KPIs for Mobile Apps and Digital Data Management in Healthcare

2021 ◽  
pp. 238-265
Author(s):  
Rebeca Antolín-Prieto ◽  
Nuria Ruiz-Lacaci ◽  
Ana Reyes-Menendez
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Big Data ◽  
Product Quality ◽  
Mobile Apps ◽  
Healthcare Management ◽  
Digital Data ◽  
Mobile Technologies ◽  
Review Of The Literature ◽  
Scientific Databases

This study aimed to identify quantitative and qualitative KPIs for the implementation of apps and use of digital data in healthcare management. To this end, a systematic review of the literature was undertaken to analyze relevant scientific articles downloaded from reputed scientific databases (Scopus, PubMed, PsyINFO, ScienceDirect, and WOS). The databases were searched using the following keywords: “Big Data,” “Artificial Intelligence,” “Mobile Technologies,” “APP,” “Disease,” “Geolocation,” and “Health.” Subsequently, 25 qualitative and quantitative KPI values, as rating, product quality, or unique users, were identified for the successful preparation and management of healthcare based on apps and the use of digital data.

scholarly journals A Study on the Transformation of Accounting Based on New Technologies: Evidence from Korea

2020 ◽  
Vol 12 (20) ◽  
pp. 8669
Author(s):  
Sora Yoon
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Big Data ◽  
Research Methodology ◽  
New Technologies ◽  
Actual Practice ◽  
Review Of The Literature ◽  
Analytical Process ◽  
Academic Publications ◽  
Technology Transformation

This study identifies the new accounting technologies into Cloud, Artificial Intelligence, Big Data, and Blockchain, and introduces the case of Korean companies applying new technologies to their accounting process. The purpose of this study is to help understand accounting technologies and provide examples of the adoption of these technologies in actual practice. To achieve this aim of the study, a systematic review of the literature of the major academic publications and professional reports and websites was used as a research methodology. In order to select the cases, it performed the analytical process of reviewing Korean major business and economic newspaper articles. This study provides evidence from Korea to companies contemplating the transformation of their accounting process using technology. Such companies can consider the cases presented in this study as a benchmark. It also offers guidance for the application of technologies to accounting practices for businesses and related researchers. The technology transformation is expected to be accelerated, especially after COVID-19. Therefore, it is necessary to understand and explore ways to effectively apply them. Further, while new technologies offer many opportunities, associated risks and threats should be addressed.

Disrupting Agriculture

2021 ◽  
pp. 771-812
Author(s):  
Omar F. El-Gayar ◽  
Martinson Q. Ofori
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Big Data ◽  
New Paradigm ◽  
Agricultural Industry ◽  
Decision Systems ◽  
Food And Agriculture ◽  
Key Stakeholders ◽  
Smart Agriculture ◽  
Key Aspects

The United Nations (UN) Food and Agriculture (FAO) estimates that farmers will need to produce about 70% more food by 2050. To accommodate the growing demand, the agricultural industry has grown from labor-intensive to smart agriculture, or Agriculture 4.0, which includes farm equipment that are enhanced using autonomous unmanned decision systems (robotics), big data, and artificial intelligence. In this chapter, the authors conduct a systematic review focusing on big data and artificial intelligence in agriculture. To further guide the literature review process and organize the findings, they devise a framework based on extant literature. The framework is aimed to capture key aspects of agricultural processes, supporting supply chain, key stakeholders with a particular emphasis on the potential, drivers, and challenges of big data and artificial intelligence. They discuss how this new paradigm may be shaped differently depending on context, namely developed and developing countries.

Artificial intelligence techniques in asthma: a systematic review and critical appraisal of the existing literature

2020 ◽  
Vol 56 (3) ◽  
pp. 2000521 ◽  
Author(s):  
Konstantinos P. Exarchos ◽  
Maria Beltsiou ◽  
Chainti-Antonella Votti ◽  
Konstantinos Kostikas
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Critical Appraisal ◽  
Unmet Needs ◽  
Asthma Management ◽  
Scientific Field ◽  
Patient Classification ◽  
Review Of The Literature ◽  
Qualitative And Quantitative ◽  
Screening And Diagnosis

