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Hamzehlou M. System dynamics model for an agile pharmaceutical supply chain during COVID‑19 pandemic in Iran. PLoS One 2024; 19:e0290789. [PMID: 38206960 PMCID: PMC10783738 DOI: 10.1371/journal.pone.0290789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 08/15/2023] [Indexed: 01/13/2024] Open
Abstract
Unpredictable changes in the current business environment have made organizations pay attention to the concept of agility. This concept is a key feature to survive and compete in turbulent markets while considering customers' fluctuating needs. An organization's agility is a function of its supply chain's agility. The present study offers a System Dynamics (SD) model for Iran's Pharmaceutical Supply Chain (PSC). The model is presented in three steps. First, the Supply Chain (SC) indicators were extracted based on theoretical foundations and literature review results. Second, an SD model of the PSC was extracted in the context of the COVID‑19 pandemic with the necessary analyses. Finally, the desired outputs and strategies were obtained by conducting a case study. The results indicated that the PSC's highest agility could be guaranteed by the simultaneous implementation of three strategies: investment, Human Capital Development (HCD), and accelerated completion of ongoing projects on a priority basis. According to these results, the organization had better determine the amount of capital and workforce required for ongoing projects, then design funding solutions to implement these projects and implement them according to the projects' priority.
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Huang L, Lv W, Huang Q, Zhang H, Jin S, Chen T, Shen B. Transforming medical equipment management in digital public health: a decision-making model for medical equipment replacement. Front Med (Lausanne) 2024; 10:1239795. [PMID: 38239616 PMCID: PMC10795183 DOI: 10.3389/fmed.2023.1239795] [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: 06/17/2023] [Accepted: 11/29/2023] [Indexed: 01/22/2024] Open
Abstract
Introduction In the rapidly evolving field of digital public health, effective management of medical equipment is critical to maintaining high standards of healthcare service levels and operational efficiency. However, current decisions to replace large medical equipment are often based on subjective judgments rather than objective analyses and lack a standardized approach. This study proposes a multi-criteria decision-making model that aims to simplify and enhance the medical equipment replacement process. Methods The researchers developed a multi-criteria decision-making model specifically for the replacement of medical equipment. The model establishes a system of indicators for prioritizing and evaluating the replacement of large medical equipment, utilizing game theory to assign appropriate weights, which uniquely combines the weights of the COWA and PCA method. In addition, which uses the GRA method in combination with the TOPSIS method for a more comprehensive decision-making model. Results The study validates the model by using the MRI equipment of a tertiary hospital as an example. The results of the study show that the model is effective in prioritizing the most optimal updates to the equipment. Significantly, the model shown a higher level of differentiation compared to the GRA and TOPSIS methods alone. Discussion The present study shows that the multi-criteria decision-making model presented provides a powerful and accurate tool for optimizing decisions related to the replacement of large medical equipment. By solving the key challenges in this area as well as giving a solid basis for decision making, the model makes significant progress toward the field of management of medical equipment.
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Affiliation(s)
- Luying Huang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Wenqian Lv
- Shanghai General Hospital, Shanghai, China
| | - Qingming Huang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
- School of Medical Imaging, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Haikang Zhang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Siyuan Jin
- Shanghai General Hospital, Shanghai, China
| | - Tong Chen
- Shanghai General Hospital, Shanghai, China
| | - Bing Shen
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
- Shanghai Tenth People’s Hospital of Tongji University, Shanghai, China
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Rony MKK, Parvin MR, Wahiduzzaman M, Debnath M, Bala SD, Kayesh I. "I Wonder if my Years of Training and Expertise Will be Devalued by Machines": Concerns About the Replacement of Medical Professionals by Artificial Intelligence. SAGE Open Nurs 2024; 10:23779608241245220. [PMID: 38596508 PMCID: PMC11003342 DOI: 10.1177/23779608241245220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 03/08/2024] [Accepted: 03/15/2024] [Indexed: 04/11/2024] Open
Abstract
Background The rapid integration of artificial intelligence (AI) into healthcare has raised concerns among healthcare professionals about the potential displacement of human medical professionals by AI technologies. However, the apprehensions and perspectives of healthcare workers regarding the potential substitution of them with AI are unknown. Objective This qualitative research aimed to investigate healthcare workers' concerns about artificial intelligence replacing medical professionals. Methods A descriptive and exploratory research design was employed, drawing upon the Technology Acceptance Model (TAM), Technology Threat Avoidance Theory, and Sociotechnical Systems Theory as theoretical frameworks. Participants were purposively sampled from various healthcare settings, representing a diverse range of roles and backgrounds. Data were collected through individual interviews and focus group discussions, followed by thematic analysis. Results The analysis revealed seven key themes reflecting healthcare workers' concerns, including job security and economic concerns; trust and acceptance of AI; ethical and moral dilemmas; quality of patient care; workforce role redefinition and training; patient-provider relationships; healthcare policy and regulation. Conclusions This research underscores the multifaceted concerns of healthcare workers regarding the increasing role of AI in healthcare. Addressing job security, fostering trust, addressing ethical dilemmas, and redefining workforce roles are crucial factors to consider in the successful integration of AI into healthcare. Healthcare policy and regulation must be developed to guide this transformation while maintaining the quality of patient care and preserving patient-provider relationships. The study findings offer insights for policymakers and healthcare institutions to navigate the evolving landscape of AI in healthcare while addressing the concerns of healthcare professionals.
