An Evidence-Based Framework for Evidence-Based Management in Healthcare Organizations: A Delphi Study

BACKGROUND: Evidence-based management (EBMgt) is a growing literature concept in management sciences which claims that management decision-making must be based on the best available evidence. The aim of this paper is to present and provide an evidence-based framework for EBMgt to improve decision-making in healthcare organizations.METHODS: A two-round Delphi survey was used to collect the factors affecting EBMgt. Purposive and snowball sampling methods were used in both rounds. In round 1, we conducted a systematic review and a series of semi-structured interviews (n=45). In round 2, a specific questionnaire with four main parts was designed. The experts (n=21) were asked to rate on a 9-point Likert scale the importance of each factor. The data was collected through Google Forms (n=11) and paper forms (n=10).RESULTS: Participants were mostly men (73%). Overall, 126 factors were selected in round 1. Factors were classified into 4 categories: facilitators, barriers, the sources of evidence and EBMgt process that consisted of 48, 46, 22 and 10 factors, respectively. In round 2, based on median scores, many factors (n=114) were found to be very important. Only, 12 factors have a median score of less than 3 and were excluded from the study. Finally, 114 factors were confirmed.CONCLUSIONS: Confirmed factors played significant roles in affecting the practice of EBMgt among healthcare managers. We tried to facilitate interaction between these factors in the framework. Depending on the type of problem, using six steps of EBMgt process, managers will select the best evidence among six sources of evidence.KEYWORDS: Evidence-based management, evidence-based framework, healthcare organization

SGHRP: Secure Greedy Highway Routing Protocol with authentication and increased privacy in vehicular ad hoc networks.

VANETs are networks of connected intelligent vehicles that can communicate with each other, as well as with infrastructure and fixed roadside equipment. As a result of the lack of fixed infrastructure and open-access environment, security is crucial when sending packets. Secure routing protocols have been proposed for VANETs, but most are focused on authenticating nodes and creating a secure route, without considering confidentiality after the route is created. Using a chain of source keys validated by a one-way function, we have proposed a secure routing protocol called Secure Greedy Highway Routing Protocol (GHRP), which provides increased confidentiality over other protocols. As part of the proposed protocol, the source, destination, and intermediate nodes are authenticated using a hashing chain in the first stage, and in the second stage, one-way hashing has been used to increase data security. In order to resist routing attacks such as black hole attacks, the proposed protocol is based on the GHRP routing protocol. The proposed protocol is simulated using the NS2 simulator, and its performance is compared with that of the SAODV protocol. Based on the simulation results, the proposed protocol performs better than the mentioned protocol in terms of packet delivery rate, overhead, and average end-to-end delay

SGHRP: Secure Greedy Highway Routing Protocol with authentication and increased privacy in vehicular ad hoc networks

VANETs are networks of connected intelligent vehicles that can communicate with each other, as well as with infrastructure and fixed roadside equipment. As a result of the lack of fixed infrastructure and open-access environment, security is crucial when sending packets. Secure routing protocols have been proposed for VANETs, but most are focused on authenticating nodes and creating a secure route, without considering confidentiality after the route is created. Using a chain of source keys validated by a one-way function, we have proposed a secure routing protocol called Secure Greedy Highway Routing Protocol (GHRP), which provides increased confidentiality over other protocols. As part of the proposed protocol, the source, destination, and intermediate nodes are authenticated using a hashing chain in the first stage, and in the second stage, one-way hashing has been used to increase data security. In order to resist routing attacks such as black hole attacks, the proposed protocol is based on the GHRP routing protocol. The proposed protocol is simulated using the NS2 simulator, and its performance is compared with that of the SAODV protocol. Based on the simulation results, the proposed protocol performs better than the mentioned protocol in terms of packet delivery rate, overhead, and average end-to-end delay

DLJSF: Data-Locality Aware Job Scheduling IoT tasks in fog-cloud computing environments

