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Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. In appreciating these ‘capacity-dependent’ deaths, this paper reports on the clinically-led development of a stochastic discrete event simulation model designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients. With application to a large public hospital in England during an early stage of the pandemic, the purpose of this study was to estimate the extent to which such capacity-dependent deaths can be mitigated through demand-side initiatives involving non-pharmaceutical interventions and supply-side measures to increase surge capacity. Based on information available at the time, results suggest that total capacity-dependent deaths can be reduced by 75% through a combination of increasing capacity from 45 to 100 beds, reducing length of stay by 25%, and flattening the peak demand to 26 admissions per day. Accounting for the additional ‘capacity-independent’ deaths, which occur even when appropriate care is available within the intensive care setting, yields an aggregate reduction in total deaths of 30%. The modelling tool, which is freely available and open source, has since been used to support COVID-19 response planning at a number of healthcare systems within the UK National Health Service. Electronic supplementary material The online version of this article (10.1007/s10729-020-09511-7) contains supplementary material, which is available to authorized users.

PurposeThe 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/approachThis 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.FindingsThe 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/implicationsThe 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/valueIn 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.

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. Methods The 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. Results The 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. Conclusions The 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.

Background During the COVID-19 pandemic, many intensive care units have been overwhelmed by unprecedented levels of demand. Notwithstanding ethical considerations, the prioritization of patients with better prognoses may support a more effective use of available capacity in maximizing aggregate outcomes. This has prompted various proposed triage criteria, although in none of these has an objective assessment been made in terms of impact on number of lives and life-years saved. Design An open-source computer simulation model was constructed for approximating the intensive care admission and discharge dynamics under triage. The model was calibrated from observational data for 9505 patient admissions to UK intensive care units. To explore triage efficacy under various conditions, scenario analysis was performed using a range of demand trajectories corresponding to differing nonpharmaceutical interventions. Results Triaging patients at the point of expressed demand had negligible effect on deaths but reduces life-years lost by up to 8.4% (95% confidence interval: 2.6% to 18.7%). Greater value may be possible through “reverse triage”, that is, promptly discharging any patient not meeting the criteria if admission cannot otherwise be guaranteed for one who does. Under such policy, life-years lost can be reduced by 11.7% (2.8% to 25.8%), which represents 23.0% (5.4% to 50.1%) of what is operationally feasible with no limit on capacity and in the absence of improved clinical treatments. Conclusions The effect of simple triage is limited by a tradeoff between reduced deaths within intensive care (due to improved outcomes) and increased deaths resulting from declined admission (due to lower throughput given the longer lengths of stay of survivors). Improvements can be found through reverse triage, at the expense of potentially complex ethical considerations.

Timely and adequate provision of blood following mass casualty events (MCEs) is critical to reducing mortality rates amongst casualties transported to hospital following an event. Developing planning strategies to ensure the blood transfusion demands of casualties are met is challenging. Discrete event simulation (DES) offers a novel solution to this problem which is financially efficient, less disruptive to services and allows for rich experimentation compared to the current industry standards of live exercises, round-table discussion or tabletop planning. There are currently no published models of this type for investigating blood provision in MCEs. The objective of this study was to develop a working model which could be used to target the in-hospital 'levers' and 'supply levels' of the transfusion system and improve outcomes during the response to future events. This was achieved through the robust design of a DES model using exclusive access to qualitative and quantitative data as well as a panel of experts from the field of transfusion and MCE management. The completed model was extensively and formally evaluated with secondary data from the 7th of July 2005 London bombings, the largest UK based civilian MCE in over 50 years. A subsequent sensitivity analysis revealed the five factors displaying the greatest influence on casualty outcomes. Experimental themes based on these findings have generated new solutions for managing future events which have since been presented to MCE stakeholders and policy makers.

The development and implementation of innovation by health care providers is widely understood as a multi-determinant and multi-level process. Theories at different analytical levels (i.e. both micro and organisational) are needed in order to capture the processes that influence innovation by health care providers. This paper combines a micro theory of innovation, actor-network theory, with organisational level processes using the “resource based view of the firm”, to examine the influence of, and interplay between, innovation-seeking teams (micro) and underlying organisational capabilities (meso) during the development and implementation of service innovation. The study is based on two examples of service innovation in relation to ophthalmology services run by a specialist English NHS Trust at multiple locations in a large city. We conducted interviews (28) with stakeholders within and outside the Trust and non-participant observation (40.5 hours) of outpatient clinics and service and board level meetings. Operational Research techniques were used to map the care process in the clinics and establish the theoretical improvement possible through service redesign. In practice, innovation at the micro service level was constrained by the complexity of and strain on existing outpatient clinics. Deficiencies in organisational capabilities for supporting innovation were identified, including the nature of manager-clinician relations and organisation-wide resources. The study concludes that actor-network theory can be combined with the resource-based view to highlight the influence of organisational capabilities on the management of innovation. Equally, actor-network theory helps to address the lack of theory in the resource-based view on the micro practices of implementing change.

