Attitudes toward and preparedness for lung transplantation among individuals with cystic fibrosis in the era of highly effective modulators.

BACKGROUND: Outcomes for individuals with cystic fibrosis (CF) have improved due to highly effective modulator therapy (HEMT). However, lung transplant (LTx) remains an important treatment for people with advanced lung disease. This study assessed attitudes and knowledge about LTx in the HEMT era. METHODS: All patients from the University of Washington CF clinic were surveyed March 25-May 30, 2020. Questions addressed self-rated LTx preparedness and knowledge, as well as barriers and facilitators to discussing LTx. Demographic and clinical data were extracted from the electronic health record. RESULTS: There were 159/224 (71%) responses. Respondents had a median forced expiratory volume in one second (FEV1) of 70%, and 142 (89%) were on modulatory therapy. One hundred thirteen (71%) respondents felt that it was moderately or very important to be prepared to make decisions about LTx, though only 56 (35%) felt moderately or very prepared. Only 83 (30%) and 47 (52%) participants correctly answered questions about life expectancy and improved quality of life after LTx, respectively. Respondents with Medicaid insurance less frequently answered questions correctly. The most common barriers to discussing LTx were fear of being a burden on loved ones for 58 respondents (36%) and cost of LTx for 46 (29%). Most participants (94%) trusted their CF doctor, and 75% of participants selected trust as a facilitator for LTx discussions. CONCLUSIONS: Many individuals with CF, especially those with lower socioeconomic status, lacked knowledge and did not feel very prepared for decisions about LTx. Earlier education and discussions about LTx represent an area for improvement in CF care.

Approaches for Implementing App-Based Digital Treatments for Drug Use Disorders Into Primary Care: A Qualitative, User-Centered Design Study of Patient Perspectives.

BACKGROUND: Digital interventions, such as websites and smartphone apps, can be effective in treating drug use disorders (DUDs). However, their implementation in primary care is hindered, in part, by a lack of knowledge on how patients might like these treatments delivered to them. OBJECTIVE: This study aims to increase the understanding of how patients with DUDs prefer to receive app-based treatments to inform the implementation of these treatments in primary care. METHODS: The methods of user-centered design were combined with qualitative research methods to inform the design of workflows for offering app-based treatments in primary care. Adult patients (n=14) with past-year cannabis, stimulant, or opioid use disorder from 5 primary care clinics of Kaiser Permanente Washington in the Seattle area participated in this study. Semistructured interviews were recorded, transcribed, and analyzed using qualitative template analysis. The coding scheme included deductive codes based on interview topics, which primarily focused on workflow design. Inductive codes emerged from the data. RESULTS: Participants wanted to learn about apps during visits where drug use was discussed and felt that app-related conversations should be incorporated into the existing care whenever possible, as opposed to creating new health care visits to facilitate the use of the app. Nearly all participants preferred receiving clinician support for using apps over using them without support. They desired a trusting, supportive relationship with a clinician who could guide them as they used the app. Participants wanted follow-up support via phone calls or secure messaging because these modes of communication were perceived as a convenient and low burden (eg, no copays or appointment travel). CONCLUSIONS: A user-centered implementation of treatment apps for DUDs in primary care will require health systems to design workflows that account for patients' needs for structure, support in and outside of visits, and desire for convenience.

Capturing the patient voice: implementing patient-reported outcomes across the health system.

