Modelling trauma hip fracture hospital activities

Hip fracture is the most common reason for an elderly person to be admitted to an acute orthopaedic ward. The main aim of this research is to provide a statistical evaluation of a hip fracture database, and then to use Operational Research (OR) techniques, using the statistical output, to model activities associated with the care of hip fracture patients. OR techniques employed in this thesis include simulation and queuing theory. This research focuses on hip fracture admissions to the University Hospital of Wales in Cardiff, with a primary aim of ascertaining whether the time between admission and surgical intervention has any impact upon patient outcome. Outcome is considered in terms of mortality, hospital length of stay and discharge destination. Statistical analyses are performed, via regression and CART analysis, to investigate length of stay and mortality variables. The results from these statistical tests are compiled, compared and investigated in more depth. Additionally, a principal component analysis is performed to investigate whether it would be feasible to reduce the dimensionality of the dataset, and subsequently principal component regression methodology is used to complement the output. Simulation is used to model activities in both the hip fracture ward and the trauma theatre. These models incorporate output from the statistical analysis and encompass complexities within the patient group and theatre process. The models are then used to test a number of ‘what-if’ type scenarios, including the future anticipated increase in demand. Finally, results from queuing theory are applied to the trauma theatre in order to determine a desired daily theatre allocation for these patients. Specifically, the M | G | 1 queuing system and results from queues with vacations are utilised. The thesis concludes with some discussion of how this research could be further expanded. In particular, two areas are considered; risk scoring systems and the Fenton-Wilkinson approximation

Modelling and solving healthcare decision making problems under uncertainty

The efficient management of healthcare services is a great challenge for healthcare managers because of ageing populations, rising healthcare costs, and complex operation and service delivery systems. The challenge is intensified due to the fact that healthcare systems involve various uncertainties. Operations Research (OR) can be used to model and solve several healthcare decision making problems at strategic, tactical and also operational levels. Among different stages of healthcare decision making, resoure allocation and capacity planning play an important role for the overall performance of the complex systems. This thesis aims to develop modelling and solution tools to support healthcare decision making process within dynamic and stochastic systems. In particular, we are concerned with stochastic optimization problems, namely i) capacity planning in a stem-cell donation network, ii) resource allocation in a healthcare outsourcing network and iii) real-time surgery planning. The patient waiting times and operational costs are considered as the main performance indicators in these healthcare settings. The uncertainties arising in patient arrivals and service durations are integrated into the decision making as the most significant factors affecting the overall performance of the underlying healthcare systems. We use stochastic programming, a collection of OR tools for decision-making under uncertainty, to obtain robust solutions against these uncertainties. Due to complexities of the underlying stochastic optimization models such as large real-life problem instances and non-convexity, these models cannot be solved efficiently by exact methods within reasonable computation time. Thus, we employ approximate solution approaches to obtain feasible decisions close to the optimum. The computational experiments are designed to illustrate the performance of the proposed approximate methods. Moreover, we analyze the numerical results to provide some managerial insights to aid the decision-making processes. The numerical results show the benefits of integrating the uncertainty into decision making process and the impact of various factors in the overall performance of the healthcare systems

Improving the analysis and use of patient complaints in the English National Health Service

The English National Health Service (NHS) receives over 200,000 patient complaints annually. Complaints provide rich narratives of poor and unsafe care, and are often submitted with the aim of preventing harm from occurring to others. Inquiries into safety failures have demonstrated that complaints signal problems where internal systems fail. Yet, their insights remain underutilised due to their complex unstructured nature, a disregard for their informational value, and a complaints process designed for case-by-case redress. This work develops evidence-based and theory-informed approaches towards improving the analysis and use of complaints in the English NHS. Using process modelling and realist review methods, this thesis generates theory on how and under what conditions healthcare settings can achieve both case-by-case redress and system-wide analysis of complaints. Findings identify the need for a robust coding taxonomy to detect systemic problems with healthcare delivery, and support the prioritisation of deeper qualitative analysis and investigation. The inter-rater reliability of the existing NHS complaints reporting scheme ‘KO41a’ is tested across four NHS Trusts, and compared to the psychometrically robust and theory-informed Healthcare Complaints Analysis Tool (HCAT). Results highlight the limited discriminative value of KO41a, and indicate HCAT as a reliable alternative in most investigated settings. Drawing from social science approaches to safety, the final study conducts data linkage and narrative analysis of complaints and staff incident reports, and demonstrates the contributions of using complainants’ interpretation and sense-making of adverse events to test, challenge, and complement staff representations of the causes and severity of harm. Collectively, the work in this thesis demonstrates why patient and staff perspectives need to be combined for a more holistic understanding of patient safety, and provides a pragmatic, evidence-based pathway towards integrating complaints into the historically staff-driven quality monitoring and improvement systems.Open Acces

Medical Celebrity in Eighteenth-Century Britain

Medical Celebrity in Eighteenth-Century Britain argues that the cultural mechanisms responsible for creating and sustaining celebrity culture helped create and sustain commercialized medicine in eighteenth-century Britain. I identify the process by which celebrity and medical culture impact one another as a sociocultural phenomenon that I term medical celebrity. The following chapters present four case studies of how medical celebrity impacted the development of the medical profession, the medical market, cultural representations and perceptions of health and illness, and the patient experience. I engage with the work done by scholars in contemporary and early celebrity studies to shed light on the memoirs, biographies, letters, etc. of culturally significant medical practitioners and patients. As such, I create case studies of the famous surgeons John and William Hunter, the infamous pamphlet war following Robert Walpole’s medical treatment and death, two notoriously ill and healthy actresses, Susannah Cibber and Margaret “Peg” Woffington, and Frances Burney’s infamous letter detailing her mastectomy. This dissertation serves as a typology of medical celebrity, thereby identifying the sociocultural phenomenon at the root of the discussions by medical historians and eighteenth-century scholars who examine intersections between celebrity and medical culture. Ultimately, I argue that eighteenth-century medical celebrity was foundational to contemporary British and American culture and we must examine the function of medical celebrity in contemporary culture in order to understand the development of the medical profession, lived experiences of patients and practitioners, cultural representations and perceptions of medicine, and the medical market

Joint optimization of allocation and release policy decisions for surgical block time under uncertainty

The research presented in this dissertation contributes to the growing literature on applications of operations research methodology to healthcare problems through the development and analysis of mathematical models and simulation techniques to find practical solutions to fundamental problems facing nearly all hospitals. In practice, surgical block schedule allocation is usually determined regardless of the stochastic nature of case demand and duration. Once allocated, associated block time release policies, if utilized, are often simple rules that may be far from optimal. Although previous research has examined these decisions individually, our model considers them jointly. A multi-objective model that characterizes financial, temporal, and clinical measures is utilized within a simulation optimization framework. The model is also used to test “conventional wisdom” solutions and to identify improved practical approaches. Our result from scheduling multi-priority patients at the Stafford hospital highlights the importance of considering the joint optimization of block schedule and block release policy on quality of care and revenue, taking into account current resources and performance. The proposed model suggests a new approach for hospitals and OR managers to investigate the dynamic interaction of these decisions and to evaluate the impact of changes in the surgical schedule on operating room usage and patient waiting time, where patients have different sensitivities to waiting time. This study also investigated the performance of multiple scheduling policies under multi-priority patients. Experiments were conducted to assess their impacts on the waiting time of patients and hospital profit. Our results confirmed that our proposed threshold-based reserve policy has superior performance over common scheduling policies by preserving a specific amount of OR time for late-arriving, high priority demand

Robust optimisation of operating theatre schedules

Hospitals in the UK are increasingly having to cancel a large proportion of elective operations due to the unavailability of beds on hospital wards for post-operative recovery. The availability of post-operative beds is therefore critical to the scheduling of surgical procedures and the throughput of patients in a hospital. The focus of this research is to investigate, via data-driven modelling, systematic reasons for the unavailability of beds and to demonstrate how the Master Surgery Schedule (MSS) can be constructed using Operational Research techniques to minimise the number of cancellations of elective operations. Statistical analysis of data provided by the University Hospital of Wales, Cardiff was performed, providing information on patient demand and length of stay distributions. A two-stage modelling process was developed to construct and simulate an MSS that minimises the number of cancellations. The first stage involves a novel set partitioning based optimisation model that incorporates operating room and bed constraints. The second stage simulates the resulting optimal schedule to provide measures on how well the schedule would perform if implemented. The results from this two-stage model provide insights into when best to schedule surgical specialties and how best the beds are distributed between wards. Two optimisation under uncertainty techniques are then employed to incorporate the uncertainty associated with the bed requirements into the optimisation process. A robust optimisation (RO) approach that uses protection functions in each bed constraint is developed. Investigations into varying levels of protection are performed in order to gain insight into the so called `price of robustness'. Results show that MSSs that are constructed from protecting more of the uncertainty result in fewer cancellations and a smaller probability of requiring more beds than are available. The deterministic optimisation model is then extended to become a scenario-based optimisation model in which more scenarios of bed requirement are incorporated into a single optimisation model. Results show that as more scenarios are included, a more robust schedule is generated and fewer cancellations are expected. Results from the different approaches are compared to assess the benefits of using RO techniques. Future research directions following from this work are discussed, including the construction of the MSS based on sub-specialties and investigation of different working practices within the case study hospital