Burnout and the Quantified Workplace: Tensions around Personal Sensing Interventions for Stress in Resident Physicians

Abstract

Recent research has explored computational tools to manage workplace stress via personal sensing, a measurement paradigm in which behavioral data streams are collected from technologies including smartphones, wearables, and personal computers. As these tools develop, they invite inquiry into how they can be appropriately implemented towards improving workers' well-being. In this study, we explored this proposition through formative interviews followed by a design provocation centered around measuring burnout in a U.S. resident physician program. Residents and their supervising attending physicians were presented with medium-fidelity mockups of a dashboard providing behavioral data on residents' sleep, activity and time working; self-reported data on residents' levels of burnout; and a free text box where residents could further contextualize their well-being. Our findings uncover tensions around how best to measure workplace well-being, who within a workplace is accountable for worker stress, and how the introduction of such tools remakes the boundaries of appropriate information flows between worker and workplace. We conclude by charting future work confronting these tensions, to ensure personal sensing is leveraged to truly improve worker well-being.

Understanding Mental Health Clinicians’ Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study

“…Although our results do not contest the objectivity of using digitally tracked data to measure behavior, they do question the objectivity of these data with respect to measuring a patient’s mental health. Researchers in human-computer interaction have echoed this point that digital measures may reduce mental health to biobehavioral data points, missing more interpretive aspects of mental health that cannot be easily quantified using passive tracking [ 20 , 33 ]. These critiques may also apply to specific forms of active PGHD, for example, behavioral data collected during health-related active tasks, although participants did not discuss these forms of active data during the interviews.…”

“…We created an interview guide composed of 3 sections following the speculative design tradition [ 31 , 33 ]. The interview opened with an introductory section to learn about the participants’ clinical practice.…”

W4-Groups: Modeling the Who, What, When and Where of Group Behavior via Mobility Sensing

“…There has been increasing research in the CSCW space where group modeling techniques are applied to gather behavioral data streams of individuals and their groups on topics related to health, engagement, and performance. Alder et al 's study on how passive personal sensing technologies could help resident physicians with their burnout symptoms revealed the potential for tracking a resident's work deviations from their cohort's norm [1]. Separately, Zakaria et al reported a significant predictor of stress is the deviations between the student and their group's norm, distinguishing casual groups from workgroups [75] and their social identification to respective groups [76].…”

“…Consider our case study of analyzing longitudinal behavioral change among students on campus in Section 6.2 to separately analyze the behaviors of resident physicians in their workplaces [1] as an example. Such a system would require individual stakeholders to trust the institution enormously.…”

“…Without violating individual privacy, aggregated insights ensure consistent deployment of community crisis resources is within reach of potential target users. Aside from direct interventions, enabling a data-sharing use case to encourage training programs better manage residents' well-being [1] should only be at the discretion of the individual, coupled with policies for data accountability.…”