Systems modelling and simulation in health service design, delivery and decision making

Does a GP service package matter in addressing the absence of health management by the occupational population? A modelling study

OBJECTIVE

To assess the influence of supply and demand factors on the contract behavior of occupational populations with general practitioner (GP) teams.

METHODS

We employed a system dynamics approach to assess and predict the effect of the general practitioner service package (GPSP) and complementary incentive policies on the contract rate for 2015-2030. First, the GPSP is designed to address the unique needs of occupational populations, enhancing the attractiveness of GP contracting services, including three personalized service contents tailored to demand-side considerations: work-related disease prevention (WDP), health education & counseling (HEC), and health-care service (HCS). Second, the complementary incentive policies on the supply-side included income incentives (II), job title promotion (JTP), and education & training (ET). Considering the team collaboration, the income distribution ratio (IDR) was also incorporated into supply-side factors.

FINDINGS

The contract rate is predicted to increase to 57.8% by 2030 after the GPSP intervention, representing a 15.4% increase on the non-intervention scenario. WDP and HEC have a slightly higher (by 2%) impact on the contract rate than that from HCS. Regarding the supply-side policies, II have a more significant impact on the contract rate than JTP and ET by 3-5%. The maximum predicted contract rate of 75.2% is expected by 2030 when the IDR is 0.5, i.e., the GP receives 50% of the contract income and other members share 50%.

CONCLUSION

The GP service package favorably increased the contract rate among occupational population, particularly after integrating the incentive policies. Specifically, for a given demand level, the targeted content of the package enhanced the attractiveness of contract services. On the supply side, the incentive policies boost GPs' motivation, and the income distribution motivated other team members.

Improving the usability of open health service delivery simulation models using Python and web apps

One aim of Open Science is to increase the accessibility of research. Within health services research that uses discrete-event simulation, Free and Open Source Software (FOSS), such as Python, offers a way for research teams to share their models with other researchers and NHS decision makers. Although the code for healthcare discrete-event simulation models can be shared alongside publications, it may require specialist skills to use and run. This is a disincentive to researchers adopting Free and Open Source Software and open science practices. Building on work from other health data science disciplines, we propose that web apps offer a user-friendly interface for healthcare models that increase the accessibility of research to the NHS, and researchers from other disciplines. We focus on models coded in Python deployed as streamlit web apps. To increase uptake of these methods, we provide an approach to structuring discrete-event simulation model code in Python so that models are web app ready. The method is general across discrete-event simulation Python packages, and we include code for both simpy and ciw implementations of a simple urgent care call centre model. We then provide a step-by-step tutorial for linking the model to a streamlit web app interface, to enable other health data science researchers to reproduce and implement our method.

Using stochastic simulation modelling to study occupancy levels of decentralised admission avoidance units in Norway

Identifying alternatives to acute hospital admission is a priority for many countries. Over 200 decentralised municipal acute units (MAUs) were established in Norway to divert low-acuity patients away from hospitals. MAUs have faced criticism for low mean occupancy and not relieving pressures on hospitals. We developed a discrete time simulation model of admissions and discharges to MAUs to test scenarios for increasing absolute mean occupancy. We also used the model to estimate the number of patients turned away as historical data was unavailable. Our experiments suggest that mergers alone are unlikely to substantially increase MAU absolute mean occupancy as unmet demand is generally low. However, merging MAUs offers scope for up to 20% reduction in bed capacity, without affecting service provision. Our work has relevance for other admissions avoidance units and provides a method for estimating unconstrained demand for beds in the absence of historical data.

Learning to See: Using Mixed OR Methods to Model Radiology Staff Workload and Support Decision Making in CT

Demand for Computer Tomography (CT) is growing year on year and the population of Ireland is increasingly aging and ailing. Anecdotally, radiology staff reported increasing levels of workload associated with the patient profile. In this paper, we propose a framework combining discrete event simulation (DES) modeling and soft systems methodologies (SSM) for use in healthcare which captures the staff experience and metrics to evidence workload. The framework was applied in a single-scanner CT department, which completes circa 6000 examinations per year. The scanner case load consists of unscheduled work [inpatient (IP) and emergency department (ED)] and scheduled work [outpatient (OP) and general practitioner (GP)]. The three stage framework is supported by qualitative and quantitative methods and uses DES as a decision support tool. Firstly, workflow mapping and system dynamics are used to conceptualize the problem situation and instigate a preliminary data analysis. Secondly, SSM tools are used to identify components for a DES model and service improvement scenarios. Lastly, the DES model results are used to inform decision-making and identify a satisficing solution. Data from the DES model provided evidence of the differing workload (captured in staff time) for the IP and OP cohorts. For non-contrast examinations, inpatient workload is 2.5 times greater than outpatient. Average IP process delays of 11.9 min were demonstrated compared to less than 1 min for OP. The findings recommend that OP and IP diagnostic imaging be provided separately, for efficiency, workload management and infection control reasons.

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

BACKGROUND

Transferring 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.

AIMS

The 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.

METHODS

Discrete 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.

RESULTS

Without 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.

CONCLUSIONS

This 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.

Presenting a comprehensive multi-scale evaluation framework for participatory modelling programs: A scoping review

INTRODUCTION

Systems modelling and simulation can improve understanding of complex systems to support decision making, better managing system challenges. Advances in technology have facilitated accessibility of modelling by diverse stakeholders, allowing them to engage with and contribute to the development of systems models (participatory modelling). However, despite its increasing applications across a range of disciplines, there is a growing need to improve evaluation efforts to effectively report on the quality, importance, and value of participatory modelling. This paper aims to identify and assess evaluation frameworks, criteria, and/or processes, as well as to synthesize the findings into a comprehensive multi-scale framework for participatory modelling programs.

