Contact Tracing in Healthcare Settings During the COVID-19 Pandemic Using Bluetooth Low Energy and Artificial Intelligence-A Viewpoint

The Emergence of AI in Public Health Is Calling for Operational Ethics to Foster Responsible Uses

This paper discusses the responsible use of artificial intelligence (AI) in public health and in medicine, and questions the development of AI ethics in international guidelines from a public health perspective. How can a global ethics approach help conceive responsible AI development and use for improving public health? By analysing key international guidelines in AI ethics (UNESCO, WHO, European High-Level Expert Group on AI) and the available literature, this paper advocates conceiving proper ethical and legal frameworks and implementation tools for AI in public health, based on a pragmatic risk-based approach. It highlights how ethical AI principles meet public health objectives and focuses on their value by addressing the meaning of human-centred innovations, transparency, accountability, diversity, equity, privacy protection, technical robustness, environmental protection, and post-marketing surveillance. It concludes that AI technology can reconcile individual and collective ethical approaches to public health, but requires specific legal frameworks and interdisciplinary efforts. Prospects include the development of supporting data infrastructures, of stakeholders' involvement to ensure long-term commitment and trust, of the public's and users' education, and of international organisations' capacity to coordinate and monitor AI developments. It formulates a proposal to reflect on an integrated transparent public health functionality in digital applications processing data.

Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow

BACKGROUND

In this study, we implemented an indoor positioning system to track the activities of healthcare professionals during their shifts in an emergency department, aiming to gain a better understanding of the emergency care production process.

METHODS

An ultrawideband-based tracking system was used in an experiment at the emergency department of Le Corbusier Hospital in Firminy, France. Over a 46-day period, healthcare professionals, including assistant nurses, nurses, doctors, and managers, wore a sensor to record their location within the emergency department. We analyzed a substantial amount of quasi-real-time data to objectively assess physicians' time allocation and movement patterns and their correlation with the emergency department's occupancy. Additionally, we developed a user recognition algorithm (i.e., random forest classifier) capable of detecting the job category of the participant wearing the sensor.

RESULTS

The proportion of time spent on care-related activities ranges from 26% to 39% for doctors. In contrast, this share reaches approximately half of the time for triage nurses and intensive care unit nurses. The burden of non-care-related activities appears to be largely induced by the time spent on administrative duties and transit. For doctors, the share of non-care-related activities is found to be correlated with the occupancy level. The hourly distance walked by nurses (except triage nurses) is found to increase with occupancy, while for doctors, the walking distance remains invariant to patient load. The random forest classifier predicts job categories with 96% accuracy.

CONCLUSIONS

Indoor tracking systems offer additional perspectives for enhancing the understanding of emergency department systems. The technology tested in this study demonstrates its potential to quantify physicians' time allocation and movements.

Digital Contact Tracing Implementation Among Leaders and Health Care Workers in a Pediatric Hospital During the COVID-19 Pandemic: Qualitative Interview Study

BACKGROUND

Health systems had to rapidly implement infection control strategies to sustain their workforces during the COVID-19 pandemic. Various outbreak response tools, such as digital contact tracing (DCT), have been developed to monitor exposures and symptoms of health care workers (HCWs). Limited research evidence exists on the experiences with these technologies and the impacts of DCT innovations from the perspective of stakeholders in health care environments.

OBJECTIVE

This study aims to identify the factors influencing the adoption of DCT, highlight variations in perspectives across 3 key stakeholder groups concerning the impact of DCT, and provide benchmarking evidence for future pandemic preparedness.

METHODS

Guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, we conducted an exploratory qualitative study to investigate the implementation and impact of DCT at the Children's Hospital of Eastern Ontario between December 2022 and April 2023. We conducted 21 semistructured interviews with key stakeholders, including health care administrators (6/21, 29%), occupational health and safety specialists (8/21, 38%), and HCWs (7/21, 33%). Stakeholders were asked about the factors influencing engagement with the DCT tool, organizational-level uptake, the implementation process, long-term use and sustainability of DCT, and unintended consequences. Verbatim transcripts were subject to thematic analysis using NVivo (QSR International).

