Data-Driven and Practice-Based Evidence

2016 ◽  
pp. 336-368
Author(s):  
Hamzah Osop ◽  
Tony Sahama
Keyword(s):  
Data Warehouse ◽  
Data Privacy ◽  
Health Data ◽  
Data Driven ◽  
Systematic Research ◽  
Evidence Based ◽  
Patient Health ◽  
Data Driven Approach ◽  
The Right

Decision making is such an integral aspect in health care routine that the ability to make the right decisions at crucial moments can lead to patient health improvements. Evidence-based practice, the paradigm used to make those informed decisions, relies on the use of current best evidence from systematic research such as randomized controlled trials. Limitations of the outcomes from RCT, such as “quantity” and “quality” of evidence generated, has lowered healthcare professionals' confidence in using EBP. An alternate paradigm of Practice-Based Evidence has evolved with the key being evidence drawn from practice settings. Through the use of health information technology, electronic health records capture relevant clinical practice “evidence”. A data-driven approach is proposed to capitalize on the benefits of EHR. The issues of data privacy, security and integrity are diminished by an information accountability concept. Data warehouse architecture completes the data-driven approach by integrating health data from multi-source systems, unique within the healthcare environment.

Data-Driven and Practice-Based Evidence

2016 ◽  
pp. 295-328 ◽  
Author(s):  
Hamzah Osop ◽  
Tony Sahama
Keyword(s):  
Data Warehouse ◽  
Data Privacy ◽  
Health Data ◽  
Data Driven ◽  
Systematic Research ◽  
Evidence Based ◽  
Patient Health ◽  
Data Driven Approach ◽  
The Right

Decision making is such an integral aspect in health care routine that the ability to make the right decisions at crucial moments can lead to patient health improvements. Evidence-based practice, the paradigm used to make those informed decisions, relies on the use of current best evidence from systematic research such as randomized controlled trials. Limitations of the outcomes from RCT, such as “quantity” and “quality” of evidence generated, has lowered healthcare professionals' confidence in using EBP. An alternate paradigm of Practice-Based Evidence has evolved with the key being evidence drawn from practice settings. Through the use of health information technology, electronic health records capture relevant clinical practice “evidence”. A data-driven approach is proposed to capitalize on the benefits of EHR. The issues of data privacy, security and integrity are diminished by an information accountability concept. Data warehouse architecture completes the data-driven approach by integrating health data from multi-source systems, unique within the healthcare environment.

scholarly journals The Use of Evidence-Based Acupuncture: Current Evidence

2022 ◽  
Author(s):  
Dedi Ardinata
Keyword(s):  
Clinical Evidence ◽  
Scientific Evidence ◽  
Current Evidence ◽  
Systematic Research ◽  
Evidence Based ◽  
Clinical Expertise ◽  
Quality Of Evidence ◽  
Assessment Development ◽  
Evidence Quality

Evidence-based medicine (EBM), which emphasizes that medical decisions must be based on the most recent best evidence, is gaining popularity. Individual clinical expertise is combined with the best available external clinical evidence derived from systematic research in the practice of EBM. The key and core of EBM is the hierarchical system for categorizing evidence. The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) system divides evidence quality into four categories: high, moderate, low, and very low. GRADE is based on the lowest quality of evidence for any of the outcomes that are critical to making a decision, reducing the risk of mislabeling the overall evidence quality, when evidence for a critical outcome is lacking. This principle is also used in acupuncture as a complementary and integrative treatment modality, but incorporating scientific evidence is more difficult due to a number of factors. The goal of this chapter is to discuss how to establish a clinical evidence system for acupuncture, with a focus on the current quality of evidence for a variety of conditions or diseases.