MATERIALS AND METHODS

A scoping review approach was utilized, which involved a systematic literature search via Scopus in consultation with experts to identify and appraise records that described an evaluation framework, criteria, and/or process in the context of participatory modelling. This scoping review is registered with the Open Science Framework.

RESULTS

The review identified 11 studies, which varied in evaluation purposes, terminologies, levels of examination, and time points. The review of studies highlighted areas of overlap and opportunities for further development, which prompted the development of a comprehensive multi-scale evaluation framework to assess participatory modelling programs across disciplines and systems modelling methods. The framework consists of four categories (Feasibility, Value, Change/Action, Sustainability) with 30 evaluation criteria, broken down across project-, individual-, group- and system-level impacts.

DISCUSSION & CONCLUSION

The presented novel framework brings together a significant knowledge base into a flexible, cross-sectoral evaluation effort that considers the whole participatory modelling process. Developed through the rigorous synthesis of multidisciplinary expertise from existing studies, the application of the framework can provide the opportunity to understand practical future implications such as which aspects are particularly important for policy decisions, community learning, and the ongoing improvement of participatory modelling methods.

Expediting workflow in the acute stroke pathway for endovascular thrombectomy in the northern Netherlands: a simulation model

OBJECTIVE

The objective of this study is to identify barriers for the timely delivery of endovascular thrombectomy (EVT) and to investigate the effects of potential workflow improvements in the acute stroke pathway.

DESIGN

Hospital data prospectively collected in the MR CLEAN Registry were linked to emergency medical services data for each EVT patient and used to build two Monte Carlo simulation models. The 'mothership (MS) model', reflecting patients who arrived directly at the comprehensive stroke centre (CSC); and the 'drip and ship' (DS) model, reflecting patients who were transferred to the CSC from primary stroke centres (PSCs).

SETTING

Northern region of the Netherlands. One CSC provides EVT, and its catchment area includes eight PSCs.

PARTICIPANTS

248 patients who were treated with EVT between July 2014 and November 2017.

OUTCOME MEASURES

The main outcome measures were total delay from stroke onset until groin puncture, functional independence at 90 days (modified Rankin Scale 0-2) and mortality.

RESULTS

Barriers identified included fast-track emergency department routing, prealert for transfer to the CSC, reduced handover time between PSC and ambulance, direct transfer from CSC arrival to angiography suite entry, and reducing time to groin puncture. Taken together, all workflow improvements could potentially reduce the time from onset to groin puncture by 59 min for the MS model and 61 min for the DS model. These improvements could thus result in more patients-3.7% MS and 7.4% DS-regaining functional independence after 90 days, in addition to decreasing mortality by 3.0% and 5.0%, respectively.

CONCLUSIONS

In our region, the proposed workflow improvements might reduce time to treatment by about 1 hour and increase the number of patients regaining functional independence by 6%. Simulation modelling is useful for assessing the potential effects of interventions aimed at reducing time from onset to EVT.

Assessing Patient Engagement in Health Care: Proposal for a Modeling and Simulation Framework for Behavioral Analysis

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/30092.

Reducing failures in daily medical practice: Healthcare failure mode and effect analysis combined with computer simulation

This study proposes a risk analysis approach for complex healthcare processes that combines qualitative and quantitative methods to improve patient safety. We combine Healthcare Failure Mode and Effect Analysis with Computer Simulation (HFMEA-CS), to overcome widely recognised HFMEA drawbacks regarding the reproducibility and validity of the outcomes due to human interpretation, and show the application of this methodology in a complex healthcare setting. HFMEA-CS is applied to analyse drug adherence performance in the surgical admission to discharge process of pheochromocytoma patients. The multidisciplinary team identified and scored the failure modes, and the simulation model supported in prioritisation of failure modes, uncovered dependencies between failure modes, and predicted the impact of measures on system behaviour. The results show that drug adherence, defined as the percentage of required drugs received at the right time, can be significantly improved with 12%, to reach a drug adherence of 99%. We conclude that HFMEA-CS is both a viable and effective risk analysis approach, combining strengths of expert opinion and quantitative analysis, for analysing human-system interactions in socio-technical systems. Practitioner summary: We propose combining Healthcare Failure Mode and Effects Analysis with Computer Simulation (HFMEA-CS) for prospective risk analysis of complex and potentially harmful processes, to prevent critical incidents from occurring. HFMEA-CS combines expert opinions with quantitative analyses, such that the results are more reliable, reproducible, and fitting for complex healthcare settings.

Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units

STUDY OBJECTIVE

This study aimed to develop and validate 2 machine learning models that use historical and current-visit patient data from electronic health records to predict the probability of patient admission to either an inpatient unit or ICU at each hour (up to 24 hours) of an emergency department (ED) encounter. The secondary goal was to provide a framework for the operational implementation of these machine learning models.

METHODS

Data were curated from 468,167 adult patient encounters in 3 EDs (1 academic and 2 community-based EDs) of a large academic health system from August 1, 2015, to October 31, 2018. The models were validated using encounter data from January 1, 2019, to December 31, 2019. An operational user dashboard was developed, and the models were run on real-time encounter data.

