Interprofessional Healthcare Collaboration on the Front Lines: A Qualitative Exploration of the Facilitators and Barriers to Effective Collaboration in Qatar.

Background:
Healthcare in Qatar is undergoing a period of major reform, driven by a strong economy and vision for a world-class healthcare system. One area identified as a potential contributor to developing a world-class healthcare system is interprofessional education (IPE), with the goal of facilitating healthcare workers to work together collaboratively. Several key steps have been taken towards developing IPE in Qatar, such as the formation of the Qatar Interprofessional Health Council (QIHC), the development of an IPE program for undergraduate healthcare students, the development of a set of shared core competencies, the receipt of substantial buy-in from leaders across the healthcare system, and recent approval of funding to develop a post-licensure healthcare IPE program. In order to improve IPE in Qatar, it is important to better understand the facilitators and barriers to interprofessional collaboration in Qatar. This study seeks to do so by qualitatively exploring facilitators and barriers to interprofessional collaboration for healthcare professional in Qatar from the perspective of health care professionals. By better understanding how health care workers give meaning to interprofessional education and collaboration, this research can assist in improving interprofessional activities in healthcare in Qatar.

Objectives
The purpose of this paper-presentation is to report on finding from a qualitative study that explored different facilitators and barriers of interprofessional practice in Qatar.

Method:
Ten healthcare professionals who work in Qatar were interviewed using semi-structured, open-ended interviews. Interview questions were organized by phenomenological (e.g. exploring the lived-experiences of healthcare workers) and ethnographic interviewing techniques (e.g. focusing on what people do). The questions explored the barriers, facilitators, and what is working well in terms of interprofessional practice for health care professional in Qatar.

Findings and Implications:
Different factors associated with interprofessional collaborations will be discussed. In doing so, this research adds to the literature on IPE by shedding light on interprofessional collaboration and education in the Middle East. Furthermore, this study identifies barriers for health care workers to work collaboratively in health care settings in Qatar. Addressing such barriers, and building off of what is working well, will facilitate Qatar in reaching one of the Vision 2030 goals of improving Qatar’s health and wellness.

Interprofessional Healthcare Collaboration on the Front Lines: A Qualitative Exploration of the Facilitators and Barriers to Effective Collaboration in Qatar.

Background:

Healthcare in Qatar is undergoing a period of major reform, driven by a strong economy and vision for a world-class healthcare system. One area identified as a potential contributor to developing a world-class healthcare system is interprofessional education (IPE), with the goal of facilitating healthcare workers to work together collaboratively. Several key steps have been taken towards developing IPE in Qatar, such as the formation of the Qatar Interprofessional Health Council (QIHC), the development of an IPE program for undergraduate healthcare students, the development of a set of shared core competencies, the receipt of substantial buy-in from leaders across the healthcare system, and recent approval of funding to develop a post-licensure healthcare IPE program. In order to improve IPE in Qatar, it is important to better understand the facilitators and barriers to interprofessional collaboration in Qatar. This study seeks to do so by qualitatively exploring facilitators and barriers to interprofessional collaboration for healthcare professional in Qatar from the perspective of health care professionals. By better understanding how health care workers give meaning to interprofessional education and collaboration, this research can assist in improving interprofessional activities in healthcare in Qatar.

Objectives

The purpose of this paper-presentation is to report on finding from a qualitative study that explored different facilitators and barriers of interprofessional practice in Qatar.

Method:

Ten healthcare professionals who work in Qatar were interviewed using semi-structured, open-ended interviews. Interview questions were organized by phenomenological (e.g. exploring the lived-experiences of healthcare workers) and ethnographic interviewing techniques (e.g. focusing on what people do). The questions explored the barriers, facilitators, and what is working well in terms of interprofessional practice for health care professional in Qatar.

Findings and Implications:

Different factors associated with interprofessional collaborations will be discussed. In doing so, this research adds to the literature on IPE by shedding light on interprofessional collaboration and education in the Middle East. Furthermore, this study identifies barriers for health care workers to work collaboratively in health care settings in Qatar. Addressing such barriers, and building off of what is working well, will facilitate Qatar in reaching one of the Vision 2030 goals of improving Qatar’s health and wellness.