Connecting Teacher-Made Assessments to Course Learning Outcomes Through Learning Analytics

2019 ◽  
pp. 101-125
Author(s):  
Julia Chen ◽  
Dennis Foung
Keyword(s):  
Learning Outcomes ◽  
Learning Analytics ◽  
Structural Equation ◽  
Data Driven ◽  
Equation Modeling ◽  
Curriculum Alignment ◽  
Outcome Based Education ◽  
Data Driven Approach ◽  
The Subject

This chapter explores the possibility of adopting a data-driven approach to connecting teacher-made assessments with course learning outcomes. The authors begin by describing several key concepts, such as outcome-based education, curriculum alignment, and teacher-made assessments. Then, the context of the research site and the subject in question are described and the use of structural equation modeling (SEM) in this curriculum alignment study is explained. After that, the results of these SEM analyses are presented, and the various models derived from the analyses are discussed. In particular, the authors highlight how a data-driven curriculum model can benefit from input by curriculum leaders and how SEM provides insights into course development and enhancement. The chapter concludes with recommendations for curriculum leaders and front-line teachers to improve the quality of teacher-made assessments.

scholarly journals Correlating Espresso Quality with Coffee-Machine Parameters by Means of Association Rule Mining

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 100
Author(s):  
Daniele Apiletti ◽  
Eliana Pastor
Keyword(s):  
Real World ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Driven ◽  
Rule Mining ◽  
Electronic Noses ◽  
Data Driven Approach ◽  
The Many ◽  
Coffee Machine

Coffee is among the most popular beverages in many cities all over the world, being both at the core of the busiest shops and a long-standing tradition of recreational and social value for many people. Among the many coffee variants, espresso attracts the interest of different stakeholders: from citizens consuming espresso around the city, to local business activities, coffee-machine vendors and international coffee industries. The quality of espresso is one of the most discussed and investigated issues. So far, it has been addressed by means of human experts, electronic noses, and chemical approaches. The current work, instead, proposes a data-driven approach exploiting association rule mining. We analyze a real-world dataset of espresso brewing by professional coffee-making machines, and extract all correlations among external quality-influencing variables and actual metrics determining the quality of the espresso. Thanks to the application of association rule mining, a powerful data-driven exhaustive and explainable approach, results are expressed in the form of human-readable rules combining the variables of interest, such as the grinder settings, the extraction time, and the dose amount. Novel insights from real-world coffee extractions collected on the field are presented, together with a data-driven approach, able to uncover insights into the espresso quality and its impact on both the life of consumers and the choices of coffee-making industries.

scholarly journals X-ETL: A Data-Driven Approach for Designing Star Schemas

2019 ◽  
Vol 7 (1) ◽  
pp. 4
Author(s):  
Nawfal El Moukhi ◽  
Ikram El Azami ◽  
Abdelaaziz Mouloudi ◽  
Abdelali Elmounadi
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Data Driven ◽  
Critical Stage ◽  
Model Driven ◽  
New Methods ◽  
Star Models ◽  
Data Driven Approach ◽  
Data Source ◽  
Warehouse Design

The data warehouse design is currently recognized as the most important and complicated phase in any project of decision support system implementation. Its complexity is primarily due to the proliferation of data source types and the lack of a standardized and well-structured method, hence the increasing interest from researchers who have tried to develop new methods for the automation and standardization of this critical stage of the project. In this paper, the authors present the set of developed methods that follows the data-driven paradigm, and they propose a new data-driven method called X-ETL. This method aims to automating the data warehouse design by generating star models from relational data. This method is mainly based on a set of rules derived from the related works, the Model-Driven Architecture (MDA) and the XML language.

scholarly journals Estimating Wait Time and Passenger Load in a Saturated Metro Network: A Data-Driven Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Hezhou Qu ◽  
Xiaoyue Xu ◽  
Steven Chien
Keyword(s):  
Joint Probability ◽  
Wait Time ◽  
Service Planning ◽  
Data Driven ◽  
Passenger Demand ◽  
Passenger Travel ◽  
Data Driven Approach ◽  
System Data ◽  
Transit Performance

The service quality of public transit, such as comfort and convenience, is an important factor influencing ridership and fare revenue, which also reflects the passengers’ perception to the transit performance. Passengers are frustrated while waiting to board a crowded train especially during the peak hours, while the fail-to-board (FtB) situation commonly exists. The service performance measures determined by deterministic passenger demand and service frequency cannot reflect the perceived service of passengers. With the automatic fare collection system data provided by Chengdu Metro, we develop a data-driven approach considering the joint probability of spatiotemporal passenger demand at stations based on posted train schedule to approximate passenger travel time (e.g., in-vehicle and out-of-vehicle times). It was found that the estimated wait time can reflect the actual situation as passengers FtB. The proposed modeling approach and analysis results would be useful and beneficial for transit providers to improve system performance and service planning.

