OBJECTIVE: To characterize and demonstrate how to reduce the administrative burden experienced by patients when navigating medication affordability resources in the United States. MATERIALS AND METHODS: Informed by administrative burden theory, we conducted an environmental scan of medication affordability resources for atrial fibrillation, and four common comorbidities (diabetes, heart failure, hypertension, and lipid disorder). We systematically searched for resources (eg, patient assistance programs, savings cards and nonprofit support) and extracted information about types, eligibility criteria, needed documentation, and application processes. RESULTS: We identified 66 resources across 12 categories across the five conditions. The resources' varied eligibility criteria, application processes, and requirements for providing sensitive financial documents could introduce multiple administrative costs for patients. DISCUSSION: The volume and complexity of medication affordability resources and related application processes may create substantial administrative burden for patients that could prevent their use-especially when prescribed multiple medications. CONCLUSION: Medication affordability resource informatics tools that reduce administrative burden could advance equitable medication access.

COVID Long Haul (CLH) is an emerging chronic illness with varied patient experiences. Our understanding of CLH is often limited to data from electronic health records (EHRs), such as diagnoses or problem lists, which do not capture the volatility and severity of symptoms or their impact. To better understand the unique presentation of CLH, we conducted a 3-month long cohort study with 14 CLH patients, collecting objective (EHR, daily Fitbit logs) and subjective (weekly surveys, interviews) data. Our findings reveal a complex presentation of symptoms, associated uncertainty, and the ensuing impact CLH has on patients’ personal and professional lives. We identify patient needs, practices, and challenges around adhering to clinical recommendations, engaging with health data, and establishing "new normals" post COVID. We reflect on the potential found at the intersection of these various data streams and the persuasive heuristics possible when designing for this new population and their specific needs.

OBJECTIVE: We investigated patient experiences with medication- and test-related cost conversations with healthcare providers to identify their preferences for future informatics tools to facilitate cost-sensitive care decisions. MATERIALS AND METHODS: We conducted 18 semistructured interviews with diverse patients (ages 24-81) in a Midwestern health system in the United States. We identified themes through 2 rounds of qualitative coding. RESULTS: Patients believed their providers could help reduce medication-related costs but did not see how providers could influence test-related costs. Patients viewed cost conversations about medications as beneficial when providers could adjust medical recommendations or provide resources. However, cost conversations did not always occur when patients felt they were needed. Consequently, patients faced a "cascade of work" to address affordability challenges. To prevent this, collaborative informatics tools could facilitate cost conversations and shared decision-making by providing information about a patient's financial constraints, enabling comparisons of medication/testing options, and addressing transportation logistics to facilitate patient follow-through. DISCUSSION: Like providers, patients want informatics tools that address patient out-of-pocket costs. They want to discuss healthcare costs to reduce the frequency of unaffordable costs and obtain proactive assistance. Informatics interventions could minimize the cascade of patient work through shared decision-making and preventative actions. Such tools might integrate information about efficacy, costs, and side effects to support decisions, present patient decision aids, facilitate coordination among healthcare units, and eventually improve patient outcomes. CONCLUSION: To prevent a burdensome cascade of work for patients, informatics tools could be designed to support cost conversations and decisions between patients and providers.

The user study is a fundamental method used in HCI. In designing user studies, we often use compensation strategies to incentivize recruitment. However, compensation can also lead to ethical issues, such as coercion. The CHI community has yet to establish best practices for participant compensation. Through a systematic review of manuscripts at CHI and other associated publication venues, we found high levels of variation in the compensation strategies used within the community and how we report on this aspect of the study methods. A qualitative analysis of justifications offered for compensation sheds light into how some researchers are currently contextualizing this practice. This paper provides a description of current compensation strategies and information that can inform the design of compensation strategies in future studies. The findings may be helpful to generate productive discourse in the HCI community towards the development of best practices for participant compensation in user studies.

A growing body of research in HCI focuses on understanding how social media and other social technologies impact a given user’s mental health, including eating disorders. In this paper, we review the results of an interview study with 10 clinicians spanning various specialties who treat people with eating disorders, in order to understand the clinical contexts of eating disorders and social media use. We found various tensions related to clinician comfort and education into the (mis)use of technologies and balancing the positive and negative aspects of social media use within active disease states as well as in recovery. Understanding these tensions as well as the variation in the current process of diagnosing patients is a critical component in connecting HCI research focused on eating disorders to clinical practice and ultimately assessing how digital self-harm could be addressed clinically in the future.

The use of legal and illegal drugs has grown to such an acute level that it now represents a public health crisis in the United States. To support clinical treatments of substance use disorders (SUDs), formal non-clinical peer recovery support programs pairing coaches with people new to recovery are gaining in popularity. Using a user-centered design approach, we designed a mobile application to support the peer coach recovery program of a health system. The application addresses the needs associated with the coaches' workflows, encompasses social supports for recoverees, and provides a space for fostering the coach-recoveree relationship. Finally, we then evaluated a prototype with recoverees and program coaches. Through this process, we identified tensions between stakeholder needs and translated these tensions into design features and future design considerations.