RESULTS

The implementation of DCT was viable and well received. End users indicated that their engagement with the DCT tool was facilitated by its perceived ease of use and the ability to gain awareness of probable COVID-19 exposures; however, risk assessment consequences and access concerns were reported as barriers (reach). Participants commonly agreed that the DCT technology had a positive influence on the hospital's capacity to meet the demands of COVID-19 (effectiveness). Implementors and occupational specialists referred to negative staffing impacts and the loss of nuanced information as unintended consequences (effectiveness). Safety-focused communication strategies and having a DCT tool that was human-centered were crucial factors driving staff adoption of the technology. Conversely, adoption was challenged by the misaligned delivery of the DCT tool with HCWs' standard practices, alongside the evolving perceived threat of COVID-19. Stakeholders collectively agreed on the viability of DCT and its applicability to infectious disease practices (maintenance).

CONCLUSIONS

Hospital stakeholders were highly satisfied with DCT technology and it was perceived as feasible, efficient, and having a positive impact on organizational safety. Challenges related to the alignment and delivery of DCT, alongside the evolving perspectives on COVID-19, posed obstacles to continued adoption by HCWs. Our findings contribute to evidence-based practices and present benchmarks that can inform preparedness for future pandemics and infectious disease outbreaks and help other organizations implement similar technologies.

COVID-19 and beyond: leveraging artificial intelligence for enhanced outbreak control

COVID-19 has brought significant changes to our political, social, and technological landscape. This paper explores the emergence and global spread of the disease and focuses on the role of Artificial Intelligence (AI) in containing its transmission. To the best of our knowledge, there has been no scientific presentation of the early pictorial representation of the disease's spread. Additionally, we outline various domains where AI has made a significant impact during the pandemic. Our methodology involves searching relevant articles on COVID-19 and AI in leading databases such as PubMed and Scopus to identify the ways AI has addressed pandemic-related challenges and its potential for further assistance. While research suggests that AI has not fully realized its potential against COVID-19, likely due to data quality and diversity limitations, we review and identify key areas where AI has been crucial in preparing the fight against any sudden outbreak of the pandemic. We also propose ways to maximize the utilization of AI's capabilities in this regard.

Contact tracing using real-time location system (RTLS): a simulation exercise in a tertiary hospital in Singapore

OBJECTIVE

We aim to assess the effectiveness of contact tracing using real-time location system (RTLS) compared with the conventional (electronic medical records (EMRs)) method via an emerging infectious disease (EID) outbreak simulation exercise. The aims of the study are: (1) to compare the time taken to perform contact tracing and list of contacts identified for RTLS versus EMR; (2) to compare manpower and manpower-hours required to perform contact tracing for RTLS versus EMR; and (3) to extrapolate the cost incurred by RTLS versus EMR.

DESIGN

Prospective case study.

SETTING

Sengkang General Hospital, a 1000-bedded public tertiary hospital in Singapore.

PARTICIPANTS

1000 out of 4000 staff wore staff tags in this study.

INTERVENTIONS

A simulation exercise to determine and compare the list of contacts, time taken, manpower and manpower-hours required between RTLS and conventional methods of contact tracing. Cost of both methods were compared.

PRIMARY AND SECONDARY OUTCOME MEASURES

List of contacts, time taken, manpower required, manpower-hours required and cost incurred.

RESULTS

RTLS identified almost three times the number of contacts compared with conventional methods, while achieving that with a 96.2% reduction in time taken, 97.6% reduction in manpower required and 97.5% reduction in manpower-hours required. However, RTLS incurred significant equipment cost and might take many contact tracing episodes before providing economic benefit.

CONCLUSION

Although costly, RTLS is effective in contact tracing. RLTS might not be ready at present time to replace conventional methods, but with further refinement, RTLS has the potential to be the gold standard in contact tracing methods of the future, particularly in the current pandemic.