Spitzer infrared spectrograph point source classification in the Small Magellanic Cloud

The Magellanic Clouds are uniquely placed to study the stellar contribution to dust emission. Individual stars can be resolved in these systems even in the mid-infrared, and they are close enough to allow detection of infrared excess caused by dust. We have searched the Spitzer Space Telescope data archive for all Infrared Spectrograph (IRS) staring-mode observations of the Small Magellanic Cloud (SMC) and found that 209 Infrared Array Camera (IRAC) point sources within the footprint of the Surveying the Agents of Galaxy Evolution in the Small Magellanic Cloud (SAGE-SMC) Spitzer Legacy programme were targeted, within a total of 311 staring-mode observations. We classify these point sources using a decision tree method of object classification, based on infrared spectral features, continuum and spectral energy distribution shape, bolometric luminosity, cluster membership and variability information. We find 58 asymptotic giant branch (AGB) stars, 51 young stellar objects, 4 post-AGB objects, 22 red supergiants, 27 stars (of which 23 are dusty OB stars), 24 planetary nebulae (PNe), 10 Wolf-Rayet stars, 3 H II regions, 3 R Coronae Borealis stars, 1 Blue Supergiant and 6 other objects, including 2 foreground AGB stars. We use these classifications to evaluate the success of photometric classification methods reported in the literature.

A Scheme Design of Cloud + End Technology in Demand Side Management

Demand Side Management (DSM) plays an important role in Smart Grid. It has large scale access points, massive users, heterogeneous infrastructure and dispersive participants. Moreover, cloud computing which is a service model is characterized by resource on-demand, high reliability and large scale integration and so on and the game theory is a useful tool to the dynamic economic phenomena. In this study, a scheme design of cloud + end technology is proposed to solve technical and economic problems of the DSM. The architecture of cloud + end is designed to solve technical problems in the DSM. In particular, a construct model of cloud + end is presented to solve economic problems in the DSM based on game theories. The proposed method is tested on a DSM cloud + end public service system construction in a city of southern China. The results demonstrate the feasibility of these integrated solutions which can provide a reference for the popularization and application of the DSM in china.

Incorporation of spiritual care as a component of healthcare and medical education: comparison of viewpoints of healthcare providers from a rural community hospital in Uganda with those of medical students from United Kingdrom

This study addresses cultural differences regarding views on the place for spirituality within healthcare training and delivery. A questionnaire was devised using a 5-point ordinal scale, with additional free text comments assessed by thematic analysis, to compare the views of Ugandan healthcare staff and students with those of (1) visiting international colleagues at the same hospital; (2) medical faculty and students in United Kingdom. Ugandan healthcare personnel were more favourably disposed towards addressing spiritual issues, their incorporation within compulsory healthcare training, and were more willing to contribute themselves to delivery than their European counterparts. Those from a nursing background also attached a greater importance to spiritual health and provision of spiritual care than their medical colleagues. Although those from a medical background recognised that a patient’s religiosity and spirituality can affect their response to their diagnosis and prognosis, they were more reticent to become directly involved in provision of such care, preferring to delegate this to others with greater expertise. Thus, differences in background, culture and healthcare organisation are important, and indicate that the wide range of views expressed in the current literature, the majority of which has originated in North America, are not necessarily transferable between locations; assessment of these issues locally may be the best way to plan such training and incorporation of spiritual care into clinical practice.

Cloud benchmarking for performance

How can applications be deployed on the cloud to achieve maximum performance? This question has become significant and challenging with the availability of a wide variety of Virtual Machines (VMs) with different performance capabilities in the cloud. The above question is addressed by proposing a six step benchmarking methodology in which a user provides a set of four weights that indicate how important each of the following groups: memory, processor, computation and storage are to the application that needs to be executed on the cloud. The weights along with cloud benchmarking data are used to generate a ranking of VMs that can maximise performance of the application. The rankings are validated through an empirical analysis using two case study applications, the first is a financial risk application and the second is a molecular dynamics simulation, which are both representative of workloads that can benefit from execution on the cloud. Both case studies validate the feasibility of the methodology and highlight that maximum performance can be achieved on the cloud by selecting the top ranked VMs produced by the methodology.

Container-based Cloud Virtual Machine Benchmarking

With the availability of a wide range of cloud Virtual Machines (VMs) it is difficult to determine which VMs can maximise the performance of an application. Benchmarking is commonly used to this end for capturing the performance of VMs. Most cloud benchmarking techniques are typically heavyweight - time consuming processes which have to benchmark the entire VM in order to obtain accurate benchmark data. Such benchmarks cannot be used in real-time on the cloud and incur extra costs even before an application is deployed.

