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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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

The advent of antiretroviral therapy (ART) has transformed HIV infection from a deadly disease to a manageable chronic condition. The life expectancy of people living with HIV has been prolonged dramatically. Therefore, health systems are now confronted with new challenges, with ever-increasing number of newly diagnosed cases, fuelling the pool of existing patients, with many comorbidities and requiring hospital admissions. Are health systems prepared to handle large and increasing numbers of people with HIV? We developed a HIV-Management Support System (MSS) to support service evaluation and management using simulation by capturing individual patient's pathways within HIV services in the United Kingdom. Two scenarios were tested: (1) the impact of increasing the number of diagnosed cases in steps of 5% on human resources and (2) the impact of treating all patients with ART on hospital admissions. A 5% increase in newly diagnosed HIV cases increases human resource requirements between 4% and 8%, whereas the impact of treating all HIV patients with ART on hospital admissions is far greater. HIV services are under intense pressure and managing patient and service needs are far more important than ever, hence the development of our HIV MSS is timely, to support better planning of services. Note that the HIV simulation model presented in this study is the first of its kind., (© 2021 John Wiley & Sons Ltd.)

In the United Kingdom, one in seven babies require specialist neonatal care after birth, with a noticeable increase in demand. Coupled with budgeting constraints and lack of investment means that neonatal units are struggling. This will inevitably have an impact on baby's length of stay (LoS) and the performance of the service. Models have previously been developed to capture individual babies' pathways to investigate the longitudinal cycle of care. However, no models have been developed to examine the joint analysis of LoS and babies' pathways. LoS at each stage of care is a critical driver of both the clinical outcomes and economic performance of the neonatal system. Using the generalized linear mixed modelling approach, extended to accommodate multiple outcomes, the association between neonate's pathway to discharge and LoS is examined. Using the data about 1002 neonates, we noticed that there is a high positive association between baby's pathway and total LoS, suggesting that discharge policies needs to be looked at more carefully. A novel statistical approach that examined the association of key outcomes and how it evolved over time is developed. Its applicability can be extended to other types of long-term care or diseases, such as heart failure and stroke., (© 2019 John Wiley & Sons, Ltd.)

Background: Because of increasing demand, hospitals in England are currently under intense pressure resulting in shortages of beds, nurses, clinicians, and equipment. To be able to effectively cope with this demand, the management needs to accurately find out how many patients are expected to use their services in the future. This applies not just to one service but for all hospital services., Purpose: A forecasting modelling framework is developed for all hospital's acute services, including all specialties within outpatient and inpatient settings and the accident and emergency (A&E) department. The objective is to support the management to better deal with demand and plan ahead effectively., Methodology/approach: Having established a theoretical framework, we used the national episodes statistics dataset to systematically capture demand for all specialties. Three popular forecasting methodologies, namely, autoregressive integrated moving average (ARIMA), exponential smoothing, and multiple linear regression were used. A fourth technique known as the seasonal and trend decomposition using loess function (STLF) was applied for the first time within the context of health-care forecasting., Results: According to goodness of fit and forecast accuracy measures, 64 best forecasting models and periods (daily, weekly, or monthly forecasts) were selected out of 760 developed models; ie, demand was forecasted for 38 outpatient specialties (first referrals and follow-ups), 25 inpatient specialties (elective and non-elective admissions), and for A&E., Conclusion: This study has confirmed that the best demand estimates arise from different forecasting methods and forecasting periods (ie, one size does not fit all). Despite the fact that the STLF method was applied for the first time, it outperformed traditional time series forecasting methods (ie, ARIMA and exponential smoothing) for a number of specialties., Practise Implications: Knowing the peaks and troughs of demand for an entire hospital will enable the management to (a) effectively plan ahead; (b) ensure necessary resources are in place (eg, beds and staff); (c) better manage budgets, ensuring enough cash is available; and (d) reduce risk., (© 2019 John Wiley & Sons, Ltd.)

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.