A decision support tool with health economic modelling for better management of DVT patients.

BACKGROUND: Responding to the increasing demand for Deep Vein Thrombosis (DVT) treatment in the United Kingdom (UK) at times of limited budgets and resources is a great challenge for decision-makers. Therefore, there is a need to find innovative policies, which improve operational efficiency and achieve the best value for money for patients. This study aims to develop a Decision Support Tool (DST) that assesses the impact of implementing new DVT patients' management and care policies aiming at improving efficiency, reducing costs, and enhancing value for money. METHODS: With the involvement of stakeholders from a number of DVT services in the UK, we developed a DST combining discrete event simulation (DES) for DVT pathways and the Socio Technical Allocation of Resources (STAR) approach, an agile health economics technique. The model was inputted with data from the literature, local datasets from DVT services, and interviews conducted with DVT specialists. The tool was validated and verified by various stakeholders and two policies, namely shifting more patients to community services (CSs) and increasing the usage of the Novel Oral Anticoagulant (NOAC) drug were selected for testing on the model. RESULTS: Sixteen possible scenarios were run on the model for a period of 5 years and generated treatment activity, human resources, costing, and value for money outputs. The results indicated that hospital visits can be reduced by up to 50%. Human resources' usage can be greatly lowered driven mainly by offering NOAC treatment to more patients. Also, combining both policies can lead to cost savings of up to 50%. The STAR method, which considers both service and patient perspectives, produced findings that implementing both policies provide a significantly higher value for money compared to the situation when neither is applied. CONCLUSIONS: The combination of DES and STAR can help decision-makers determine the interventions that have the highest benefits from service providers' and patients' perspectives. This is important given the mismatch between care demand and resources and the resulting need for improving operational and economic outcomes. The DST tool has the potential to inform policymaking in DVT services in the UK to improve performance.

Assessing the impact of COVID-19 measures on COPD management and patients: a simulation-based decision support tool for COPD services in the UK.

OBJECTIVES: To develop a computer-based decision support tool (DST) for key decision makers to safely explore the impact on chronic obstructive pulmonary disease (COPD) care of service changes driven by restrictions to prevent the spread of COVID-19. DESIGN: The DST is powered by discrete event simulation which captures the entire patient pathway. To estimate the number of COPD admissions under different scenario settings, a regression model was developed and embedded into the tool. The tool can generate a wide range of patient-related and service-related outputs. Thus, the likely impact of possible changes (eg, COVID-19 restrictions and pandemic scenarios) on patients with COPD and care can be estimated. SETTING: COPD services (including outpatient and inpatient departments) at a major provider in central London. RESULTS: Four different scenarios (reflecting the UK government's Plan A, Plan B and Plan C in addition to a benchmark scenario) were run for 1 year. 856, 616 and 484 face-to-face appointments (among 1226 clinic visits) are expected in Plans A, B and C, respectively. Clinic visit quality in Plan A is found to be marginally better than in Plans B and C. Under coronavirus restrictions, lung function tests decreased more than 80% in Plan C as compared with Plan A. Fewer COPD exacerbation-related admissions were seen (284.1 Plan C vs 395.1 in the benchmark) associated with stricter restrictions. Although the results indicate that fewer quality-adjusted life years (in terms of COPD management) would be lost during more severe restrictions, the wider impact on physical and mental health must also be established. CONCLUSIONS: This DST will enable COPD services to examine how the latest developments in care delivery and management might impact their service during and beyond the COVID-19 pandemic, and in the event of future pandemics.

Evaluation of a whole system approach to diet and healthy weight in the east of Scotland: Study protocol.

