ABSTRACT
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. © 2023 ACM.
ABSTRACT
Algorithms as a component of decision-making in healthcare are becoming increasingly prevalent and AI in healthcare has become a topic of mass consideration. However, pursuing these methods without a human-centered framework can lead to bias, thus incorporating discrimination on behalf of the algorithm upon implementation. By examining each step of the design process from a human-centered perspective and incorporating stakeholder motivations, algorithmic implementation can become vastly useful, and more accurately tailored to stakeholder needs. We examine previous work in healthcare executed with a human-centered design, to analyze the multiple frameworks which effectively create human-centered application, as extended to healthcare. © 2023 Owner/Author.