Responsible AI (RAI) practices are increasingly important for practitioners in anticipating and addressing potential harms of AI, and emerging research suggests that AI practitioners often learn about RAI on-the-job.More generally, learning at work is social; thus, this work explores the interpersonal aspects of learning about RAI on-the-job.Through workshops with 21 industry-based RAI educators, we offer the first empirical investigation into interpersonal processes and dimensions of learning about RAI at work.This study finds key phases of RAI are sites for ongoing interpersonal learning, such as critical reflection about potential RAI impacts and collective sense-making about operationalizing RAI principles.We uncover a significant gap between these interpersonal learning processes and current approaches to learning about RAI.Finally, we identify barriers and supports for interpersonal learning about RAI.We close by discussing opportunities to better enable interpersonal learning about RAI on-the-job and the broader implications of interpersonal learning for RAI.

Community-based partnerships are essential to creating inclusive and equitable technologies and design practices. Though recent scholarship in HCI focuses on equitable design practices, there is less focus on understanding the experiences of community-based nonprofit organizations (CBOs) when partnering with technology companies. In this paper, we focus on understanding the perspectives of CBOs by answering the following research question: What are the experiences of CBOs that have collaborated with technology companies? Through a series of design workshops with 18 participants who work at community-based nonprofits that have collaborated with technology firms, we identified four elements of community-industry collaborations that collectively shape the overall experience: divergences in cultural and organizational norms, ''setting the table,'' project relationship dynamics, and affective qualities. We conclude by discussing the power structures that impact community-industry collaboration and suggest reflective practices to guide equitable collaborations between CBOs and tech companies.

Fairness in AI and ML systems is increasingly linked to the proper treatment and recognition of data workers involved in training dataset development. Yet, those who collect and annotate the data, and thus have the most intimate knowledge of its development, are often excluded from critical discussions. This exclusion prevents data annotators, who are domain experts, from contributing effectively to dataset contextualization. Our investigation into the hiring and engagement practices of 52 data work requesters on platforms like Amazon Mechanical Turk reveals a gap: requesters frequently hold naive or unchallenged notions of worker identities and capabilities and rely on ad-hoc qualification tasks that fail to respect the workers’ expertise. These practices not only undermine the quality of data but also the ethical standards of AI development. To rectify these issues, we advocate for policy changes to enhance how data annotation tasks are designed and managed and to ensure data workers are treated with the respect they deserve.

Calls for engagement with the public in Artificial Intelligence (AI) research, development, and governance are increasing, leading to the use of surveys to capture people's values, perceptions, and experiences related to AI. In this paper, we critically examine the state of human participant surveys associated with these topics. Through both a reflexive analysis of a survey pilot spanning six countries and a systematic literature review of 44 papers featuring public surveys related to AI, we explore prominent perspectives and methodological nuances associated with surveys to date. We find that public surveys on AI topics are vulnerable to specific Western knowledge, values, and assumptions in their design, including in their positioning of ethical concepts and societal values, lack sufficient critical discourse surrounding deployment strategies, and demonstrate inconsistent forms of transparency in their reporting. Based on our findings, we distill provocations and heuristic questions for our community, to recognize the limitations of surveys for meeting the goals of engagement, and to cultivate shared principles to design, deploy, and interpret surveys cautiously and responsibly.

Prompt-based interfaces for Large Language Models (LLMs) have made prototyping and building AI-powered applications easier than ever before. However, identifying potential harms that may arise from AI applications remains a challenge, particularly during prompt-based prototyping. To address this, we present Farsight, a novel in situ interactive tool that helps people identify potential harms from the AI applications they are prototyping. Based on a user’s prompt, Farsight highlights news articles about relevant AI incidents and allows users to explore and edit LLM-generated use cases, stakeholders, and harms. We report design insights from a co-design study with 10 AI prototypers and findings from a user study with 42 AI prototypers. After using Farsight, AI prototypers in our user study are better able to independently identify potential harms associated with a prompt and find our tool more useful and usable than existing resources. Their qualitative feedback also highlights that Farsight encourages them to focus on end-users and think beyond immediate harms. We discuss these findings and reflect on their implications for designing AI prototyping experiences that meaningfully engage with AI harms. Farsight is publicly accessible at: https://pair-code.github.io/farsight.

