MedAI-SciTS: Enhancing Interdisciplinary Collaboration between AI Researchers and Medical ExpertsIntegrating AI in healthcare requires effective interdisciplinary collaboration, yet challenges like methodological differences, terminology barriers, and divergent objectives persist. To address the issues, we introduce MedAI-SciTS, a structured approach combining a theoretical framework and a toolkit to improve collaboration across disciplines. The framework builds on a formative study (N=12) and literature review, identifying the key challenges and potential solutions in medical-AI projects. We further develop an innovative toolkit with twelve tools, featuring an AI-enhanced research glossary with personalized analogies, an agile co-design platform, and an integrated resource management system. A three-month case study involving AI and medical professionals (N=16 total) applying a segmentation algorithm for adrenal CT images confirmed the toolkit’s effectiveness in enhancing team engagement, communication, trust, and collaboration outcomes. We envision MedAI-SciTS could potentially be applied to a wide range of medical applications and facilitate broader medical-AI collaboration.2025CCChen Cao et al.university of sheffield, Information schoolEV Charging & Eco-Driving InterfacesHand Gesture RecognitionKnowledge Worker Tools & WorkflowsCHI
T2 Coach: A Qualitative Study of an Automated Health Coach for Diabetes Self-ManagementComputational intelligence is increasingly common in interactive systems in many domains, including health. Health coaching with conversational agents (CA) can reach wide populations, but the level of computational intelligence needed for a positive coaching experience is unclear. We conducted a study with sixteen individuals with diabetes and prediabetes who used a CA for health coaching, T2 Coach. Qualitative interviews revealed that participants saw T2 Coach as reliable in helping them stay on track with self-management, appreciated the flexibility in choosing personally meaningful goals and engaging on their own terms, and felt it provided encouragement and even compared it favorably with human coaches. However, they also noted that coaching experience could be improved with more fluid conversations, more tailoring to their personal preferences and lifestyles, and more sensitivity to specific contexts, all of which require more computational intelligence. We discuss implications and design directions for more intelligent coaching CA in health.2025EMElliot G Mitchell et al.Geisinger HealthAugmentative & Alternative Communication (AAC)Mental Health Apps & Online Support CommunitiesChronic Disease Self-Management (Diabetes, Hypertension, etc.)CHI
The Voice of Endo: Leveraging Speech for an Intelligent System That Can Forecast Illness Flare-upsManaging complex chronic illness is challenging due to its unpredictability. This paper explores the potential of voice for automated flare-up forecasts. We conducted a six-week speculative design study with individuals with endometriosis, tasking participants to submit daily voice recordings and symptom logs. Through focus groups, we elicited their experiences with voice capture and perceptions of its usefulness in forecasting flare-ups. Participants were enthusiastic and intrigued at the potential of flare-up forecasts through the analysis of their voice. They highlighted imagined benefits from the experience of recording in supporting emotional aspects of illness and validating both day-to-day and overall illness experiences. Participants reported that their recordings revolved around their endometriosis, suggesting that the recordings’ content could further inform forecasting. We discuss potential opportunities and challenges in leveraging the voice as a data modality in human-centered AI tools that support individuals with complex chronic conditions.2025APAdrienne Pichon et al.Columbia University, Department of Biomedical InformaticsIntelligent Voice Assistants (Alexa, Siri, etc.)Chronic Disease Self-Management (Diabetes, Hypertension, etc.)CHI
Are We Healthier Together? Two Strategies for Supporting Macronutrient Assessment Skills and How the Crowd Can Help (or Not)Learning macronutrient assessment skills can support improved health outcomes and overall wellbeing. We conducted two Mechanical Turk studies to investigate how users might benefit from the crowd's input in macronutrient assessment education. We first determined whether the wisdom of the crowd alone would provide users with enough insight to arrive at accurate macronutrient estimates. Next, we tested two methods of teaching macronutrient assessment skills (Comparison and Decomposition) and analyzed their effectiveness. Results from these studies indicate that while the crowd alone may not be sufficient to support this type of education, users may yet benefit from access to community-generated photos and labels while they use either the Comparison or Decomposition strategy.2022SHSarah M. Harmon et al.Crowdwork & Crowd-powered Systems; Crowdwork & Crowd-powered SystemsCSCW
Do people engage cognitively with AI? Impact of AI assistance on incidental learningWhen people receive advice while making difficult decisions, they often make better decisions in the moment and also increase their knowledge in the process. However, such incidental learning can only occur when people cognitively engage with the information they receive and process this information carefully and thoughtfully. How do people process the information and advice they receive from AI, and do they engage with it deeply enough to enable learning? To answer these questions, we conducted three experiments in which individuals were asked to make nutritional decisions and received simulated AI recommendations and explanations. In the first experiment, we found that when people were presented with both a recommendation and an explanation before making their choice, they made better decisions than they did when they received no such help, but they did not learn. In the second experiment, participants first made their own choice, and then examined a