Unwarranted variations modelling and analysis of healthcare services based on heterogeneous service data

There is a growing demand worldwide to increase the quality and productivity of healthcare services thereby increasing the value of the healthcare services delivered. To deal with these demands, increasingly importance is being placed on analysing and reducing unwarranted variations in healthcare services to achieve significant savings in healthcare expenditure. Unwarranted variations are defined as the variations in the utilisation of healthcare services that cannot be explained by variation in patient illness or patient preferences. Current modelling and simulation approaches for healthcare service efficiency and effectiveness improvements in hospitals do not utilise multiple types of heterogeneous service data such as qualitative information about hospital services and quantitative data such as historic system data, electronic patient records (EPR), and real time tracking data for analysing unwarranted variations in hospital. Consequently, due to the presence of large amount of unwarranted variations in the service delivery systems, service improvement efforts are often inadequate or ineffective. Therefore, there is urgent need to: (i) accurately and efficiently model complex care delivery services provided in hospital; (ii) develop integrated simulation model to analyse unwarranted variations on a care pathway of a hospitals; and, (iii) develop analytical and simulation models to analyse unwarranted variations from a care pathway. Current process modelling methods to represent healthcare services rely on simplified flowchart of patient flow obtained based on on-site observations and clinician workshops. However, gathering and documenting qualitative data from workshops is challenging. Furthermore, resulting models are insufficient in modelling important service interactions and hence the resulting models are often inaccurate. Therefore, a detailed and accurate process modelling methodology is proposed together with a systematic knowledge acquisition approach based on staff interviews. Traditional simulation models utilised simplified flow diagrams as an input together with the historic system data for analysing unwarranted variations on a care pathway. The resulting simulation models are often incomplete leading to oversimplified outputs from the conducted simulations. Therefore, an integrated simulation modelling approach is presented together with the capability to systematically use heterogeneous data to analyse unwarranted variations on service delivery process of a hospital. Maintaining and using care services pathway within hospitals to provide complex care to patients have challenges related to unwarranted variations from a care pathway. These variations from care pathway predominantly occur due ineffective decision making processes, unclear process steps, their interactions, conflicting performance measures for speciality units, and availability of resources. These variations from care pathway are largely unnecessary and lead to longer waiting times, delays, and lower productivity of care pathways. Therefore, methodologies for analysing unwarranted variations from a care pathway such as: (i) system variations (decision makers (roles) and decision making process); (ii) patient variations (patient diversion from care pathway); are discussed in this thesis. A system variations modelling methodology to model system variations in radiology based on real time tracking data is proposed. The methodology employs generalised concepts from graph theory to identify and represent system variations. In particular, edge coloured directed multi-graphs (ECDMs) are used to model system variations which are reflected in paths adopted by staff, i.e., sequence of rooms/areas traversed while delivering services. A pathway variations analysis (PVA) methodology is proposed which simulates patient diversions from the care pathway by modelling hospital operational parameters, assessing the accuracy of clinical decisions, and performance measures of speciality units involved in care pathway to suggest set-based solutions for reducing variations from care pathway. PVA employs the detailed service model of care pathway together with the electronic patient records (EPRs) and historic data. The main steps of the methodology are: (i) generate sample of patients for analysis; (ii) simulate patient diversions from care pathway; and, (iii) simulation analysis to suggest set-based solutions. The aforementioned unwarranted variations analysis approaches have been applied to Magnetic Resonance (MR) scanning process of radiology and stroke care pathway of a large UK hospital as a case study. Proposed improvement options contributed to achieve the performance target of stroke services.