Generative AI is expected to have transformative effects in multiple knowledge industries. To better understand how knowledge workers expect generative AI may affect their industries in the future, we conducted participatory research workshops for seven different industries, with a total of 54 participants across three US cities. We describe participants’ expectations of generative AI’s impact, including a dominant narrative that cut across the groups’ discourse: participants largely envision generative AI as a tool to perform menial work, under human review. Participants do not generally anticipate the disruptive changes to knowledge industries currently projected in common media and academic narratives. Participants do however envision generative AI may amplify four social forces currently shaping their industries: deskilling, dehumanization, disconnection, and disinformation. We describe these forces, and then we provide additional detail regarding attitudes in specific knowledge industries. We conclude with a discussion of implications and research challenges for the HCI community.

Technology companies continue to invest in efforts to incorporate responsibility in their Artificial Intelligence (AI) advancements, while efforts to audit and regulate AI systems expand. This shift towards Responsible AI (RAI) in the tech industry necessitates new practices and adaptations to roles—undertaken by a variety of practitioners in more or less formal positions, many of whom focus on the user-centered aspects of AI. To better understand practices at the intersection of user experience (UX) and RAI, we conducted an interview study with industrial UX practitioners and RAI subject matter experts, both of whom are actively involved in addressing RAI concerns throughout the early design and development of new AI-based prototypes, demos, and products, at a large technology company. Many of the specific practices and their associated challenges have yet to be surfaced in the literature, and distilling them offers a critical view into how practitioners’ roles are adapting to meet present-day RAI challenges. We present and discuss three emerging practices in which RAI is being enacted and reified in UX practitioners’ everyday work. We conclude by arguing that the emerging practices, goals, and types of expertise that surfaced in our study point to an evolution in praxis, with associated challenges that suggest important areas for further research in HCI.

We present a cross-cultural diary study with 64 transgender (trans) and non-binary adults in Mexico, the U.S., and India, to understand experiences keeping track of and managing aspects of personal health and well-being. Based on a reflexive thematic analysis of diary data, we highlight sociotechnical interactions that shape how trans and non-binary people track and manage aspects of their health and well-being. Specifically, we surface the ways in which trans and non-binary people infrastructure forms of care, by assembling together elements of informal social ecologies, formalized knowledge sources, and self-reflective media. We examine the forms of precarity that interact with care infrastructure and shape management of health and well-being, including management of gender identity transitions. We discuss the ways in which our findings extend knowledge at the intersection of technology and marginalized health needs, and conclude by arguing for the importance of a research agenda to move toward TGNB-inclusive design.

As new forms of data capture emerge to power new AI applications, questions abound about the ethical implications of these data collection practices. In this paper, we present clinicians' perspectives on the prospective benefits and harms of voice data collection during health consultations. Such data collection is being proposed as a means to power models to assist clinicians with medical data entry, administrative tasks, and consultation analysis. Yet, clinicians' attitudes and concerns are largely absent from the AI narratives surrounding these use cases, and the academic literature investigating them. Our qualitative interview study used the concept of an informed consent process as a type of design fiction, to support elicitation of clinicians' perspectives on voice data collection and use associated with a fictional, near-term AI assistant. Through reflexive thematic analysis of in-depth sessions with physicians, we distilled eight classes of potential risks that clinicians are concerned about, including workflow disruptions, self-censorship, and errors that could impact patient eligibility for services. We conclude with an in-depth discussion of these prospective risks, reflect on the use of the speculative processes that illuminated them, and reconsider evaluation criteria for AI-assisted clinical documentation technologies in light of our findings.

HCI researchers have been gradually shifting attention from individual users to communities when engaging in research, design, and system development. However, our field has yet to establish a cohesive, systematic understanding of the challenges, benefits, and commitments of community-collaborative approaches to research. We conducted a systematic review and thematic analysis of 47 computing research papers discussing participatory research with communities for the development of technological artifacts and systems, published over the last two decades. From this review, we identified seven themes associated with the evolution of a project: from establishing community partnerships to sustaining results. Our findings suggest that several tensions characterize these projects, many of which relate to the power and position of researchers, and the computing research environment, relative to community partners. We discuss the implications of our findings and offer methodological proposals to guide HCI, and computing research more broadly, towards practices that center communities.