PURPOSE: Supporting the capture and use of patient-reported outcomes (PROs) at the point-of-care enriches information about important clinical and quality of life outcomes. Yet the ability to scale PROs across healthcare systems has been limited by knowledge gaps around how to manage the diversity of PRO uses and leverage health information technology. In this study, we report learnings and practice insights from UW Medicine's practice transformation efforts to incorporate patient voice into multiple areas of care. METHODS: Using a participatory, action research approach, we engaged with UW Medicine clinical and administrative stakeholders experienced with PRO implementation to inventory PRO implementations across the health system, characterize common clinical uses for PROs, and develop recommendations for system-wide governance and implementation of PROs. RESULTS: We identified a wide breadth of PRO implementations (n = 14) in practice and found that nearly half (47%) of employed PRO measures captured shared clinical domains (e.g., depression). We developed three vignettes (use cases) that illustrate how users interact with PROs, characterize common ways PRO implementations support clinical care across the health system (1) Preventive care, (2) Chronic/Specialty care, and (3) Surgical/Interventional care), and elucidate opportunities to enhance efficient PRO implementations through system-level standards and governance. CONCLUSIONS: Practice transformation efforts increasingly require integration of the patient voice into clinical care, often through the use of PROs. Learnings from our work highlight the importance of proactively considering how PROs will be used across the layers of healthcare organizations to optimize the design and governance of PROs.

Roles and Functions of Community Health Workers in Primary Care.

Community health workers have potential to enhance primary care access and quality, but remain underutilized. To provide guidance on their integration, we characterized roles and functions of community health workers in primary care through a literature review and synthesis. Analysis of 30 studies identified 12 functions (ie, care coordination, health coaching, social support, health assessment, resource linking, case management, medication management, remote care, follow-up, administration, health education, and literacy support) and 3 prominent roles representing clusters of functions: clinical services, community resource connections, and health education and coaching. We discuss implications for community health worker training and clinical support in primary care.

Understanding What Is Most Important to Individuals with Multiple Chronic Conditions: A Qualitative Study of Patients' Perspectives.

BACKGROUND: To improve care for individuals living with multiple chronic conditions, patients and providers must align care planning with what is most important to patients in their daily lives. We have a limited understanding of how to effectively encourage communication about patients' personal values during clinical care. OBJECTIVE: To identify what patients with multiple chronic conditions describe as most important to their well-being and health. DESIGN: We interviewed individuals with multiple chronic conditions in their homes and analyzed results qualitatively, guided by grounded theory. PARTICIPANTS: A total of 31 patients (mean age 68.7 years) participated in the study, 19 of which included the participation of family members. Participants were from Kaiser Permanente Washington, an integrated health care system in Washington state. APPROACH: Qualitative analysis of home visits, which consisted of semi-structured interviews aided by photo elicitation. KEY RESULTS: Analysis revealed six domains of what patients described as most important for their well-being and health: principles, relationships, emotions, activities, abilities, and possessions. Personal values were interrelated and rarely expressed as individual values in isolation. CONCLUSIONS: The domains describe the range and types of personal values multimorbid older adults deem important to well-being and health. Understanding patients' personal values across these domains may be useful for providers when developing, sharing, and following up on care plans.

"How Did We Get Here?": Topic Drift in Online Health Discussions.

BACKGROUND: Patients increasingly use online health communities to exchange health information and peer support. During the progression of health discussions, a change of topic-topic drift-can occur. Topic drift is a frequent phenomenon linked to incoherence and frustration in online communities and other forms of computer-mediated communication. For sensitive topics, such as health, such drift could have life-altering repercussions, yet topic drift has not been studied in these contexts. OBJECTIVE: Our goals were to understand topic drift in online health communities and then to develop and evaluate an automated approach to detect both topic drift and efforts of community members to counteract such drift. METHODS: We manually analyzed 721 posts from 184 threads from 7 online health communities within WebMD to understand topic drift, members' reaction towards topic drift, and their efforts to counteract topic drift. Then, we developed an automated approach to detect topic drift and counteraction efforts. We detected topic drift by calculating cosine similarity between 229,156 posts from 37,805 threads and measuring change of cosine similarity scores from the threads' first posts to their sequential posts. Using a similar approach, we detected counteractions to topic drift in threads by focusing on the irregular increase of similarity scores compared to the previous post in threads. Finally, we evaluated the performance of our automated approaches to detect topic drift and counteracting efforts by using a manually developed gold standard. RESULTS: Our qualitative analyses revealed that in threads of online health communities, topics change gradually, but usually stay within the global frame of topics for the specific community. Members showed frustration when topic drift occurred in the middle of threads but reacted positively to off-topic stories shared as separate threads. Although all types of members helped to counteract topic drift, original posters provided the most effort to keep threads on topic. Cosine similarity scores show promise for automatically detecting topical changes in online health discussions. In our manual evaluation, we achieved an F1 score of .71 and .73 for detecting topic drift and counteracting efforts to stay on topic, respectively. CONCLUSIONS: Our analyses expand our understanding of topic drift in a health context and highlight practical implications, such as promoting off-topic discussions as a function of building rapport in online health communities. Furthermore, the quantitative findings suggest that an automated tool could help detect topic drift, support counteraction efforts to bring the conversation back on topic, and improve communication in these important communities. Findings from this study have the potential to reduce topic drift and improve online health community members' experience of computer-mediated communication. Improved communication could enhance the personal health management of members who seek essential information and support during times of difficulty.

Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text.

BACKGROUND: The prevalence and value of patient-generated health text are increasing, but processing such text remains problematic. Although existing biomedical natural language processing (NLP) tools are appealing, most were developed to process clinician- or researcher-generated text, such as clinical notes or journal articles. In addition to being constructed for different types of text, other challenges of using existing NLP include constantly changing technologies, source vocabularies, and characteristics of text. These continuously evolving challenges warrant the need for applying low-cost systematic assessment. However, the primarily accepted evaluation method in NLP, manual annotation, requires tremendous effort and time. OBJECTIVE: The primary objective of this study is to explore an alternative approach-using low-cost, automated methods to detect failures (eg, incorrect boundaries, missed terms, mismapped concepts) when processing patient-generated text with existing biomedical NLP tools. We first characterize common failures that NLP tools can make in processing online community text. We then demonstrate the feasibility of our automated approach in detecting these common failures using one of the most popular biomedical NLP tools, MetaMap. METHODS: Using 9657 posts from an online cancer community, we explored our automated failure detection approach in two steps: (1) to characterize the failure types, we first manually reviewed MetaMap's commonly occurring failures, grouped the inaccurate mappings into failure types, and then identified causes of the failures through iterative rounds of manual review using open coding, and (2) to automatically detect these failure types, we then explored combinations of existing NLP techniques and dictionary-based matching for each failure cause. Finally, we manually evaluated the automatically detected failures. RESULTS: From our manual review, we characterized three types of failure: (1) boundary failures, (2) missed term failures, and (3) word ambiguity failures. Within these three failure types, we discovered 12 causes of inaccurate mappings of concepts. We used automated methods to detect almost half of 383,572 MetaMap's mappings as problematic. Word sense ambiguity failure was the most widely occurring, comprising 82.22% of failures. Boundary failure was the second most frequent, amounting to 15.90% of failures, while missed term failures were the least common, making up 1.88% of failures. The automated failure detection achieved precision, recall, accuracy, and F1 score of 83.00%, 92.57%, 88.17%, and 87.52%, respectively. CONCLUSIONS: We illustrate the challenges of processing patient-generated online health community text and characterize failures of NLP tools on this patient-generated health text, demonstrating the feasibility of our low-cost approach to automatically detect those failures. Our approach shows the potential for scalable and effective solutions to automatically assess the constantly evolving NLP tools and source vocabularies to process patient-generated text.

Improving Vaccine Equity: How Community Engagement and Informatics Facilitate Health System Outreach to Underrepresented Groups.