recommendation and an explanation from AI; this condition also resulted in improved decisions, but no learning. However, in our third experiment, participants were presented with just an AI explanation but no recommendation and had to arrive at their own decisions. This condition led to both more accurate decisions and learning gains. We hypothesize that learning gains in this condition were due to deeper engagement with explanations needed to arrive at the decisions. This work provides some of the most direct evidence to date that it may not be sufficient to provide people with AI-generated recommendations and explanations to ensure that people engage carefully with the AI-provided information. This work also presents one technique that enables incidental learning and, by implication, can help people process AI recommendations and explanations more carefully.2022KGKrzysztof Gajos et al.Explainable AI (XAI)AI-Assisted Decision-Making & AutomationIUI
Examining AI Methods for Micro-Coaching DialogsConversational interaction, for example through chatbots, is well-suited to enable automated health coaching tools to support self-management and prevention of chronic diseases. However, chatbots in health are predominantly scripted or rule-based, which can result in a stagnant and repetitive user experience in contrast with more dynamic, data-driven chatbots in other domains. Consequently, little is known about the tradeoffs of pursuing data-driven approaches for health chatbots. We examined multiple artificial intelligence (AI) approaches to enable micro-coaching dialogs in nutrition — brief coaching conversations related to specific meals, to support achievement of nutrition goals — and compared, reinforcement learning (RL), rule-based, and scripted approaches for dialog management. While the data-driven RL chatbot succeeded in shorter, more efficient dialogs, surprisingly the simplest, scripted chatbot was rated as higher quality, despite not fulfilling its task as consistently. These results highlight tensions between scripted and more complex, data-driven approaches for chatbots in health.2022EMElliot G Mitchell et al.Columbia UniversityConversational ChatbotsAI-Assisted Decision-Making & AutomationCHI
Automated vs. Human Health Coaching: Exploring Participant and Practitioner ExperiencesHealth coaching can be an effective intervention to support self-management of chronic conditions like diabetes, but there are not enough coaching practitioners to scalably reach the growing population in need of support. Conversational technology, like chatbots, presents an opportunity to extend health coaching support to broader and more diverse populations. However, some have suggested that the human element is essential to health coaching and cannot be replicated with technology. In this research, we examine automated health coaching using a theory-grounded, wizard-of-oz chatbot, in comparison with text-based virtual coaching from human practitioners who start with the same protocol as the chatbot but have the freedom to embellish and adjust as needed. We found that even a scripted chatbot can create a coach-like experience for participants. While human coaches displayed advantages expressing empathy and using probing questions to tailor their support, they also encountered tremendous barriers and frustrations adapting to text-based virtual coaching. The chatbot coach had advantages in being persistent, as well as more consistently giving choices and options to foster client autonomy. We discuss implications for the design of virtual health coaching interventions.2021EMElliot G Mitchell et al.Care and CaregivingCSCW
From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal RecommendationsSelf-tracking can help personalize self-management interventions for chronic conditions like type 2 diabetes (T2D), but reflecting on personal data requires motivation and literacy. Machine learning (ML) methods can identify patterns, but a key challenge is making actionable suggestions based on personal health data. We introduce GlucoGoalie, which combines ML with an expert system to translate ML output into personalized nutrition goal suggestions for individuals with T2D. In a controlled experiment, participants with T2D found that goal suggestions were understandable and actionable. A 4-week in-the-wild deployment study showed that receiving goal suggestions augmented participants’ self-discovery, choosing goals highlighted the multifaceted nature of personal preferences, and the experience of following goals demonstrated the importance of feedback and context. However, we identified tensions between abstract goals and concrete eating experiences and found static text too ambiguous for complex concepts. We discuss implications for ML-based interventions and the need for systems that offer more interactivity, feedback, and negotiation.2021EMElliot G. Mitchell et al.Columbia UniversityAI-Assisted Decision-Making & AutomationChronic Disease Self-Management (Diabetes, Hypertension, etc.)CHI
Divided We Stand: The Collaborative Work of Patients and Providers in an Enigmatic Chronic DiseaseIn chronic conditions, patients and providers need support in making sense of and managing illness over time. Focusing on endometriosis, an enigmatic chronic condition, we conducted interviews with specialists and focus groups with patients to elicit their work in care specifically pertaining to dealing with an enigmatic disease, both independently and in partnership, and how technology could support these efforts. We found that the work to care for the illness, including reflecting on the illness experience and planning for care, is significantly compounded by the complex nature of the disease: enigmatic condition means uncertainty and frustration in care and management; the multi-factorial and systemic features of endometriosis without any guidance to interpret them overwhelm patients and providers; the different temporal resolutions of this chronic condition confuse both patients and provides; and patients and providers negotiate medical knowledge and expertise in an attempt to align their perspective. We note how this added complexity demands that patients and providers work together to find common ground and align perspectives, and propose three design opportunities (considerations to construct a holistic picture of the patient, design features to reflect and make sense of the illness, and opportunities and mechanisms to correct misalignments and plan for care) and implications to support patients and providers in their care work. Specifically, the enigmatic nature of endometriosis necessitates complementary approaches from human-centered computing and artificial intelligence, and thus opens a number of future research avenues.2020APAdrienne Pichon et al.Health, Caregiving, and ChatbotsCSCW
