Hospital waste management is crucial for healthcare operations, ensuring safe disposal and handling of waste types. The integration of IoT and deep learning technologies offers a promising solution to address waste volumes and environmental concerns. This chapter presents an advanced research overview on hospital waste management using IoT and DL, exploring its potential benefits and applications. IoT-DL integration enables real-time monitoring of waste fill levels, temperature, and other parameters, while advanced DL techniques improve waste collection efficiency, segregation accuracy, and data-driven decision-making for optimized waste management practices. The chapter discusses challenges, benefits, challenges, and considerations in IoT implementation, waste sorting techniques, and ethical and environmental aspects, including sustainability and circular economy principles.
TopIntroduction
Hospital waste management is a crucial aspect of modern healthcare operations that demands effective and sustainable solutions. The healthcare industry generates a significant amount of diverse waste, including hazardous materials, infectious substances, and pharmaceutical residues, posing environmental and public health risks if not managed appropriately. Conventional waste management practices often fall short in efficiently handling this complex waste stream, leading to potential pollution and health hazards (Harikaran, Boopathi, Gokulakannan, et al., 2023).
In recent years, there has been a growing interest in leveraging cutting-edge technologies to address the challenges of hospital waste management. Among these technologies, the integration of the Internet of Things (IoT) and Deep Learning (DL) has shown great promise in revolutionizing waste management practices in healthcare facilities. Conventional methods are often labour-intensive, error-prone, and lack real-time monitoring capabilities, leading to suboptimal waste disposal and increased risks of contamination. Additionally, inadequate waste segregation and classification can result in improper treatment, further exacerbating the environmental impact (Janardhana et al., 2023).
The advent of IoT has opened up new avenues for waste management by enabling continuous monitoring and tracking of waste generation, storage, and transportation processes. Simultaneously, DL techniques, particularly in image recognition and segmentation, offer powerful tools to automate waste sorting and classification, ensuring proper disposal and recycling of different waste categories (Boopathi, 2023a; Harikaran, Boopathi, Gokulakannan, et al., 2023).
Hospital waste management is a critical aspect of modern healthcare facilities that demands effective strategies to ensure the proper handling, disposal, and recycling of various types of waste generated within hospitals and other healthcare settings. Waste Segregation and Classification: One of the primary research aspects in hospital waste management is the development of efficient waste segregation and classification methods. This involves exploring technologies like deep learning and computer vision to automatically identify and sort different types of waste, facilitating proper disposal and recycling (Harikaran, Boopathi, Gokulakannan, et al., 2023; Koshariya, Khatoon, et al., 2023).
IoT-based Waste Monitoring and Tracking: Internet of Things (IoT) technologies offer opportunities for real-time waste monitoring and tracking throughout the waste management process. Research in this area focuses on the deployment of IoT sensors and devices to monitor waste generation, storage, transportation, and disposal, enabling data-driven decision-making and optimization (Janardhana et al., 2023; Selvakumar, Adithe, et al., 2023). Environmental and Health Impact Assessment: Understanding the environmental and health impacts of hospital waste is crucial for developing effective waste management strategies.
Investigating and promoting sustainable waste management practices in healthcare facilities is another key research aspect. This includes exploring the feasibility of waste-to-energy technologies, recycling methods, and waste reduction strategies to minimize the environmental footprint of hospital waste. Policy and Hospital waste management is subject to a range of local, national, and international regulations. Research in this aspect involves analyzing the existing policies and regulations related to hospital waste and proposing improvements or best practices to ensure compliance and better waste management outcomes (Boopathi, 2023f; Maguluri et al., 2023a; Subha et al., 2023).
Integrating advanced technologies like IoT and deep learning into hospital waste management systems presents various implementation challenges. Research in this aspect focuses on addressing issues related to data privacy, security, scalability, and cost-effectiveness to ensure successful technology adoption (Reddy, Reddy, et al., 2023a; Syamala et al., 2023b). Public Awareness and Engagement: Public awareness and engagement play a crucial role in promoting responsible waste disposal practices and supporting hospital waste management initiatives.