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BackgroundIt is a stated ambition of many healthcare systems to eliminate delayed transfers of care (DTOCs) between acute and step-down community services.ObjectiveThis study aims to demonstrate how, counter to intuition, pursual of such a policy is likely to be uneconomical, as it would require large amounts of community capacity to accommodate even the rarest of demand peaks, leaving much capacity unused for much of the time.MethodsSome standard results from queueing theory—a mathematical discipline for considering the dynamics of queues and queueing systems—are used to provide a model of patient flow from the acute to community setting. While queueing models have a track record of application in healthcare, they have not before been used to address this question.ResultsResults show that ‘eliminating’ DTOCs is a false economy: the additional community costs required are greater than the possible acute cost saving. While a substantial proportion of DTOCs can be attributed to inefficient use of resources, the remainder can be considered economically essential to ensuring cost-efficient service operation. For England’s National Health Service (NHS), our modelling estimates annual cost savings of £117m if DTOCs are reduced to the 12% of current levels that can be regarded as economically essential.ConclusionThis study discourages the use of ‘zero DTOC’ targets and instead supports an assessment based on the specific characteristics of the healthcare system considered.

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.

IPACS overview

Aim: Identify, simulate, and evaluate the formal and informal patient-level and unit-level factors that nurse managers use to determine the number of nurses for each shift. Background: Nurse staffing schedules are commonly set based on metrics such as midnight census that do not account for seasonality or midday turnover, resulting in last-minute adjustments or inappropriate staffing levels. Methods: Staffing schedules at a pediatric intensive care unit (PICU) were simulated based on nurse-to-patient assignment rules from interviews with nursing management. Multivariate regression modeled the discrepancies between scheduled and historical staffing levels and constructed rules to reduce these discrepancies. The primary outcome was the median difference between simulated and historical staffing levels.Results: Nurse-to-patient ratios underestimated staffing by a median of 1.5 nurses per shift. Multivariate regression identified patient turnover as the primary factor accounting for this difference and subgroup analysis revealed that patient age and weight were also important. New rules reduced the difference to a median of 0.07 nurses per shift.Conclusion: Measurable, predictable indicators of patient acuity and historical trends may allow for schedules that better match demand.Implications for Nursing Management: Data-driven methods can quantify what drives unit demand and generate nurse schedules that require fewer last-minute adjustments.

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.

Despite the many examples where simulation has successfully demonstrated its capability in modelling patient flow, there is little evidence of enduring widespread use. Bespoke academic projects provide little scope for repeatable practical application and efforts to develop generic reusable solutions have neither clearly identified the end users nor fully appreciated their multifaceted requirements. Influenced by a review of existing findings and recommendations in addition to a customised survey of prospective end users, a set of practical design principles were elicited in guiding development of a new open-source and versatile tool. Capturing variability in arrivals and length of stay as well as the stochastic nature of dynamic delays in patient discharge and transfer, the PathSimR tool can be used to model various user-defined configurations of outpatient and inpatient pathways. In addition to reporting on early use and engagement initiatives to promote the tool, this study also offers a blueprint for how simulation models can be developed and deployed within the healthcare setting.

Objectives While there has been significant research on the pressures facing acute hospitals during the COVID-19 pandemic, there has been less interest in downstream community services which have also been challenged in meeting demand. This study aimed to estimate the theoretical cost-optimal capacity requirement for ‘step down’ intermediate care services within a major healthcare system in England, at a time when considerable uncertainty remained regarding vaccination uptake and the easing of societal restrictions. Methods Demand for intermediate care was projected using an epidemiological model (for COVID-19 demand) and regressing upon public mobility (for non-COVID-19 demand). These were inputted to a computer simulation model of patient flow from acute discharge readiness to bedded and home-based Discharge to Assess (D2A) intermediate care services. Cost-optimal capacity was defined as that which yielded the lowest total cost of intermediate care provision and corresponding acute discharge delays. Results Increased intermediate care capacity is likely to bring about lower system-level costs, with the additional D2A investment more than offset by substantial reductions in costly acute discharge delays (leading also to improved patient outcome and experience). Results suggest that completely eliminating acute ‘bed blocking’ is unlikely economical (requiring large amounts of downstream capacity), and that health systems should instead target an appropriate tolerance based upon the specific characteristics of the pathway. Conclusions Computer modelling can be a valuable asset for determining optimal capacity allocation along the complex care pathway. With results supporting a Business Case for increased downstream capacity, this study demonstrates how modelling can be applied in practice and provides a blueprint for use alongside the freely-available model code.

ObjectivesWhile there has been significant research on the pressures facing acute hospitals during the COVID-19 pandemic, there has been less interest in downstream community services which have also been challenged in meeting demand. This study aimed to estimate the theoretical cost-optimal capacity requirement for ‘step down’ intermediate care services within a major healthcare system in England, at a time when considerable uncertainty remained regarding vaccination uptake and the easing of societal restrictions.MethodsDemand for intermediate care was projected using an epidemiological model (for COVID-19 demand) and regressing upon public mobility (for non-COVID-19 demand). These were inputted to a computer simulation model of patient flow from acute discharge readiness to bedded and home-based Discharge to Assess (D2A) intermediate care services. Cost-optimal capacity was defined as that which yielded the lowest total cost of intermediate care provision and corresponding acute discharge delays.ResultsIncreased intermediate care capacity is likely to bring about lower system-level costs, with the additional D2A investment more than offset by substantial reductions in costly acute discharge delays (leading also to improved patient outcome and experience). Results suggest that completely eliminating acute ‘bed blocking’ is unlikely economical (requiring large amounts of downstream capacity), and that health systems should instead target an appropriate tolerance based upon the specific characteristics of the pathway.ConclusionsComputer modelling can be a valuable asset for determining optimal capacity allocation along the complex care pathway. With results supporting a Business Case for increased downstream capacity, this study demonstrates how modelling can be applied in practice and provides a blueprint for use alongside the freely-available model code.