Personal Health Oracle: Explorations of Personalized Predictions in Diabetes Self-ManagementThe increasing availability of health data and knowledge about computationally modeling human physiology opens new opportunities for personalized predictions in health. Yet little is known about how individuals interact and reason with personalized predictions. To explore these questions, we developed a smartphone app, GlucOracle, that uses self-tracking data of individuals with type 2 diabetes to generate personalized forecasts for post-meal blood glucose levels. We pilot-tested GlucOracle with two populations: members of an online diabetes community, knowledgeable about diabetes and technologically savvy; and individuals from a low socio-economic status community, characterized by high prevalence of diabetes, low literacy and limited experience with mobile apps. Individuals in both communities engaged with personal glucose forecasts and found them useful for adjusting immediate meal options, and planning future meals. However, the study raised new questions as to appropriate time, form, and focus of forecasts and suggested new research directions for personalized predictions in health.2019PDPooja M. Desai et al.Columbia University Teachers CollegeAI-Assisted Decision-Making & AutomationChronic Disease Self-Management (Diabetes, Hypertension, etc.)Biosensors & Physiological MonitoringCHI
Designing in the Dark: Eliciting Self-tracking Dimensions for Understanding Enigmatic DiseaseThe design of personal health informatics tools has traditionally been explored in self-monitoring and behavior change. There is an unmet opportunity to leverage self- tracking of individuals and study diseases and health conditions to learn patterns across groups. An open research question, however, is how to design engaging self-tracking tools that also facilitate learning at scale. Furthermore, for conditions that are not well understood, a critical question is how to design such tools when it is unclear which data types are relevant to the disease. We outline the process of identifying design requirements for self-tracking endometriosis, a highly enigmatic and prevalent disease, through interviews (N=3), focus groups (N=27), surveys (N=741), and content analysis of an online endometriosis community (1500 posts, N=153 posters) and show value in triangulating across these methods. Finally, we discuss tensions inherent in designing self-tracking tools for individual use and population analysis, making suggestions for overcoming these tensions.2018MMMollie McKillop et al.Columbia UniversityMental Health Apps & Online Support CommunitiesChronic Disease Self-Management (Diabetes, Hypertension, etc.)CHI
Lost in Migration: Information Management and Community Building in an Online Health CommunityThe ever-growing volume of information within online health communities (OHCs) presents an urgent need for new solutions that improve the efficiency of information organization and retrieval for their members. To meet this need, OHCs may choose to adopt off-the-shelf platforms that provide novel features for information management, but were not specifically designed to meet these communities’ needs. The questions remain, however, as to the impact of these new platforms on social dynamics within OHCs and their well-being. To examine these questions, we qualitatively studied a migration of a popular OHC, focusing on diabetes self-management, between two off-the-shelf social computing platforms. Despite improving information management, the migration served as a catalyst to reveal the importance of features for identity management and closed circle communication that were not apparent to either the management or the membership of the community. We describe the study and draw implications for research and design for OHCs.2018DNDrashko Nakikj et al.Columbia UniversityMental Health Apps & Online Support CommunitiesChronic Disease Self-Management (Diabetes, Hypertension, etc.)CHI
Pictures Worth a Thousand Words: Reflections on Visualizing Personal Blood Glucose Forecasts for Individuals with Type 2 DiabetesType 2 Diabetes Mellitus (T2DM) is a common chronic condition that requires management of one’s lifestyle, including nutrition. Critically, patients often lack a clear understanding of how everyday meals impact their blood glucose. New predictive analytics approaches can provide personalized mealtime blood glucose forecasts. While communicating forecasts can be challenging, effective strategies for doing so remain little explored. In this study, we conducted focus groups with 13 participants to identify approaches to visualizing personalized blood glucose forecasts that can promote diabetes self-management and understand key styles and visual features that resonate with individuals with diabetes. Focus groups demonstrated that individuals rely on simple heuristics and tend to take a reactive approach to their health and nutrition management. Further, the study highlighted the need for simple and explicit, yet information-rich design. Effective visualizations were found to utilize common metaphors alongside words, numbers, and colors to convey a sense of authority and encourage action and learning.2018PDPooja M. Desai et al.Columbia UniversityMental Health Apps & Online Support CommunitiesChronic Disease Self-Management (Diabetes, Hypertension, etc.)CHI