Objectives Overcrowding in the emergency department (ED) is common in the UK as in other countries worldwide. Computer simulation is one approach used for understanding the causes of ED overcrowding and assessing the likely impact of changes to the delivery of emergency care. However, little is known about the usefulness of computer simulation for analysis of ED patient flow. We undertook a systematic review to investigate the different computer simulation methods and their contribution for analysis of patient flow within EDs in the UK.Methods We searched eight bibliographic databases (MEDLINE, EMBASE, COCHRANE, WEB OF SCIENCE, CINAHL, INSPEC, MATHSCINET and ACM DIGITAL LIBRARY) from date of inception until 31 March 2016. Studies were included if they used a computer simulation method to capture patient progression within the ED of an established UK National Health Service hospital. Studies were summarised in terms of simulation method, key assumptions, input and output data, conclusions drawn and implementation of results.Results Twenty-one studies met the inclusion criteria. Of these, 19 used discrete event simulation and 2 used system dynamics models. The purpose of many of these studies (n=16; 76%) centred on service redesign. Seven studies (33%) provided no details about the ED being investigated. Most studies (n=18; 86%) used specific hospital models of ED patient flow. Overall, the reporting of underlying modelling assumptions was poor. Nineteen studies (90%) considered patient waiting or throughput times as the key outcome measure. Twelve studies (57%) reported some involvement of stakeholders in the simulation study. However, only three studies (14%) reported on the implementation of changes supported by the simulation.Conclusions We found that computer simulation can provide a means to pretest changes to ED care delivery before implementation in a safe and efficient manner. However, the evidence base is small and poorly developed. There are some methodological, data, stakeholder, implementation and reporting issues, which must be addressed by future studies.

IntroductionMassive open online courses (MOOCs) offer a flexible approach to online and distance learning, and are growing in popularity. Several MOOCs are now available, to help learners build on their knowledge in a number of healthcare topics. More research is needed to determine the effectiveness of MOOCs as an online education tool, and explore their long-term impact on learners’ professional practice. We present a protocol describing the design of comprehensive, mixed-methods evaluation of a MOOC, ‘QualityImprovement (QI) inHealthcare: the Case for Change’, which aims to improve learner’s knowledge and understanding of QI approaches in healthcare, and to increase their confidence in participating, and possibly leading QI projects.Methods and analysisA pre-post study design using quantitative and qualitative methods will be used to evaluate the QI MOOC. Different elements of the RE-AIM (reach, effectiveness and maintenance) and Kirkpatrick (reaction, learning and behaviour) models will be used to guide the evaluation. All learners who register for the course will be invited to participate in the QI MOOC evaluation study. Those who consent will be asked to complete a presurvey to assess baseline QI knowledge (self-report and objective) and perceived confidence in engaging in QI activities. On completion of the course, participants will complete a postsurvey measuring again knowledge and perceived confidence. Feedback on the course content and how it can be improved. A subset of participants will be invited to take part in a follow-up qualitative interview, 3 months after taking the course, to explore in depth how the MOOC impacted their behaviour in practice.Ethics and disseminationThe study has been approved by the University of Bath Human Research Ethics Committee (reference: 2958). Study findings will be published in peer-reviewed journals, and disseminated at conference and departmental presentations, and more widely using social media, microblogging sites and periodicals aimed at healthcare professionals.

We describe a modelling toolkit that was developed with the aim of assisting those responsible for introducing stepped care systems to local mental health services in the UK. The toolkit was pre-populated with real patient flow data collected from four sites that piloted the stepped care system design. Two analytical models were developed and coded as part of the toolkit to provide insights concerning workload, patient throughput, and changes in waiting times and waiting list size. An interface was built to allow users to specify their own stepped care system and input their own estimates or data of service demands and capacities at different steps. Despite the challenges and limitations, the use of modelling to inform the design of new service configurations is an important step in the right direction and we would recommend this as a reasonable way forward.