In this paper, we present lightweight cloud benchmarking techniques that execute quickly and can be used in near real-time on the cloud. The exploration of lightweight benchmarking techniques are facilitated by the development of DocLite - Docker Container-based Lightweight Benchmarking. DocLite is built on the Docker container technology which allows a user-defined portion (such as memory size and the number of CPU cores) of the VM to be benchmarked. DocLite operates in two modes, in the first mode, containers are used to benchmark a small portion of the VM to generate performance ranks. In the second mode, historic benchmark data is used along with the first mode as a hybrid to generate VM ranks. The generated ranks are evaluated against three scientific high-performance computing applications. The proposed techniques are up to 91 times faster than a heavyweight technique which benchmarks the entire VM. It is observed that the first mode can generate ranks with over 90% and 86% accuracy for sequential and parallel execution of an application. The hybrid mode improves the correlation slightly but the first mode is sufficient for benchmarking cloud VMs.

Executing bag of distributed tasks on the cloud: Investigating the trade-offs between performance and cost

Bag of Distributed Tasks (BoDT) can benefit from decentralised execution on the Cloud. However, there is a trade-off between the performance that can be achieved by employing a large number of Cloud VMs for the tasks and the monetary constraints that are often placed by a user. The research reported in this paper is motivated towards investigating this trade-off so that an optimal plan for deploying BoDT applications on the cloud can be generated. A heuristic algorithm, which considers the user's preference of performance and cost is proposed and implemented. The feasibility of the algorithm is demonstrated by generating execution plans for a sample application. The key result is that the algorithm generates optimal execution plans for the application over 91% of the time.

Optimal deployment of geographically distributed workflow engines on the cloud

When orchestrating Web service workflows, the geographical placement of the orchestration engine (s) can greatly affect workflow performance. Data may have to be transferred across long geographical distances, which in turn increases execution time and degrades the overall performance of a workflow. In this paper, we present a framework that, given a DAG-based workflow specification, computes the optimal Amazon EC2 cloud regions to deploy the orchestration engines and execute a workflow. The framework incorporates a constraint model that solves the workflow deployment problem, which is generated using an automated constraint modelling system. The feasibility of the framework is evaluated by executing different sample workflows representative of scientific workloads. The experimental results indicate that the framework reduces the workflow execution time and provides a speed up of 1.3x-2.5x over centralised approaches.

LS-ADT: Lightweight and Scalable Anomaly Detection for Cloud Datacentres

Cloud data centres are implemented as large-scale clusters with demanding requirements for service performance, availability and cost of operation. As a result of scale and complexity, data centres typically exhibit large numbers of system anomalies resulting from operator error, resource over/under provisioning, hardware or software failures and security issus anomalies are inherently difficult to identify and resolve promptly via human inspection. Therefore, it is vital in a cloud system to have automatic system monitoring that detects potential anomalies and identifies their source. In this paper we present a lightweight anomaly detection tool for Cloud data centres which combines extended log analysis and rigorous correlation of system metrics, implemented by an efficient correlation algorithm which does not require training or complex infrastructure set up. The LADT algorithm is based on the premise that there is a strong correlation between node level and VM level metrics in a cloud system. This correlation will drop significantly in the event of any performance anomaly at the node-level and a continuous drop in the correlation can indicate the presence of a true anomaly in the node. The log analysis of LADT assists in determining whether the correlation drop could be caused by naturally occurring cloud management activity such as VM migration, creation, suspension, termination or resizing. In this way, any potential anomaly alerts are reasoned about to prevent false positives that could be caused by the cloud operator’s activity. We demonstrate LADT with log analysis in a Cloud environment to show how the log analysis is combined with the correlation of systems metrics to achieve accurate anomaly detection.

DocLite: A Docker-Based Lightweight Cloud Benchmarking Tool

Existing benchmarking methods are time consuming processes as they typically benchmark the entire Virtual Machine (VM) in order to generate accurate performance data, making them less suitable for real-time analytics. The research in this paper is aimed to surmount the above challenge by presenting DocLite - Docker Container-based Lightweight benchmarking tool. DocLite explores lightweight cloud benchmarking methods for rapidly executing benchmarks in near real-time. DocLite is built on the Docker container technology, which allows a user-defined memory size and number of CPU cores of the VM to be benchmarked. The tool incorporates two benchmarking methods - the first referred to as the native method employs containers to benchmark a small portion of the VM and generate performance ranks, and the second uses historic benchmark data along with the native method as a hybrid to generate VM ranks. The proposed methods are evaluated on three use-cases and are observed to be up to 91 times faster than benchmarking the entire VM. In both methods, small containers provide the same quality of rankings as a large container. The native method generates ranks with over 90% and 86% accuracy for sequential and parallel execution of an application compared against benchmarking the whole VM. The hybrid method did not improve the quality of the rankings significantly.