Resource productivity management in the services sector

School of Managemen

A framework for measuring quality in the emergency department

There is increasing concern that medical care is of variable quality, with variable outcomes, safety, costs and experience for patients. Despite substantial efforts to improve patient safety, some studies suggest little evidence of reductions in adverse events. Furthermore, there is limited agreement about what outcomes are expected and whether increased expenditure results in a real improvement in outcome or experience. In emergency medicine, many countries have developed specific indicators to help drive improvements in patient care. Most of these are time based and there is a lack of consensus regarding which indicators are high priority and what an appropriate framework for measuring quality should look like

Metrics for Measuring Data Quality - Foundations for an Economic Oriented Management of Data Quality

The article develops metrics for an economic oriented management of data quality. Two data quality dimensions are focussed: consistency and timeliness. For deriving adequate metrics several requirements are stated (e. g. normalisation, cardinality, adaptivity, interpretability). Then the authors discuss existing approaches for measuring data quality and illustrate their weaknesses. Based upon these considerations, new metrics are developed for the data quality dimensions consistency and timeliness. These metrics are applied in practice and the results are illustrated in the case of a major German mobile services provider

Tars: Timeliness-aware Adaptive Replica Selection for Key-Value Stores

In current large-scale distributed key-value stores, a single end-user request may lead to key-value access across tens or hundreds of servers. The tail latency of these key-value accesses is crucial to the user experience and greatly impacts the revenue. To cut the tail latency, it is crucial for clients to choose the fastest replica server as much as possible for the service of each key-value access. Aware of the challenges on the time varying performance across servers and the herd behaviors, an adaptive replica selection scheme C3 is proposed recently. In C3, feedback from individual servers is brought into replica ranking to reflect the time-varying performance of servers, and the distributed rate control and backpressure mechanism is invented. Despite of C3's good performance, we reveal the timeliness issue of C3, which has large impacts on both the replica ranking and the rate control, and propose the Tars (timeliness-aware adaptive replica selection) scheme. Following the same framework as C3, Tars improves the replica ranking by taking the timeliness of the feedback information into consideration, as well as revises the rate control of C3. Simulation results confirm that Tars outperforms C3.Comment: 10pages,submitted to ICDCS 201

Frequency of financial reports

Interim reports are summary statements that are usually prepared in semi-annual format in the UK. Until the EU’s Transparency Directive was put into practice in the UK in 2007, there was no legal necessity for companies to provide interim financial reports. (Note 1) Instead such preparation was only a regulatory requirement of the London Stock Exchange. The responsibility on companies listed on the London Stock Exchange to provide these financial reports was first prepared as an suggestion in 1964, to meet the requirements for updates by financial analysts (May, 1971). In 1973, this advice to provide the market with interim information became a requirement for the admission of stocks and securities to be listed on the stock Exchange (Lunt, 1982). This study investigates the preparation of interim reports, and accounting standards for interim reporting. Also, this study discusses the main purpose of interim reports, the methods of preparation and the benefits of reporting frequency

A systematic review of data quality issues in knowledge discovery tasks

Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust

Data Accuracy and Completeness of Monthly Midwifery Returns Indicators of Ejisu Juaben Health Directorate of Ghana

The broad range of activities contained in the provision of Primary Health Care (PHC) places a burden on providers to make optimal use of limited resources to achieve maximal health benefit to the population served. All too often, ad hoc decisions and personal preferences guide PHC resource allocations, making accountability for results impossible. Problems constraining Routine Health Information System (RHIS) performance in low-income countries include: poor data quality; limited use of available information; weaknesses in how data are analyzed and poor RHIS management practices. This study sought to investigate these constraints. A non-experimental before and after study involving bassline assessment of data accuracy and completeness, application of innovative strategies such as mentoring and coaching of Health Information Officers in data quality improvement process. Coincidentally, the intervention improved both data accuracy and completeness performance significantly among the participating facilities. The outstanding performance may be attributed to management's new orientation and growing interest towards quality data. Engaging frontline staff in data quality improvement work and provision of regular feedback leads to improvement in data accuracy and completeness. This has implications for decision-making and resource allocation, especially in low-income countries, where the routine health information management system relies heavily on paper work