RESULTS

For the intermediate admission model, the area under the receiver operating characteristic curve was 0.873 and the area under the precision-recall curve was 0.636. For the ICU admission model, the area under the receiver operating characteristic curve was 0.951 and the area under the precision-recall curve was 0.461. The models had similar performance in both the academic- and community-based settings as well as across the 2019 and real-time encounter data.

CONCLUSION

Machine learning models were developed to accurately make predictions regarding the probability of inpatient or ICU admission throughout the entire duration of a patient's encounter in ED and not just at the time of triage. These models remained accurate for a patient cohort beyond the time period of the initial training data and were integrated to run on live electronic health record data, with similar performance.

Variation in referral rates to emergency departments and inpatient services from a GP out of hours service and the potential impact of alternative staffing models

Introduction

Out of hours (OOHs) primary care is a critical component of the acute care system overnight and at weekends. Referrals from OOH services to hospital will add to the burden on hospital assessment in the ED and on-call specialties.

Methods

We studied the variation in referral rates (to the ED and direct specialty admission) of individual clinicians working in the Oxfordshire, UK OOH service covering a population of 600 000 people. We calculated the referral probability for each clinician over a 13-month period of practice (1 December 2014 to 31 December 2015), stratifying by clinician factors and location and timing of assessment. We used Simul8 software to determine the range of hospital referrals potentially due to variation in clinician referral propensity.

Results

Among the 119 835 contacts with the service, 5261 (4.4%) were sent directly to the ED and 3474 (3.7%) were admitted directly to specialties. More referrals were made to ED by primary care physicians if they did not work in the local practices (5.5% vs 3.5%, p=0.011). For clinicians with >1000 consultations, percentage of patients referred varied from 1% to 21% of consultations. Simulations where propensity to refer was made less extreme showed a difference in maximum referrals of 50 patients each week.

Conclusions

There is substantial variation in clinician referral rates from OOHs primary care to the acute hospital setting. The number of patients referred could be influenced by this variation in clinician behaviour. Referral propensity should be studied including casemix adjustment to determine if interventions targeting such behaviour are effective.

"What's the evidence?"-Towards more empirical evaluations of the impact of OR interventions in healthcare

Despite an increasing number of papers reporting applications of operational research (OR) to problems in healthcare, there remains little empirical evidence of OR improving healthcare delivery in practice. Without such evidence it is harder both to justify the usefulness of OR to a healthcare audience and to learn and continuously improve our approaches. To progress, we need to build the evidence-base on whether and how OR improves healthcare delivery through careful empirical evaluation. This position paper reviews evaluation standards in healthcare improvement research and dispels some common myths about evaluation. It highlights the current lack of robust evaluation of healthcare OR and makes the case for addressing this. It then proposes possible ways for building better empirical evaluations of OR interventions in healthcare.

COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care

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.

A simulation modelling toolkit for organising outpatient dialysis services during the COVID-19 pandemic

This study presents two simulation modelling tools to support the organisation of networks of dialysis services during the COVID-19 pandemic. These tools were developed to support renal services in the South of England (the Wessex region caring for 650 dialysis patients), but are applicable elsewhere. A discrete-event simulation was used to model a worst case spread of COVID-19, to stress-test plans for dialysis provision throughout the COVID-19 outbreak. We investigated the ability of the system to manage the mix of COVID-19 positive and negative patients, the likely effects on patients, outpatient workloads across all units, and inpatient workload at the centralised COVID-positive inpatient unit. A second Monte-Carlo vehicle routing model estimated the feasibility of patient transport plans. If current outpatient capacity is maintained there is sufficient capacity in the South of England to keep COVID-19 negative/recovered and positive patients in separate sessions, but rapid reallocation of patients may be needed. Outpatient COVID-19 cases will spillover to a secondary site while other sites will experience a reduction in workload. The primary site chosen to manage infected patients will experience a significant increase in outpatients and inpatients. At the peak of infection, it is predicted there will be up to 140 COVID-19 positive patients with 40 to 90 of these as inpatients, likely breaching current inpatient capacity. Patient transport services will also come under considerable pressure. If patient transport operates on a policy of one positive patient at a time, and two-way transport is needed, a likely scenario estimates 80 ambulance drive time hours per day (not including fixed drop-off and ambulance cleaning times). Relaxing policies on individual patient transport to 2-4 patients per trip can save 40-60% of drive time. In mixed urban/rural geographies steps may need to be taken to temporarily accommodate renal COVID-19 positive patients closer to treatment facilities.

Integrating HIV, syphilis, malaria and anaemia point-of-care testing (POCT) for antenatal care at dispensaries in western Kenya: discrete-event simulation modelling of operational impact

BACKGROUND

Despite WHO advocating for an integrated approach to antenatal care (ANC), testing coverage for conditions other than HIV remains low and women are referred to distant laboratories for testing. Using point-of-care tests (POCTs) at peripheral dispensaries could improve access to testing and timely treatment. However, the effect of providing additional services on nurse workload and client wait times are unknown. We use discrete-event simulation (DES) modelling to understand the effect of providing four point-of-care tests for ANC on nurse utilization and wait times for women seeking maternal and child health (MCH) services.

METHODS

We collected detailed time-motion data over 20 days from one high volume dispensary in western Kenya during the 8-month implementation period (2014-2015) of the intervention. We constructed a simulation model using empirical arrival distributions, activity durations and client pathways of women seeking MCH services. We removed the intervention from the model to obtain wait times, length-of-stay and nurse utilization rates for the baseline scenario where only HIV testing was offered for ANC. Additionally, we modelled a scenario where nurse consultations were set to have minimum durations for sufficient delivery of all WHO-recommended services.

