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.

A hybrid analytical model for an entire hospital resource optimisation.

Given the escalating healthcare costs around the world (more than 10% of the world's GDP) and increasing demand hospitals are under constant scrutiny in terms of managing services with limited resources and tighter budgets. Hospitals endeavour to find sustainable solutions for a variety of challenges ranging from productivity enhancements to resource allocation. For instance, in the UK, evidence suggests that hospitals are struggling due to increased delayed transfers of care, bed-occupancy rates well above the recommended levels of 85% and unmet A&E performance targets. In this paper, we present a hybrid forecasting-simulation-optimisation model for an NHS Foundation Trust in the UK. Using the Hospital Episode Statistics dataset for A&E, outpatient and inpatient services, we estimate the future patient demands for each speciality and model how it behaves with the forecasted activity in the future. Discrete event simulation is used to capture the entire hospital within a simulation environment, where the outputs is used as inputs into a multi-period integer linear programming (MILP) model to predict three vital resource requirements (on a monthly basis over a 1-year period), namely beds, physicians and nurses. We further carry out a sensitivity analysis to establish the robustness of solutions to changes in parameters, such as nurse-to-bed ratio. This type of modelling framework is developed for the first time to better plan the needs of hospitals now and into the future.

HIV-MSS: A user-friendly management support system for better planning of HIV care services.

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.

Healthcare systems and Covid19: Lessons to be learnt from efficient countries.

BACKGROUND: The novel coronavirus is rapidly spreading over the world and puts the health systems of countries under intense pressure. High hospitalization levels due to the pandemic outbreak have caused the intensive care units to work above capacity. PURPOSE: A data envelopment analysis (DEA) based modelling approach was developed to evaluate the effectiveness of regions (i.e. city, country or clinical commissioning groups) against the pandemic outbreak. The objective is to enable related authorities better manage the struggle against the outbreak and put in place the emergency action plans immediately. METHODOLOGY/APPROACH: DEA method was used to measure the efficiency scores of countries. Super efficiency DEA method was also applied to countries based on the level of efficiencies they have achieved. Sixteen countries were selected that have been facing with Covid19 pandemic outbreak for at least 5 consecutive weeks after their 100th confirmed case. RESULTS: A total of 80 DEA models were developed, that is, 16 DEA models for each week. The percentage of efficient countries decreased dramatically over time, from 43.75% in the first week to 25% in the fifth week. Unlike most European countries, China and South Korea increased their effectiveness after first week of implementing all the necessary measures. CONCLUSION: This study sheds light into better understanding the effectiveness of policies adopted by countries and their management strategy in dealing with Covid19 pandemic. Our model will enable political leaders to identify inadequate policies as quickly as possible and learn from their peers for more effective decisions.

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.

Flexible ureterorenoscopy and laser lithotripsy with regional anesthesia vs general anesthesia: A prospective randomized study

ABSTRACT Purpose To compare the effect of general anesthesia (GA) and regional anesthesia (RA) on f-URS outcomes and surgeon comfort. Material and Methods The study was conducted between June 2017 to January 2018 and data collection was applied in a prospective, randomized fashion. 120 patients participated in the study and were divided into RA group (n=56) and GA group (n=64). Demographic, operative and post-operative parameters of patients were analysed. The end point of this study was the effect of two anesthesia regimens on the comfort of the surgeon, and the comparability of feasibility and safety against perioperative complications. Results The study including 120 randomized patients, 14 patients were excluded from the study and completed with 106 patients (45 in RA group and 61 in GA group). No difference was detected between the two groups in terms of preoperative data. During the monitorization of operative vital signs, 3 patients in RA group experienced bradycardia, and this finding was significant when compared with GA group (p=0.041). Additionally, 2 patients in RA group experienced mucosal tears and 1 patient experienced hemorrhage during the operation, but no complications were observed in the GA group (p=0.041). Postoperative surgeon comfort evaluation revealed statistically significant results in favor of GA group (p=0.001). Conclusions Both GA and RA are equally effective and safe anesthesia methods for f-URS procedures. However, RA group showed significantly increased likelihood of bradycardia and mucosal injury during surgery, and significantly decreased surgeon comfort during surgery.

Flexible ureterorenoscopy and laser lithotripsy with regional anesthesia vs general anesthesia: A prospective randomized study.

