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.

COVID Long Haul (CLH) is an emerging chronic illness for which the healthcare system continues to seek a common understanding of symptoms, diagnosis, and treatment. CLH experiences can differ drastically, necessitating personalized care plans. Because patients interact with different clinicians during their CLH journey, it becomes important to ensure interoperability and understand clinical relevance of different data that can support clinicians in making appropriate recommendations. We conducted qualitative research where we interviewed 13 patients, conducted a focus group with 8 clinicians, and analyzed care plan follow-up records. We report patient and clinician expectations from and interactions with clinic data. We uncover logistical challenges, personal contexts, and health barriers impacting patient compliance. As researchers embedded in the clinical system, we identify the potential of using multiple patient data streams to support personalized treatment and clinical decisions. We discuss technology design opportunities and provide actionable recommendations for improving clinical workflows and cross-provider collaboration.

The COVID-19 pandemic transformed many aspects of health and daily life. A subset of people who were infected with the virus have ongoing chronic health issues that range in type of symptom and severity. In this study, we conducted a qualitative assessment of self-reported post-COVID symptoms from patients' electronic health records (EHR, n=564) and a randomized collection of Reddit and Twitter posts (n=500 for each). We show the inconsistencies in what types of symptoms are shared between platforms in addition to assessing the severity of the symptoms and how social media characterizations of post-COVID do not tell a complete story of this phenomenon. This research contributes to CSCW health literature by connecting digital traces of post-COVID with EHR data, critiquing the use of social media as a health proxy and points to its potential to add context to the analysis of traditional health data extracted from the EHR.