RESULTS

A total of 183 women visited the dispensary for MCH services and 14 of these women received point-of-care testing (POCT). The mean difference in total waiting time was 2 min (95%CI: < 1-4 min, p = 0.026) for MCH women when integrated POCT was given, and 9 min (95%CI: 4-14 min, p < 0.001) when integrated POCT with adequate ANC consult times was given compared to the baseline scenario. Mean length-of-stay increased by 2 min (95%CI: < 1-4 min, p = 0.015) with integrated POCT and by 16 min (95%CI: 10-21 min, p < 0.001) with integrated POCT and adequate consult times compared to the baseline scenario. The two nurses' overall daily utilization in the scenario with sufficient minimum consult durations were 72 and 75%.

CONCLUSION

The intervention had a modest overall impact on wait times and length-of-stay for women seeking MCH services while ensuring pregnant women received essential diagnostic testing. Nurse utilization rates fluctuated among days: nurses experienced spikes in workload on some days but were under-utilized on the majority of days. Overall, our model suggests there was sufficient time to deliver all WHO's required ANC activities and offer integrated testing for ANC first and re-visits with the current number of healthcare staff. Further investigations on improving healthcare worker, availability, performance and quality of care are needed. Delivering four point-of-care tests together for ANC at dispensary level would be a low burden strategy to improve ANC.

The 85% bed occupancy fallacy: The use, misuse and insights of queuing theory

Queuing theory can and has been used to inform bed pool capacity decision making, though rarely by managers themselves. The insights it brings are also not widely and properly understood by healthcare managers. These two shortcomings lead to the persistent fallacy of there being a globally applicable optimum average occupancy target, for example 85%, which can in turn lead to over- or under-provision of resources. Through this paper, we aim both to make queuing models more accessible and to provide visual demonstrations of the general insights managers should absorb from queuing theory. Occupancy is a consequence of the patient arrival rate and 'treatment' rate (the number of beds and length of stay). There is a trade-off between the average occupancy and access to beds (measured by, for example, the risk of access block due to all beds being full or the average waiting time for a bed). Managerially, the decision-making input should be the level of access to beds required, and so bed occupancy should be an output. Queuing models are useful to quickly draw the shape of these access-occupancy trade-off curves. Moreover, they can explicitly show the effect that variation (lack of regularity) in the times between arrivals and in the lengths of stay of individual patients has on the shape of the trade-off curves. In particular, with the same level of access, bed pools subject to lower variation can operate at higher average occupancy. Further, to improve access to a bed pool, reducing variation should be considered.

Implementation of simulation modelling to improve service planning in specialist orthopaedic and neurosurgical outpatient services

BACKGROUND

Advanced physiotherapist-led services have been embedded in specialist orthopaedic and neurosurgical outpatient departments across Queensland, Australia, to ameliorate capacity constraints. Simulation modelling has been used to inform the optimal scale and professional mix of services required to match patient demand. The context and the value of simulation modelling in service planning remain unclear. We aimed to examine the adoption, context and costs of using simulation modelling recommendations to inform service planning.

METHODS

Using an implementation science approach, we undertook a prospective, qualitative evaluation to assess the use of discrete event simulation modelling recommendations for service re-design and to explore stakeholder perspectives about the role of simulation modelling in service planning. Five orthopaedic and neurosurgical services in Queensland, Australia, were selected to maximise variation in implementation effectiveness. We used the consolidated framework for implementation research (CFIR) to guide the facilitation and analysis of the stakeholder focus group discussions. We conducted a prospective costing analysis in each service to estimate the costs associated with using simulation modelling to inform service planning.

RESULTS

Four of the five services demonstrated adoption by inclusion of modelling recommendations into proposals for service re-design. Four CFIR constructs distinguished and two CFIR constructs did not distinguish between high versus mixed implementation effectiveness. We identified additional constructs that did not map onto CFIR. The mean cost of implementation was AU$34,553 per site (standard deviation = AU$737).

CONCLUSIONS

To our knowledge, this is the first time the context of implementing simulation modelling recommendations in a health care setting, using a validated framework, has been examined. Our findings may provide valuable insights to increase the uptake of healthcare modelling recommendations in service planning.

Making Smarter Decisions Faster: Systems Engineering to Improve the Global Public Health Response to HIV

PURPOSE OF REVIEW

This review offers an operational definition of systems engineering (SE) as applied to public health, reviews applications of SE in the field of HIV, and identifies opportunities and challenges of broader application of SE in global health.

RECENT FINDINGS

SE involves the deliberate sequencing of three steps: diagnosing a problem, evaluating options using modeling or optimization, and providing actionable recommendations. SE includes diverse tools (from process improvement to mathematical modeling) applied to decisions at various levels (from local staffing decisions to planning national-level roll-out of new interventions). Contextual factors are crucial to effective decision-making, but there are gaps in understanding global decision-making processes. Integrating SE into pre-service training and translating SE tools to be more accessible could increase utilization of SE approaches in global health. SE is a promising, but under-recognized approach to improve public health response to HIV globally.