Problem statement: Nowadays, devices generate copious quantities of high-speed data streams due to Internet of Things (IoT) applications. For the most part, cloud computing platforms handle and manage all of these data and requests. However, for certain applications, the data transmission delay that comes with transferring data from edge devices to the cloud could be unbearable. When there are a lot of devices connected to the internet, the public network actually becomes a bottleneck for data transfer. In this setting, power management, data storage, resource management, and service management all necessitate more robust infrastructure and complex processes. More efficient use of network and cloud resources is achievable with fog computing's “intelligent gateway” capability. Methodology: Planning and managing resources is one of the most important factors affecting system performance (especially latency) in a fog-cloud environment. Planning in an environment with fog and clouds is an NP-hard problem. This paper delves into the optimisation difficulty of longevity for data-intensive job scheduling in fog and cloud-based IoT systems. The issue is initially expressed as an optimisation model for integer linear programming (ILP). Next, we provide a heuristic algorithm known as DLJSF (Data-Locality Aware Job Scheduling in Fog-Cloud) that is based on the suggested formulation. Results: The results of the tests showed that the performance of the proposed algorithm is close to the results by an average of 87 %. Also, on average, it is 99.16 % better than the LP results obtained from the optimal solution obtained from the solver obtained from the solution that the data is processed locally. To check the efficiency of the simulation solution, it was repeated for tasks with different entry rates and data with different sizes. Conclusion: According to the obtained documents, the data transfer approach can be valuable and the proposed algorithm has not lost its performance in different conditions

An efficient approach for multi-label classification based on Advanced Kernel-Based Learning System

The importance of data quality and quantity cannot be overstated in automatic data analysis systems. An important factor to take into account is the capability to assign a data item to many classes. In Lithuania, there is currently no mechanism for classifying textual data that permits allocating a data item to multiple classes. Multi-label categorization learning offers a multi-dimensional viewpoint for objects with several meanings and has emerged as a prominent area of study in machine learning in recent times. Within the context of big data, it is imperative to develop a high-speed and effective algorithm for multi-label classification. This paper utilized the Machine Learning Advanced Kernel-Based Learning System for Multi-Label Classification Problem (ML-AKLS) to eliminate the need for repetitive learning operations. Concurrently, a thresholding function that is both dynamic and self-adaptive was developed to address the conversion from the ML-AKLS network's actual value outputs to a binary multi-label vector. ML-AKLS offers the ideal solution with the least squares method, requiring less parameters to be set. It ensures steady execution, faster convergence speed, and superior generalization performance. Extensive experiments in multi-label classification were conducted on datasets of varying scales. The comparative analysis reveals that ML-AKLS has superior performance when applied to extensive datasets characterized by high-dimensional sample features

Occupational stress and cognitive failure of nurses and associations with self-reported adverse events: A national cross-sectional survey

Aim: To determine correlations for nurse self-reported occupational stress, prevalence of cognitive failure (CF), and adverse events. Design: Cross-sectional nationwide survey. Methods: Tertiary-level public hospitals (N = 115) from 13 provinces in Iran were recruited and 2,895 nurses surveyed (August 2016–December 2017). Participants’ self-reported demographic information, occupational stress, CF, and frequency of adverse events were analysed using chi-square, t tests, and binary logistic regression. Results: This study showed that 29.1% of nurses had experienced adverse events in the past six months. Significant predictors for reported adverse events from logistic regression were ‘Role stressors’, ‘Interpersonal relations stressors’, and ‘Action’, while ‘Working environment stressors’ was protective for reported adverse events. Demographic predictors of adverse events were longer work hours and male gender, while those working in critical care units, general wards, and other wards had higher reported adverse events than for emergency wards. Conclusions: Occupational stress and CF are associated with the reporting of adverse events. Further research is needed to assess interventions to address occupational stress and CF to reduce adverse events. Impact: Adverse events compromise patient safety, lead to increased healthcare costs, and impact nursing staff. Higher self-reported adverse events were associated with higher reported stressors and CF. Understanding the factors that influence occupational stress, CF, and adverse events will support quality patient care and safety