We examine the use of personal protective equipment (PPE) in two interdisciplinary medical settings to inform the design of just-intime alerts and reminders for correcting PPE noncompliance. We reviewed videos of 26 pediatric resuscitations occurring over the course of the COVID-19 pandemic at an urban pediatric teaching hospital. Through video review, we identified causes for PPE non-compliance, activities that were frequently performed without PPE, instances in which PPE was intentionally removed, and mechanisms by which healthcare providers corrected PPE noncompliance. We also interviewed 18 registered nurses working in the hospital's emergency department (ED) and intensive care unit (ICU) to better understand observed PPE behaviors and practices. Our results suggest that alert design will require considering the urgency of correcting PPE noncompliance against the urgency of tasks being performed. We discuss our findings through the lens of the COM-B framework and conclude by exploring design opportunities for just-in-time alerts and reminders for prompting PPE noncompliance corrections in dynamic medical work.
AI technologies in healthcare hold great promise for addressing numerous challenges, but ensuring that patients understand, trust, and adopt these technologies remains a significant hurdle. While the HCI community has proposed AI documentation frameworks (e.g., model cards) to enhance understanding, patient perspectives in the healthcare AI documentation remain underexplored. To address this gap, we designed prototypes based on existing frameworks and gathered feedback from 18 participants to explore their perspectives on AI documentation in cardiology, a domain where high-stakes AI tools are increasingly used and understanding users' trust in AI is essential. Our findings revealed patient needs for more detailed information about healthcare AI technologies, the importance of extrinsic trust cues (e.g., regulatory status), and the integration of AI documentation into existing care processes. Based on these findings, we discuss two design implications: enhancing patient-centeredness in AI documentation and leveraging extrinsic trust cues to improve its design. This study contributes to the HCI community by amplifying the patient voice in designing AI documentation and offering actionable insights into leveraging extrinsic trust cues effectively.
Focusing on deficits in research with historically marginalized communities, such as Indigenous communities, perpetuates negative stereotypes and overlooks their strengths and resilience, contributing to mistrust and epistemic injustice. Shifting to strengths-based research approaches promotes more constructive narratives, respects Indigenous knowledge systems, and aligns with ethical frameworks emphasizing Indigenous community ownership and collaboration. We use photo elicitation to investigate values for community-level health research results dissemination with Alaska Native communities, exploring online image search as a tool for collaborative photo elicitation during the co-design ideation process. Our strengths-based approach demonstrates how our methods aid in recalling cultural values during design ideation. Using a deductive qualitative approach, we examined data through lenses of resilience, socioecological strengths, and sociocultural strengths. Cultural representations of community, health and wellness, storytelling, and research are emphasized to illustrate strengths while supporting ideation. We discuss the design implications of using image search for collaborative photo elicitation and its potential for revealing cultural strengths that may otherwise be missed.
Audio descriptions (AD) make videos accessible for blind and low vision (BLV) users by describing visual elements that cannot be understood from the main audio track. AD created by professionals or novice describers is time-consuming and offers little customization or control to BLV viewers on description length and content and when they receive it. To address this gap, we explore user-driven AI-generated descriptions, enabling BLV viewers to control both the timing and level of detail of the descriptions they receive. In a study, 20 BLV participants activated audio descriptions for seven different video genres with two levels of detail: concise and detailed. Our findings reveal differences in the preferred frequency and level of detail of ADs for different videos, participants' sense of control with this style of AD delivery, and its limitations. We discuss the implications of these findings for the development of future AD tools for BLV users.
Stories about overcoming personal struggles can effectively illustrate the application of psychological theories in real life, yet they may fail to resonate with individuals' experiences. In this work, we employ large language models (LLMs) to create tailored narratives that acknowledge and address unique challenging thoughts and situations faced by individuals. Our study, involving 346 young adults across two settings, demonstrates that personalized LLM-enhanced stories were perceived to be better than human-written ones in conveying key takeaways, promoting reflection, and reducing belief in negative thoughts. These stories were not only seen as more relatable but also similarly authentic to human-written ones, highlighting the potential of LLMs in helping young adults manage their struggles. The findings of this work provide crucial design considerations for future narrative-based digital mental health interventions, such as the need to maintain relatability without veering into implausibility and refining the wording and tone of AI-enhanced content.
The number of older adults who are homebound with depressive symptoms is increasing. Due to their homebound status, they have limited access to trained mental healthcare support, which leaves this support often to untrained family caregivers. To increase access, a growing interest is placed on using technology-mediated solutions, such as voice-assisted intelligent personal assistants (VIPAs), to deliver mental health services to older adults. To better understand how older adults and family caregivers intend to interact with a VIPA for mental health interventions, we conducted a participatory design study during which 6 older adults and 7 caregivers designed VIPA-human dialogues for various scenarios. Using conversation style preferences as a starting point, we present aspects of human-likeness older adults and family caregivers perceived as helpful or uncanny, specifically in the context of the delivery of mental health interventions, which helps inform potential roles VIPAs can play in mental healthcare for older adults.
