Using simulation modelling to transform hospital planning and management to address health inequalities.

Health inequalities are a perennial concern for policymakers and in service delivery to ensure fair and equitable access and outcomes. As health inequalities are socially influenced by employment, income, and education, this impacts healthcare services among socio-economically disadvantaged groups, making it a pertinent area for investigation in seeking to promote equitable access. Researchers widely acknowledge that health equity is a multi-faceted problem requiring approaches to understand the complexity and interconnections in hospital planning as a precursor to healthcare delivery. Operations research offers the potential to develop analytical models and frameworks to aid in complex decision-making that has both a strategic and operational function in problem-solving. This paper develops a simulation-based modelling framework (SimulEQUITY) to model the complexities in addressing health inequalities at a hospital level. The model encompasses an entire hospital operation (including inpatient, outpatient, and emergency department services) using the discrete-event simulation method to simulate the behaviour and performance of real-world systems, processes, or organisations. The paper makes a sustained contribution to knowledge by challenging the existing population-level planning approaches in healthcare that often overlook individual patient needs, especially within disadvantaged groups. By holistically modelling an entire hospital, socio-economic variations in patients' pathways are developed by incorporating individual patient attributes and variables. This innovative framework facilitates the exploration of diverse scenarios, from processes to resources and environmental factors, enabling key decision-makers to evaluate what intervention strategies to adopt as well as the likely scenarios for future patterns of healthcare inequality. The paper outlines the decision-support toolkit developed and the practical application of the SimulEQUITY model through to implementation within a hospital in the UK. This moves hospital management and strategic planning to a more dynamic position where a software-based approach, incorporating complexity, is implicit in the modelling rather than simplification and generalisation arising from the use of population-based models.

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.

Impact of pre-exposure and post-exposure prophylaxes prevention programme on HIV burden and services in a low-resource setting: a simulation modelling approach.

INTRODUCTION: sub-Saharan African countries contribute substantially to the global HIV disease burden. Despite this burden, and the promises that prevention could deliver, the implementation and uptake of HIV prevention programmes are still low. The study used the decision support system model to explore the potential impacts of prevention implementation on HIV burden (incidence) and service delivery. METHODS: an operational research technique known as discrete event simulation model was used to capture an individual patient´s pathways through the HIV care process from diagnosis to treatment and monitoring. The regular monitoring, over a 5-year period, including all the activities and resources utilized at each stage of the pathway were analysed, and the impact of increasing prevention measures for an HIV treatment service in a treatment centre in Nigeria was tested using the simulation model. RESULTS: forty-three patients currently access the Pre-Exposure Prophylaxis (PrEP) and Post Exposure Prophylaxis (PEP) annually, with a 20% and 80% split in the number of patients offered PrEP and PEP, respectively. Scenarios-based on increasing the number of people offered PrEP and PEP from 43 to 250 with a 50/50 split were tested. The outputs revealed improved preventive care by averting new HIV cases, reduction in service demand and utilization, but an increase in the required human resource as well as financial burden. In the next 5 years, the cumulative averted HIV cases are expected to increase from 2 and 5 people (baseline) to 24 and 20 people for PrEP and PEP, respectively. The potentially averted 2 cases per infected persons based on the basic reproductive number of HIV. CONCLUSION: the effective implementation of PrEP/PEP programme offers an additional safety measure to prevent HIV transmission in at-risk individuals and possibility of ending HIV epidemic.

Operational Modeling with Health Economics to Support Decision Making for COPD Patients.

OBJECTIVE: To assess the impact of interventions for improving the management of chronic obstructive pulmonary disease (COPD), specifically increased use of pulmonary rehabilitation (PR) on patient outcomes and cost-benefit analysis. DATA SOURCES: We used the national Hospital Episode Statistics (HES) datasets in England, local data and experts from the hospital setting, National Prices and National Tariffs, reports and the literature around the effectiveness of PR programs. STUDY DESIGN: The COPD pathway was modeled using discrete event simulation (DES) to capture the patient pathway to an adequate level of detail as well as randomness in the real world. DES was further enhanced by the integration of a health economic model to calculate the net benefit and cost of treating COPD patients based on key sets of interventions. DATA COLLECTION/EXTRACTION METHODS: A total of 150 input parameters and 75 distributions were established to power the model using the HES dataset, outpatient activity data from the hospital and community services, and the literature. PRINCIPAL FINDINGS: The simulation model showed that increasing referral to PR (by 10%, 20%, or 30%) would be cost-effective (with a benefit-cost ratio of 5.81, 5.95, and 5.91, respectively) by having a positive impact on patient outcomes and operational metrics. Number of deaths, admissions, and bed days decreased (ie, by 3.56 patients, 4.90 admissions, and 137.31 bed days for a 30% increase in PR referrals) as well as quality of life increased (ie, by 5.53 QALY among 1540 patients for the 30% increase). CONCLUSIONS: No operational model, either statistical or simulation, has previously been developed to capture the COPD patient pathway within a hospital setting. To date, no model has investigated the impact of PR on COPD services, such as operations, key performance, patient outcomes, and cost-benefit analysis. The study will support policies around extending availability of PR as a major intervention.