Economic efficiency versus accessibility: Planning of the hospital landscape in rural regions using a linear model on the example of paediatric and obstetric wards in the northeast of Germany

BACKGROUND

Costs for the provision of regional hospital care depend, among other things, on the population density and the maximum reasonable distance to the nearest hospital. In regions with a low population density, it is a challenge to plan the number and location of hospitals with respect both to economic efficiency and to the availability of hospital care close to residential areas. We examined whether the hospital landscape in rural regions can be planned on the basis of a regional economic model using the example which number of paediatric and obstetric wards in a region in the Northeast of Germany is economically efficient and what would be the consequences for the accessibility when one or more of the three current locations would be closed.

METHODS

A model of linear programming was developed to estimate the costs and revenues under different scenarios with up to three hospitals with both a paediatric and an obstetric ward in the investigation region. To calculate accessibility of the wards, geographic analyses were conducted.

RESULTS

With three hospitals in the study region, there is a financial gap of €3.6 million. To get a positive contribution margin for all three hospitals, more cases have to be treated than the region can deliver. Closing hospitals in the parts of the region with the smallest population density would lead to reduced accessibility for about 8% of the population under risk.

CONCLUSIONS

Quantitative modelling of the costs of regional hospital care provides a basis for planning. A qualitative discussion to the locations of the remaining departments and the implementation of alternative healthcare concepts should follow.

Reducing pharmacy patient waiting time

PURPOSE

Pharmacy services start right from prescribing medicines and continue as the medication's effect is monitored. Hospital and community pharmacy staff promote rational prescribing and medicine use. Consequentially, pharmacy is a complex and busy field. Often there are peak workload hours when patients must wait, which is associated with patient dissatisfaction that may negatively affect patient experience and the organisation's reputation. The purpose of this paper is to enlist techniques, methods and technological advancements that have been successfully employed to reduce patient waiting time.

DESIGN/METHODOLOGY/APPROACH

A database search was conducted in 2017 to locate articles addressing methods and technologies that reduce pharmacy waiting time. The literature revealed various techniques and technologies like queuing theory, tele-pharmacy, evidence-based pharmacy design, automated pharmacy systems (robotics), system modelling and simulation and the Six Sigma method for identifying potential problems associated with increased wait time.

FINDINGS

The authors conclude that various techniques and methods, including automated queuing technology, tele-pharmacy, automated pharmacy devices/machines for quick and accurate filling and dispensing, computer simulation modelling, evidence-based pharmacy infrastructure for smooth workflow and Six Sigma can maintain customer satisfaction, reduce waiting time, attract new customers, decrease workload and improve the organisation's reputation.

PRACTICAL IMPLICATIONS

The authors conclude that various techniques and methods, including automated queuing technology, tele-pharmacy, automated pharmacy devices/machines for quick and accurate filling and dispensing, computer simulation modelling, evidence-based pharmacy infrastructure for smooth workflow and Six Sigma methodology can maintain customer satisfaction, reduce waiting time, attract new customers, decrease workload and improve the organisation's reputation.

ORIGINALITY/VALUE

The authors carried out a literature search and identified the techniques that have been successfully implemented to reduce pharmacy patient waiting time and methods that can identify potential process behind medication dispensation delays.

Exploring the utility of internal whistleblowing in healthcare via agent-based models

OBJECTIVE

The benefits of internal whistleblowing or speaking-up in the healthcare sector are significant. The a priori assumption that employee whistleblowing is always beneficial is, however, rarely examined. While recent research has begun to consider how the complex nature of healthcare institutions impact speaking-up rates, few have investigated the institutional processes and factors that facilitate or retard the benefits of speaking up. Here we consider how the efficacy of formal inquiries within organisations in response to employees' speaking up about their concerns affects the utility of internal whistleblowing.

DESIGN

Using computational models, we consider how best to improve patient care through internal whistleblowing when resource and practical limitations constrain healthcare operation. We analyse the ramifications of varying organisational responses to employee concerns, given organisational and practical limitations.

SETTING

Drawing on evidence from international research, we test the utility of whistleblowing policies in a variety of organisational settings. This includes institutions where whistleblowing inquiries are handled with varying rates of efficiency and accuracy.

RESULTS

We find organisational inefficiencies can negatively impact the benefits of speaking up about bad patient care. We find that, given resource limitations and review inefficiencies, it can actually improve patient care if whistleblowing rates are limited. However, we demonstrate that including softer mechanisms for internal adjustment of healthcare practice (eg, peer to peer conversation) alongside whistleblowing policy can overcome these organisational limitations.

CONCLUSION

Healthcare organisations internationally have a variable record of responding to employees who speak up about their workplace concerns. Where organisations get this wrong, the consequences can be serious for patient care and staff well-being. The results of this study, therefore, have implications for researchers, policy makers and healthcare organisations internationally. We conclude with a call for further research on a more holistic understanding of the interplay between organisational structure and the benefits of whistleblowing to patient care.

From behavioural simulation to computer models: how simulation can be used to improve healthcare management and policy

Simulation is a technique that evokes or replicates substantial aspects of the real world, in order to experiment with a simplified imitation of an operations system, for the purpose of better understanding and/or improving that system. Simulation provides a safe environment for investigating individual and organisational behaviour and a risk-free testbed for new policies and procedures. Therefore, it can complement or replace direct field observations and trial-and-error approaches, which can be time consuming, costly and difficult to carry out. However, simulation has low adoption as a research and improvement tool in healthcare management and policy-making. The literature on simulation in these fields is dispersed across different disciplinary traditions and typically focuses on a single simulation method. In this article, we examine how simulation can be used to investigate, understand and improve management and policy-making in healthcare organisations. We develop the rationale for using simulation and provide an integrative overview of existing approaches, using examples of in vivo behavioural simulations involving live participants, pure in silico computer simulations and intermediate approaches (virtual simulation) where human participants interact with computer simulations of health organisations. We also discuss the combination of these approaches to organisational simulation and the evaluation of simulation-based interventions.

