Organisational and systems factors impacting on patient safety in acute care organisations: lessons from four multi-site research studies

Background Patient safety is concerned with preventable harm in healthcare, a subject that became a focus for study in the UK in the late 1990s. How to improve patient safety, presented both a practical and a research challenge in the early 2000s, leading to the eleven publications presented in this thesis. Research question The overarching research question was: What are the key organisational and systems factors that impact on patient safety, and how can these best be researched? Methods Research was conducted in over 40 acute care organisations in the UK and Europe between 2006 and 2013. The approaches included surveys, interviews, documentary analysis and non-participant observation. Two studies were longitudinal. Results The findings reveal the nature and extent of poor systems reliability and its effect on patient safety; the factors underpinning cases of patient harm; the cultural issues impacting on safety and quality; and the importance of a common language for quality and safety across an organisation. Across the publications, nine key organisational and systems factors emerged as important for patient safety improvement. These include leadership stability; data infrastructure; measurement capability; standardisation of clinical systems; and creating an open and fair collective culture where poor safety is challenged. Conclusions and contribution to knowledge The research presented in the publications has provided a more complete understanding of the organisation and systems factors underpinning safer healthcare. Lessons are drawn to inform methods for future research, including: how to define success in patient safety improvement studies; how to take into account external influences during longitudinal studies; and how to confirm meaning in multi-language research. Finally, recommendations for future research include assessing the support required to maintain a patient safety focus during periods of major change or austerity; the skills needed by healthcare leaders; and the implications of poor data infrastructure.

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.

Business Process Modelling based on formal temporal theory with an application to hospital patient flows

Existing Workflow Management Systems (WFMSs) follow a pragmatic approach. They often use a proprietary modelling language with an intuitive graphical layout. However the underlying semantics lack a formal foundation. As a consequence, analysis issues, such as proving correctness i.e. soundness and completeness, and reliable execution are not supported at design level. This project will be using an applied ontology approach by formally defining key terms such as process, sub-process, action/task based on formal temporal theory. Current business process modelling (BPM) standards such as Business Process Modelling Notation (BPMN) and Unified Modelling Language (UML) Activity Diagram (AD) model their constructs with no logical basis. This investigation will contribute to the research and industry by providing a framework that will provide grounding for BPM to reason and represent a correct business process (BP). This is missing in the current BPM domain, and may result in reduction of the design costs and avert the burden of redundant terms used by the current standards. A graphical tool will be introduced which will implement the formal ontology defined in the framework. This new tool can be used both as a modelling tool and at the same time will serve the purpose of validating the model. This research will also fill the existing gap by providing a unified graphical representation to represent a BP in a logically consistent manner for the mainstream modelling standards in the fields of business and IT. A case study will be conducted to analyse a catalogue of existing ‘patient pathways’ i.e. processes, of King’s College Hospital NHS Trust including current performance statistics. Following the application of the framework, a mapping will be conducted, and new performance statistics will be collected. A cost/benefits analysis report will be produced comparing the results of the two approaches.

A general framework for Business Process Modelling (BPM) based on Formal Temporal Theory with an application to Hospital Patient flows

Existing Workflow Management Systems (WFMSs) follow a pragmatic approach. They often use a proprietary modelling language with an intuitive graphical layout. However the underlying semantics lack a formal foundation. As a consequence, analysis issues, such as proving correctness i.e. soundness and completeness, and reliable execution are not supported at design level. This project will be using an applied ontology approach by formally defining key terms such as process, sub-process, action/task based on formal temporal theory. Current business process modelling (BPM) standards such as Business Process Modelling Notation (BPMN) and Unified Modelling Language (UML) Activity Diagram (AD) model their constructs with no logical basis. This investigation will contribute to the research and industry by providing a framework that will provide grounding for BPM to reason and represent a correct business process (BP). This is missing in the current BPM domain, and may result in reduction of the design costs and avert the burden of redundant terms used by the current standards. A graphical tool will be introduced which will implement the formal ontology defined in the framework. This new tool can be used both as a modelling tool and at the same time will serve the purpose of validating the model. This research will also fill the existing gap by providing a unified graphical representation to represent a BP in a logically consistent manner for the mainstream modelling standards in the fields of business and IT. A case study will be conducted to analyse a catalogue of existing ‘patient pathways’ i.e. processes, of King’s College Hospital NHS Trust including current performance statistics. Following the application of the framework, a mapping will be conducted, and new performance statistics will be collected. A cost/benefits analysis report will be produced comparing the results of the two approaches.