Health Systems最新文献

Literature reviews over five decades have reported the paucity of examples of OR methods being routinely used to support decision-making in health and social care. This paper presents insights from an independent evaluation of a project intended to overcome some of the barriers to implementation by establishing a "community of practice" in Kent (England). The project itself was undertaken by practitioners, and had two main aims: providing training in system dynamics modelling to analysts, and making senior managers aware of the benefits of modelling. The findings largely confirmed previous studies, but also raised issues about style of training delivery and selection of problems to be modelled. Project leaders fully understood the barriers to embedding OR modelling skills, and made considerable efforts to avoid them, but nevertheless the main barrier, pressures on people's time, remained an obstacle. The paper concludes with general reflections and advice.

We examine how physicians' perceptions of two computerized provider order entry (CPOE) capabilities, standardisation of care protocols and documentation quality, are associated with their perceptions of turnaround time, medical error, and job demand at three phases of CPOE implementation: pre-go-live, initial use, and continued use. Through a longitudinal study at a large urban hospital, we find standardisation of care protocols is positively associated with turnaround time reduction in all phases but positively associated with job demand increase only in the initial use phase. Standardisation also has a positive association with medical error reduction in the initial use phase, but later this effect becomes fully mediated through turnaround time reduction in the continued use phase. Documentation quality has a positive association with medical error reduction in the initial use phase and this association strengthens in the continued use phase. Our findings provide insights to effectively manage physicians' response to CPOE implementation.

Nowadays, the increase in patient demand and the decline in resources are lengthening patient waiting times in many chemotherapy oncology departments. Therefore, enhancing healthcare services is necessary to reduce patient complaints. Reducing the patient waiting times in the oncology departments represents one of the main goals of healthcare managers. Simulation models are considered an effective tool for identifying potential ways to improve patient flow in oncology departments. This paper presents a new agent-based simulation model designed to be configurable and adaptable to the needs of oncology departments which have to interact with an external pharmacy. When external pharmacies are utilised, a courier service is needed to deliver the individual therapies from the pharmacy to the oncology department. An oncology department located in southern Italy was studied through the simulation model and different scenarios were compared with the aim of selecting the department configuration capable of reducing the patient waiting times.

Malaria remains an important public health concern. Sub-Saharan African countries carry over 95% of the global burden. Unfortunately, there are also major resource constraints that have limited efforts to reduce the burden. Our study sought to estimate efficiency in the use of malaria resources and to identify potential determinants. We used primary data collected from district-level health facilities in three administrative regions in Ghana from 2014 to 2016. The Data Envelopment Analysis technique was used to estimate efficiency. The Malmquist productivity index was estimated and disaggregated to reflect the sources of productivity change. The findings show an average technical efficiency score of 0.61 with private facilities being more efficient. Productivity changes were driven by changes in technology/innovation advancements. Facility revenue mix and ownership type were important determinants of efficiency. The findings highlight the need to improve resource use in the delivery of specific services such as malaria.

Decontamination centres provide sterilisation services (sort, disinfect, package, and sterilise) for reusable surgical instruments that have a vital impact on patient safety. The market trend is to increase the level of automation in the decontamination process, to increase productivity, and reduce the risk of human error and musculoskeletal injuries. The goal of this research is to study the use of automated guided vehicles (AGVs) in sterilisation departments, to improve safety and efficiency. A generic simulation model is created based on data gathering of various decontamination centres and is validated for a specific centre to analyse various aspects of applying AGVs to automate the internal transfer. Centre's potential to increase capacity through AGV application is analysed and a Design of Experiments is conducted to identify the most promising implementation scenarios. Results show reductions in treatment time and work in process, while ,maintaining the accessibility of medical instruments, and ensuring worker safety.

The assessment of the hospital supply chain management represents a key challenge by virtue of the complexity of the healthcare sector. The purpose of this study is to introduce a hybrid approach that helps hospital administrators to clearly identify, evaluate, and narrow the key performance criteria for their supply chain. The methodology attempts to minimise information loss, reduce the fuzziness and subjectivity of the collected data and describes the interdependence among criteria. The proposed generic framework can be valuable for hospitals organisations aiming for a sustainable performance decision-making process. The combination of the Fuzzy Delphi method and Structural Equation Modelling proved to be effective in determining the pillars driving the sustainable performance of the hospital supply chain.

This study identified the risk factors for type 2 diabetes (T2D) and proposed a machine learning (ML) technique for predicting T2D. The risk factors for T2D were identified by multiple logistic regression (MLR) using p-value (p<0.05). Then, five ML-based techniques, including logistic regression, naïve Bayes, J48, multilayer perceptron, and random forest (RF) were employed to predict T2D. This study utilized two publicly available datasets, derived from the National Health and Nutrition Examination Survey, 2009-2010 and 2011-2012. About 4922 respondents with 387 T2D patients were included in 2009-2010 dataset, whereas 4936 respondents with 373 T2D patients were included in 2011-2012. This study identified six risk factors (age, education, marital status, SBP, smoking, and BMI) for 2009-2010 and nine risk factors (age, race, marital status, SBP, DBP, direct cholesterol, physical activity, smoking, and BMI) for 2011-2012. RF-based classifier obtained 95.9% accuracy, 95.7% sensitivity, 95.3% F-measure, and 0.946 area under the curve.

Computer-assisted Parkinson's disease-specific gait pattern recognition has gained more attention in the past decade due to its extensive application. In this research study, vision-based gait feature extraction is obtained from the observed skeleton points to support the real-time Parkinson disease prediction and diagnosis in the smart healthcare environment. So, a novel kernel-based principal component analysis (KPCA) is introduced for establishing respective feature extraction and dimensionality reduction on the patient's video data. In this research study, a vision-based Parkinson disease identification system (VPDIS) is developed with a feature-weighted minimum distance classifier model to support the clinical assessment of Parkinson's disease. At the time of experimentation, a steady-state walking style of the patient was captured using the cameras fixed in the smart healthcare environment. Then, the accumulated walking frames from the remote patients were transformed into the required binary silhouettes for the sake of noise minimisation and compression purpose. The resulting experimentation shows that the proposed feature extraction approach has significant improvements on the recognition of target gait patterns from the video-based gait analysis of Parkinson's and normal patients. Accordingly, the proposed VPDIS using feature-weighted minimum distance classifier model provides better prediction time and classification accuracy against the existing healthcare systems that is developed using support vector machine and ensemble learning classifier models.

In many households, preparation of food in normal times proves to be problematic, particularly when parents endeavour to keep their children on a balanced diet. The COVID-19 pandemic has further exacerbated this problem imposing the requirement of social distancing, which led to disruptions in the food supply chain and multiplication of responsibilities faced by families with children. The present study revisits the standard "Diet Problem" to address these challenges and to develop a participatory approach to provide a diversified weekly meal plan that is easy and fun but simultaneously complies with the unique requirements of each participant. This is done by providing a novel framework, which combines linear optimisation with the Parsimonious Analytic Hierarchy Process, a method for individual choices. This novel approach to participatory modelling is tested within two young family settings in Brazil. The model produced through this contemporary framework provides a weekly menu that best meets expectations of the members of a young family in the context of the COVID-19 pandemic.