Artificial intelligence (AI) when coupled with large amounts of well characterised data can yield models that are expected to facilitate clinical practice and contribute to the delivery of better care, especially in chronic diseases such as asthma.The purpose of this paper is to review the utilisation of AI techniques in all aspects of asthma research, i.e. from asthma screening and diagnosis, to patient classification and the overall asthma management and treatment, in order to identify trends, draw conclusions and discover potential gaps in the literature.We conducted a systematic review of the literature using PubMed and DBLP from 1988 up to 2019, yielding 425 articles; after removing duplicate and irrelevant articles, 98 were further selected for detailed review.The resulting articles were organised in four categories, and subsequently compared based on a set of qualitative and quantitative factors. Overall, we observed an increasing adoption of AI techniques for asthma research, especially within the last decade.AI is a scientific field that is in the spotlight, especially the last decade. In asthma there are already numerous studies; however, there are certain unmet needs that need to be further elucidated.

scholarly journals Artificial intelligence and machine learning: changing paradigm in diagnostics and imaging

2019 ◽  
Vol 8 (1) ◽  
pp. 382
Author(s):  
Rahul Badwaik
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Setting ◽  
Digital Transformation ◽  
Current Status ◽  
Healthcare Industry ◽  
Review Of The Literature

Healthcare industry is currently undergoing a digital transformation, and Artificial Intelligence (AI) is the latest buzzword in the healthcare domain. The accuracy and efficiency of AI-based decisions are already been heard across countries. Moreover, the increasing availability of electronic clinical data can be combined with big data analytics to harness the power of AI applications in healthcare. Like other countries, the Indian healthcare industry has also witnessed the growth of AI-based applications. A review of the literature for data on AI and machine learning was conducted. In this article, we discuss AI, the need for AI in healthcare, and its current status. An overview of AI in the Indian healthcare setting has also been discussed.

scholarly journals Behavioral Functionality of Mobile Apps in Health Interventions: A Systematic Review of the Literature

2015 ◽  
Vol 3 (1) ◽  
pp. e20 ◽  
Author(s):  
Hannah E Payne ◽  
Cameron Lister ◽  
Joshua H West ◽  
Jay M Bernhardt
Keyword(s):  
Systematic Review ◽  
Mobile Apps ◽  
Health Interventions ◽  
Review Of The Literature

Disrupting Agriculture

2020 ◽  
pp. 174-215 ◽  
Author(s):  
Omar F. El-Gayar ◽  
Martinson Q. Ofori
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Big Data ◽  
New Paradigm ◽  
Agricultural Industry ◽  
Decision Systems ◽  
Food And Agriculture ◽  
Key Stakeholders ◽  
Smart Agriculture ◽  
Key Aspects

The United Nations (UN) Food and Agriculture (FAO) estimates that farmers will need to produce about 70% more food by 2050. To accommodate the growing demand, the agricultural industry has grown from labor-intensive to smart agriculture, or Agriculture 4.0, which includes farm equipment that are enhanced using autonomous unmanned decision systems (robotics), big data, and artificial intelligence. In this chapter, the authors conduct a systematic review focusing on big data and artificial intelligence in agriculture. To further guide the literature review process and organize the findings, they devise a framework based on extant literature. The framework is aimed to capture key aspects of agricultural processes, supporting supply chain, key stakeholders with a particular emphasis on the potential, drivers, and challenges of big data and artificial intelligence. They discuss how this new paradigm may be shaped differently depending on context, namely developed and developing countries.

scholarly journals Artificial Intelligence Techniques for Distance Education: A Systematic Literature Review

TEM Journal ◽  
2021 ◽  
pp. 1621-1629
Author(s):  
Aayat Aljarrah ◽  
Mustafa Ababneh ◽  
Damla Karagozlu ◽  
Fezile Ozdamli
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Distance Education ◽  
Big Data ◽  
Distance Learning ◽  
Affective Computing ◽  
Learning System ◽  
Educational Systems ◽  
Artificial Intelligence Techniques ◽  
Educational Processes

In the current era, education, like other fields, relies heavily on big data. Moreover, artificial intelligence, including affective computing, is one of the most essential and popular technologies adopted by educational institutions to process and analyze big data. In this systematic review, many previous research types related to improving educational systems using artificial intelligence techniques were studied, such as: deep learning, machine learning, and affective computing. This systematic review aims to identify the gaps in students' emotional understanding in distance education systems. The world has recently witnessed the spread of educational processes for distance learning, especially in the university and the enormous open online courses (MOOCs). Besides, the COVID-19 pandemic has been involved in changing all educational processes to a distance learning system. The results indicated that these systems recorded a high success rate. However, the teacher does not fully understand the student’s emotional state during the educational session. It also lacks monitoring or monitoring during the electronic exams, which are electronic exams. So, it is a widespread problem in distance learning.

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