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The last several years have shown a strong growth of Artificial Intelligence (AI) technologies with promising results for many areas of healthcare. HCI has contributed to these discussions, mainly with studies on explainability of advanced algorithms. However, there are only few AI-systems based on machine learning algorithms that make it to the real world and everyday care. This challenging move has been named the “last mile” of AI in healthcare, emphasizing the sociotechnical uncertainties and unforeseen learnings from involving users in the design or use of AI-based systems. The aim of this workshop is to set the stage for a new wave of HCI research that accounts for and begins to develop new insights, concepts, and methods, for transitioning from development to implementation and use of AI in healthcare. Participants are invited to collaboratively define an HCI research agenda focused on healthcare AI in the wild, which will require examining end-user engagements and questioning underlying concepts of AI in healthcare.

Large-scale educational settings have been common domains for affect detection and recognition research. Most research emphasizes improvements in the accuracy of affect measurement to enhance instructors' efficiency in managing large numbers of students. However, these technologies are not designed from students' perspectives, nor designed for students' own usage. To identify the unique design considerations for affect sensors that consider student capacities and challenges, and explore the potential of affect sensors to support students' self-learning, we conducted semi-structured interviews and surveys with both online students and on-campus students enrolled in large in-person classes. Drawing on these studies we: (a) propose using affect data to support students' self-regulated learning behaviors through a "scaling for empowerment'' design perspective, (b) identify design guidelines to mitigate students' concerns regarding the use of affect data at scale, (c) provide design recommendations for the physical design of affect sensors for large educational settings.

Adolescents with chronic conditions must work with family caregivers to manage their illness experiences. To explore how technology can support collaborative documentation of these experiences, we designed and distributed a paper diary probe kit in a two-week field deployment with 12 adolescent-parent dyads (24 participants). Three insights emerged from the study that highlight how technology can support shared illness management: 1) provide scaffolds to recognize physical and emotional experiences in the context of daily activities; 2) help families reconstruct patient experiences; and 3) adapt to individual preferences for capturing, representing and sharing experiences. We discuss opportunities for HCI research that follow from these findings and conclude by reflecting on the benefits and limitations of using diary probes with adolescent patients and their parental caregivers.

Deep learning algorithms promise to improve clinician workflows and patient outcomes. However, these gains have yet to be fully demonstrated in real world clinical settings. In this paper, we describe a human-centered study of a deep learning system used in clinics for the detection of diabetic eye disease. From interviews and observation across eleven clinics in Thailand, we characterize current eye-screening workflows, user expectations for an AI-assisted screening process, and post-deployment experiences. Our findings indicate that several socio-environmental factors impact model performance, nursing workflows, and the patient experience. We draw on these findings to reflect on the value of conducting human-centered evaluative research alongside prospective evaluations of model accuracy.

Increases in data complexity in personal informatics systems require new ways of contextualizing personal data to facilitate meaningful reflection. An emerging approach for providing such context includes augmenting one's personal data with the data of others "like them" to help individuals make sense of their data. However, we do not yet understand how an individual's self-reflection process is affected when the data of others is made available. In this paper, we investigate how people reflect on three types of personal data when presented alongside a large set of aggregated data of multiple cohorts. We conducted personal and cohort data reviews using a subset of participants from a mobile-sensing study that collected physical activity, digital social activity, and perceived stress, from 47 students over three weeks. Participants preferred to use characteristics of the data (e.g., maxima, minima) and graphical presentation (e.g., appearance of trends) along with demographic identities (e.g., age, gender) when relating to cohorts. We further characterize how participants incorporated cohort data into their self-reflection process, and conclude with discussion of the implications for personal informatics systems that leverage the data of "people like me" to enable meaningful reflection.

The authors report on the PervasiveHealth 2017 workshop, Leveraging Patient-Generated Data (PGD) for Collaborative Decision Making in Healthcare. They discuss characteristics of PGD, followed by scenarios demonstrating the data-sharing practice among patients, clinicians, and caregivers. The authors also highlight current challenges and opportunities, and outline a future research agenda to envision ways to harness the power of PGD.

Ecological Momentary Assessment (EMA) methods have emerged as an approach that enhances the ecological validity of data collected for the study of human behavior and experience. In particular, EMA methods are used to capture individuals' experiences (e.g., symptoms, affect, and behaviors) in real-world contexts and in near-real time. However, work investigating participants' experiences in EMA studies and in particular, how these experiences may influence the collected data, is limited. We conducted in-depth focus groups with 32 participants following an EMA study on mental well-being in college students. In doing so, we probed how the elicitation of high-quality, reflective responses is related to the design of EMA interactions. Through our study, we distilled three primary considerations for designing EMA interactions, based on observations of 1) response strategies to repeated questions, 2) the perceived burden of EMA prompts, and 3) challenges to the validity and robustness of EMA data. We present these considerations in the context of two microinteraction-based EMA approaches that we tested: lock-screen EMA and image-based question prompts. We conclude by characterizing design tensions in the presentation and delivery of EMA prompts, and outline directions for future work to address these tensions.

This forum is dedicated to personal health in all its many facets: decision making, goal setting, celebration, discovery, reflection, and coordination, among others. We look at innovations in interactive technologies and how they help address current critical healthcare challenges. --- Yunan Chen, Editor

Interaction design is playing an increasingly prominent role in computing research, while professional user experience roles expand. These forces drive the demand for more de- sign instruction in HCI classrooms. In this paper, we distill the popular approaches to teaching design to undergraduate and graduate students of HCI. Through a review of existing research on design pedagogy, an international survey of 61 HCI educators, and an analysis of popular textbooks, we ex- plore the prominent disciplinary perspectives that shape design education in the HCI classroom. We draw on our analyses to discuss the differences we see in forms of design taught, approaches to adapting design instruction in computing-based courses, and the tensions faced by instructors of these classes. We conclude by arguing for the importance of pedagogical research on design instruction as a vital and foundational area of inquiry in Interaction Design and HCI.

Identifying useful features of health and fitness technologies has the potential to lead to more effective pervasive technology interventions. Through collaborative creative processes, co-design provides useful insight and direction for the design of these technologies from populations such as older adults who are often neglected in design. Additionally, extensive technology experience prior to co-design may elicit richer feedback from users that may otherwise be unfamiliar with the intervention of interest. In this paper, we examine the value of experience-based co-design among older adults. We report on seven design sessions conducted with a total of 25 older adults, following a 10-week study of fitness app effectiveness. Sessions were grouped into users and non-users of the assigned app following the study. We found that continued use with the assigned application led to more robust, detailed feedback in design sessions, suggesting that long-term, prior use of sample technologies is an important prerequisite to ideating useful features for new health technology. We draw on these findings to make design recommendations, and discuss the values and guidelines of implementing experience-based co-design.

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.

Previous research suggests an important role for self-tracking in promoting mental wellness. Recent studies with college student populations have examined the feasibility of collecting everyday mood, activity, and social data. However, these studies do not account for students’ experiences and challenges adopting self-tracking technologies to support mental wellness goals. We present two studies conducted to better understand self-tracking for stress management and mental wellness in student populations. First, focus groups and card sorting activities with 14 student health professionals reveal expert perspectives on the usefulness of tracking for three scenarios. Second, an online survey of 297 students examines personal experiences with self-tracking and attitudes toward sharing self-tracked data with others. We draw on findings from these studies to characterize students’ motivations, challenges, and preferences in collecting and viewing self-tracked data related to mental wellness, and we compare findings between students with diagnosed mental illnesses and those without. We conclude with a discussion of challenges and opportunities in leveraging self-tracking for mental wellness, highlighting several design considerations.

Diagnostic radiology reports are increasingly being made available to patients and their family members. However, these reports are not typically comprehensible to lay recipients, impeding effective communication about report findings. In this paper, we present three studies informing the design of a prototype to foster patient-clinician communication about radiology report content. First, analysis of questions posted in online health forums helped us identify patients' information needs. Findings from an elicitation study with seven radiologists provided necessary domain knowledge to guide prototype design. Finally, a clinical field study with 14 pediatric patients, their parents and clinicians, revealed positive responses of each stakeholder when using the prototype to interact with and discuss the patient's current CT or MRI report and allowed us to distill three use cases: co-located communication, preparing for the consultation, and reviewing radiology data. We draw on our findings to discuss design considerations for supporting each of these use cases.

Adolescents with complex chronic illnesses, such as cancer and blood disorders, must partner with family and clinical caregivers to navigate risky procedures with life-altering implications, burdensome symptoms and lifelong treatments. Yet, there has been little investigation into how technology can support these partnerships. We conducted 38 in-depth interviews (15 with teenage adolescents with chronic forms of cancer and blood disorders, 15 with their parents, and eight with clinical caregivers) along with nine non-participant observations of clinical consultations to better understand common challenges and needs that could be supported through design. Participants faced challenges primarily concerning: 1) teens' limited participation in their care, 2) communicating emotionally-sensitive information, and 3) managing physical and emotional responses. We draw on these findings to propose design goals for sociotechnical systems to support teens in partnering in their care, highlighting the need for design to support gradually evolving partnerships in care.