BACKGROUND: Given the inequities in access to health care resources like COVID-19 vaccination, health systems should carefully consider how to reach underrepresented groups. Reflecting on vaccine rollout efforts holds insight on the role of community engagement and informatics support in promoting health equity. OBJECTIVES: This study aimed to assess the effectiveness of four outreach strategies deployed by University of Washington (UW) Medicine in improving vaccine equity over traditional vaccine scheduling online or by phone, we report on appointment scheduling and completion of appointments (i.e., vaccine administration) through (1) automated outreach to individuals from underrepresented groups, (2) temporary "pop-up" clinics in neighborhoods highly impacted by COVID-19, (3) vulnerable population clinics, and (4) mobile vaccine vans. METHODS: We conducted a 6-month retrospective analysis of electronic health records (EHR) to describe the sociodemographic characteristics of individuals who scheduled appointments using the outreach strategies and characteristics associated with a greater likelihood of vaccine administration based on appointment completion. To help explain trends in the EHR data, we engaged 10 health system leaders and staff who spearheaded the outreach strategies in follow-up conversations to identify qualitative insights into what worked and why. RESULTS: Compared with traditional scheduling, all outreach strategies except vulnerable population clinics had higher vaccine appointment completion rates, including automated outreach (N = 3,734 [94.7%], p < 0.001), pop-up clinics (N = 4,391 [96.0%], p < 0.001), and mobile vans (N = 4,198 [99.1%], p < 0.001); and lower cancellation rates, including automated outreach (N = 166 [1.1%], p < 0.001), pop-up clinics (N = 155 [0.6%], p < 0.001), and mobile vans (N = 0 [0%], p < 0.001). Qualitative insights emphasized ongoing community partnerships and information resources in successful outreach. CONCLUSION: Vaccine equity outreach strategies improved the proportion of patients who scheduled and completed vaccination appointments among populations disproportionately impacted by COVID-19. Engaging community partners and equity-focused informatics tools can facilitate outreach. Lessons from these outreach strategies carry practical implications for health systems to amplify their health equity efforts.

Designing Communication Feedback Systems To Reduce Healthcare Providers' Implicit Biases In Patient Encounters.

Healthcare providers' implicit bias, based on patients' physical characteristics and perceived identities, negatively impacts healthcare access, care quality, and outcomes. Feedback tools are needed to help providers identify and learn from their biases. To incorporate providers' perspectives on the most effective ways to present such feedback, we conducted semi-structured design critique sessions with 24 primary care providers. We found that providers seek feedback designed with transparent metrics indicating the quality of their communication with a patient and trends in communication patterns across visits. Based on these metrics and trends, providers want this feedback presented in a dashboard paired with actionable, personalized tips about how to improve their communication behaviors. Our study provides new insights for interactive systems to help mitigate the impact of implicit biases in patient-provider communication. New systems that build upon these insights could support providers in making healthcare more equitable, particularly for patients from marginalized communities.

Qualitative Analysis of Perspectives on Lung Transplant among People with Cystic Fibrosis.

Rationale: Lung transplant (LTx) is a potentially lifesaving treatment option for individuals with advanced cystic fibrosis (CF), but more people with CF (PwCF) and advanced lung disease die each year than undergo transplant in the United States. Little is known about these individuals' LTx information needs and factors influencing their decision-making process related to transplant. Objectives: To examine PwCF's experiences with and preferences for provision of LTx information and to identify transplant information needs that CF clinicians are well positioned to address. Methods: We performed semistructured qualitative interviews in two separate cohorts: PwCF without LTx and PwCF with LTx between July 2019 and June 2020. Questions focused on awareness and knowledge about LTx, perspectives related to communication about transplant in the CF clinic, and experiences with LTx. Thematic analysis was used to organize the qualitative data. Exemplar quotes were chosen to llustrate domains that emerged pertaining to the research objectives. Results: Fifty-five PwCF, including 35 without LTx and 20 with LTx, participated. One-third of PwCF without LTx had normal or near-normal lung function. Key common domains among PwCF with and without LTx were identified, including information needs, connections with LTx recipients, and conversations with CF clinicians. For PwCF with and without transplant, concrete information needs were identified: success or survival, social support, surgery, recovery/pain, and quality of life post-transplant. The importance of connecting with LTx recipients to hear their stories and experiences was emphasized by both PwCF with and without transplant. Important considerations for timing and content of discussions with CF clinicians were identified, including having information presented early (before LTx referral is needed) and in limited detail at first. PwCF without LTx wanted to understand how LTx was relevant to them, with a focus on the unique experience of CF. PwCF with LTx emphasized the need for a centralized resource for LTx information. Conclusions: The findings provide content areas for CF clinicians to focus on as they proactively initiate conversations about LTx and support the development of tools to aid in discussions about LTx for PwCF.

ConverSense: An Automated Approach to Assess Patient-Provider Interactions using Social Signals.

Patient-provider communication influences patient health outcomes, and analyzing such communication could help providers identify opportunities for improvement, leading to better care. Interpersonal communication can be assessed through "social-signals" expressed in non-verbal, vocal behaviors like interruptions, turn-taking, and pitch. To automate this assessment, we introduce a machine-learning pipeline that ingests audio-streams of conversations and tracks the magnitude of four social-signals: dominance, interactivity, engagement, and warmth. This pipeline is embedded into ConverSense, a web-application for providers to visualize their communication patterns, both within and across visits. Our user study with 5 clinicians and 10 patient visits demonstrates ConverSense's potential to provide feedback on communication challenges, as well as the need for this feedback to be contextualized within the specific underlying visit and patient interaction. Through this novel approach that uses data-driven self-reflection, ConverSense can help providers improve their communication with patients to deliver improved quality of care.

Stakeholder engagement: a key component of integrating genomic information into electronic health records.

Integrating genomic information into clinical care and the electronic health record can facilitate personalized medicine through genetically guided clinical decision support. Stakeholder involvement is critical to the success of these implementation efforts. Prior work on implementation of clinical information systems provides broad guidance to inform effective engagement strategies. We add to this evidence-based recommendations that are specific to issues at the intersection of genomics and the electronic health record. We describe stakeholder engagement strategies employed by the Electronic Medical Records and Genomics Network, a national consortium of US research institutions funded by the National Human Genome Research Institute to develop, disseminate, and apply approaches that combine genomic and electronic health record data. Through select examples drawn from sites of the Electronic Medical Records and Genomics Network, we illustrate a continuum of engagement strategies to inform genomic integration into commercial and homegrown electronic health records across a range of health-care settings. We frame engagement as activities to consult, involve, and partner with key stakeholder groups throughout specific phases of health information technology implementation. Our aim is to provide insights into engagement strategies to guide genomic integration based on our unique network experiences and lessons learned within the broader context of implementation research in biomedical informatics. On the basis of our collective experience, we describe key stakeholder practices, challenges, and considerations for successful genomic integration to support personalized medicine.

Validity and reliability of a smartphone application for the assessment of penile deformity in Peyronie's disease.

INTRODUCTION: Available methods to evaluate men with Peyronie's disease (PD) are limited by the inability to accurately and reproducibly measure penile deformity. AIM.: The study aims to evaluate the performance of a smartphone application for the measurement of penile curvature and narrowing. METHODS: A smartphone application, the University of Washington Peyronie's Examination Network (UWPEN), was developed for this purpose. To assess penile curvature, 15 single cylinders of malleable penile prostheses were molded to varying curvature angles. Three blinded observers nonsequentially measured the angle of curvature for each prosthetic cylinder using a protractor, goniometer, and UWPEN. To assess girth narrowing, six clay models of the penile shaft were constructed to represent conditions of normal, partial hourglass, circumferential hourglass, and pencil narrowing. Girth was measured using a ruler and UWPEN by the same blinded observers. MAIN OUTCOME MEASURES: Statistical analyses compared intertest, interobserver, and intraobserver reliability using the interclass correlation coefficient (ICC). An ICC above 0.75 indicates excellent reproducibility among measurements. RESULTS: Intertest reliability for angle measurements yielded an ICC for the three methods of 1.000. Separately, the ICC for UWPEN vs. the goniometer and protractor was 0.999 and 0.999, respectively. The interobserver ICC for UWPEN, goniometer, and protractor was 0.998, 0.999, and 1.000, respectively. Intertest reliability for girth narrowing measurements yielded an ICC of 0.991. The interobserver ICC for girth narrowing for UWPEN and the ruler was 0.978 and 0.986, respectively. Intraobserver ICC for angle measurements and girth narrowing showed high reliability for all observers and methods. CONCLUSIONS: The performance of UWPEN is comparable with and highly correlated with angle measurements obtained from the goniometer and protractor as well as with girth narrowing measurements obtained from a ruler. Measurements are reproducible among different observers. UWPEN may provide a noninvasive, accurate, reliable, and widely accessible method to characterize and track PD over time.

Implementation Fidelity of Chatbot Screening for Social Needs: Acceptability, Feasibility, Appropriateness.

OBJECTIVES: Patient and provider-facing screening tools for social determinants of health have been explored in a variety of contexts; however, effective screening and resource referral remain challenging, and less is known about how patients perceive chatbots as potential social needs screening tools. We investigated patient perceptions of a chatbot for social needs screening using three implementation outcome measures: acceptability, feasibility, and appropriateness. METHODS: We implemented a chatbot for social needs screening at one large public hospital emergency department (ED) and used concurrent triangulation to assess perceptions of the chatbot use for screening. A total of 350 ED visitors completed the social needs screening and rated the chatbot on implementation outcome measures, and 22 participants engaged in follow-up phone interviews. RESULTS: The screened participants ranged in age from 18 to 90 years old and were diverse in race/ethnicity, education, and insurance status. Participants (n = 350) rated the chatbot as an acceptable, feasible, and appropriate way of screening. Through interviews (n = 22), participants explained that the chatbot was a responsive, private, easy to use, efficient, and comfortable channel to report social needs in the ED, but wanted more information on data use and more support in accessing resources. CONCLUSION: In this study, we deployed a chatbot for social needs screening in a real-world context and found patients perceived the chatbot to be an acceptable, feasible, and appropriate modality for social needs screening. Findings suggest that chatbots are a promising modality for social needs screening and can successfully engage a large, diverse patient population in the ED. This is significant, as it suggests that chatbots could facilitate a screening process that ultimately connects patients to care for social needs, improving health and well-being for members of vulnerable patient populations.

Integrating patient voices into the extraction of social determinants of health from clinical notes: ethical considerations and recommendations.

Identifying patients' social needs is a first critical step to address social determinants of health (SDoH)-the conditions in which people live, learn, work, and play that affect health. Addressing SDoH can improve health outcomes, population health, and health equity. Emerging SDoH reporting requirements call for health systems to implement efficient ways to identify and act on patients' social needs. Automatic extraction of SDoH from clinical notes within the electronic health record through natural language processing offers a promising approach. However, such automated SDoH systems could have unintended consequences for patients, related to stigma, privacy, confidentiality, and mistrust. Using Floridi et al's "AI4People" framework, we describe ethical considerations for system design and implementation that call attention to patient autonomy, beneficence, nonmaleficence, justice, and explicability. Based on our engagement of clinical and community champions in health equity work at University of Washington Medicine, we offer recommendations for integrating patient voices and needs into automated SDoH systems.

Geospatial divide in real-world EHR data: Analytical workflow to assess regional biases and potential impact on health equity.

Real-world data (RWD) like electronic health records (EHR) has great potential for secondary use by health systems and researchers. However, collected primarily for efficient health care, EHR data may not equitably represent local regions and populations, impacting the generalizability of insights learned from it. We assessed the geospatial representativeness of regions in a large health system EHR data using a spatial analysis workflow, which provides a data-driven way to quantify geospatial representation and identify adequately represented regions. We applied the workflow to investigate geospatial patterns of overweight/obesity and depression patients to find regional "hotspots" for potential targeted interventions. Our findings show the presence of geospatial bias in EHR and demonstrate the workflow to identify spatial clusters after adjusting for bias due to the geospatial representativeness. This work highlights the importance of evaluating geospatial representativeness in RWD to guide targeted deployment of limited healthcare resources and generate equitable real-world evidence.

Improving physical activity among prostate cancer survivors through a peer-based digital walking program.

Physical activity is important for prostate cancer survivors. Yet survivors face significant barriers to traditional structured exercise programs, limiting engagement and impact. Digital programs that incorporate fitness trackers and peer support via social media have potential to improve the reach and impact of traditional support. Using a digital walking program with prostate cancer survivors, we employed mixed methods to assess program outcomes, engagement, perceived utility, and social influence. After 6 weeks of program use, survivors and loved ones (n=18) significantly increased their average daily step count. Although engagement and perceived utility of using a fitness tracker and interacting with walking buddies was high, social media engagement and utility were limited. Group strategies associated with social influence were driven more by group attraction to the collective task of walking than by interpersonal bonds. Findings demonstrate the feasibility of a digital walking program to improve physical activity and extend the reach of traditional support.

Co-designing mind-body technologies for sleep with adolescents.

Sleep is critical for well-being, yet adolescents do not get enough sleep. Mind-body approaches can help. Despite the potential of technology to support mind-body approaches for sleep, there is a lack of research on adolescent preferences for digital mind-body technology. We use co-design to examine adolescent perspectives on mind-body technologies for sleep. From our analysis of design sessions with 16 adolescents, four major themes emerged: system behavior, modality, content, and context. In light of these key findings, we recommend that technology-based mind-body approaches to sleep for adolescents be designed to 1) serve multiple functions while avoiding distractions, 2) provide intelligent content while maintaining privacy and trust, 3) provide a variety of content with the ability to customize and personalize, 4) offer multiple modalities for interaction with technology, and 5) consider the context of adolescent and their families. Findings provide a foundation for designing mind-body technologies for adolescent sleep.

Imagining Improved Interactions: Patients' Designs To Address Implicit Bias.

Implicit biases may negatively influence healthcare providers' behaviors toward patients from historically marginalized communities, impacting providers' communication style, clinical decision-making, and delivery of quality care. Existing interventions to mitigate negative experiences of implicit biases are primarily designed to increase recognition and management of stereotypes and prejudices through provider-facing tools and resources. However, there is a gap in understanding and designing interventions from patient perspectives. We conducted seven participatory co-design workshops with 32 Black, Indigenous, People of Color (BIPOC), Lesbian, Gay, Bisexual, Transgender, Queer/Questioning (LGBTQ+), and Queer, Transgender, Black, Indigenous, People of Color (QTBIPOC) individuals to design patient-centered interventions that help them address and recover from provider implicit biases in primary care. Participants designed four types of solutions: accountability measures, real-time correction, patient enablement tools, and provider resources. These informatics interventions extend the research on implicit biases in healthcare through inclusion of valuable, firsthand patient perspectives and experiences.

Identifying opportunities for informatics-supported suicide prevention: the case of Caring Contacts.

Suicide is the tenth leading cause of death in the United States. Caring Contacts (CC) is a suicide prevention intervention involving care teams sending brief messages expressing unconditional care to patients at risk of suicide. Despite solid evidence for its effectiveness, CC has not been broadly adopted by healthcare organizations. Technology has the potential to facilitate CC if barriers to adoption were better understood. This qualitative study assessed the needs of organizational stakeholders for a CC informatics tool through interviews that investigated barriers to adoption, workflow challenges, and participant-suggested design opportunities. We identified contextual barriers related to environment, intervention parameters, and technology use. Workflow challenges included time-consuming simple tasks, risk assessment and management, the cognitive demands of authoring follow-up messages, accessing and aggregating information across systems, and team communication. To address these needs, we propose design considerations that focus on automation, cognitive support, and data and workflow integration. Future work will incorporate these findings to design informatics tools supporting broader adoption of Caring Contacts.