Hi, everyone.
My name is Megh Marathe, and I use they/them pronouns.
I'm a PhD candidate at the University of Michigan,
and my research interests are situated
at the intersection of workplace studies
and medical anthropology.
I'm here today to present joint work with my advisor, Professor
Kentaro Toyama.
The central emphasis of our paper
is that in practice, medical diagnosis
accounts for information well beyond the patient's
physiology, even for conditions that
are considered physiological.
This has important implications for those
who seek to support medical diagnosis through decision
support systems.
We use the case of epilepsy to present
an in-depth socio-technical understanding
of diagnostic work in hospital settings.
We drop on observational fieldwork and interviews
conducted with neurologists over a period of 15 months.
Epilepsy is a chronic illness and disability
characterized by, recurrent and unpredictable seizures.
Electroencephalograms are the primary diagnostic test
for epilepsy, and EEGs are devices
that record electro-chemical activity in the patient's brain
and display it in the form of brainwaves.
Outside the epilepsy sub-specialty,
the common understanding is that EEG brainwaves
and other physiological data, like MRI images,
are by themselves sufficient for diagnosis.
For example, seizure detection systems
are typically trained and evaluated
on data sets of brainwaves.
However, what we see in practice is quite different.
One day, neurologist Dr. Brenda was
reading the EEG for a patient being
evaluated for infantile spasms.
Infantile spasms are an epilepsy syndrome
characterized by, short, full body
seizures in very young babies.
Infants are known to be particularly wiggly,
that is they move around a lot, and hence their EEGs
are allowed a very high threshold of abnormality.
Dr. Brenda had already acquired a sense for this patient's
normal brain waves, and she was scrolling through the EEG
at a steady pace, watchful for deviations in the brainwaves
and in the video recording from the patient's room.
Suddenly, the baby stretched out,
almost threw out its arms and legs in a movement
indicative of an infantile spasm.
Dr. Brenda also noticed the abrupt and distinctive
abnormal pattern that erupted in the baby's brainwaves
and paused.
She used her mouse to position the EEG cursor a few seconds
before the pattern, and then, keeping her eyes trained
on the video, she slowly dragged the cursor over the brainwaves
and watched the spasm like movement in slow motion.
She then watched the movement once more,
this time balancing her gaze between the brainwaves
and the video because in a real seizure,
brainwaves start off normal before the start of patient
symptoms.
But it was hard to tell which came first.
However, in the blurry background of the video,
Dr. Brenda noticed that someone left the baby's room
and closed the door.
She repositioned the EEG cursor to
before the potential seizure, turned up
the volume of her computer's speakers,
and set the brainwaves and video to play automatically.
We heard the loud, [INAUDIBLE] bash of a hospital door closing
immediately before the baby threw out its arms and legs.
Shaking her head in disbelief, Dr. Brenda
labeled the event, artifact, question mark, door closed.
In discussion with the attending neurologist,
Dr. Brenda removed the question mark from her label
a few hours later,
This scene previews some of our findings and shows
that, in practice, medical diagnosis
is a relational, situated, and evolving process.
In particular, normal and abnormal brainwave patterns
are not universally defined, but specific to the patient
and their context.
The brainwaves of babies, for example,
are allowed a high threshold of abnormality.
In addition, diagnosis is partly determined
by social and environmental factors such as,
the activities of caregivers and other health workers,
and even the closing of a door.
Finally, diagnostic classifications
are collaborative, and they change over time.
An event that was originally considered a seizure
comes to be seen as clinically irrelevant artifact,
and vice versa.
Our paper contributes a broader socio-technical perspective
to discussions on intelligent and data driven
decision support in healthcare.
Thank you for watching this video,
and we invite you to read our paper for more information.