Obesity is a global epidemic affecting all age groups, populations and income levels across continents. The causes of obesity are complex and are routed in health behaviours, environmental factors, government policy and the cultural and built environment. Consequently, a Whole System Approach (WSA) which considers the many causes of obesity and shifts the focus away from individuals as points of intervention and puts an emphasis on understanding and improving the system in which people live in is required. This protocol describes a programme of research that will: critically evaluate the evidence for WSAs; assess longitudinally the implementation of a WSA to diet and healthy weight to explore the range of levers (drivers) and opportunities to influence relevant partnerships and interventions to target obesity in East Scotland. The programme consists of four workstreams within a mixed methods framework: 1) Systematic review of reviews of WSAs to diet and healthy weight; 2) Longitudinal qualitative process evaluation of implementing two WSAs in Scotland; 3) Quantitative and Qualitative momentary analysis evaluation of a WSA; and 4) the application of System Dynamics Modelling (SDM) methodology to two council areas in Scotland. A Public Involvement in Research group (PIRg) have informed each stage of the research process. The research programme's breadth and its novel nature, mean that it will provide valuable findings for the increasing numbers who commission, deliver, support and evaluate WSAs to diet and healthy weight nationally and internationally.

Using system dynamics modelling to assess the economic efficiency of innovations in the public sector - a systematic review.

BACKGROUND: Decision-makers for public policy are increasingly utilising systems approaches such as system dynamics (SD) modelling, which test alternative interventions or policies for their potential impact while accounting for complexity. These approaches, however, have not consistently included an economic efficiency analysis dimension. This systematic review aims to examine how, and in what ways, system dynamics modelling approaches incorporate economic efficiency analyses to inform decision-making on innovations (improvements in products, services, or processes) in the public sector, with a particular interest in health. METHODS AND FINDINGS: Relevant studies (n = 29) were identified through a systematic search and screening of four electronic databases and backward citation search, and analysed for key characteristics and themes related to the analytical methods applied. Economic efficiency analysis approaches within SD broadly fell into two categories: as embedded sub-models or as cost calculations based on the outputs of the SD model. Embdedded sub-models within a dynamic SD framework can reveal a clear allocation of costs and benefits to periods of time, whereas cost calculations based on the SD model outputs can be useful for high-level resource allocation decisions. CONCLUSIONS: This systematic review reveals that SD modelling is not currently used to its full potential to evaluate the technical or allocative efficiency of public sector innovations, particularly in health. The limited reporting on the experience or methodological challenges of applying allocated efficiency analyses with SD, particularly with dynamic embedded models, hampers common learning lessons to draw from and build on. Further application and comprehensive reporting of this approach would be welcome to develop the methodology further.

Stakeholders' views on the organisational factors affecting application of artificial intelligence in healthcare: a scoping review protocol.

INTRODUCTION: Artificial intelligence (AI) offers great potential for transforming healthcare delivery leading to better patient-outcomes and more efficient care delivery. However, despite these advantages, integration of AI in healthcare has not kept pace with technological advancements. Previous research indicates the importance of understanding various organisational factors that shape integration of new technologies in healthcare. Therefore, the aim of this study is to provide an overview of the existing organisational factors influencing adoption of AI in healthcare from the perspectives of different relevant stakeholders. By conducting this review, the various organisational factors that facilitate or hinder AI implementation in healthcare could be identified. METHODS AND ANALYSIS: This study will follow the Joanna Briggs Institute framework, which includes the following stages: (1) defining and aligning objectives and questions, (2) developing and aligning the inclusions criteria with objectives and questions, (3) describing the planned approach to evidence searching and selection, (4) searching for the evidence, (5) selecting the evidence, (6) extracting the evidence, (7) charting the evidence, and summarising the evidence with regard to the objectives and questions.The databases searched will be MEDLINE (Ovid), CINAHL (Plus), PubMed, Cohrane Library, Scopus, MathSciNet, NICE Evidence, OpenGrey, O'REILLY and Social Care Online from January 2000 to June 2021. Search results will be reported based on The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews guidelines. The review will adopt diffusion of innovations theory, technology acceptance model and stakeholder theory as guiding conceptual models. Narrative synthesis will be used to integrate the findings. ETHICS AND DISSEMINATION: Ethics approval will not be sought for this scoping review as it only includes information from previously published studies. The results will be disseminated through publication in a peer-reviewed journal. In addition, to ensure its findings reach relevant stakeholders, they will be presented at relevant conferences.