Eating disorders (EDs) are a worldwide public health concern that impact approximately 10% of the U.S. population. Our previous research characterized these behaviors across online spaces. These characterizations have used clinical terminology, and their lexical variants, to identify ED content online. However, previous HCI research on EDs (including our own) suffers from a lack of gender and cultural diversity. In this paper, we designed a follow-up study of online ED characterizations, extending our previous methodologies to focus specifically on male/masculine-related content. We highlight the similarities and differences found in the terminology utilized and media archetypes associated with the social media content. Finally, we discuss other considerations highlighted through our analysis of the male-related content that is missing from the previous research.

Applying a user-centered design (UCD) process within the framework of a learning healthcare system (LHS), this study unearths role-specific performance measures to support operational and financial decision making in community health centers (CHCs). We first built a multidimensional EHR/Practice Management data warehouse through a large collaborative of seven CHCs in the state of Indiana aimed at improving efficiency and access to care. A UCD process that comprised contextual interviews, card sorting and high fidelity dashboard prototyping was used to uncover over 45 different operational performance measures, many of which were unique and hitherto unknown measures of relevance to different individual roles within a CHC that included frontline staff, providers, clinical support, and executive management. Within the LHS paradigm, the study highlights the value of role-specific performance measurement and their delivery through interactive user-centered visualizations, while continuing to guide the development of future informatics tools.

OBJECTIVE: Researchers conduct studies with selection biases, which may limit generalizability and outcomes of intervention research. In this methodological reflection, we examined demographic and health characteristics of implantable cardioverter defibrillator patients who were excluded from an informatics intervention due to lack of access to a computer and/or the internet. MATERIALS AND METHODS: Using information gathered from surveys and electronic health records, we compared the intervention group to excluded patients on demographic factors, computer skills, patient activation, and medical history. RESULTS: Excluded patients were older, less educated, less engaged and activated in their health, and had worse health (ie, more medical comorbidities) than nonexcluded patients. DISCUSSION: Although excluded from the intervention based solely on lack of access to a computer and/or internet, excluded patients may have needed the intervention more because they were sicker with more comorbidities. CONCLUSION: Researchers must be mindful of enrollment biases and demographic and health-related inequities that may exist during recruitment for technology-based interventions.

Digital self-harm encompasses a variety of activities, including the use of social media to facilitate or amplify mental illness-related behaviors. To understand the extent of these behaviors and their impacts, we conducted an in-depth case study with three patients who are in recovery from an eating disorder. We collected survey data, conducted interviews reflecting back to their technology use during their active disease state, and reviewed up to 18 months of their social media data leading up to the start of their initial point of recovery. Through the triangulation of this data, we explore the role of social media and social technologies in relation to their eating disorder. By utilizing this methodology, we were able to provide a contextually rich and nuanced lens for exploring the impacts of digital self-harm on this group of patients. We found that patients acknowledged that the eating disordered content on social media had a negative impact on their health, often contributing to a worsening of the physical manifestations of their disorder. Conversely, while they actively consumed this content, our participants did not produce online content related to eating disordered activities or behaviors. Finally, we discuss the patterns within their social media data and how platform designers and operators could use these findings in the future through design considerations for future platform-based interventions.

OBJECTIVES: To determine the impact of tethered personal health record (PHR) use on patient engagement and intermediate health outcomes among patients with coronary artery disease (CAD). METHODS: Adult CAD patients (N = 200) were enrolled in this prospective, quasi-experimental observational study. Each patient received a PHR account and training on its use. PHRs were populated with information from patient electronic medical records, hosted by a Health Information Exchange. Intermediate health outcomes including blood pressure, body mass index, and hemoglobin A1c (HbA1c) were evaluated through electronic medical record review or laboratory tests. Trends in patient activation measure® (PAM) were determined through three surveys conducted at baseline, 6 and 12 months. Frequency of PHR use data was collected and used to classify participants into groups for analysis: Low, Active, and Super users. RESULTS: There was no statistically significant improvement in patient engagement as measured by PAM scores during the study period. HbA1c levels improved significantly in the Active and Super user groups at 6 months; however, no other health outcome measures improved significantly. Higher PAM scores were associated with lower body mass index and lower HbA1c, but there was no association between changes in PAM scores and changes in health outcomes. Use of the PHR health diary increased significantly following PHR education offered at the 6-month study visit and an elective group refresher course. CONCLUSIONS: The study findings show that PHR use had minimal impact on intermediate health outcomes and no significant impact on patient engagement among CAD patients.

In this paper we describe findings from two studies aimed at understanding how health monitoring technology affects the parent-child relationship, examining emotional response and barriers to using this type of technology. We present suggestions for the design of health monitoring technology intended to enhance self-care in children without creating parent-child conflict. Our recommendations integrate the study findings, developmental stage specific concerns, and prior HCI research aimed at children's health.

Childhood offers a number of opportunities for parents to shape the health related attitudes and behaviors of their children. The proposed research described in this paper aims to better understand the ways in which a child's personal health data can be leveraged to educate and provide a transition to healthy adult behaviors. The target population for this project is children with Type 1 Diabetes and their parents but many of the design issues may be relevant to the management of other chronic diseases as well as general health in childhood.

Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people's activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity.