Cancer survivors face unique mental health challenges, yet nearly half report unmet psychosocial needs. Smartphone interventions could help, but a major obstacle is knowing if, when, and how to intervene because inferring affective states with low-burden methods is hard. We test whether ultra-brief mobile diaries can infer contextual information approximating survivors’ affect, desire to regulate affect, and potential availability for brief digital behavioral interventions. Analyzing 24,183 entries from 407 survivors, administrative and health-related situations align with higher negative affect, whereas leisure/social situations align with higher positive affect. We introduce a Context-Aware LLM (CALLM) framework, which curates context via similarity-aligned peer cases and short personal trajectories, achieving balanced accuracy of 72.96% (positive affect), 73.29% (negative affect), 73.72% (regulation desire), and 60.09% (intervention availability), outperforming baselines. Post-hoc analyses show LLM confidence tracks accuracy, longer entries aid inference, and brief calibration improves personalization. Findings inform future just-in-time adaptive interventions for this underrepresented population.
Serious Illness Conversations (SICs)—discussions about values and care preferences for patients with life-threatening illness—rarely occur in Emergency Departments (EDs), despite evidence that early conversations improve care alignment and reduce unnecessary interventions. We interviewed 11 ED providers to identify challenges in SICs and opportunities for technology support, with a focus on AI. Our analysis revealed a four-stage SIC workflow (identification, preparation, conduction, documentation) and barriers at each stage, including fragmented patient information, limited time and space, lack of conversational guidance, and burdensome documentation. Providers expressed interest in AI systems for synthesizing information, supporting real-time conversations, and automating documentation, but emphasized concerns about preserving human connection and clinical autonomy. This tension highlights the need for technologies that enhance efficiency without undermining the interpersonal nature of SICs. We propose design guidelines for ambient and peripheral AI systems to support providers while preserving the essential humanity of these conversations.
Care quality improvement systems (CQIS) allow hospitals to monitor and improve their care by analysing performance data. However, many CQIS fail at helping clinicians improve as they see decreased use and stagnating care quality. In this paper, we investigate the use of one CQIS through ten qualitative activities carried out across five years. We synthesize and present our results regarding specific problems various stakeholders face, exact data visualization tasks that clinicians struggle with, and concrete solutions described by clinicians. Our research proposes steps that designers can take towards integrating data analysis interfaces and automating tasks in CQIS to assist with concrete data visualization problems identified by the 74 clinicians who participated in our study.
Referrals from the emergency department (ED) to inpatient teams are routine but high-stakes interactions, yet little is known about how they are accomplished in practice. Prior work often treats referrals as information transfer and emphasises structural tensions between departments, paying limited attention to the interactional detail of referral calls. To address this gap,we draw on seventeen recorded referral calls collected during a year of ethnographic fieldwork in a metropolitan ED. We show how clinicians manage jurisdictional seams through fine-grained conversational moves,and identify navigation strategies: tentative problem framing, preference-sensitive questioning, implicit boundary negotiation, offering of assistance and follow-through,and balancing displays of competence and humility. We demonstrate how the patient’s case is not treated as a fixed record but is reshaped in talk to align with different specialties,operating as a dynamic object. These insights extend accounts of boundary work by offering a micro-interactional view of how clinicians negotiate responsibility at jurisdictional seams,and contribute training implications that treat referral talk as interactional expertise,and design directions for improved referral tool technologies that surface seams,scaffold rather than script calls,and keep automation in service of clinical judgement.
Psychotherapy delivery relies on a negotiation between patient self-reports and clinical intuition. Growing evidence for technological support of psychotherapy suggests opportunities to aid the mediation of this tension. To explore this prospect, we designed a prototype of a clinical decision support system (CDSS) for treating veterans with post-traumatic stress disorder in a Prolonged Exposure (PE) therapy intensive outpatient program. We conducted a two-phase interview study to collect perspectives from practicing PE clinicians and former PE patients who are United States veterans. Our analysis distills opportunities for a CDSS (e.g., offering homework review at a glance, aiding patient conceptualization) and larger challenges related to context and deployment (e.g., navigating Veterans Affairs). By reframing our findings through three human-centered perspectives (distributed cognition, situated learning, infrastructural inversion), we highlight the complexities of designing a CDSS for psychotherapists in this context and offer theory-aligned design considerations.
Emergency departments (EDs) often experience overcrowding, staff shortages, and long waiting times, which put a strain on clinicians and negatively impact patients' experiences.
Although AI is commonly proposed to optimize clinical workflows, the perspectives of patients are often overlooked in AI design.
We report on a four-phase study at a university hospital combining preparatory fieldwork, co-creation workshops, design phase, and storyboard-guided interviews.
Our findings reveal a misalignment: clinicians framed the contribution of AI around triage efficiency, while patients emphasized reassurance, empathy, and guidance. Addressing both needs, we developed and evaluated a concept of an ephemeral AI companion for the ED waiting area, designed to provide orientation, support reflection, and prepare patients for consultations without substituting human contact.
We contribute: empirical evidence of patients’ needs, concerns, and expectations for AI support, design principles for an ephemeral AI companion concept, and findings from a study conducted with patients during their ED waits.
Virtual reality exposure therapy (VRET) enables controlled exposure to trauma-related stimuli to facilitate memory access and emotional processing. However, the field remains underexplored for complex post-traumatic stress disorder (C-PTSD). Unlike single-trauma PTSD, C-PTSD requires highly individualized triggers that are difficult to identify and implement safely. We conducted a feasibility study with 11 patients, two trauma therapists, and a VR developer to explore integrating VRET into C-PTSD treatment while safeguarding all stakeholders. Initial findings indicate that simple objects can be just as effective as complex scenes, therapeutic success does not correlate with VR presence levels, and the design process itself became integral to therapy rather than preparatory. However, involving developers in therapy sessions led to considerable emotional stress and role confusion, which required a cautious approach. Based on these insights, we provide methodological recommendations for safe and patient-centered VRET studies that balance therapeutic effectiveness with stakeholder safety across the research process.