Process improvement for a complex dual medical procedure

Pancreatic extracorporeal shock wave lithotripsy followed by endoscopic retrograde cholangiopancreatography is accepted worldwide as a treatment for a large, symptomatic, obstructing pancreatic stones. However, timely completion of the combined process requires coordination of equipment and personnel from two different complex procedures.

We used Lean management tools in a week-long event to redesign the process around the patient. Using idea-generated Plan Do Study Act cycles to refine the process, from scheduling to postprocedure recovery, equipment and personnel were aligned to allow these two procedures to occur in immediate succession.

The redesigned process resulted in all patients receiving both procedures without delay. This eliminated over 8 hours of wait time. Standard work and a newly created complex scheduler improved flow. We reduced the number of anaesthetics for patients without prolonging the procedure length.

A systematic literature review of simulation models for non-technical skill training in healthcare logistics

Background

Resource allocation in patient care relies heavily on individual judgements of healthcare professionals. Such professionals perform coordinating functions by managing the timing and execution of a multitude of care processes for multiple patients. Based on advances in simulation, new technologies that could be used for establishing realistic representations have been developed. These simulations can be used to facilitate understanding of various situations, coordination training and education in logistics, decision-making processes, and design aspects of the healthcare system. However, no study in the literature has synthesized the types of simulations models available for non-technical skills training and coordination of care.

Methods

A systematic literature review, following the PRISMA guidelines, was performed to identify simulation models that could be used for training individuals in operative logistical coordination that occurs on a daily basis. This article reviewed papers of simulation in healthcare logistics presented in the Web of Science Core Collections, ACM digital library, and JSTOR databases. We conducted a screening process to gather relevant papers as the knowledge foundation of our literature study. The screening process involved a query-based identification of papers and an assessment of relevance and quality.

Results

Two hundred ninety-four papers met the inclusion criteria. The review showed that different types of simulation models can be used for constructing scenarios for addressing different types of problems, primarily for training and education sessions. The papers identified were classified according to their utilized paradigm and focus areas. (1) Discrete-event simulation in single-category and single-unit scenarios formed the most dominant approach to developing healthcare simulations and dominated all other categories by a large margin. (2) As we approached a systems perspective (cross-departmental and cross-institutional), discrete-event simulation became less popular and is complemented by system dynamics or hybrid modeling. (3) Agent-based simulations and participatory simulations have increased in absolute terms, but the share of these modeling techniques among all simulations in this field remains low.

Conclusions

An extensive study analyzing the literature on simulation in healthcare logistics indicates a growth in the number of examples demonstrating how simulation can be used in healthcare settings. Results show that the majority of studies create situations in which non-technical skills of managers, coordinators, and decision makers can be trained. However, more system-level and complex system-based approaches are limited and use methods other than discrete-event simulation.

Simple rules for evidence translation in complex systems: A qualitative study

BACKGROUND

Ensuring patients benefit from the latest medical and technical advances remains a major challenge, with rational-linear and reductionist approaches to translating evidence into practice proving inefficient and ineffective. Complexity thinking, which emphasises interconnectedness and unpredictability, offers insights to inform evidence translation theories and strategies. Drawing on detailed insights into complex micro-systems, this research aimed to advance empirical and theoretical understanding of the reality of making and sustaining improvements in complex healthcare systems.

METHODS

Using analytical auto-ethnography, including documentary analysis and literature review, we assimilated learning from 5 years of observation of 22 evidence translation projects (UK). We used a grounded theory approach to develop substantive theory and a conceptual framework. Results were interpreted using complexity theory and 'simple rules' were identified reflecting the practical strategies that enhanced project progress.

RESULTS

The framework for Successful Healthcare Improvement From Translating Evidence in complex systems (SHIFT-Evidence) positions the challenge of evidence translation within the dynamic context of the health system. SHIFT-Evidence is summarised by three strategic principles, namely (1) 'act scientifically and pragmatically' - knowledge of existing evidence needs to be combined with knowledge of the unique initial conditions of a system, and interventions need to adapt as the complex system responds and learning emerges about unpredictable effects; (2) 'embrace complexity' - evidence-based interventions only work if related practices and processes of care within the complex system are functional, and evidence-translation efforts need to identify and address any problems with usual care, recognising that this typically includes a range of interdependent parts of the system; and (3) 'engage and empower' - evidence translation and system navigation requires commitment and insights from staff and patients with experience of the local system, and changes need to align with their motivations and concerns. Twelve associated 'simple rules' are presented to provide actionable guidance to support evidence translation and improvement in complex systems.

CONCLUSION

By recognising how agency, interconnectedness and unpredictability influences evidence translation in complex systems, SHIFT-Evidence provides a tool to guide practice and research. The 'simple rules' have potential to provide a common platform for academics, practitioners, patients and policymakers to collaborate when intervening to achieve improvements in healthcare.

