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Chalmeta R, Navarro-Ruiz A, Soriano-Irigaray L. A computer architecture based on disruptive information technologies for drug management in hospitals. PeerJ Comput Sci 2023; 9:e1455. [PMID: 37409078 PMCID: PMC10319265 DOI: 10.7717/peerj-cs.1455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 06/06/2023] [Indexed: 07/07/2023]
Abstract
The drug management currently carried out in hospitals is inadequate due to several factors, such as processes carried out manually, the lack of visibility of the hospital supply chain, the lack of standardized identification of medicines, inefficient stock management, an inability to follow the traceability of medicines, and poor data exploitation. Disruptive information technologies could be used to develop and implement a drug management system in hospitals that is innovative in all its phases and allows these problems to be overcome. However, there are no examples in the literature that show how these technologies can be used and combined for efficient drug management in hospitals. To help solve this research gap in the literature, this article proposes a computer architecture for the whole drug management process in hospitals that uses and combines different disruptive computer technologies such as blockchain, radio frequency identification (RFID), quick response code (QR), Internet of Things (IoT), artificial intelligence and big data, for data capture, data storage and data exploitation throughout the whole drug management process, from the moment the drug enters the hospital until it is dispensed and eliminated.
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Affiliation(s)
- Ricardo Chalmeta
- Grupo de Integración y Re-Ingeniería de sistemas, Departamento de Lenguajes y sistemas Informáticos, Universitat Jaume I de Castellón, Castellón, Spain
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Le NT, Thwe Chit MM, Truong TL, Siritantikorn A, Kongruttanachok N, Asdornwised W, Chaitusaney S, Benjapolakul W. Deployment of Smart Specimen Transport System Using RFID and NB-IoT Technologies for Hospital Laboratory. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23010546. [PMID: 36617144 PMCID: PMC9823357 DOI: 10.3390/s23010546] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/25/2022] [Accepted: 12/28/2022] [Indexed: 06/12/2023]
Abstract
In this study, we propose a specimen tube prototype and smart specimen transport box using radio frequency identification (RFID) and narrow band-Internet of Things (NB-IoT) technology to use in the Department of Laboratory Medicine, King Chulalongkorn Memorial Hospital. Our proposed method replaces the existing system, based on barcode technology, with shortage usage and low reliability. In addition, tube-tagged barcode has not eliminated the lost or incorrect delivery issues in many laboratories. In this solution, the passive RFID tag is attached to the surface of the specimen tube and stores information such as patient records, required tests, and receiver laboratory location. This information can be written and read multiple times using an RFID device. While delivering the specimen tubes via our proposed smart specimen transport box from one clinical laboratory to another, the NB-IoT attached to the box monitors the temperature and humidity values inside the box and tracks the box's GPS location to check whether the box arrives at the destination. The environmental condition inside the specimen transport box is sent to the cloud and can be monitored by doctors. The experimental results have proven the innovation of our solution and opened a new dimension for integrating RFID and IoT technologies into the specimen logistic system in the hospital.
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Affiliation(s)
- Ngoc Thien Le
- Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
| | - Mya Myet Thwe Chit
- Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
| | - Thanh Le Truong
- Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
| | - Atchasai Siritantikorn
- Department of Laboratory Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
| | - Narisorn Kongruttanachok
- Department of Laboratory Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
| | - Widhyakorn Asdornwised
- Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
| | - Surachai Chaitusaney
- Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
| | - Watit Benjapolakul
- Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
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Mohd Mazlan MF, Salleh SZ, Ab Karim MS, Mohd Amran NA, Abd Rashid R, Abd Razak NA, Kadri NA, Zahari Z. Development and Performance Evaluation of Automated Methadone Dispenser for Drug Addiction Therapy. JOURNAL OF TESTING AND EVALUATION 2022; 50:20210709. [DOI: 10.1520/jte20210709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
- Muhamad Farhan Mohd Mazlan
- Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia, https://orcid.org/0000-0002-4740-6070 (M.F.M.M.), https://orcid.org/0000-0001-7216-263X (S.Z.S.)
| | - Siti Zuliana Salleh
- Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia, https://orcid.org/0000-0002-4740-6070 (M.F.M.M.), https://orcid.org/0000-0001-7216-263X (S.Z.S.)
| | - Mohd Sayuti Ab Karim
- Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia (Corresponding author), e-mail: , https://orcid.org/0000-0003-2379-5080
| | - Nor Amirah Mohd Amran
- Advanced Manufacturing and Materials Processing (AMMP) Research Centre, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia, https://orcid.org/0000-0001-5105-7937
| | - Rusdi Abd Rashid
- Department of Psychological Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia, https://orcid.org/0000-0002-1295-7382
| | - Nasrul Anuar Abd Razak
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia, https://orcid.org/0000-0002-1911-015X (N.A.A.R.), https://orcid.org/0000-0001-9694-4337 (N.A.K.)
| | - Nahrizul Adib Kadri
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia, https://orcid.org/0000-0002-1911-015X (N.A.A.R.), https://orcid.org/0000-0001-9694-4337 (N.A.K.)
| | - Zalina Zahari
- Faculty of Pharmacy, University Sultan Zainal Abidin, Kampus Besut, Besut Terengganu Darul Iman 22200, Malaysia, https://orcid.org/0000-0003-1459-8958
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