The COVID-19 pandemic disrupted processes interaction designers took for granted, challenging some of our most commonplace design practices. Participatory and situated approaches have been impacted the most: where we engaged stakeholders in-person and in-context, during this time we must co-design remotely and in virtual environments. Such a dramatic change calls for new co-design methods. In this article, we present a novel remote strategy for involving stakeholders to co-design interactive technology: Designerly Tele-Experiences (DTE). Our methodological proposal enables participants to experience early design concepts in-the-wild as a provocation to contribute new ideas that resonate with their experiential preferences. Here we describe the rationale for DTE, unpack how it builds on and extends existing methods, and provide actionable guidelines from our experience of using it in our work. Our contribution will empower interaction designers to embrace participatory and situated approaches even when engaging stakeholders in person is not possible or desirable.

As the global population ages and the prevalence of chronic conditions and acute infections rise, it is becoming imperative to understand the many forms of caregiving labor and create sociotechnical systems that support them. In this paper, we report results of a large survey study with 2000 informal caregivers in the USA, highlighting the fundamental challenges that different types of caregivers face when coordinating care with others. Our findings support previous findings on caregivers’ coordination challenges, while also offering insights into the situational, mediating factors that influence the extent to which care coordination challenges are felt. These mediating factors include caregivers’ relationships, access to a variety of resources including physical, social, and financial support, and physical and mental barriers. We discuss these challenges and mediating factors, and conclude with a discussion on how they can be considered in the design of future sociotechnical systems.

In this paper, we present an annotated portfolio of speculative ideas that emerged from a co-design process where we investigated the playful potential of day-to-day mealtime. Our portfolio illustrates the learnings from our participatory engagements: it embodies ours and our participants' ideas of how technology might support increasingly playful and socio-emotionally rich experiences around food. We contribute: (1) a list of play potentials of mealtime-i.e. people's existing playful practices with food-that will point designers towards socio-emotionally desirable play-food experiences; (2) a portfolio of speculative design ideas that illustrate how mealtime technology could help to realize that playful potential; and (3) a discussion of our participants' experiences with and responses to lo-fi prototypes of our ideas. Our work will provoke designers to carefully consider the impact of food-tech innovation on the quality of people's social eating experiences and inspire them to cultivate forms of food-play that are socio-emotionally rich.?

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.

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.

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.

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.

Although rapid advances in machine learning have made it increasingly applicable to expert decision-making, the delivery of accurate algorithmic predictions alone is insufficient for effective human-AI collaboration. In this work, we investigate the key types of information medical experts desire when they are first introduced to a diagnostic AI assistant. In a qualitative lab study, we interviewed 21 pathologists before, during, and after being presented deep neural network (DNN) predictions for prostate cancer diagnosis, to learn the types of information that they desired about the AI assistant. Our findings reveal that, far beyond understanding the local, case-specific reasoning behind any model decision, clinicians desired upfront information about basic, global properties of the model, such as its known strengths and limitations, its subjective point-of-view, and its overall design objective--what it's designed to be optimized for. Participants compared these information needs to the collaborative mental models they develop of their medical colleagues when seeking a second opinion: the medical perspectives and standards that those colleagues embody, and the compatibility of those perspectives with their own diagnostic patterns. These findings broaden and enrich discussions surrounding AI transparency for collaborative decision-making, providing a richer understanding of what experts find important in their introduction to AI assistants before integrating them into routine practice.

Maker culture is on the rise in healthcare with the adoption of consumer-grade fabrication technologies. However, little is known about the activities and resources involved in prototyping medical devices to improve patient care. In this paper, we refer to such activity asmedical making to report findings based on a qualitative study of stakeholder engagement in physical prototyping (making) experiences. We examine perspectives from diverse stakeholders including clinicians, engineers, administrators, and medical researchers. Through 18 semi-structured interviews with medical-makers in the US and Canada, we analyze making activity in medical settings. We find that medical makers share strategies to address risks, adopt labor roles, and acquire resources within traditional medical practice. Our findings outline how medical-makers mitigate risks for patient safety, collaborate with local and global stakeholder networks, and overcome constraints of co-location and material practices. We recommend a clinician-aided software system, partially-open repositories, and a collaborative skill-sharing social network to extend their strategies in support of medical making.

