Showing 3 results for Artificial Intelligence
Mohammadhossein Ronaghi,
Volume 19, Issue 2 (8-2020)
Abstract
Background: The fourth industrial revolution consists of combining network devices with cloud computing methods and analyzing large data and artificial intelligence, which makes it possible to call such an infrastructure smart. In a Smart Hospital, all things and devices are designed to be connected and integrated, thus achieving better patient care, increasing efficiency and reducing time waste. Therefore, the aim of this paper was to recognize the components of smart hospital based on disruptive technologies of industry 4.0.
Materials and Methods: This applied research has been done in two phases using qualitative approach in winter 2019. In the first step, the components of smart hospital were recognized from previous studies. In the second step, research experts evaluate conceptual model by Delphi method. The expert panel consists of 15 individuals active in information technology in healthcare according to targeted sampling.
Results: According to research results the main components of smart hospital are eight technologies: Internet of things technology, robotic, blockchain technology, cloud computing, big data, augmented and virtual reality technology, additive manufacturing and artificial intelligence.
Conclusion: According to components of smart hospital, Hospitals managers should equip their organization and adopt process and equipment by disruptive technologies. Due to sanctions, investment in Iranian knowledge-based companies active in new technologies field and Joint venture with them can be a suitable solution for healthcare policymakers.
Javad Pourgholam Sarivi, Fatemeh Rahmaty, Maryam Yaghoubi,
Volume 22, Issue 2 (9-2023)
Abstract
Introduction : The Internet of Things (IoT) enables the connectivity of all devices in our daily lives, and it has had a positive impact on healthcare, specifically in disease diagnosis and prevention, especially during times of crisis. The objective of this research is to identify the factors that influence the use of IoT in combating COVID-19 in hospitals.
Materials and Methods: This qualitative study was conducted in 2023, using semi-structured face-to-face and telephone interviews with ten experts in health information technology and the Internet of Things. Data analysis was conducted using directed content analysis with MAXQDA software, version 2018.
Results: The data analysis resulted in 124 codes, which were then divided into 32 subcategories. These subcategories were further classified into six categories according to the model of the International Telecommunication Union. The categories include Network (five subcategories), Application (ten subcategories), Equipment (four subcategories), Support (three subcategories), Management (six subcategories), and Security (four subcategories). The Application category had the highest frequency, while the Support category had the lowest.
Conclusion: Infrastructure and legal aspects are among the most significant factors in the implementation of the Internet of Things in healthcare, particularly in the fight against COVID-19.
Mohammadreza Shahraki, Hamidreza Esmaeili,
Volume 22, Issue 4 (1-2024)
Abstract
Background and Purpose: Artificial intelligence (AI) plays a crucial role in the optimal management of hospital waste, particularly in predicting the volume and type of waste generated. This study aims to identify and rank the risks associated with the use of AI systems in hospital waste management by employing a multi-criteria decision-making approach.
Methods: This descriptive, cross-sectional study was conducted in 2023 (1402 in the Iranian calendar) at two hospitals, Ali Ibn Abitaleb and Khatam al-Anbiah, in Zahedan. Ten hospital staff members were selected as expert participants for the Delphi panel. The Shannon entropy method was utilized for risk weighting, and the TOPSIS method was applied to rank the identified risks.
Results: Kendall's coordination coefficient was used to assess the level of consensus among the Delphi panel members, with the coefficient values for the first, second, and third Delphi rounds being 6.3, 7.1, and 7.3, respectively. The indicators were weighted using the Shannon entropy method, based on three criteria: impact intensity (0.3), probability of occurrence (0.4), and detection probability (0.32). The TOPSIS method was then employed to rank the identified risks, with the most significant risks being the need for necessary infrastructure (0.847), the requirement for accurate and complete data (0.751), and budget constraints (0.749).
Conclusion: By applying multi-criteria decision-making methods, healthcare managers can effectively identify and prioritize the risks associated with using AI systems in hospital waste management, enabling them to focus on strengthening waste management practices based on these priorities.
