Algorithms and the future of work

New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges

Despite a standardized diagnostic examination, cancer of unknown primary (CUP) is a rare metastatic malignancy with an unidentified tissue of origin (TOO). Patients diagnosed with CUP are typically treated with empiric chemotherapy, although their prognosis is worse than those with metastatic cancer of a known origin. TOO identification of CUP has been employed in precision medicine, and subsequent site-specific therapy is clinically helpful. For example, molecular profiling, including genomic profiling, gene expression profiling, epigenetics and proteins, has facilitated TOO identification. Moreover, machine learning has improved identification accuracy, and non-invasive methods, such as liquid biopsy and image omics, are gaining momentum. However, the heterogeneity in prediction accuracy, sample requirements and technical fundamentals among the various techniques is noteworthy. Accordingly, we systematically reviewed the development and limitations of novel TOO identification methods, compared their pros and cons and assessed their potential clinical usefulness. Our study may help patients shift from empirical to customized care and improve their prognoses.

A system science perspective on burn-out: development of an expert-based causal loop diagram

Introduction

Burn-out leads to reduced worker well-being, long-term absenteeism, and high costs for employers and society. Determinants at different levels may affect burn-out in an interrelated and dynamic manner. The aim of the present study was to apply a broader systems perspective by exploring and visualizing the complex system of determinants at different levels (living conditions, working conditions, and societal developments) underlying the prevalence of burn-out in the Netherlands.

Methods

During three group model building (GMB) sessions with in total eight experts on workers' mental health, a causal loop diagram (CLD) was developed and relevant feedback loops were identified. For the selection of determinants to be included in the CLD a recently published overview of determinants on burn-out at different levels was used. Experts could also add factors that were not listed in the overview.

Results

The final CLD consists of 20 factors and depicts a central position of working conditions. Societal developments (e.g., access to mental health care, size of the working population, rougher social climate, etc.) were mostly located at the outside of the CLD and barely integrated in feedback loops. Several reinforcing feedback loops resulting in an increase of the prevalence of burn-out were identified in which the factors (very) high workload, imbalance between work and private life, and insufficient recovery time play an important role. Also, several balancing loops were found that visualize the crucial role of functional support from supervisors to prevent burn-out among workers.

Discussion

Applying a broader systems perspective, including determinants at different levels, offers new insights into dynamic feedback loops that contribute to the prevalence of burn-out. Supervisors, amongst others, have a considerable impact on the system underlying the high prevalence of burn-out and may therefore contribute to its prevention. Even though societal developments were less integrated in feedback loops, they might be considered drivers of existing feedback loops. The results from this study confirm that determinants at various levels underly the prevalence of burn-out. To be able to address the diversity of determinants underlying a high prevalence of burn-out, a complex system approach can be helpful.

Artificial intelligence and the work-health interface: A research agenda for a technologically transforming world of work

The labor market is undergoing a rapid artificial intelligence (AI) revolution. There is currently limited empirical scholarship that focuses on how AI adoption affects employment opportunities and work environments in ways that shape worker health, safety, well-being and equity. In this article, we present an agenda to guide research examining the implications of AI on the intersection between work and health. To build the agenda, a full day meeting was organized and attended by 50 participants including researchers from diverse disciplines and applied stakeholders. Facilitated meeting discussions aimed to set research priorities related to workplace AI applications and its impact on the health of workers, including critical research questions, methodological approaches, data needs, and resource requirements. Discussions also aimed to identify groups of workers and working contexts that may benefit from AI adoption as well as those that may be disadvantaged by AI. Discussions were synthesized into four research agenda areas: (1) examining the impact of stronger AI on human workers; (2) advancing responsible and healthy AI; (3) informing AI policy for worker health, safety, well-being, and equitable employment; and (4) understanding and addressing worker and employer knowledge needs regarding AI applications. The agenda provides a roadmap for researchers to build a critical evidence base on the impact of AI on workers and workplaces, and will ensure that worker health, safety, well-being, and equity are at the forefront of workplace AI system design and adoption.

Workers' Health under Algorithmic Management: Emerging Findings and Urgent Research Questions

Algorithms are increasingly used instead of humans to perform core management functions, yet public health research on the implications of this phenomenon for worker health and well-being has not kept pace with these changing work arrangements. Algorithmic management has the potential to influence several dimensions of job quality with known links to worker health, including workload, income security, task significance, schedule stability, socioemotional rewards, interpersonal relations, decision authority, and organizational trust. To describe the ways algorithmic management may influence workers' health, this review summarizes available literature from public health, sociology, management science, and human-computer interaction studies, highlighting the dimensions of job quality associated with work stress and occupational safety. We focus on the example of work for platform-based food and grocery delivery companies; these businesses are growing rapidly worldwide and their effects on workers and policies to address those effects have received significant attention. We conclude with a discussion of research challenges and needs, with the goal of understanding and addressing the effects of this increasingly used technology on worker health and health equity.