Unlocking Data Collection: Practical Tips for School Nurses

2017 ◽  
Vol 33 (1) ◽  
pp. 27-28 ◽  
Author(s):  
Angela M. Lepkowski
Keyword(s):  
Data Collection ◽  
Evidence Based Practice ◽  
School Nurses ◽  
Health Data ◽  
Evidence Based ◽  
Quality Of Data ◽  
Specific Health

School nurses contend with a variety of challenges related to collecting and using their own data. Seemingly small steps can be taken to overcome these challenges, which will result in significant improvements in data collection and use. Improving the quality of data collection assists school nurses to identify and define practice issues and guide implementation of evidence-based practice within their schools and districts. This article provides school nurses with practical steps to collect and use school or district specific health data.

Degree Distribution and Quality in Complex Engineered Systems

2011 ◽  
Vol 133 (10) ◽  
Author(s):  
Manuel Sosa ◽  
Jürgen Mihm ◽  
Tyson Browning
Keyword(s):  
Complex System ◽  
Degree Distribution ◽  
Data Driven ◽  
Small Subset ◽  
System Quality ◽  
Complex Engineered Systems ◽  
Data Driven Approach ◽  
Engineered Systems ◽  
System Designs

Complex engineered systems tend to have architectures in which a small subset of components exhibits a disproportional number of linkages. Such components are known as hubs. This paper examines the degree distribution of systems to identify the presence of hubs and quantify the fraction of hub components. We examine how the presence and fraction of hubs relate to a system’s quality. We provide empirical evidence that the presence of hubs in a system’s architecture is associated with a low number of defects. Furthermore, we show that complex engineered systems may have an optimal fraction of hub components with respect to system quality. Our results suggest that architects and managers aiming to improve the quality of complex system designs must proactively identify and manage the use of hubs. Our paper provides a data-driven approach for identifying appropriate target levels of hub usage.

scholarly journals Personnel management in the sustainable transport and logistics industry

2021 ◽  
Vol 128 ◽  
pp. 04022
Author(s):  
Katarína Hercegová ◽  
Alexander Pyanov ◽  
Oksana Mukhoryanova
Keyword(s):  
Human Resource ◽  
Data Driven ◽  
Human Resource Managers ◽  
Sustainable Transport ◽  
Comprehensive Overview ◽  
Logistics Industry ◽  
Resource Managers ◽  
Transport Industry ◽  
Data Driven Approach ◽  
The Right

This paper describes the methods and techniques of the ways personnel security is ensured and maintained in the sustainable transport industry. In addition, it focuses on the novel methods and technologies used by the human resource managers for selecting and hiring candidates for jobs in the transport and logistic sector. Furthermore, it gives a comprehensive overview of human capital management in the transport industry and provides a detailed analysis of several segments covered. It offers a detailed insight into the growth markets and their impact on the human resource management market in the transport industry. Our results demonstrate that the majority of the world's largest transportation and logistics companies believe that data-driven decision-making is essential to supply chain activities and is hiring the right employees. The paper shows that this data-driven approach might be the best solution for optimizing performance and achieving the standards of sustainable and environmentally-friendly business both at the personnel level and at the level of operation and efficient management. Moreover, it stresses the importance of the artificial intelligence and deep learning in the development of the sustainable transport industry.

scholarly journals Adoption of Big Data by Global Chemical Industries

2021 ◽  
Author(s):  
Ashiff Khan ◽  
A Seetharaman ◽  
Abhijit Dasgupta
Keyword(s):  
Big Data ◽  
Data Science ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Time Data ◽  
New Era ◽  
Potential Applications ◽  
Data Driven Approach ◽  
Process Plants ◽  
The Right

The new era of Big Data (BD) is influencing the chemical industries tremendously, providing several opportunities to reshape the way they operate and for shifting towards smart manufacturing. Given the availability of free software, and the large amount of real-time data generated and stored in process plants why many chemical industries are still not fully adopting BD? The industry is just starting to realize the importance of a large amount of data that they own to make the right decisions and to support their strategies. This article is exploring the importance of professional competencies and data science that influence BD in chemical industries for shifting towards smart manufacturing in a fast and reliable manner. This article utilizes a literature review and identifies potential applications in the chemical industry to shift from conventional methods towards a data-driven approach.

Sign in / Sign up

Export Citation Format

Share Document