Health Systems最新文献

With greater demand for outpatient services, the importance of patient-centric clinic layout design that improves timeliness of patient care has become more elucidated. In this paper, a novel simulation-optimisation (SO) framework is proposed focusing on the physical and process flows of patients in the design of a paediatric orthopaedic outpatient clinic. A discrete-event simulation model is used to estimate the frequency of movements between clinic units. The resulting information is utilised as input to a mixed integer programming (MIP) model, optimising the clinic layout design. In order to solve the MIP model, Particle Swarm Optimisation (PSO), a metaheuristic approach enhanced with several heuristics is utilised. Finally, the optimisation model outputs are evaluated with the simulation model. The results demonstrate that improvements to the quality of the patient experience can be achieved through incorporating SO methods into the clinic layout design process.

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.

Automated communication systems are increasingly common in mobile and ehealth contexts. Yet, there is reason to believe that some high risk segments of the population might be prone to avoid automated systems even though they are often designed to reach these groups. To facilitate research in this area, avoidance of automated communication (AAC) is theorized - and a measurement instrument validated - across two studies. In study 1, an AAC scale was found to be unidimensional and internally reliable as well as negatively correlated with comfort, perceptions, and intentions to use technology. Moreover, individuals with social phobia had lower AAC scores which was consistent with the idea that they preferred non-human interaction facilitated by automated communication. In study 2, confirmatory factor analysis supported the unidimensional structure of the measure and the instrument once again proved to be reliable. Individuals with lower AAC had greater intentions to utilize automated communication, EHRs, and an automated virtual nurse program. AAC is a disposition that predicts significant variance in intentions and comfort with various automated communication technologies. Avoidance increases with age but may be mitigated by systems that allow participants to opt-out or immediately interact with a live person.

Primary care (PC) has always been underestimated and underinvested by the United States health system. Our goal was to investigate the effect of Medicaid expansion and the Affordable Care Act (ACA) provisions on PC access in Broome County, NY, a county that includes both rural and urban areas, and can serve as a benchmark for other regions. We developed a spatial system dynamics model to capture different stages of PC access for the Medicaid population by using the health belief model constructs and simulate the effect of several hypothetical interventions on PC utilisation. The government data portals used as data sources for calibrating our model include the New York State Department of Health, the Medicaid Delivery System Reform Incentive Payment (DSRIP) dashboards, and the US census. In our unique approach, we integrated the simulation results within Geographical Information System (GIS) maps, to assess the influence of geospatial factors on PC access. Our results identify hot spot demographic areas that have poor access to PC service facilities due to transportation constraints and a shortage in PC providers. Our decision support tool informs policymakers about programmes with the strongest impact on improving access to care, considering spatial and temporal characteristics of a region.

In the nineties and noughties, Hollocks surveyed the use of Discrete Event Simulation (DES) in industry and listed (although he could not quantify the value of) benefits. This paper explores how DES is now used to design healthcare facilities and services, developing a value-for-money case with a protocol on collecting information. We present a set of five DES case studies from the US care system and, following Hollocks, focus on modelling as part of a rigorous design process, capturing as many of the benefits as possible. Healthcare offers the possibility of ascribing value to health improvement, but in these cases it is primarily the operational benefits of a better service that are reported and monetarised. By estimated the cost of modelling and the value of the operation gains, this paper contributes significantly to the literature. We conclude with a protocol for collecting information and a discussion of methods by which different types of benefit may be captured.

Emergency departments (EDs) provide medical treatment for a broad spectrum of illnesses and injuries to patients who arrive at all hours of the day. The quality and efficient delivery of health care in EDs are associated with a number of factors, such as patient overall length of stay (LOS) and admission, prompt ambulance diversion, quick and accurate triage, nurse and physician assessment, diagnostic and laboratory services, consultations and treatment. One of the most important ways to plan the healthcare delivery efficiently is to make forecasts of ED processes. The aim this study is thus to provide an exhaustive review for ED stakeholders interested in applying forecasting methods to their ED processes. A categorisation, analysis and interpretation of 102 papers is performed for review. This exhaustive review provides an insight for researchers and practitioners about forecasting in EDs in terms of showing current state and potential areas for future attempts.

Few studies have examined how to identify future readmission of patients with a large number of repeat emergency department (ED) visits. We explore 30-day readmission risk prediction using Microsoft's AZURE machine learning software and compare five classification methods: Logistic Regression, Boosted Decision Trees (BDTs), Support Vector Machine (SVM), Bayes Point Machine (BPM), and Two-Class Neural Network (TCNN). We predict the last readmission visit of frequent ED patients extracted from the electronic health records of their 8455 penultimate visits. The methods show differential improvement, with the BDT indicating marginally better AUC (area under the ROC curve) than logistic regression and BPM, followed by the TCNN and SVM. A comparison of BDT and Logistic Regression results for correct and incorrect classification highlights the similarities and differences in the significant predictors identified by each method. Future research may incorporate time-varying covariates to identify other longitudinal factors that can lead to readmission risk reduction.

We study the intra-facility patient movement problem in large, multi-floor hospitals, where many patients are moved each year by patient movers using wheelchairs or gurneys. Using a simulation model, and the University of Michigan Health System (UMHS) hospital as the problem setting, we compare alternative rules for dispatching the patient movers via a centralised, computer-controlled system. Included in the comparison is the rule used currently at the hospital as well as new alternative dispatching rules we develop for the study. Our results suggest that significant improvements in system performance can be obtained by using better dispatching rules that consider not only the proximity of the patient mover to the patient pick-up point but also the type of equipment needed (wheelchair versus gurney) and the location of the equipment marshalling areas, which also play a key role. In conjunction with the dispatching rules, we investigate the number and location of the marshalling areas, and show empirically that carefully locating them based on usage improves the system performance as much as, if not more than, a more efficient dispatching rule. Although the UMHS hospital serves as the problem setting, our study would apply to most hospitals with dedicated patient movers and centralised dispatching.

The current study outlines the creation of an online community designed to connect patients to providers of Complementary and Alternative Medicine (CAM) and western biomedicine. The purpose of the site was to create a forum for patients and healthcare providers to share information and social support regarding eight popular CAM treatments. First, we created a prototype and pilot tested it through a usability analysis. Second, we conducted semi-structured interviews with 12 key stakeholders from the CAM, biomedicine, and patient populations. Third, we conducted a content analysis of the discussion forums to examine common posting behaviour. We found that CAM providers were the most active contributors to the forums. This project provides proof of concept for using an online community platform to connect patients and CAM providers. Future work should attempt to engage Western medicine providers while studying techniques and features that best engage users.

In the period from January 2013 to July 2014, three process change initiatives were undertaken at a major UK hospital to improve the patient discharge process. These initiatives were inspired by the findings of a study of the discharge process using Soft Systems Methodology. The first initiative simplified time-consuming paperwork and the second introduced more regular reviews of patient progress through daily multi-disciplinary "Situation Reports". These two initiatives were undertaken in parallel across the hospital, and for the average patient they jointly led to a 41% reduction between a patient being declared medically stable and their being discharged from the hospital. The third initiative implemented more proactive alerting of Social Care Practitioners to patients with probable social care needs at the front door, and simplified capture of important patient information (using a "SPRING" form). This initiative saw a 20% reduction in total length of stay for 88 patients on three wards where the SPRING form was used, whilst 248 patients on five control wards saw no significant change in total length of stay in the same period. Taken together, these initiatives have reduced total length of stay by 67% from 55.8 days to 18.6 days for the patients studied.