Analyst-driven development of an open-source simulation tool to address poor uptake of O.R. in healthcare

Computer simulation studies of health and care problems have been reported extensively in the academic literature, but the one-off research projects typically undertaken have failed to create an enduring legacy of widespread use by healthcare practitioners. Simulation and other modelling tools designed and developed to be used routinely have not fared much better either. Following a review of the literature and a survey of frontline analysts in the UK NHS, we found that one reason for this is because simulation tools have, to date, not been developed with the requirements of the end-user in the heart of the development process. Starting with a thorough needs assessment of NHS based healthcare analysts, this study outlines a set of practical design principles to guide development of simulation software tool for conducting patient flow simulation studies. The overall requirement is that patient flow be modelled over a number of inter-connected points of delivery while capturing the stochastic nature of patient arrivals and hospital length of stay, as well as the dynamic delays to patient discharge and transfer of care between different points of care delivery. In ensuring a cost-free solution that is both versatile and user-friendly, and coded in an increasingly popular language among the envisaged end users, the tool was implemented is the R programming language and software environment, with the user interface implemented in the interactive R-Shiny application. The talk will provide an overview of the project lifecycle including an illustrative example of an empirical simulation study concerning the centralisation of an acute stroke pathway

Setting up a rapid diagnostic clinic for patients with vague symptoms of cancer: a mixed method process evaluation study

Background The study sought to evaluate the impact of a Rapid Diagnostic Clinic (RDC) service designed to improve general practitioner (GP) referral processes for patients who do not meet existing referral criteria yet present with vague - but potentially concerning - symptoms of cancer. We sought to investigate how well the RDC has performed in the views of local GPs and patients, and through analysis of its activity and performance in the first two years of operation.MethodsThe study setting was a single, hospital-based RDC clinic in a University Health Board in South Wales. We used a mixed-method process evaluation study, including routinely collected activity and diagnosis data. All GPs were invited to participate in an online survey (34/165 responded), and a smaller group (n = 8) were interviewed individually. Two focus groups with patients and their carers (n = 7) provided in-depth personal accounts of their experiences.ResultsThe focus groups revealed high rates of patient satisfaction with the RDC. GPs were also overwhelmingly positive about the value of the RDC to their practice. There were 574 clinic attendances between July 2017 and March 2019; the mean age of attendees was 68, 57% were female, and approximately 30% had three or more vague symptoms. Of those attending, we estimated between 42 to 71 (7.3% and 12.3%) received preliminary cancer diagnoses. Median time from GP referral to RDC appointment was 12 days; from GP referral to cancer diagnosis was 34 days. Overall, 73% of RDC patients received either a new diagnosis (suspected cancer 23.2%, non-cancer 35.9%) or an onward referral to secondary care for further investigation with no new diagnosis (13.9%), and 27% were referred to primary care with no new diagnosis.ConclusionsThe RDC appears to enable a good patient experience in cancer diagnosis. Patients are seen in timely fashion, and the service is highly regarded by them, their carers, and referring GPs. Although too early to draw conclusions about long-term patient outcomes, there are strong indications to suggest that this model of service provision can set higher standards for a strongly patient-centred service. <br/

Evaluating the sustainability of complex health system transformation in the context of population ageing: An empirical system dynamics study

Demographic changes, particularly population ageing, and rising morbidity from chronic conditions contribute to ever-increasing pressures on health and care systems in developed countries. Partly as a response, new models of care and service innovations are being piloted and introduced. However, the effectiveness and sustainability of these complex health system transformations are often not well understood and most modelling studies fail to capture both system configuration and populating dynamics. In this paper, we present a comprehensive system dynamics modelling approach to capture both population ageing and the organisation of the health and care services from a whole system perspective. The development of the model was directly informed by an ambitious care system transformation project designed to offer a different pathway for those patients deemed to be complex. The model input parameters were populated using estimates from empirical data. A series of simulation experiments were conducted to inform the design of the new service and its sustainability. We found that, subject to the model’s limitations and assumptions, the new pathway could have a stabilising effect against increasing demand provided hospital readmission fractions and length of stay for complex patients can be managed effectively