PURPOSE: To compare the effect of general anesthesia (GA) and regional anesthesia (RA) on f-URS outcomes and surgeon comfort. MATERIAL AND METHODS: The study was conducted between June 2017 to January 2018 and data collection was applied in a prospective, randomized fashion. 120 patients participated in the study and were divided into RA group (n=56) and GA group (n=64). Demographic, operative and post-operative parameters of patients were analysed. The end point of this study was the effect of two anesthesia regimens on the comfort of the surgeon, and the comparability of feasibility and safety against perioperative complications. RESULTS: The study including 120 randomized patients, 14 patients were excluded from the study and completed with 106 patients (45 in RA group and 61 in GA group). No difference was detected between the two groups in terms of preoperative data. During the monitorization of operative vital signs, 3 patients in RA group experienced bradycardia, and this finding was significant when compared with GA group (p=0.041). Additionally, 2 patients in RA group experienced mucosal tears and 1 patient experienced hemorrhage during the operation, but no complications were observed in the GA group (p=0.041). Postoperative surgeon comfort evaluation revealed statistically significant results in favor of GA group (p=0.001). CONCLUSIONS: Both GA and RA are equally effective and safe anesthesia methods for f-URS procedures. However, RA group showed significantly increased likelihood of bradycardia and mucosal injury during surgery, and significantly decreased surgeon comfort during surgery.

A decision support system for demand and capacity modelling of an accident and emergency department.

Accident and emergency (A&E) departments in England have been struggling against severe capacity constraints. In addition, A&E demands have been increasing year on year. In this study, our aim was to develop a decision support system combining discrete event simulation and comparative forecasting techniques for the better management of the Princess Alexandra Hospital in England. We used the national hospital episodes statistics data-set including period April, 2009 - January, 2013. Two demand conditions are considered: the expected demand condition is based on A&E demands estimated by comparing forecasting methods, and the unexpected demand is based on the closure of a nearby A&E department due to budgeting constraints. We developed a discrete event simulation model to measure a number of key performance metrics. This paper presents a crucial study which will enable service managers and directors of hospitals to foresee their activities in future and form a strategic plan well in advance.

Psoas Muscle Area Measured with Computed Tomography at Admission to Intensive Care Unit: Prediction of In-Hospital Mortality in Patients with Pulmonary Embolism.

AIM: Sarcopenia, a core component of physical frailty, is an independent risk factor for suboptimal health outcomes in hospitalized patients, especially in the intensive care patients. Psoas muscle areas can be assessed to identify sarcopenia. The aim of this study was to determine the prognostic value of psoas muscle area measured with CT for the prediction of in-hospital mortality in patients with pulmonary embolism at admission to the intensive care unit. METHODS: Patients with an admission abdominal computed tomography scan and requiring intensive care unit (ICU) stay were reviewed. Selected clinical data of patients admitted to intensive care unit for the management of pulmonary embolism were collected. Using CT scan images at the level of L3 vertebra, the psoas muscle area value was obtained by dividing the sum of the right and left psoas muscle areas into the body surface area. RESULTS: In-hospital mortality rate was 22.5% in 89 patients. The pulmonary embolism patients with in-hospital mortality had higher PESI and lower value of psoas muscle area, in addition to the lower systolic blood pressure and arterial oxygen saturation at admission. The increase in the value of psoas muscle area is associated with a decrease in the rate of in-hospital mortality. In patients with in-hospital mortality related to pulmonary embolism, the higher PESI and the lower value of psoas muscle area were considered in accordance with the outcome of patients. CONCLUSIONS: For the prediction of in-hospital mortality risk in patients with pulmonary embolism managed in intensive care unit, the psoas muscle area value has a merit to be used among the routine diagnostic procedures after further studies conducted with different severity of pulmonary embolism.

Modelling the neonatal system: A joint analysis of length of stay and patient pathways.

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.

A comprehensive modelling framework to forecast the demand for all hospital services.

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.

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 simulation tool for better management of retinal services.