Resources management impact on neonatal services performance in the United Kingdom: A system dynamics modelling approach.

Demand for neonatal care in the United Kingdom (UK) has increased in recent years. This care is provided by neonatal services, which are chronically saturated due to years of budget austerity in the UK. The aim of this paper is to investigate the possible impact of increasing resources to these services to improve their operational performance and alleviate the pressure they are facing. To achieve this aim, a system dynamics (SD) simulation model was built and validated in a UK neonatal unit. The SD model was used initially to evaluate the impact of increasing resources on the unit performance and the results showed that this policy will have a limited effect on performance. The model was then extended to predict the effect of reducing the length of stay (LoS) in conjunction with increasing resources. These joint interventions will have a positive impact on the unit performance if LoS is reduced for all care categories and resources are slightly increased. Results' implications and SD's modelling usefulness to guide decision making in complex health settings are discussed.

How the health-seeking behaviour of pregnant women affects neonatal outcomes: findings of system dynamics modelling in Pakistan.

BACKGROUND: Limited studies have explored how health-seeking behaviour during pregnancy through to delivery affect neonatal outcomes. We modelled health-seeking behaviour across urban and rural settings in Pakistan, where poor neonatal outcomes persist with wide disparities. METHODS AND FINDINGS: A system dynamics model was developed and parameterised. Following validation tests, the model was used to determine neonatal mortality for pregnant women considering their decisions to access, refuse and switch antenatal care services in four provider sectors: public, private, traditional and charitable. Four health-seeking scenarios were tested across different pregnancy trimesters. Health-seeking behaviour in different subgroups by geographical locations and social network effect was modelled. The largest reduction in neonatal mortality was achieved with antenatal care provided by skilled providers in public, private or charitable sectors, combined with the use of institutional delivery. Women's social networks had strong influences on if, when and where to seek care. Interventions by Lady Health Workers had a minimal impact on health-seeking behaviour and neonatal outcomes after trimester 1. Optimal benefits were achieved for urban women when antenatal care was accessed within trimester 2, but for rural women within trimester 1. Antenatal care access delayed to trimester 3 had no protective impact on neonatal mortality. CONCLUSIONS: System dynamics modelling enables capturing the complexity of health-seeking behaviours and impact on outcomes, informing intervention design, implementation of targeted policies and uptake of services specific to urban/rural settings considering structural enablers/barriers to access, cultural contexts and strong social network influences.

A discrete event simulation model to evaluate the treatment pathways of patients with cataract in the United Kingdom.

BACKGROUND: The number of people affected by cataract in the United Kingdom (UK) is growing rapidly due to ageing population. As the only way to treat cataract is through surgery, there is a high demand for this type of surgery and figures indicate that it is the most performed type of surgery in the UK. The National Health Service (NHS), which provides free of charge care in the UK, is under huge financial pressure due to budget austerity in the last decade. As the number of people affected by the disease is expected to grow significantly in coming years, the aim of this study is to evaluate whether the introduction of new processes and medical technologies will enable cataract services to cope with the demand within the NHS funding constraints. METHODS: We developed a Discrete Event Simulation model representing the cataract services pathways at Leicester Royal Infirmary Hospital. The model was inputted with data from national and local sources as well as from a surgery demand forecasting model developed in the study. The model was verified and validated with the participation of the cataract services clinical and management teams. RESULTS: Four scenarios involving increased number of surgeries per half-day surgery theatre slot were simulated. Results indicate that the total number of surgeries per year could be increased by 40% at no extra cost. However, the rate of improvement decreases for increased number of surgeries per half-day surgery theatre slot due to a higher number of cancelled surgeries. Productivity is expected to improve as the total number of doctors and nurses hours will increase by 5 and 12% respectively. However, non-human resources such as pre-surgery rooms and post-surgery recovery chairs are under-utilized across all scenarios. CONCLUSIONS: Using new processes and medical technologies for cataract surgery is a promising way to deal with the expected higher demand especially as this could be achieved with limited impact on costs. Non-human resources capacity need to be evenly levelled across the surgery pathway to improve their utilisation. The performance of cataract services could be improved by better communication with and proactive management of patients.