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Affiliation(s)
- Moustaq Karim Khan Rony
- Master of Public Health, Bangladesh Open University, Gazipur, Bangladesh
- Institute of Social Welfare and Research, University of Dhaka, Dhaka, Bangladesh
| | - Mst. Rina Parvin
- Armed Forces Nursing Service, Major at Bangladesh Army (AFNS Officer), Combined Military Hospital, Dhaka, Bangladesh
| | - Md. Wahiduzzaman
- School of Medical Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Mitun Debnath
- Master of Public Health, National Institute of Preventive and Social Medicine, Dhaka, Bangladesh
| | - Shuvashish Das Bala
- College of Nursing, International University of Business Agriculture and Technology, Dhaka, Bangladesh
| | - Ibne Kayesh
- Institute of Social Welfare and Research, University of Dhaka, Dhaka, Bangladesh
- Faculty of Graduate Studies, University of Kelaniya, Colombo, Sri Lanka
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Long P, Lu L, Chen Q, Chen Y, Li C, Luo X. Intelligent selection of healthcare supply chain mode - an applied research based on artificial intelligence. Front Public Health 2023; 11:1310016. [PMID: 38164449 PMCID: PMC10758214 DOI: 10.3389/fpubh.2023.1310016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 11/23/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction Due to the inefficiency and high cost of the current healthcare supply chain mode, in order to adapt to the great changes in the global economy and public health, it is urgent to choose an effective mode for sustainable development of healthcare supply chain. The aim of this paper is to use artificial intelligence systems to make intelligent decisions for healthcare supply chain mode selection. Methods Firstly, according to the economic benefits, social benefits and environmental benefits of healthcare supply chain, this paper identifies different healthcare supply chain modes in combination with artificial intelligence technology. Secondly, this paper presents the intelligent choice optimization method of healthcare supply chain mode based on deep reinforcement learning algorithm. Finally, the effect of artificial intelligence in healthcare supply chain mode selection is verified by simulation experiment. Results and Discussion The experimental results show that healthcare supply chain mode selected by artificial intelligence is basically consistent with the target mode, while healthcare supply chain mode selected by the basic selection method, BP neural network method and big data method is different from the target mode, which indicates that AI has more advantages in the selection of medical supply chain mode. Therefore, we recommend the application of artificial intelligence to healthcare supply chain management. This study not only makes up for the ineffective problems of existing methods, but also makes up for the gaps in the application of AI technology in the field of healthcare supply chain. The scientific value of this paper is that the proposed framework and the artificial intelligence algorithm enrich the relevant theories of healthcare supply chain research and provide methodological guidance for intelligent decision-making of healthcare supply chain. At the same time, for medical enterprises, this research provides a new practical guideline for the application of artificial intelligence in the sustainable development and modern management of healthcare supply chain.
