The impact of management practices on relative patient mortality: Evidence from public hospitals

A small, but growing, body of empirical evidence shows that the material and persistent variation in many aspects of the performance of healthcare organisations can be related to variation in their management practices. This study uses public data on hospital patient mortality outcomes, the Summary Hospital-level Mortality Indicator (SHMI) to extend this programme of research. We assemble a five-year dataset combining SHMI with potential confounding variables for all English NHS non-specialist acute hospital trusts. The large number of providers working within a common system provides a powerful environment for such investigations. We find considerable variation in SHMI between trusts and a high degree of persistence of high- or low performance. This variation is associated with a composite metric for management practices based on the NHS National Staff Survey. We then use a machine learning technique to suggest potential clusters of individual management practices related to patient mortality performance and test some of these using traditional multivariate regression. The results support the hypothesis that such clusters do matter for patient mortality, and so we conclude that any systematic effort at improving patient mortality should consider adopting an optimal cluster of management practices

A review on the relation between simulation and improvement in hospitals

<p>Abstract</p> <p>Background</p> <p>Simulation applications on operations management in hospitals are frequently published and claim to support decision-making on operations management subjects. However, the reported implementation rates of recommendations are low and the actual impact of the changes recommended by the modeler has hardly been examined. This paper examines: 1) the execution rate of simulation study recommendations, 2) the research methods used to evaluate implementation of recommendations, 3) factors contributing to implementation, and 4) the differences regarding implementation between literature and practice.</p> <p>Results</p> <p>Altogether 16 hospitals executed the recommendations (at least partially). Implementation results were hardly reported upon; 1 study described a before-and-after design, 2 a partial before and after design. Factors that help implementation were grouped according to 1) technical quality, of which data availability, validation/verification with historic data/expert opinion, and the development of the conceptual model were mentioned most frequently 2) process quality, with client involvement and 3) outcome quality with, presentation of results. The survey response rate of traceable authors was 61%, 18 authors implemented the results at least partially. Among these responses, evaluation methods were relatively better with 3 time series designs and 2 before-and-after designs.</p> <p>Conclusions</p> <p>Although underreported in literature, implementation of recommendations seems limited; this review provides recommendations on project design, implementation conditions and evaluation methods to increase implementation.</p> <p>Methods</p> <p>A literature review in PubMed and Business Source Elite on stochastic simulation applications on operations management in individual hospitals published between 1997 and 2008. From those reporting implementation, cross references were added. In total, 89 papers were included. A scoring list was used for data extraction. Two reviewers evaluated each paper separately; in case of discrepancies, they jointly determined the scores. The findings were validated with a survey to the original authors.</p

Overcrowding in emergency department: an international issue

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Assessing the queuing process using data envelopment analysis:an application in health centres

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages

The development of an intelligent maintenance optimisation system

This thesis describes the background to and development of a computer-baseddecision support system (DSS) known as IMOS, the intelligent maintenanceoptimisation system. The aim of the system is to help industrial maintenanceengineers improve the planned preventive maintenance policies applied to large andcomplex technical systems. IMOS attempts to achieve this by providing someautomated analysis of the huge amounts of maintenance history information which isaccumulating in the computerised data bases of many large industrial companies. Thekeys to this analysis are a set of mathematical models of the effects of maintenanceactivities and expert judgement about which of the models is most suitable under aparticular set of circumstances. These features are incorporated in the IMOSsoftware as a 'model base1 module, consisting of a set of routines for eachmathematical model, and a 'rule base' module which selects the most appropriatemodels by recognising characteristic patterns in the historical data for each item ofequipment. There are no previous attempts in the maintenance literature to formulatesuch a list of rules to guide model selection.The study and modelling of industrial maintenance is reviewed, as is relevant work onthe support of management decision making and the features and evolution of DSS isalso discussed. The need for and benefits of a system such as IMOS are describedand the suitability of the intelligent decision support system approach is discussed.The mathematical models, the selection rules, and optimisation criteria andtechniques are detailed, and the development of the software, written in C for anIBM compatible PC, is described. The research was conducted in collaboration withtwo major oil exploration and production companies and data from several North Seaoil-production platforms are analysed and discussed. Finally, achievements andshortcomings of the system are discussed and some suggestions for further researchoutlined

Impact of intact rock properties on proneness to rockbursting

Stakeholder engagement in simulation projects is important, especially in healthcare where there is a plurality of stakeholder opinions, objectives and power. One promising approach for increasing engagement is facilitated modelling. Currently, the complexity of producing a simulation model means that the ‘model coding’ stage is performed without the involvement of stakeholders, interrupting the possibility of a fully-facilitated project. Early work demonstrated that with currently-available software tools we can represent a simple healthcare process using Business Process Model and Notation (BPMN) and generate a simulation model automatically. However, for more complex processes, BPMN currently has a number of limitations, namely the ability to represent queues and data-driven decision points. To address these limitations, we propose a conceptual design for an extension to BPMN (BPMN4SIM) using Model Driven Architecture. Application to an elderly emergency care pathway in a UK hospital shows that BPMN4SIM is able to represent a more-complex business process