A discrete event simulation model to evaluate the use of community services in the treatment of patients with Parkinson's disease in the United Kingdom.

BACKGROUND: The number of people affected by Parkinson's disease (PD) is increasing in the United Kingdom driven by population ageing. The treatment of the disease is complex, resource intensive and currently there is no known cure to PD. The National Health Service (NHS), the public organisation delivering healthcare in the UK, is under financial pressures. There is a need to find innovative ways to improve the operational and financial performance of treating PD patients. The use of community services is a new and promising way of providing treatment and care to PD patients at reduced cost than hospital care. The aim of this study is to evaluate the potential operational and financial benefits, which could be achieved through increased integration of community services in the delivery of treatment and care to PD patients in the UK without compromising care quality. METHODS: A Discrete Event Simulation model was developed to represent the PD care structure including patients' pathways, treatment modes, and the mix of resources required to treat PD patients. The model was parametrised with data from a large NHS Trust in the UK and validated using information from the same trust. Four possible scenarios involving increased use of community services were simulated on the model. RESULTS: Shifting more patients with PD from hospital treatment to community services will reduce the number of visits of PD patients to hospitals by about 25% and the number of PD doctors and nurses required to treat these patients by around 32%. Hospital based treatment costs overall should decrease by 26% leading to overall savings of 10% in the total cost of treating PD patients. CONCLUSIONS: The simulation model was useful in predicting the effects of increased use of community services on the performance of PD care delivery. Treatment policies need to reflect upon and formalise the use of community services and integrate these better in PD care. The advantages of community services need to be effectively shared with PD patients and carers to help inform management choices and care plans.

Using a decision support systems computer simulation model to examine HIV and tuberculosis: the Russian Federation.

The aim of this paper is to describe the development and use of a computer simulation model that can be used as a Decision Support System (DSS) to tackle the critical public health issues of HIV and HIV-related tuberculosis in the Russian Federation. This country has recently witnessed an explosion of HIV infections and a worrying spread of the Multi-Drug Resistant form of Tuberculosis (MDRTB). The conclusions drawn are that a high population coverage with Highly Active Anti-Retroviral Treatment (HAART) (75% or higher), allied with high MDRTB cure rates, reduces cumulative deaths by 60%, with limited impact below this level. This research offers a simulation model that can be applied as a DSS by public health officials to inform policy making. By doing so, ways of controlling the spread of HIV and MDRTB and reduce mortality from these serious public health threats is provided.

High coverage with HAART is required to substantially reduce the number of deaths from tuberculosis: system dynamics simulation.

We used a system dynamics simulation model of the transmission dynamics of drug-sensitive tuberculosis (DSTB), multidrug-resistant tuberculosis (MDRTB) and HIV to estimate the impact of coverage with highly active antiretroviral therapy (HAART) and different cure rates for MDRTB in settings of explosive HIV epidemics and high MDRTB levels. Population coverage levels at 0%, 25%, 50%, 75% and 100% for HAART, and 5% and 80% of MDRTB treatment cure rates were simulated over a 10-year period and cumulative deaths from tuberculosis and HIV-associated tuberculosis were estimated for populations with latent tuberculosis, DSTB, MDRTB, HIV and HIV-associated tuberculosis. Depending on levels of HAART population coverage, increasing MDRTB cure rates from 5% to 80% reduces cumulative tuberculosis deaths by 1% and 13%. High population coverage with HAART (75% or higher), allied with high MDRTB cure rates, reduces cumulative deaths by 60%, with limited impact below this level. High coverage with HAART is required to substantially reduce the number of deaths from tuberculosis.