Exploring the implementation of standardized processes in a professional setting

In this study, we investigate the process of standardising people-centric, knowledge-intensive, professional work, specifically attempts to adopt a common standard for hospital stroke care. In operations management (OM), standardization (i.e. eliminating various aspects of process variation) is central to increased control but in professional work, managerial influence is more limited because (medical) professionals deploying specialized knowledge enjoy, by definition, significant autonomy. The research is built upon an in-depth longitudinal case study in a UK hospital that confirms the value of standardization in healthcare work but also strongly emphasizes that it must be understood as a multi-dimensional puzzle. The findings confirm established but under-developed, insights regarding limitations of the dominant OM process design logic, namely flow dependency. Our analysis clearly shows that variation is also driven by sharing (i.e. sub-optimal layouts, high-utilisation and high-variability of resources) and fit (i.e. conflicting KPIs) dependency considerations. More specifically, we observed – often via the use of shared (or not) pathway artefacts (maps, etc.) - that autonomy has negative and positive impacts on inter-professional collaboration. Autonomy frequently led to minimally shared mental models of care, perspectives on the best interests of the patient, etc. and (often highly dysfunctional) competition between individuals and groups. Where we observed effective inter-professional collaboration, it had been achieved through relational resources (i.e. shared goals, interests etc.) developed via continuous interaction and knowledge sharing mechanisms. However, pathway capacity issues (i.e. staff and bed availability, etc) moderated such interactions. The paper concludes by discussing how these insights can help healthcare operations managers and other professionals to design better process and implementation strategies that improve the delivery of care

Implementing standardised flow:navigating operational and professional dependencies

Purpose The purpose of this study had two aims: (1) to extend insight regarding the challenges of implementing standardised work, via care pathways, in a healthcare setting by considering interactions with other operational (i.e. resource sharing, portfolio alignment) and professional (i.e. autonomous expertise) dependencies and (2) to develop novel insights regarding a specific flow mechanism, the stroke nurse practitioner, a form of flow "pilo" or guide. Design/methodology/approach This was a longitudinal case study of implementing the acute stroke care pathway in a National Health Service hospital in England based on 185 hours of non-participant observations and 68 semi-structured interviews. Archival documents were also analysed. Findings The combined flow, operational and professional dependency lens extends operations management understanding of the challenge of implementing standardised work in healthcare. One observed practice, the process pilot role, may be particularly valuable in dealing with these dependencies but it requires specific design and continuous support, for which the authors provide some initial guidance. Research limitations/implications The research was a single case study and was focussed on a single care pathway. The findings require replication and extension but offer a novel set of insights into the implications of standardised work in healthcare. Originality/value In addition to confirming that a multidependency lens adds conceptual and practical insight to the challenges of implementing standardised work in a healthcare setting, the findings and recommendations regarding flow "pilots" are novel. The authors' analysis of this role reveals new insights regarding the need for continued improvisation in standardised work

Exploring financially sustainable initiatives to address out-of-area placements in psychiatric ICUs:a computer simulation study

BackgroundTransferring individuals for treatment outside their geographic area occurs when healthcare demand exceeds local supply. This can result in significant financial cost while impacting patient outcomes and experience.AimsThe aim of this study was to assess initiatives to reduce psychiatric intensive care unit (PICU) out-of-area bed placements within a major healthcare system in South West England.MethodsDiscrete event computer simulation was used to model patient flow across the healthcare system’s three PICUs. A scenario analysis was performed to estimate the impact of management plans to decrease admissions and length of stay. The amount of capacity required to minimise total cost was also considered.ResultsWithout increasing in-area capacity, mean out-of-area bed requirement can be reduced by 25.6% and 19.1% respectively through plausible initiatives to decrease admissions and length of stay. Reductions of 34.7% are possible if both initiatives are employed. Adjusting the in-area bed capacity can also lead to aggregate cost savings.ConclusionsThis study supports the likely effectiveness of particular initiatives in reducing out-of-area placements for high-acuity bedded psychiatric care. This study also demonstrates the value of computer simulation in an area that has seen little such attention to date

An Open Source Decision Support System for Facility Location Analysis

This paper introduces FLP Spreadsheet Solver, an open source spreadsheet based Decision Support System for Facility Location Problems. Structure of the spreadsheets, interface of the solver, and a Tabu Search algorithm implemented within the solver are described. An integer programming formulation of the underlying facility location problem is provided. Computational tests show that FLP Spreadsheet Solver can solve benchmark p-median and capacitated p-median instances to near optimality. The paper also includes a case study consisting of the application of FLP Spreadsheet Solver to a healthcare facility location problem.</p

Developing and validating a predictive model for emergency hospital admissions

Although many emergency hospital admissions may be unavoidable, a proportion of these admissions represent a failure of the care system. The adverse consequences of avoidable emergency hospital admissions affect patients, carers, care systems and substantially increase care costs. The aim of this study was to develop and validate a risk prediction model to estimate the individual probability of emergency admission in the next 12 months within a regional population. We deterministically linked routinely collected data from secondary care with population level data, resulting in a comprehensive research dataset of 190,466 individuals. The resulting risk prediction tool is based on a logistic regression model with five independent variables. The model indicated a discrimination of area under the receiver operating characteristic curve of 0.9384 (95% CI 0.9325 – 0.9443). We also experimented with different probability cut-off points for identifying high risk patients and found the model’s overall prediction accuracy to be over 95% throughout. In summary, the internally validated model we developed can predict with high accuracy the individual risk of emergency admission to hospital within the next year. Its relative simplicity makes it easily implementable within a decision support tool to assist with the management of individual patients in the community