Teens with complex chronic illnesses have difficulty understanding and articulating symptoms such as pain and emotional distress. Yet, symptom communication plays a central role in clinical care and illness management. To understand how design can help overcome these challenges, we created a visual library of 72 sketched illustrations, informed by the Observations of Daily Living framework along with insights from 11 clinician interviews. We utilized our library with storyboarding techniques, free-form sketching, and interviews, in co-design sessions with 13 pairs of chronically-ill teens and their parents. We found that teens depicted symptoms as being interwoven with narratives of personal and social identity. Teens and parents were enthusiastic about collaboratively-generated, interactive storyboards as a tracking and communication mechanism, and suggested three ways in which they could aid in communication and coordination with informal and formal caregivers. In this paper, we detail these findings, to guide the design of tools for symptom-tracking and incorporation of patient-generated data into pediatric care.

Objective: Parents routinely access young children's medical records, but medical societies strongly recommend confidential care during adolescence, and most medical centers restrict parental records access during the teen years. We sought to assess public opinion about adolescent medical privacy. Materials and Methods: The Cornell National Social Survey (CNSS) is an annual nationwide public opinion survey. We added questions about a) whether parents should be able to see their 16-year-old child's medical record, and b) whether teens would avoid discussing sensitive issues (sex, alcohol) with doctors if parents could see the record. Hypothesizing that highlighting the rationale for adolescent privacy would change opinions, we conducted an experiment by randomizing question order. Results: Most respondents (83.0%) believed that an adolescent would be less likely to discuss sensitive issues with doctors with parental medical record access; responses did not differ by question order (P = .29). Most also believed that parents should have access to teens' records, but support for parental access fell from 77% to 69% among those asked the teen withholding question first (P = .01). Conclusions: Although medical societies recommend confidential care for adolescents, public opinion is largely in favor of parental access. A brief "nudge," asking whether parental access might harm adolescent-doctor communication, increased acceptance of adolescent confidentiality, and could be part of a strategy to prepare parents for electronic patient portal policies that medical centers impose at the beginning of adolescence.

Objective: Medical privacy policies, which are clear-cut for adults and young children, become ambiguous during adolescence. Yet medical organizations must establish unambiguous rules about patient and parental access to electronic patient portals. We conducted a national interview study to characterize the diversity in adolescent portal policies across a range of institutions and determine the factors influencing decisions about these policies. Methods: Within a sampling framework that ensured diversity of geography and medical organization type, we used purposive and snowball sampling to identify key informants. Semi-structured interviews were conducted and analyzed with inductive thematic analysis, followed by a member check. Results: We interviewed informants from 25 medical organizations. Policies established different degrees of adolescent access (from none to partial to complete), access ages (from 10 to 18 years), degrees of parental access, and types of information considered sensitive. Federal and state law did not dominate policy decisions. Other factors in the decision process were: technology capabilities; differing patient population needs; resources; community expectations; balance between information access and privacy; balance between promoting autonomy and promoting family shared decision-making; and tension between teen privacy and parental preferences. Some informants believed that clearer standards would simplify policy-making; others worried that standards could restrict high-quality polices. Conclusions: In the absence of universally accepted standards, medical organizations typically undergo an arduous decision-making process to develop teen portal policies, weighing legal, economic, social, clinical, and technological factors. As a result, portal access policies are highly inconsistent across the United States and within individual states.

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.

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.

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.

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.

