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Using SAS technologies, Creative Computing
is working to systematically address
weaknesses within healthcare organizations
that cause patients to receive substandard
care. Our goal is to find new and creative
areas to apply analytical applications
to save lives.
Leveraging analytics to improve patient
care is what drives many of us at Creative
Computing. According to a report released
by the Institute of Medicine in 2000,
between 44,000 and 98,000 medical error-related
deaths occur each year in the US, exceeding
deaths attributable to motor vehicle
accidents, breast cancer, and AIDS.
According to the 2006 “State of Health
Care Quality,” published by the National
Committee for Quality Assurance, an
additional 37,600 to 81,000 deaths are
estimated to occur each year in the
US as a result of unjustifiable variation
in care caused by physician training,
geographical locations, and other medically
irrelevant factors.
Four reoccurring themes Creative Computing
has observed within healthcare facilities
that may result in substandard care
include:
- • Overwhelmed clinicians who
occasionally overlook medically-relevant
information as a result of being
bombarded with data
- [E.g. Patient charts
are often comprised of large quantities
of unanalyzed raw data]
-
• Failure of physicians to follow
treatment best practice and clinical
pathways
- [E.g.
Clinician style of medicine may
not adhere to accepted best practice]
- • Disparate and decentralized
data sources within hospitals and
clinics
- [E.g.
Patient Registration, laboratory,
data recording instruments]
- • Inability to identify, quantify,
and provide preventative care for
patients at risk of future illness
- [E.g. Forecasting
risk of cardiovascular disease in
ten years]
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 By
networking and collaborating with
physicians, mathematicians, and
scholars from around the world,
Creative Computing is beginning
to confront these and other clinical
challenges on a broad scale that
impacts individual patients, practitioners,
organizations, and populations.
We seek to improve clinical practice
through real-time measurement, analysis,
and automation of processes to ensure
adherence to best practice. An abridged
list of sample questions that fall
within our scope and interest are
listed below.
Population Level
- 1. What happened when the
first aid guideline was changed?
- 2. Why did the incidence
of reported tuberculosis increase
over the last five weeks?
- 3. Where should we invest
additional bioterrorism prevention
resources?
- 4. How fast
would a small pox epidemic be
detected in a specific region?
- 5. How can hospital data
systems best be networked for
early epidemic detection?
Organization Level
- 1. What happened when we
implemented the new infectious
disease control plan?
- 2. Why did the incidence
of nosocomial infections increase
over the past year?
-
3. Which department requires
additional nurses staffed to
improve its performance?
- 4. How many HIV patients
will the hospital receive in
the next month?
- 5. Which
bed placement strategy will
minimize patient fall risk?
Department Level
- 1. What benefit did providing
ER physicians with a data organizer
provide?
- 2. Why did
the average ICU length of stay
decrease over the past two months?
- 3. Which physicians are
most closely adhering to antibiotic
treatment protocol?
-
4. How many ECG tests will be
ordered by the department next
year?
- 5. How much should
the department invest in training?
Clinician Level
- 1. Did changing my questioning
technique improve the quality
of care I render?
- 2.
Why did the frequency of my
patients following discharge
orders decline?
- 3. Which
patients are optimal candidates
for a new treatment?
- 4. How many of my patients
will adhere to their annual
check-up schedule this year?
- 5. How will the mean length
of stay for my patients compare
to my colleagues next month?
Patient Level
- 1. Has this change in my
diet decreased my weight?
- 2. How many calories should
I consume per day given my medical
history?
- 3. Is my daily
intake of a particular vitamin
too high given my personal history?
- 4. What is my risk of having
a heart attack in the next seven
years?
- 5. Which healthcare
facility is best suited to treat
my condition?
Symptom Level
- 1. Do these signs, symptoms,
and patient history indicate
an underlying chronic illness?
- 2. What order should diagnostic
tests be performed for this
set of symptoms?
- 3.
Which combination of symptom
and history most likely supports
a disease?
- 4. Given
symptoms and patient history,
which follow-up tests should
be performed?
- 5. How
can diagnostic accuracy be maximized?
Therapy Level
- 1. Will this therapy effectively
treat this condition in a specific
patient?
- 2. What dosage
of medication is best given
a specific patient’s history?
- 3. Which therapy will best
preserve quality of life while
treating the disease?
- 4. Given a history, what
will be the most likely outcome
for this treatment path?
- 5. Which treatment is likely
to yield the shortest recovery
time?
Several long-term goals of Creative
Computing include designing
SAS systems to:
- • Automate epidemiological
surveillance to include the
real-time detection and activation
of countermeasures against outbreaks
or bioterrorism events via live
data links between healthcare
organizations in New England.
- • Centralize medical data
sources across most all healthcare
facilities in New England.
- • Automate detection and
ranking of patient risk factors,
treatment priorities, triage,
and physician/procedure scheduling.
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