Analysing barriers to service improvement using a multi-level theory of innovation: the case of glaucoma outpatient clinics

The development and implementation of innovation by healthcare providers is understood as a multi-determinant and multi-level process. Theories at different analytical levels (i.e. micro and organisational) are needed to capture the processes that influence innovation by providers. This article combines a micro theory of innovation, actor-network theory, with organisational level processes using the 'resource based view of the firm'. It examines the influence of, and interplay between, innovation-seeking teams (micro) and underlying organisational capabilities (meso) during innovation processes. We used ethnographic methods to study service innovations in relation to ophthalmology services run by a specialist English NHS Trust at multiple locations. Operational research techniques were used to support the ethnographic methods by mapping the care process in the existing and redesigned clinics. Deficiencies in organisational capabilities for supporting innovation were identified, including manager-clinician relations and organisation-wide resources. The article 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.

A stakeholder visioning exercise to enhance chronic care and the integration of community pharmacy services

BACKGROUND

Collaboration between relevant stakeholders in health service planning enables service contextualization and facilitates its success and integration into practice. Although community pharmacy services (CPSs) aim to improve patients' health and quality of life, their integration in primary care is far from ideal. Key stakeholders for the development of a CPS intended at preventing cardiovascular disease were identified in a previous stakeholder analysis. Engaging these stakeholders to create a shared vision is the subsequent step to focus planning directions and lay sound foundations for future work.

OBJECTIVES

This study aims to develop a stakeholder-shared vision of a cardiovascular care model which integrates community pharmacists and to identify initiatives to achieve this vision.

METHODS

A participatory visioning exercise involving 13 stakeholders across the healthcare system was performed. A facilitated workshop, structured in three parts (i.e., introduction; developing the vision; defining the initiatives towards the vision), was designed. The Chronic Care Model inspired the questions that guided the development of the vision. Workshop transcripts, researchers' notes and materials produced by participants were analyzed using qualitative content analysis.

RESULTS

Stakeholders broadened the objective of the vision to focus on the management of chronic diseases. Their vision yielded 7 principles for advanced chronic care: patient-centered care; multidisciplinary team approach; shared goals; long-term care relationships; evidence-based practice; ease of access to healthcare settings and services by patients; and good communication and coordination. Stakeholders also delineated six environmental factors that can influence their implementation. Twenty-four initiatives to achieve the developed vision were defined.

CONCLUSIONS

The principles and factors identified as part of the stakeholder shared-vision were combined in a preliminary model for chronic care. This model and initiatives can guide policy makers as well as healthcare planners and researchers to develop and integrate chronic disease services, namely CPSs, in real-world settings.

Improving the production of applied health research findings: insights from a qualitative study of operational research

BACKGROUND

Knowledge produced through applied health research is often of a form not readily accessible to or actionable by policymakers and practitioners, which hinders its implementation. Our aim was to identify research activities that can support the production of knowledge tailored to inform policy and practice. To do this, we studied an operational research approach to improving the production of applied health research findings.

METHODS

A 2-year qualitative study was conducted of the operational research contribution to a multidisciplinary applied health research project that was successful in rapidly informing national policy. Semi-structured interviews (n = 20) were conducted with all members of the project's research team and advisory group (patient and health professional representatives and academics). These were augmented by participant (> 150 h) and non-participant (> 15 h) observations focusing on the process and experience of attempting to support knowledge production. Data were analysed thematically using QSR NVivo software.

RESULTS

Operational research performed a knowledge mediation role shaped by a problem-focused approach and an intent to perform those tasks necessary to producing readily implementable knowledge but outwith the remit of other disciplinary strands of the project. Three characteristics of the role were found to support this: engaging and incorporating different perspectives to improve services by capturing a range of health professional and patient views alongside quantitative and qualitative research evidence; rendering data meaningful by creating and presenting evidence in forms that are accessible to and engage different audiences, enabling them to make sense of it for practical use; and maintaining perceived objectivity and rigour by establishing credibility, perceived neutrality and confidence in the robustness of the research in order to unite diverse professionals in thinking creatively about system-wide service improvement.

CONCLUSIONS

Our study contributes useful empirical insights about knowledge mediation activities within multidisciplinary applied health research projects that support the generation of accessible, practice-relevant and actionable knowledge. Incorporating such activities, or a dedicated role, for mediating knowledge production within such projects could help to enhance the uptake of research findings into routine healthcare and warrants further consideration.

Combining qualitative and quantitative operational research methods to inform quality improvement in pathways that span multiple settings

Background

Improving integration and continuity of care across sectors within resource constraints is a priority in many health systems. Qualitative operational research methods of problem structuring have been used to address quality improvement in services involving multiple sectors but not in combination with quantitative operational research methods that enable targeting of interventions according to patient risk. We aimed to combine these methods to augment and inform an improvement initiative concerning infants with congenital heart disease (CHD) whose complex care pathway spans multiple sectors.

Methods

Soft systems methodology was used to consider systematically changes to services from the perspectives of community, primary, secondary and tertiary care professionals and a patient group, incorporating relevant evidence. Classification and regression tree (CART) analysis of national audit datasets was conducted along with data visualisation designed to inform service improvement within the context of limited resources.

Results

A ‘Rich Picture’ was developed capturing the main features of services for infants with CHD pertinent to service improvement. This was used, along with a graphical summary of the CART analysis, to guide discussions about targeting interventions at specific patient risk groups. Agreement was reached across representatives of relevant health professions and patients on a coherent set of targeted recommendations for quality improvement. These fed into national decisions about service provision and commissioning.

Conclusions

When tackling complex problems in service provision across multiple settings, it is important to acknowledge and work with multiple perspectives systematically and to consider targeting service improvements in response to confined resources. Our research demonstrates that applying a combination of qualitative and quantitative operational research methods is one approach to doing so that warrants further consideration.