BACKGROUND: Advances in the management of retinal diseases have been fast-paced as new treatments become available, resulting in increasing numbers of patients receiving treatment in hospital retinal services. These patients require frequent and long-term follow-up and repeated treatments, resulting in increased pressure on clinical workloads. Due to limited clinic capacity, many National Health Service (NHS) clinics are failing to maintain recommended follow-up intervals for patients receiving care. As such, clear and robust, long term retinal service models are required to assess and respond to the needs of local populations, both currently and in the future. METHODS: A discrete event simulation (DES) tool was developed to facilitate the improvement of retinal services by identifying efficiencies and cost savings within the pathway of care. For a mid-size hospital in England serving a population of over 500,000, we used 36 months of patient level data in conjunction with statistical forecasting and simulation to predict the impact of making changes within the service. RESULTS: A simulation of increased demand and a potential solution of the 'Treat and Extend' (T&E) regimen which is reported to result in better outcomes, in combination with virtual clinics which improve quality, effectiveness and productivity and thus increase capacity is presented. Without the virtual clinic, where T&E is implemented along with the current service, we notice a sharp increase in the number of follow-ups, number of Anti-VEGF injections, and utilisation of resources. In the case of combining T&E with virtual clinics, there is a negligible (almost 0%) impact on utilisation of resources. CONCLUSIONS: Expansion of services to accommodate increasing number of patients seen and treated in retinal services is feasible with service re-organisation. It is inevitable that some form of initial investment is required to implement service expansion through T&E and virtual clinics. However, modelling with DES indicates that such investment is outweighed by cost reductions in the long term as more patients receive optimal treatment and retain vision with better outcomes. The model also shows that the service will experience an average of 10% increase in surplus capacity.

The Relation between Sarcopenia and Mortality in Patients at Intensive Care Unit.

BACKGROUND AND AIM: Psoas muscle area (PMA) can reflect the status of skeletal muscle in the whole body. It has been also reported that decreased PMA was associated with postoperative mortality or morbidity after several surgical procedures. In this study, we aimed to investigate the relation between PMA and mortality in all age groups in intensive care unit (UNIT). MATERIALS AND METHOD: The study consists of 362 consecutive patients. The demographic characteristics of patients, indications for ICU hospitalization, laboratory parameters, and clinical parameters consist of mortality and length of stay, and surgery history was obtained from intensive care archive records. RESULTS: The mean age was 61.2 ± 18.2 years, and the percentage of female was 33.3%. The mean duration of stay was 10.3 ± 24.4 days. Exitus ratio, partial healing, and healing were 25%, 70%, and 5%, respectively. The mean right, left, and total PMA were 8.7 ± 3.6, 8.9 ± 3.4, and 17.6 ± 6.9, respectively. The left and total PMA averages of the nonoperation patients were statistically significantly lower (p = 0.021 p = 0.043). The mean PMA between the ex and recovered patients were statistically significantly lower (p = 0.001, p = 0.001, p < 0.001). Dyspnoea, renal insufficiency, COPD, transfusion rate, operation rate, ventilator needy, and mean duration of hospitalization were statistically significant higher in patients with exitus. There is a significant difference in operation types, anesthesia type, and clinic rates. CONCLUSION: Our data suggest that sarcopenia can be used to risk stratification in ICU patients. Future studies may use this technique to individualize postoperative interventions that may reduce the risk for an adverse discharge disposition related to critical illness, such as early mobilization, optimized nutritional support, and reduction of sedation and opioid dose.

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.

Profiling hospitals based on emergency readmission: a multilevel transition modelling approach.

Emergency readmission is seen as an important part of the United Kingdom government policy to improve the quality of care that patients receive. In this context, patients and the public have the right to know how well different health organizations are performing. Most methods for profiling estimate the expected numbers of adverse outcomes (e.g. readmission, mortality) for each organization. A number of statistical concerns have been raised, such as the differences in hospital sizes and the unavailability of relevant data for risk adjustment. Having recognized these statistical concerns, a new framework known as the multilevel transition model is developed. Hospital specific propensities of the first, second and further readmissions are considered to be measures of performance, where these measures are used to define a new performance index. During the period 1997 and 2004, the national (English) hospital episodes statistics dataset comprise more than 5 million patient readmissions. Implementing a multilevel model using the complete population dataset could possibly take weeks to estimate the parameters. To resolve the problem, we extract 1000 random samples from the original data, where each random sample is likely to lead to differing hospital performance measures. For computational efficiency a Grid implementation of the model is developed. Analysing the output from the full 1000 sample, we noticed that 4 out of the 5 worst performing hospitals treating cancer patients were in London. These hospitals are known to be the leading NHS Trusts in England, providing diverse range of services to complex patients, and therefore it is inevitable to expect higher numbers of emergency readmissions.