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Affiliation(s)
- Ping Long
- School of Economics and Management, Guangxi Normal University, Guilin, China
| | - Lin Lu
- School of Economics and Management, Guangxi Normal University, Guilin, China
| | - Qianlan Chen
- School of Economics and Management, Guangxi Normal University, Guilin, China
| | - Yifan Chen
- Adam Smith Business School, University of Glasgow, Scotland, United Kingdom
| | - Chaoling Li
- School of Economics and Management, Guangxi Normal University, Guilin, China
| | - Xiaochun Luo
- School of Economics and Management, Guangxi Normal University, Guilin, China
- School of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China
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Gupta P, Choudhury R, Kotwal A. Achieving health equity through healthcare technology: Perspective from India. J Family Med Prim Care 2023; 12:1814-1817. [PMID: 38024887 PMCID: PMC10657065 DOI: 10.4103/jfmpc.jfmpc_321_23] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 12/01/2023] Open
Abstract
India aims to provide universal health coverage to all individuals and communities thus ensuring accessibility, promotive, curative, preventive, rehabilitative, and palliative health services to all. Healthcare technologies play a critical role in ensuring eliminating healthcare disparities and encouraging quality healthcare at all levels. Technology solutions such as indigenous medical devices and diagnostic products, telemedicine, artificial intelligence, and drone technology can best integrate rural needs, improve health outcomes, patient safety, and healthcare quality and experience for patients' values and strengths and can therefore be important contributors to advancing rural health equity. These technologies can transform India's healthcare system by providing quality care and mitigating the risk of catastrophic financial hardship.
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Affiliation(s)
- Prakamya Gupta
- Division of Healthcare Technologies, National Health Systems Resource Center, Munirka, New Delhi, India
| | - Ranjan Choudhury
- Division of Healthcare Technologies, National Health Systems Resource Center, Munirka, New Delhi, India
| | - Atul Kotwal
- Executive Director, National Health Systems Resource Center, Munirka, New Delhi, India
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Kleine AK, Kokje E, Lermer E, Gaube S. Attitudes Toward the Adoption of 2 Artificial Intelligence-Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study. JMIR Hum Factors 2023; 10:e46859. [PMID: 37436801 PMCID: PMC10372564 DOI: 10.2196/46859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/08/2023] [Accepted: 05/14/2023] [Indexed: 07/13/2023] Open
Abstract
BACKGROUND Despite growing efforts to develop user-friendly artificial intelligence (AI) applications for clinical care, their adoption remains limited because of the barriers at individual, organizational, and system levels. There is limited research on the intention to use AI systems in mental health care. OBJECTIVE This study aimed to address this gap by examining the predictors of psychology students' and early practitioners' intention to use 2 specific AI-enabled mental health tools based on the Unified Theory of Acceptance and Use of Technology. METHODS This cross-sectional study included 206 psychology students and psychotherapists in training to examine the predictors of their intention to use 2 AI-enabled mental health care tools. The first tool provides feedback to the psychotherapist on their adherence to motivational interviewing techniques. The second tool uses patient voice samples to derive mood scores that the therapists may use for treatment decisions. Participants were presented with graphic depictions of the tools' functioning mechanisms before measuring the variables of the extended Unified Theory of Acceptance and Use of Technology. In total, 2 structural equation models (1 for each tool) were specified, which included direct and mediated paths for predicting tool use intentions. RESULTS Perceived usefulness and social influence had a positive effect on the intention to use the feedback tool (P<.001) and the treatment recommendation tool (perceived usefulness, P=.01 and social influence, P<.001). However, trust was unrelated to use intentions for both the tools. Moreover, perceived ease of use was unrelated (feedback tool) and even negatively related (treatment recommendation tool) to use intentions when considering all predictors (P=.004). In addition, a positive relationship between cognitive technology readiness (P=.02) and the intention to use the feedback tool and a negative relationship between AI anxiety and the intention to use the feedback tool (P=.001) and the treatment recommendation tool (P<.001) were observed. CONCLUSIONS The results shed light on the general and tool-dependent drivers of AI technology adoption in mental health care. Future research may explore the technological and user group characteristics that influence the adoption of AI-enabled tools in mental health care.
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Affiliation(s)
- Anne-Kathrin Kleine
- Department of Psychology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Eesha Kokje
- Department of Psychology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Eva Lermer
- Department of Psychology, Ludwig Maximilian University of Munich, Munich, Germany
- Technical University of Applied Sciences Augsburg, Augsburg, Germany
| | - Susanne Gaube
- Department of Psychology, Ludwig Maximilian University of Munich, Munich, Germany
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