Impact of joined-up HIV harm reduction and multidrug resistant tuberculosis control programmes in Estonia: System dynamics simulation model.

In Eastern Europe and Central Asia (ECA) the control of tuberculosis, multidrug resistant tuberculosis (MDRTB) and human immunodeficiency virus (HIV) poses important public health challenges. We used system dynamics simulation to determine impact on cumulative HIV/AIDS, tuberculosis and HIV-associated-tuberculosis deaths, over 20 years, of harm-reduction programmes to reduce needle-sharing and injection-frequency amongst injecting drug users (IDUs) and multidrug resistant tuberculosis (MDRTB) control in a population with an explosive HIV epidemic in IDUs and high MDRTB prevalence. We estimate that the number of HIV-associated-deaths will decline by 30% with effective harm-reduction programmes but double if these are ineffective. In our model, effective MDRTB and HIV control reduces cumulative tuberculosis deaths by 54%, cumulative MDRTB deaths 15-fold and cumulative HIV-associated-tuberculosis-deaths 2-fold. Effective MDRTB control, without effective harm-reduction programmes, only reduce tuberculosis deaths by 22%. However, effective harm-reduction programme with a poor MDRTB control reduce cumulative tuberculosis deaths by 34%, MDRTB by 14% and HIV-associated-tuberculosis by 56%. Even with good control programmes for drug sensitive TB, neglecting harm reduction and MDRTB control will result in 50% more tuberculosis-related deaths than if both are effectively addressed. Effective harm-reduction programmes reduces cumulative deaths from tuberculosis more substantively than effective MDRTB control. Our finding have important policy implications for communicable disease policies in post-Soviet countries, which need to substantially change if they are to effectively address the emerging HIV and MDRTB epidemics.

Impact of an effective multidrug-resistant tuberculosis control programme in the setting of an immature HIV epidemic: system dynamics simulation model.

This study sought to determine the impact of an effective programme of multidrug resistant tuberculosis control (MDRTB) on a population that is witnessing an explosive HIV epidemic among injecting drug users (IDUs), where the prevalence of MDRTB is already high. A transmission model was constructed that represents the dynamics of the drug-susceptible tuberculosis (DSTB), MDRTB and HIV spread among the adult population of Samara Oblast, Russia: from official notifications of tuberculosis and of HIV infection, estimates of MDRTB derived from surveillance studies, population data from official regional statistics, data on transmission probabilities from peer-reviewed publications and informed estimates, and policy-makers' estimates of IDU populations. Two scenarios of programme effectiveness for MDRTB were modelled and run over a period of 10 years to predict cumulative deaths. In a population of 3.3 million with a high prevalence of MDRTB, an emerging epidemic of HIV among IDUs, and a functioning directly observed therapy-short course (DOTS) programme, the model predicts that under low cure rates for MDRTB the expected cumulative deaths from tuberculosis will reach 6303 deaths including 1900 deaths from MDRTB at 10 years. Under high cure rate for MDRTB 4465 deaths will occur including 134 deaths from MDRTB. At 10 years there is little impact on HIV-infected populations from the MDRTB epidemic, but as the HIV epidemic matures the impact becomes substantial. When the model is extended to 20 years cumulative deaths from MDRTB become very high if cure rates for MDRTB are low and cumulative deaths in the HIV-infected population, likewise, are profoundly affected. In the presence of an immature HIV epidemic failure to actively control MDRTB may result in approximately a third more deaths than if effective treatment is given. As the HIV epidemic matures then the impact of MDRTB grows substantially if MDRTB control strategies are ineffective. The epidemiological starting point for these scenarios is present in many regions within the former Soviet Union and this analysis suggests control of MDRTB should be an urgent priority.