Health care systems are known to be complex and difficult to analyse. Unified Modelling Language (UML) tools have been extensively used to analyse complex systems in several domains, including health care. However, the utility of UML tools in the mapping of care processes has not been demonstrated. This paper reports on the findings of a project that employed UML tools to map the entire care process for patients presenting with symptoms of fractured neck of femur to an acute care hospital in the UK. We employed the UML activity diagram to capture the sequence and conditions of the progress of patients through care activities from a care-system perspective, use case to detail the function performed by each care activity depicted in the activity diagram, and use UML state diagram to capture the dynamic behaviour of patients by depicting states and transition specifications from a patient's perspective. We argue that UML tools can be beneficially employed in the mapping of care processes and to create an information library with the results of such mapping. We also argue that the process mapping method presented here can be used as a basis to develop requirements for simulation models of the flow of patients through the care process.

The ever increasing pressures to ensure the most efficient and effective use of limited health service resources will, over time, encourage policy makers to turn to system modelling solutions. Such techniques have been available for decades, but despite ample research which demonstrates potential, their application in health services to date is limited. This article surveys the breadth of approaches available to support delivery and design across many areas and levels of healthcare planning. A case study in emergency stroke care is presented as an exemplar of an impactful application of health system modelling. This is followed by a discussion of the key issues surrounding the application of these methods in health, what barriers need to be overcome to ensure more effective implementation, as well as likely developments in the future.

Objective To explore the quality and safety of patients’ healthcare provision by identifying whether being a medical outlier is associated with worse patient outcomes. A medical outlier is a hospital inpatient who is classified as a medical patient for an episode within a spell of care and has at least one non-medical ward placement within that spell. Data sources Secondary data from the Patient Administration System of a district general hospital were provided for the financial years 2013/2014–2015/2016. The data included 71 038 medical patient spells for the 3-year period. Study design This research was based on a retrospective, cross-sectional observational study design. Multivariate logistic regression and zero-truncated negative binomial regression were used to explore patient outcomes (in-hospital mortality, 30-day mortality, readmissions and length of stay (LOS)) while adjusting for several confounding factors. Principal findings Univariate analysis indicated that an outlying medical in-hospital patient has higher odds for readmission, double the odds of staying longer in the hospital but no significant difference in the odds of in-hospital and 30-day mortality. Multivariable analysis indicates that being a medical outlier does not affect mortality outcomes or readmission, but it does prolong LOS in the hospital. Conclusions After adjusting for other factors, medical outliers are associated with an increased LOS while mortality or readmissions are not worse than patients treated in appropriate specialty wards. This is in line with existing but limited literature that such patients experience worse patient outcomes. Hospitals may need to revisit their policies regarding outlying patients as increased LOS is associated with an increased likelihood of harm events, worse quality of care and increased healthcare costs.

Changes in medical practice, demographic shifts and financial pressures are all examples of factors that may contribute to demand for periodic changes in the configuration of health services. When reconfiguring a service, health planners often take into account projected demand for services, patient access criteria and budgetary constraints (among other things), but typically give little consideration regarding its resilience to deliver services during and after external disruptions to its capability to deliver. In this paper we discuss a study conducted in response to a direct request from the National Health Service (NHS) Resilience Project within the Department of Health to explore the feasibility of assessing resilience across local services within the NHS and developing a computer software tool to assess resilience of different service reconfigurations. We give an account of the modelling process used, including the analytical framework we developed using both optimisation and heuristic methods, and an illustrative example of usage of a prototype software tool. We also highlight the key lessons that emerged during this project, which may be helpful to OR analysts working on similar projects regarding resilience in the public sector.

Background Obesity care services are often faced with the need to adapt their resources to rising levels of demand. The main focus of thisstudy was to help prioritise planned investments in new capacity allowing the service to improve patient experience and meet future anticipated demand.\ud Methods We developed computer models of patient flows in an obesity service in an Academic Health Science Centre that provides lifestyle, pharmacotherapy and surgery treatment options for the UK’s National Health Service. Using these models we experiment with different scenarios to investigate the likely impact of\ud alternative resource configurations on patient waiting times.\ud Results Simulation results show that the timing and combination of adding extra resources (eg, surgeons and physicians) to the service are important. For example, increasing the capacity of the pharmacotherapy clinics equivalent to adding one physician reduced the relevant waiting list size and waiting times, but it then led to increased waiting times for surgical patients. Better service levels were achieved when the service operates with the resource capacity of two physicians and three surgeons. The results obtained from this study had an impact on the planning and organisation of the obesity service.\ud Conclusions Resource configuration combined with demand management (reduction in referral rates) along the care service can help improve patient waiting time targets for obesity services, such as the 18 week target of UK’s National Health Service. The use of simulation models can help stakeholders understand the\ud interconnectedness of the multiple microsystems (eg, clinics) comprising a complex clinical service for the same patient population, therefore, making stakeholders aware of the likely impact of resourcing decisions on the different microsystems.