Addressing the elephant in the room: a possible new way to increase patient adherence to medical advice

Lack of patient adherence to medical advice (PAMA) is recognized as an area of interest. None of the previous initiatives to improve PAMA, such as patient centered care and shared decision making, have proved to be successful in terms of improving patient adherence. The aim of the present study is to assess beliefs about priorities in public health care, and adherence to medical advice, to establish a novel approach to increase PAMA. The present study is based on responses to two questions in an experimental survey from the Norwegian Citizen Panel, addressing people's attitudes to priorities in public health care and adherence to medical advice. The questions on priorities in the health care sector are organized into six groups. The questions on adherence are organized into three groups. All questions are answered on a 7-point Likert scale. This study is the first to use experimental surveys to assess PAMA. The results indicate that if health care providers refer to national expertise and patient organizations' recommendations on a given treatment, PAMA could improve. Although technical and methodological interventions in health care have, to some extent, improved PAMA, medical adherence is still low. In the present study, it is shown that integrating either national expertise or collaborated messages with other health professions and patient organizations' recommendations in everyday care may help improve patients adherence to medical advice. A minor change in how treatment suggestions are presented could improve PAMA.

A stochastic model for MRSA transmission within a hospital ward incorporating environmental contamination

Methicillin-resistant Staphylococcus aureus (MRSA) transmission in hospital wards is associated with adverse outcomes for patients and increased costs for hospitals. The transmission process is inherently stochastic and the randomness emphasized by the small population sizes involved. As such, a stochastic model was proposed to describe the MRSA transmission process, taking into account the related contribution and modelling of the associated microbiological environmental contamination. The model was used to evaluate the performance of five common interventions and their combinations on six potential outcome measures of interest under two hypothetical disease burden settings. The model showed that the optimal intervention combination varied depending on the outcome measure and burden setting. In particular, it was found that certain outcomes only required a small subset of targeted interventions to control the outcome measure, while other outcomes still reported reduction in the outcome distribution with up to all five interventions included. This study describes a new stochastic model for MRSA transmission within a ward and highlights the use of the generalized Mann–Whitney statistic to compare the distribution of the outcome measures under different intervention combinations to assist in planning future interventions in hospital wards under different potential outcome measures and disease burden.

Leveraging Health Care Simulation Technology for Human Factors Research: Closing the Gap Between Lab and Bedside

OBJECTIVE

We describe health care simulation, designed primarily for training, and provide examples of how human factors experts can collaborate with health care professionals and simulationists-experts in the design and implementation of simulation-to use contemporary simulation to improve health care delivery.

BACKGROUND

The need-and the opportunity-to apply human factors expertise in efforts to achieve improved health outcomes has never been greater. Health care is a complex adaptive system, and simulation is an effective and flexible tool that can be used by human factors experts to better understand and improve individual, team, and system performance within health care.

METHOD

Expert opinion is presented, based on a panel delivered during the 2014 Human Factors and Ergonomics Society Health Care Symposium.

RESULTS

Diverse simulators, physically or virtually representing humans or human organs, and simulation applications in education, research, and systems analysis that may be of use to human factors experts are presented. Examples of simulation designed to improve individual, team, and system performance are provided, as are applications in computational modeling, research, and lifelong learning.

CONCLUSION

The adoption or adaptation of current and future training and assessment simulation technologies and facilities provides opportunities for human factors research and engineering, with benefits for health care safety, quality, resilience, and efficiency.

APPLICATION

Human factors experts, health care providers, and simulationists can use contemporary simulation equipment and techniques to study and improve health care delivery.

Operational research as implementation science: definitions, challenges and research priorities

BACKGROUND

Operational research (OR) is the discipline of using models, either quantitative or qualitative, to aid decision-making in complex implementation problems. The methods of OR have been used in healthcare since the 1950s in diverse areas such as emergency medicine and the interface between acute and community care; hospital performance; scheduling and management of patient home visits; scheduling of patient appointments; and many other complex implementation problems of an operational or logistical nature.

DISCUSSION

To date, there has been limited debate about the role that operational research should take within implementation science. I detail three such roles for OR all grounded in upfront system thinking: structuring implementation problems, prospective evaluation of improvement interventions, and strategic reconfiguration. Case studies from mental health, emergency medicine, and stroke care are used to illustrate each role. I then describe the challenges for applied OR within implementation science at the organisational, interventional, and disciplinary levels. Two key challenges include the difficulty faced in achieving a position of mutual understanding between implementation scientists and research users and a stark lack of evaluation of OR interventions. To address these challenges, I propose a research agenda to evaluate applied OR through the lens of implementation science, the liberation of OR from the specialist research and consultancy environment, and co-design of models with service users. Operational research is a mature discipline that has developed a significant volume of methodology to improve health services. OR offers implementation scientists the opportunity to do more upfront system thinking before committing resources or taking risks. OR has three roles within implementation science: structuring an implementation problem, prospective evaluation of implementation problems, and a tool for strategic reconfiguration of health services. Challenges facing OR as implementation science include limited evidence and evaluation of impact, limited service user involvement, a lack of managerial awareness, effective communication between research users and OR modellers, and availability of healthcare data. To progress the science, a focus is needed in three key areas: evaluation of OR interventions, embedding the knowledge of OR in health services, and educating OR modellers about the aims and benefits of service user involvement.