OBJECTIVE: Prior studies of computing applications that support patients' medication knowledge and self-management offer valuable insights into effective application design, but do not address inpatient settings. This study is the first to explore the design and usefulness of patient-facing tools supporting inpatient medication management and tracking. MATERIALS AND METHODS: We designed myNYP Inpatient, a custom personal health record application, through an iterative, user-centered approach. Medication-tracking tools in myNYP Inpatient include interactive views of home and hospital medication data and features for commenting on these data. In a two-phase pilot study, patients used the tools during cardiothoracic postoperative care at Columbia University Medical Center. In Phase One, we provided 20 patients with the application for 24-48 h and conducted a closing interview after this period. In Phase Two, we conducted semi-structured interviews with 12 patients and 5 clinical pharmacists who evaluated refinements to the tools based on the feedback received during Phase One. RESULTS: Patients reported that the medication-tracking tools were useful. During Phase One, 14 of the 20 participants used the tools actively, to review medication lists and log comments and questions about their medications. Patients' interview responses and audit logs revealed that they made frequent use of the hospital medications feature and found electronic reporting of questions and comments useful. We also uncovered important considerations for subsequent design of such tools. In Phase Two, the patients and pharmacists participating in the study confirmed the usability and usefulness of the refined tools. CONCLUSIONS: Inpatient medication-tracking tools, when designed to meet patients' needs, can play an important role in fostering patient participation in their own care and patient-provider communication during a hospital stay.

OBJECTIVE: To systematically review existing literature regarding patient engagement technologies used in the inpatient setting. METHODS: PubMed, Association for Computing Machinery (ACM) Digital Library, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and Cochrane databases were searched for studies that discussed patient engagement ('self-efficacy', 'patient empowerment', 'patient activation', or 'patient engagement'), (2) involved health information technology ('technology', 'games', 'electronic health record', 'electronic medical record', or 'personal health record'), and (3) took place in the inpatient setting ('inpatient' or 'hospital'). Only English language studies were reviewed. RESULTS: 17 articles were identified describing the topic of inpatient patient engagement. A few articles identified design requirements for inpatient engagement technology. The remainder described interventions, which we grouped into five categories: entertainment, generic health information delivery, patient-specific information delivery, advanced communication tools, and personalized decision support. CONCLUSIONS: Examination of the current literature shows there are considerable gaps in knowledge regarding patient engagement in the hospital setting and inconsistent use of terminology regarding patient engagement overall. Research on inpatient engagement technologies has been limited, especially concerning the impact on health outcomes and cost-effectiveness.

Patients' basic understanding of clinical events has been shown to dramatically improve patient care. We propose that the automatic generation of very short micro-explanations, suitable for real-time delivery in clinical settings, can transform patient care by giving patients greater awareness of key events in their electronic medical record. We present results of a survey study indicating that it may be possible to automatically generate such explanations by extracting individual sentences from consumer-facing Web pages. We further inform future work by characterizing physician and non-physician responses to a variety of Web-extracted explanations of medical lab tests.

We describe fieldwork in which we studied hospital ICU physicians and their strategies and documentation aids for composing patient progress notes. We then present a clinical documentation prototype, activeNotes, that supports the creation of these notes, using techniques designed based on our fieldwork. ActiveNotes integrates automated, context-sensitive patient data retrieval, and user control of automated data updates and alerts via tagging, into the documentation process. We performed a qualitative study of activeNotes with 15 physicians at the hospital to explore the utility of our information retrieval and tagging techniques. The physicians indicated their desire to use tags for a number of purposes, some of them extensions to what we intended, and others new to us and unexplored in other systems of which we are aware. We discuss the physicians' responses to our prototype and distill several of their proposed uses of tags: to assist in note content management, communication with other clinicians, and care delivery.

Electronic medical records are increasingly comprehensive, and this vast repository of information has already contributed to medical efficiency and hospital procedure. However, this information is not typically accessible to patients, who are frequently under-informed and unclear about their own hospital courses. In this paper, we propose a design for in-room, patient-centric information displays, based on iterative design with physicians. We use this as the basis for a Wizard-of-Oz study in an emergency department, to assess patient and provider responses to in-room information displays. 18 patients were presented with real-time information displays based on their medical records. Semi-structured interviews with patients, family members, and hospital staff reveal that subjective response to in-room displays was overwhelmingly positive, and through these interviews we elicited guidelines regarding specific information types, privacy, use cases, and information presentation techniques. We describe these findings, and we discuss the feasibility of a fully-automatic implementation of our design.