The use of smart environments and robots for infection prevention control: A systematic literature review

Introduction

Infection prevention and control (IPC) is essential to prevent nosocomial infections. This manuscript aims at investigating the current use and role of robots and smart environments on IPC systems in nosocomial settings In late 2019, unusual pneumonia cases in Wuhan, China were identified as a new strain of coronavirus, later named SARS-CoV-2, leading to the global COVID-19 pandemic. As of October 25, 2022, there have been 625 million confirmed cases and 6.5 million deaths reported by the WHO. The distribution of cases varies across regions, with Europe being the most affected. The pandemic has revealed the need for better preparedness for future outbreaks, given factors like the climate crisis and population growth increasing the risk of new pandemics.

This paper focuses on the development and testing of new approaches, particularly in robotics and automation, for infection prevention and control (IPC) in hospitals. Previous studies have highlighted the weaknesses of IPC guidelines, emphasizing the importance of innovative strategies. Technological innovation, driven by Health 4.0 principles, offers promising solutions for future epidemic/ pandemic responses. The article aims to provide a systematic review of robots and automation in healthcare settings, focusing on performance measures, healthcare worker compliance, and cost effectiveness compared to standard practices. The findings will inform preparedness plans for future global health emergencies.

Methods

The systematic literature review was performed following the PRISMA statement. Literature was searched for articles published in the period January 2016 to October 2022. Two authors determined the eligibility of the papers, with conflicting decisions being mitigated by a third. Relevant data was then extracted using an ad-hoc extraction table to facilitate the analysis and narrative synthesis.

Results

The search strategy returned 1520 citations and 17 papers were included. This review identified 3 main areas of interest: hand hygiene and personal protective equipment compliance, automatic infection cluster detection and environments cleaning (ie, air quality control, sterilization). This review demonstrates that IPC practices within hospitals mostly do not rely on automation and robotic technology, and few advancements have been made in this field.

Hand hygiene compliance

Several studies have examined the use of automated technologies for monitoring and promoting hand hygiene compliance among healthcare workers (HCWs). Wearable sensors were commonly used in eight of these studies, with some incorporating signaling devices to remind HCWs to sanitize their hands. One study by McCalla et al. developed an automated hand hygiene compliance system (HHCS) based on sound and light signals, which resulted in a higher number of hand hygiene opportunities recorded but a lower number of actual hand hygiene events. Another study by Xu et al. used an Internet of Things (IoT) device for monitoring hand hygiene compliance and observed a drastic increase in compliance rates but no significant difference in infection rates. The use of radio-frequency identification (RFID) technologies was also explored in three studies, showing promising results in improving overall compliance with hand hygiene protocols. Overall, these studies highlight the potential of automated technologies in enhancing hand hygiene compliance among HCWs and reducing healthcare-associated infections. However, factors such as cost, accuracy concerns, and user acceptance need to be considered for successful implementation in healthcare settings.

Cleaning and disinfection of hospital environment

In the context of infection prevention and control (IPC) processes, the cleaning and disinfection of hospital environments is a crucial aspect. Wang et al. implemented an artificial intelligence (AI) algorithm-based Plan-Do-Check-Act (PDCA) cycle for managing sterilization in supply rooms. Their approach utilized Long Short-Term Memory (LSTM) neural networks with various gating mechanisms to store and update information. The study observed higher satisfaction rates and compliance with standardized practices in the group using the PDCA cycle compared to those following conventional management methods. Furthermore, Khan et al. explored the use of cleaning robots in hospital settings, specifically during the COVID-19 pandemic. Various types of cleaning robotic technologies were employed, such as dry vacuum and mopping systems, UV radiation-based devices, intelligent navigating vacuum pumps, highly dynamic robotic grippers, and autonomous heavy-duty cleaning robots. The implementation of these robots significantly enhanced the safety and quality of healthcare management. They played a vital role in controlling the spread of COVID-19, resulting in a reduction in the number of infected patients and fatalities.

Infection cluster detection

Two studies focused on the use of automated systems for detecting infection clusters within hospital settings. Aghdassi et al. developed an automated cluster alert system (CLAR) that utilized various parameters such as the number of isolates, pathogen type, resistance patterns, sampling material, and specific hospital wards. CLAR successfully identified a substantial number of alerts, which were validated by infection prevention and control (IPC) physicians. Similarly, Stachel et al. implemented an automated surveillance system called WHONET-SaTScan to detect hospital outbreaks. This system combined statistical software (WHONET) with a software managing microbiology laboratory data (SaTScan). The integration of these tools proved to be a valuable addition to their regular IPC program, assisting in the timely identification of potential outbreaks.

Air quality control

Only 1 article dealt with air quality control, focusing their effort in the operating rooms (ORs).30 Colella et al developed a fuzzy inference system (FIS), which assesses the OR air quality and provides real-time alarms, making HCWs aware of potential risk. The risk level is decided by FIS considering 4 parameters, namely particle count, temperature, relative humidity patients and HCWs movements. Typically, FISs are an important part of fuzzy logic systems that perform decision-making and are mainly based on the Mamdani or Sugeno frameworks.

Correct use of PPE

Another important aspect of IPC is the correct use of PPE, Preda et al31 realized an AI-PPE system, with the goal of analyzing donning and doffing with real-time feedback. They validated this technology comparing it to the gold standard, that is, double buddy system. Furthermore, they included in the study participants with heterogeneous visual characteristic (ie, people with different ethnical backgrounds, age, sex, etc.) in order to lower the risk of AI bias.

Conclusions

This systematic literature review indicates that infection prevention and control (IPC) practices in hospitals primarily focus on hand hygiene (HH) and ultraviolet (UV) devices for disinfection. There is limited research on the use of Internet of Things (IoT), artificial intelligence (AI), big data technology, and robots in IPC within healthcare settings. Despite the recent COVID-19 pandemic, the existing literature on automation and robots in IPC is outdated or lacks significant impact. However, the review identified five main areas that were discussed. One notable finding is the insufficient consideration of healthcare workers (HCWs) in terms of their awareness and training regarding the design and use of healthcare technologies that affect their work and daily lives.

The active involvement of HCWs in technology co-design and training is crucial, considering the transformative impact of Health 4.0 on hospitals and HCWs as digitalization advances. The ODIN project is mentioned as a pioneering European initiative in this field, primarily focused on high-income countries. Nevertheless, it is important to prioritize research that explores implementation options tailored to lower-income countries, as they also need contextualized solutions and innovative approaches to address future pandemics.

Source:  Piaggio, Davide, et al. “The use of Smart Environments and Robots for Infection Prevention Control: a systematic literature review.” American Journal of Infection Control (2023).