistent with the substantial body of HI research, we will validate our quantitative results describing the causes of HIs against a randomly selected qualitative subsample. Through a repeated refinement of the quantitative analysis following comparison with the qualitative data we will develop a statistical model which leverages the power of our comprehensive dataset but is informed by, and grounded in, existing scholarship of HIs. Capacity Objectives C1: World's First Harmonized HI Database. The project will use the techniques developed in M1 to produce the worlds' first harmonized and fully anonymous database of HIs suitable for linking to patient records. An important project output suitable for widespread interrogation. CHI will then analyse this to produce systematic population level evidence of the number and type of incidents. C2: Develop ECR Expertise A key output of the project will be to support the development of the PDRAs who will be full partners in the project contributing to the drafting of academic outputs and impact work alongside data-analysis. It is anticipated that their consequent expertise with novel techniques and under-analysed data will serve as a foundation for a successful academic career. Policy Objectives P1: HI incident type and long-term costs. HIs generate substantial direct costs for healthcare systems. For example, the NHS paid £2.4bn in clinical negligence claims in 2018. However, little is known about the costs in terms of additional subsequent care, for both these cases and for the vastly more common less-serious HIs. We will produce the first estimates of these costs and sources and produce policy papers that describe and explain them. P2: Correlates of HI. The estimates developed in A1 will be valuable to policymakers as they seek to reduce HI. We will produce a policy paper outlining the correlates of HIs identified in our data. We will work with our advisory board and the Royal Colleges to develop best practice.

Lay abstract: Healthcare incidents, defined to include all incidents involving patients whilst under secondary healthcare including medical errors and medication errors, are a leading cause of avoidable harm in health systems globally. In England alone, the subcategory of medical errors, costs over £1.5 billion in litigation, and directly causes 712 deaths, annually. Previous research aimed at understanding the causes of healthcare incidents has focused on specific types of errors or specific contexts. They seek to establish the root causes through qualitative investigation or identify general areas of common incident for healthcare practitioners to be aware. These have led to a greater appreciation for the aetiology of incidents and a greater emphasis on the importance for organizational learning. Research to date has not engaged with the inherent complexity of the systemic factors causing incidents in modern healthcare systems, at scale. This complexity requires a new, transformative, collaborative and interdisciplinary approach, combining clinical expertise and social science techniques. Across the UK, reports of patient safety incidents have been collected digitally locally for about 15 years. We are putting forward an ambitious research project which will use these data at scale, to provide the first large-scale, comprehensive, analysis of the prevalence, causes and consequences of medical error. It will pioneer an innovative approach integrating qualitative and quantitative approaches from the social-science disciplines of Human Factors and Microeconometrics, and underpinned by an advisory board including medical, surgical, patient safety and pharmacological expertise. Using cutting edge econometric and data-science tools to master the scale of the data, alongside human factors qualitative analyses to understand the precise underlying human processes, our approach will provide evidence of the roles of human (healthcare practitioners, management, patients) and non-human actors (digital and analogue healthcare systems), and how they combine, in medical errors and causal links to patient outcomes. To accomplish this ambitious aim we propose to join existing core and restricted data sets held in the SAIL databank, e.g. Birth rates, Education Attainment, National survey for Wales, Outpatient, Outpatient referral, Primary Care GP, Welsh Health Survey, with the data on healthcare incidents from Swansea Bay University Hospital Trust. As such the project will answer the following research questions: * Will a large-scale quantitative analysis of healthcare incidents coupled with long-term patient records reveal a new model/theory of the causes of healthcare incidents ? * What are the consequences of the most common (minor) healthcare incidents for a patient's long-term outcomes? * With a new model/theory of the causes and consequences of healthcare incidents, how can healthcare be organised to minimise patient harm? To achieve this our objectives are: i) to build an anonymous, linkable, database of HIs and to compute descriptive statistics, ii) ii) develop statistical tools to study the causes and consequences of the vast majority of HIs iii) to collaboratively develop interventions for policymakers and healthcare managers to minimize the occurrence of HIs with the aim of reducing the risk of patient harm. We take a collaborative approach to economic and societal impact working with key beneficiaries and stakeholders through inception, design and delivery of the project. As such we have already started this process and have met with our advisory board members who include membership of national healthcare bodies, Royal Colleges and patient representatives.

Research activity codes: 2.4 SURVEILLANCE AND DISTRIBUTION;8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: GENERIC HEALTH RELEVANCE