interesting sources. Fitness tracking
device logs may allow a better understanding of the mobility of the patients
to determine the progression of debilitating diseases like multiple sclerosis. The merging of some very diverse
datasets including various “-omics,”
high-resolution images, and chemical
structure data is driving new medical discoveries. While, for the most
part, real-time decision making that
is powered by predictive modeling is
less crucial in such research environments, pharmaceutical companies can
improve product quality and reduce
costs by using sensor generated highly
granular data, and predictive models.
An example is a model to predict
vaccine potency using manufacturing
data. The potency of a vaccine must
be monitored to produce a high-qual-ity product that meets FDA standards.
Vaccines can be very expensive to manufacture, and take months to produce.
During this process both machine-gen-erated monitoring data and manually
entered measurements are collected.
Leveraging this data can yield a very accurate model that can prove useful in
making critical business decisions.
The primary considerations when
developing models in this particular
1. Whether the model can make
clude hospital census, emergency room
wait-time modeling, hospital readmis-
sion, gaps in care, length of stay, and
throughput modeling. Two questions
need consideration for the model to be
effective once operationalized:
1. What data is available at a particular point in time?
2. What data is actionable?
These two concepts may be illustrated using an example. A hospital
streamlining its discharge operations
by predicting the duration of a patient’s stay may perform a retrospective study using all available data. This
includes diagnoses, type of operations
performed, and the operating surgeon.
The study would allow the hospital to
attribute duration of stay to a patient’s
particular condition and to predict
length of stay for each future patient
accordingly. However, a predictive
model that helps discharge scheduling
may not have all data elements available when the decision is made. For
example, a precise diagnosis code is
not available at the time of admission
into an emergency room. Is the patient
having a panic attack or a heart attack?
This will not be known until later.
As technology evolves, the goal is
for hospitals to use more granular data
in order to become “connected hospi-
tals.” A hospital already produces a lot
of rich data that can be effectively used
in models: blood pressure, body tem-
perature, respiratory rates, and oxygen
saturation of patients. For example,
models exist that use a patient’s medi-
cal history to predict whether their con-
dition will severely deteriorate, but in
what time frame? A model may be able
to predict the outcome of the next hour
very accurately, but at that point care-
givers would not need a model to know
the extent of physiological deteriora-
tion. Models must be built to generate
alerts while action can still be taken.
In the coming years, hospitals will
continue to collect more highly granu-
lar, real-time data and feed it to a central
“brain” to drive predictive models and
take actions. Hospitals that require their
caregivers to wear wristbands that read
RFID tags at hand-washing and sanitiz-
ing stations demonstrate this trend. An
accelerometer detects how long a care-
giver spends washing their hands, and
an alarm prompts them if the action is
not done correctly. By collecting such
data in a central database, a hospital can
identify all potential contamination pat-
terns in real-time and prevent a hospital-
acquired infection from spreading.
Pharmaceutical companies. R&D
departments at pharmaceutical companies are excited about the increased
amount of rich data from new and
Figure 4. Many sensors produce data during a manufacturing process, offering a multitude of opportunities to detect anomalies,
build predictive models for quality outcomes, and use data-driven approaches to identify opportunities to tune the process.