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.

In response to societal restrictions due to the COVID‐19 pandemic, a significant proportion of physical outpatient consultations were replaced with virtual appointments within the Bristol, North Somerset and South Gloucestershire healthcare system. The objective of this study was to assess the impact of this change in informing the potential viability of a longer‐term shift to telehealth in the outpatient setting. A retrospective analysis was performed using data from the first COVID‐19 wave, comprising 2998 telehealth patient surveys and 143,321 distinct outpatient contacts through both the physical and virtual medium. Four in five specialities showed no significant change in the overall number of consultations per patient during the first wave of the pandemic when telehealth services were widely implemented. Of those surveyed following virtual consultation, more respondents ‘preferred’ virtual (36.4%) than physical appointments (26.9%) with seven times as many finding them ‘less stressful’ than ‘more stressful’. In combining both patient survey and routine activity data, this study demonstrates the importance of using data from multiple sources to derive useful insight. The results support the potential for telehealth to be rapidly employed across a range of outpatient specialities without negatively affecting patient experience.

Objective To develop and scale algorithm-enabled patient prioritization to improve population-level management of type 1 diabetes (T1D) in a pediatric clinic with fixed resources, using telemedicine and remote monitoring of patients via continuous glucose monitor (CGM) data review. Research design and methods We adapted consensus glucose targets for T1D patients using CGM to identify interpretable clinical criteria to prioritize patients for weekly provider review. The criteria were constructed to manage the number of patients reviewed weekly and identify patients who most needed provider contact. We developed an interactive dashboard to display CGM data relevant for the patients prioritized for review. Results The introduction of the new criteria and interactive dashboard was associated with a 60% reduction in the mean time spent by diabetes team members who remotely and asynchronously reviewed patient data and contacted patients, from 3.2 ± 0.20 to 1.3 ± 0.24 minutes per patient per week. Given fixed resources for review, this corresponded to an estimated 147% increase in weekly clinic capacity. Patients who qualified for and received remote review (n = 58) have associated 8.8 percentage points (pp) (95% CI = 0.6-16.9 pp) greater time-in-range (70-180 mg/dL) glucoses compared to 25 control patients who did not qualify at twelve months after T1D onset. Conclusions An algorithm-enabled prioritization of T1D patients with CGM for asynchronous remote review reduced provider time spent per patient and was associated with improved time-in-range. This article is protected by copyright. All rights reserved.

Mass vaccination is widely considered to offer the only route out of the COVID-19 pandemic in a way that social restrictions can be eased and economic activity can safely resume without compromising healthcare services. For many countries, dedicated vaccination centres are a key part of that effort. However, with no directly comparable historical experience there is little information to guide the operational configuration and management of these sites. In addressing this gap in knowledge, the objective of this study is to demonstrate the value of computer modelling. This is achieved through providing an account of its use in supporting management considerations and decisions at two major vaccination centres, at an early stage of the UK’s mass vaccination effort. We report on how modelling insight has influenced the initial setup of one site, including quantification of daily booking numbers. For the same site, we reveal how analysis has informed a significant operational shift in combining two key activities on the vaccination pathway into one. Finally, we describe how, at a second site, modelling has been used to examine pathway stability, in terms of resilience to unforeseen ‘shocks’ such as delayed arrivals and staff unavailability. Alongside the open-source simulation software, the modelling insights reported here can support managers to better plan and improve the operation of COVID-19 vaccination centres.

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.

Background: Billions of doses of medicines are donated for mass drug administrations in support of the World Health Organization’s “Roadmap to Implementation,” which aims to control, eliminate, and eradicate Neglected Tropical Diseases (NTDs). The supply chain to deliver these medicines is complex, with fragmented data systems and limited visibility on performance. This study empirically evaluates the impact of an online supply chain performance measurement system, “NTDeliver,” providing understanding of the value of information sharing towards the success of global health programs. Methods: Retrospective secondary data was extracted from NTDeliver, which included 1,484 shipments for four critical medicines ordered by over 100 countries between February 28, 2006 and December 31, 2018. We applied statistical regression models to analyze the impact on key performance metrics, comparing data before and after the system was implemented. Findings: The results suggest information sharing has a positive impact on three performance indicators: purchase order timeliness (β=1∙01, p

BackgroundManaging 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. The aim of this study is 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 or reduce length of stay.MethodsA stochastic discrete event simulation model is developed to represent the key dynamics of the intensive care admissions process for COVID-19 patients. Model inputs are aligned to levers available to planners with key outputs including duration of time at maximum capacity (to inform workforce requirements), peak daily deaths (for mortuary planning), and total deaths (as an ultimate marker of intervention efficacy). The model - freely available - is applied to the COVID-19 response at a large hospital in England for which the effect of a number of possible interventions are simulated.ResultsCapacity-dependent deaths are closely associated with both the nature and effectiveness of non-pharmaceutical interventions and availability of intensive care beds. For the hospital considered, results suggest that capacity-dependent deaths can be reduced five-fold through a combination of isolation policies, a doubling of bed capacity, and 25% reduced length of stay.ConclusionsWithout treatment or vaccination there is little that can be done to reduce deaths occurring when patients have otherwise been treated in the most appropriate hospital setting. Healthcare planners should therefore focus on minimising the capacity-dependent deaths that are within their influence.