Hi everyone! I'm Emily Tseng and I'm Dan Adler and we're fourth
year PhD students in Information Science at Cornell University.
We'll be presenting our work exploring how a certain paradigm of mental health measurement
called personal sensing might be used to help medical residents manage burnout.
This work was conducted with collaborators at Northwell Health and at Northwestern University.
So to start, let me introduce you to medical residents. Here in the U.S., a residency is an
apprenticeship that newly minted MDs go through, so folks who have just finished medical school.
This is on-the-job training as a doctor. You work in a hospital under the supervision of
a more senior doctor known as your attending. Residencies are notoriously grueling. Residents
routinely work shifts that are 24 hours or more at a time, and might pack their weeks working
upwards of 100 hours making life or death decisions for people who are extremely sick.
In recent years there's been a lot of concern around how residents experience burnout.
Burnout is an acute sense of work-related stress that begins to affect your well-being. The most
broadly accepted metric of burnout is the Maslach Burnout Inventory, which considers the condition
in three parts. First, you become emotionally exhausted. Second, you begin feeling a decreased
sense of personal accomplishment. Nothing you do excites you anymore. And third, you become you
begin to become depersonalized. You start to lose that sense of connection with others and begin
considering your colleagues and for residents your patients objects rather than people.
Burnout has all sorts of horrible downstream sequelae.
It's been linked with residents making medical errors that harm patients
and with residents themselves experiencing depression, anxiety, and even suicide.
So against this backdrop, we sought to learn whether there might be a
role for personal sensing technologies in addressing resident burnout. There's been
a lot of work at this conference and Ubicomp on what's called the Quantified Workplace:
a paradigm in which sensing is used to quantify and manage employees wellness and workloads. We
have mobile sensing, which has made available all these new data streams that help us get a grasp on
our health through fine-grained measurements of things like sleep, physical activity,
and productivity. In parallel we have lots of folks leveraging machine learning to build new
models from this data that can predict health and wellbeing and even some that extend these
models into personalized recommendations for action. We wanted to know, could these tools
be used to help drive interventions for burnout? Perhaps by giving supervisors detailed knowledge
of just how poorly residents were faring, we could drive them to improve work conditions. Perhaps by
giving residents this knowledge themselves, we can enable them to drive this change.
So we conducted a design provocation study. We started with formative interviews with 11
residents, and then conducted design sessions with five residents and five supervisors.
We showed them a medium fidelity prototype of a dashboard
depicting personal sensing data used for well-being monitoring, and then through a
semi-structured interview, we got a sense for what the prototype elicited for them:
how did they imagine such technology would change their lives and workflows?
Our provocation had four overall affordances. First, the ability to
view residents' behavioral data, the charts at the bottom, and self-reported burnout scores,
which you see just below "John Smith". Second, a place where residents could write about their
well-being, the text box at the top right. Third, highlights suggesting a prediction
model that draws users' attention to problematic values. And finally, attempts at anonymizing data,
which were a starting point for exploring privacy concerns. So, what did we find?
First, we found that it was not clear what measures of burnout and workplace well-being
reflect residents' lived experience. Residents appreciated the idea of passively collecting data
on low-level constructs like sleep and step counts which were seen as less burdensome
than filling out diaries or questionnaires. But they were concerned that passively collected data
may not accurately reflect more subjective aspects of burnout.
Second we found that residents were unwilling to have such information shared unless it was
accompanied by some form of accountability that someone in charge would do something when they
were clearly unwell. In fact residents told us that data sharing would actually make it more
difficult to approach supervisors for help, since they should already have data showing
when residents aren't doing well. Supervisors themselves were hesitant to be held accountable,
stating that their responsibility was to help the resident learn, not act as a caregiver.
Finally, residents and supervisors acknowledge the inherent privacy violations of sharing
personal data like sleep, blurring the boundary between worker and patient.
One supervisor we interviewed accidentally began saying patient instead of resident,
though the metaphor was helpful as it allowed participants to evoke how sharing data violates
a core tenet of mental healthcare: the patient's right to self-presentation, in other words, the
right to present oneself in the way that one both wants in both therapeutic and workplace contexts.
In summary our findings highlight a range of socio-technical challenges
towards implementing Quantified Workplace technologies
and point to the urgency of further work that better centers workers' needs.
First, Quantified Workplace researchers must better triangulate behavioral, biological,
and contextual measures in their assessments of well-being. Formative personal sensing research
uses subjective self-reports as ground truth for behavioral biomarkers of mental health,
but we need more sophisticated methods to account for the complexity of wellness.
Our research joins calls for a broader conception of well-being: one that connects a data-enabled
mental health to the diversity of experiences in what it means to be well and unwell.
Second, we need policy mechanisms that hold supervisors accountable
in exchange for well-being data. We think that further research
into closed-loop systems where workers experience direct benefits for engaging
in well-being programs could pave a path for effective Quantified Workplace technologies.
And finally, we need new approaches to privacy preservation, moving beyond the idea that consent
alone enables appropriate information flows. Our findings show how sharing personal sensing data
disrupts existing relational norms in mental health care, in particular the right to
self-presentation. And it's not clear yet what new norms should take the place of what we currently
understand as the clinician patient relationship or indeed the employer employee relationship.
We see these discomforts as fruitful ground for broader collaborations between not only
ubiquitous computing and clinical researchers but also information scientists, ethicists, and
privacy scholars, to ensure that the future of the Quantified Workplace truly helps us all be well.
For more you can check out the paper at the bitly or the QR code. Thanks again to our
participants and our collaborators, and to you for listening and we'll look forward to discussion.