While standalone Voice Assistants (VAs) are promising to support older adults' daily routine and wellbeing management, onboarding and setting up these devices can be challenging. Although some older adults choose to seek assistance from technicians and adult children, easy set up processes that facilitate independent use are still critical, especially for those who do not have access to external resources. We aim to understand the older adults' experience while setting up commercially available voice-only and voice-first screen-based VAs. Rooted in participants observations and semi-structured interviews, we designed a within-subject study with 10 older adults using Amazon Echo Dot and Echo Show. We identified the values of the built-in touchscreen and the instruction documents, as well as the impact of form factors, and outline important directions to support older adult independence with VAs.
Automatically integrating data within interactive clinical checklists allows for enhanced dynamic displays, while also providing information needed for checklist adaptation to the context of the medical event. In this mixed-methods study, we used user-centered design sessions with clinicians to design a checklist interface that automatically captures and displays dynamic patient data. We compared the manual and automatic checklist versions during video-guided simulation sessions, evaluating the effects of automatic capture on clinicians’ interactions with dynamic data and their situation awareness. Despite clinicians’ concerns that automatic data capture would affect situation awareness, we found no significant difference in awareness scores. Participants preferred the automatic version, highlighting its improved accuracy and completeness. From our findings, we propose a framework for capturing dynamic data and designing dynamic data interfaces within interactive checklists. We conclude by discussing barriers and design opportunities for supporting awareness of data trends through checklists.
Remote Sighted Assistance (RSA) is a popular smartphone-mediated aid for people with blindness, where a sighted individual converses with a blind individual in a one-on-one (1:1) session. Since sighted assistants outnumber blind individuals (13:1), this paper investigates what happens when more than one sighted individual assists a single blind individual in a session. Specifically, we propose paired-volunteer RSA, a new paradigm where two sighted volunteers assist a single user with blindness. We investigate the feasibility, desirability, and challenges of this paradigm and explore its opportunities. Our study with 8 sighted volunteers and 9 blind users reveals that the proposed paradigm extends the one-on-one RSA to cover a broader range of more intellectual and experiential tasks, providing new and distinctive opportunities in supporting complex, open-ended tasks (e.g., pursuing hobbies, appreciating arts, and seeking entertainment). These opportunities can not only enrich the blind users’ quality of life and independence but also offer a fun and engaging experience for the sighted volunteers. The study also reveals the costs of extended collaboration in this paradigm. Finally, we synthesize a taxonomy of tasks where the proposed RSA paradigm can succeed and outline how HCI researchers and system designers can realize this paradigm.
Studies find that older adults want control over how technologies are used in their care, but how it can be operationalized through design remains to be clarified. We present findings from a large survey (n=825) of a well-characterized U.S. online cohort that provides actionable evidence of the importance of designing for control over monitoring technologies. This uniquely large, age-diverse sample allows us to compare needs across age and other characteristics with insights about future users and current older adults (n=496 >64), including those concerned about their own memory loss (n=201). All five control options, which are not currently enabled, were very or extremely important to most people across age. Findings indicate that comfort with a range of care technologies is contingent on having privacy- and other control-enabling options. We discuss opportunities for design to meet these user needs that demand course correction through attentive, creative work.
Remote sighted assistance (RSA) has emerged as a conversational assistive service, where remote sighted workers, i.e., agents, provide real-time assistance to blind users via video-chat-like communication. Prior work identified several challenges for the agents to provide navigational assistance to users and proposed computer vision-mediated RSA service to address those challenges. We present an interactive system implementing a high-fidelity prototype of RSA service using augmented reality (AR) maps with localization and virtual elements placement capabilities. The paper also presents a confederate-based study design to evaluate the effects of AR maps with 13 untrained agents. The study revealed that, compared to baseline RSA, agents were significantly faster in providing indoor navigational assistance to a confederate playing the role of users, and agents’ mental workload was significantly reduced—all indicate the feasibility and scalability of AR maps in RSA services.
Through a process of robust co-design, we created a bespoke accessible survey platform to explore the role of co-researchers with learning disabilities (LDs) in research design and analysis. A team of co-researchers used this system to create an online survey to challenge public understanding of LDs [3]. Here, we describe and evaluate the process of remotely co-analyzing the survey data across 30 meetings in a research team consisting of academics and non-academics with diverse abilities amid new COVID-19 lockdown challenges. Based on survey data with >1,500 responses, we first co-analyzed demographics using graphs and art & design approaches. Next, co-researchers co-analyzed the output of machine learning-based structural topic modelling (STM) applied to open-ended text responses. We derived an efficient five-steps STM co-analysis process for creative, inclusive, and critical engagement of data by co-researchers. Co-researchers observed that by trying to understand and impact public opinion, their own perspectives also changed.
