Health Care Human Factors/Ergonomics Fieldwork in Home and Community Settings

Designing innovations aligned with patients’ needs and workflows requires human factors/ergonomics (HF/E) fieldwork in home and community settings. Fieldwork in these extra-institutional settings is challenged by a need to balance the occasionally competing priorities of patient and informal caregiver participants, study team members, and the overall project. We offer several strategies that HF/E professionals can use before, during, and after home and community site visits to optimize fieldwork and mitigate challenges in these settings. Strategies include interacting respectfully with participants, documenting the visit, managing the study team–participant relationship, and engaging in dialogue with institutional review boards

Beyond Disease: Technologies for Health Promotion

Health promotion is defined by the World Health Organization as “the process of enabling people to increase control over their health and its determinants, and thereby improve their health.” This is different from the bulk of formal healthcare processes, which are characterized by the treatment of an established disease. Much important human factors research and practice has been done to improve the healthcare delivery process and increasingly human factors professionals are also involved in work on health promotion. Such work has included examining the use and usability of wearable fitness tracking devices, studies of online health information seeking by healthy individuals, and human factors research on social robots for older adults, to name but a few examples. We discuss human factors applications in health promotion, focusing on examples from technology-related research

Macroergonomic factors in the patient work system: examining the context of patients with chronic illness

Human factors/ergonomics recognises work as embedded in and shaped by levels of social, physical and organisational context. This study investigates the contextual or macroergonomic factors present in the health-related work performed by patients. We performed a secondary content analysis of findings from three studies of the work of chronically ill patients and their informal caregivers. Our resulting consolidated macroergonomic patient work system model identified 17 factors across physical, social and organisational domains and household and community levels. These factors are illustrated with examples from the three studies and discussed as having positive, negative or varying effects on health and health behaviour. We present three brief case studies to illustrate how macroergonomic factors combine across domains and levels to shape performance in expected and unexpected ways. Findings demonstrate not only the importance of context for patients\u27 health-related activities but also specific factors to consider in future research, design and policy efforts. Practitioner Summary: Health-related activities of patients are embedded in and shaped by levels of social, physical and organisational context. This paper combined findings from three studies to specify 17 contextual or macroergonomic factors in home-and community-based work systems of chronically ill patients. These factors have research, design and policy implications

Mechanisms of Communicating Health Information Through Facebook: Implications for Consumer Health Information Technology Design

Background: Consumer health information technology (IT) solutions are designed to support patient health management and have the ability to facilitate patients\u27 health information communication with their social networks. However, there is a need for consumer health IT solutions to align with patients\u27 health management preferences for increased adoption of the technology. It may be possible to gain an understanding of patients\u27 needs for consumer health IT supporting their health information communication with social networks by explicating how they have adopted and adapted social networking sites, such as Facebook, for this purpose. Objective: Our aim was to characterize patients\u27 use of all communication mechanisms within Facebook for health information communication to provide insight into how consumer health IT solutions may be better designed to meet patients\u27 communication needs and preferences. Methods: This study analyzed data about Facebook communication mechanisms use from a larger, three-phase, sequential, mixed-methods study. We report here on the results of the study\u27s first phase: qualitative interviews (N=25). Participants were over 18, used Facebook, were residents or citizens of the United States, spoke English, and had a diagnosis consistent with type 2 diabetes. Participants were recruited through Facebook groups and pages. Participant interviews were conducted via Skype or telephone between July and September 2014. Data analysis was grounded in qualitative content analysis and the initial coding framework was informed by the findings of a previous study. Results: Participants\u27 rationales for the use or disuse of a particular Facebook mechanism to communicate health information reflected six broad themes: (1) characteristics and circumstances of the person, (2) characteristics and circumstances of the relationship, (3) structure and composition of the social network, (4) content of the information, (5) communication purpose, and (6) attributes of the technology. Conclusions: The results of this study showed that participants consider multiple factors when choosing a Facebook mechanism for health information communication. Factors included what information they intended to share, what they were trying to accomplish, attributes of technology, and attributes and communication practices of their social networks. There is a need for consumer health IT that allows for a range of choices to suit the intersectionality of participants\u27 rationales. Technology that better meets patients\u27 needs may lead to better self-management of health conditions, and therefore, improve overall health outcomes

The Patient in Patient Safety: Clinicians’ Experiences Engaging Patients as Partners in Safety

Patients and families play a role in the safety of care provided across clinical settings, highlighting the need to understand clinician perspectives and experiences related to their engagement. Through a panel discussion entitled, The Patient in Patient Safety: Clinicians’ Experiences Engaging Patients as Partners in Safety, we elicited clinician perspectives that have implications for human factors relevance in both, research and solution development/evaluation. We provide an overview of the panel including participants, format and content, and the resulting discussion

Risk Analysis of Artificial Intelligence in Medicine with a Multilayer Concept of System Order

Artificial intelligence (AI) is advancing across technology domains including healthcare, commerce, the economy, the environment, cybersecurity, transportation, etc. AI will transform healthcare systems, bringing profound changes to diagnosis, treatment, patient care, data, medicines, devices, etc. However, AI in healthcare introduces entirely new categories of risk for assessment, management, and communication. For this topic, the framing of conventional risk and decision analyses is ongoing. This paper introduces a method to quantify risk as the disruption of the order of AI initiatives in healthcare systems, aiming to find the scenarios that are most and least disruptive to system order. This novel approach addresses scenarios that bring about a re-ordering of initiatives in each of the following three characteristic layers: purpose, structure, and function. In each layer, the following model elements are identified: 1. Typical research and development initiatives in healthcare. 2. The ordering criteria of the initiatives. 3. Emergent conditions and scenarios that could influence the ordering of the AI initiatives. This approach is a manifold accounting of the scenarios that could contribute to the risk associated with AI in healthcare. Recognizing the context-specific nature of risks and highlighting the role of human in the loop, this study identifies scenario s.06—non-interpretable AI and lack of human–AI communications—as the most disruptive across all three layers of healthcare systems. This finding suggests that AI transparency solutions primarily target domain experts, a reasonable inclination given the significance of “high-stakes” AI systems, particularly in healthcare. Future work should connect this approach with decision analysis and quantifying the value of information. Future work will explore the disruptions of system order in additional layers of the healthcare system, including the environment, boundary, interconnections, workforce, facilities, supply chains, and others

Informatics-enabled citizen science to advance health equity.

The COVID-19 pandemic has once again highlighted the ubiquity and persistence of health inequities along with our inability to respond to them in a timely and effective manner. There is an opportunity to address the limitations of our current approaches through new models of informatics-enabled research and clinical practice that shift the norm from small- to large-scale patient engagement. We propose augmenting our approach to address health inequities through informatics-enabled citizen science, challenging the types of questions being asked, prioritized, and acted upon. We envision this democratization of informatics that builds upon the inclusive tradition of community-based participatory research (CBPR) as a logical and transformative step toward improving individual, community, and population health in a way that deeply reflects the needs of historically marginalized populations