Vital sign values during medical emergencies can help clinicians recognize and treat patients with life-threatening injuries. Identifying abnormal vital signs, however, is frequently delayed and the values may not be documented at all. In this mixed-methods study, we designed and evaluated a two-phased visual alert approach for a digital checklist in trauma resuscitation that informs users about undocumented vital signs. Using an interrupted time series analysis, we compared documentation in the periods before (two years) and after (four months) the introduction of the alerts. We found that introducing alerts led to an increase in documentation throughout the post-intervention period, with clinicians documenting vital signs earlier. Interviews with users and video review of cases showed that alerts were ineffective when clinicians engaged less with the checklist or set the checklist down to perform another activity. From these findings, we discuss approaches to designing alerts for dynamic team-based settings.
We present CORE-MI, an automated evaluation and assessment system that provides feedback to mental health counselors on the quality of their care. CORE-MI is the first system of its kind for psychotherapy, and an early example of applied machine-learning in a human service context. In this paper, we describe the CORE-MI system and report on a qualitative evaluation with 21 counselors and trainees. We discuss the applicability of CORE-MI to clinical practice and explore user perceptions of surveillance, workplace misuse, and notions of objectivity, and system reliability that may apply to automated evaluation systems generally.
Games for health (G4H) aim to improve health outcomes and encourage behavior change. While existing theoretical frameworks describe features of both games and health interventions, there has been limited systematic investigation into how disciplinary and interdisciplinary stakeholders understand design features in G4H. We recruited 18 experts from the fields of game design, behavioral health, and games for health, and prompted them with 16 sample games. Applying methods including open card sorting and triading, we elicited themes and features (e.g., real-world interaction, game mechanics) around G4H. We found evidence of conceptual differences suggesting that a G4H perspective is not simply the sum of game and health perspectives. At the same time, we found evidence of convergence in stakeholder views, including areas where game experts provided insights about health and vice versa. We discuss how this work can be applied to provide conceptual tools, improve the G4H design process, and guide approaches to encoding G4H-related data for large-scale empirical analysis.
This paper explores the workflow and use of an interactive medical checklist for trauma resuscitation--an emerging technology developed for trauma team leaders to support decision making and task coordination among team members. We used a technology probe approach and ethnographic methods, including video review, interviews, and content analysis of checklist logs, to examine how team leaders use the checklist probe during live resuscitations. We found that team leaders of various experience levels use the technology differently. Some leaders frequently glance at the checklist and take notes during task performance, while others place the checklist on a stand and only interact with the checklist when checking items. We compared checklist timestamps to task activities and found that most items are checked off after tasks are performed. We conclude by discussing design implications and new design opportunities for a future dynamic, adaptive checklist.
Eliciting, understanding, and honoring patients' values- the things most important to them in daily life-is a cornerstone of patient-centered care. However, this rarely occurs explicitly as a routine part of clinical practice. This is particularly problematic for individuals with multiple chronic conditions (MCC) because they face difficult choices about how to balance competing demands for self-care in accordance with their values. In this study, we sought to inform the design of interventions to support conversations about patient values between patients with MCC and their health care providers. We conducted a field study that included observations of 21 clinic visits for patients who have MCC, and interviews with 16 care team members involved in those visits. This paper contributes a practice-based account of ways in which providers engage with patient values, and discusses how future work in interactive systems design might extend and enrich these engagements.
We describe the design of an automated assessment and training tool for psychotherapists to illustrate challenges with creating interactive machine learning (ML) systems, particularly in contexts where human life, livelihood, and wellbeing are at stake. We explore how existing theories of interaction design and machine learning apply to the psychotherapy context, and identify "contestability" as a new principle for designing systems that evaluate human behavior. Finally, we offer several strategies for making ML systems more accountable to human actors.
To improve care for the growing number of older adults with multiple chronic conditions, physicians and other healthcare providers need to better understand what is most important in the lives of these patients. In a qualitative study of home visits with patients and family caregivers, we found that patients withhold information from providers when communicating about what they deem important to their health and well-being. We examine the various motivations and factors that explain communication boundaries between patients and their healthcare providers. Patients' disclosures reflected perceptions of what was pertinent to share, assumptions about the consequences of sharing, and the influence of interpersonal relationships with providers. Our findings revealed limitations of existing approaches to support patient-provider communication and identified challenges for the design of systems that honor patient needs and preferences.
