Other
than decision support system, there are many additional types of computer aided
support systems available to detect illnesses and diseases. One medical
profession that uses AI systems is neural networks to detect the cancer cells
in mammograms and other tests. This is useful because cancer cells have many
different shapes and stages which are hard to be recognized and differentiated
by human eyes.
Pattern recognition is another
way of Artificial Intelligence (AI) systems that assist hospitals with their
diagnoses. For example, large hospitals in this case study are very fortunate to
have obtained large amount of data through a large volume of patients. Through
all of this data, AI systems can be used to detect patterns in illnesses. When these systems see a pattern in the way
illnesses are showing up, doctors can use this information to extrapolate
possible causes of the illness by tracing it back to the origin. By using this
way, the general public can actually take preventative measures to avoid
illnesses.
2. A
big worry in the collating and aggregation of medical information across
departments and even medical institutions is that the more access there is to a
person’s medical information, the more exposed that personal information
becomes. HIPAA (Health Insurance
Portability and Accountability Act), signed into law in 1996, addresses the
security and privacy of your health data.
The law was enacted to try to ensure that medical records,
electronically stored and transferred, would be protected. Do you think that making your medical records
available to the various branches of the medical industry (doctors, therapists,
insurance companies, hospital billing, etc.) is, on the whole, good or
bad? Why? Can you think of any instances where
disclosure of medical information could cause problems for a patient?
In our point of view, using
information systems to store medical records is not a bad thing. In fact, it is
beneficial. One file per patient across all departments is good that copies
don’t have to be made continually. Document storage is saved. Besides a medical
staff could always consult the patients’ medical records, he or she does not
need to wait for someone who is using the file. In addition, the corrections
made in one part of the file are then available to everyone instead of
inconsistent information being stored.
On the contrast, when a patient
divulges information that can be traced directly, there is a potential for
harm. For example, the medical insurance
companies might decide not to cover an individual due to preexisting condition
that happened to a patient who wants to buy insurance. Besides, disclosure of medical information
could cause the employers choose not to hire the potential candidate based on
prior medical history.
However, this information leaking problem could be
avoided by HIPAA
(Health Insurance Portability and Accountability Act), which addresses the
security and privacy of the patients’ health data. All medical
records, electronically stored and transferred, would be protected.
3. Could
analytics be a part of the HHC decision support system? If so, what sort of data would it
analyze? What might it tell medical
staff? Would it be useful only to those
who are already ill or could it help healthy people? How?
Yes,
predictive analytics is a part of the HHC decision support system. Experts use
predictive analysis in health care primarily to determine which patients are at
risk of developing certain conditions, like diabetes, asthma, heart disease,
and other lifetime illnesses. This information could be analyzed from the
patients’ data of sugar level, hearth motions and etc.
Analytics
are useful to both sick and healthy people. With the correct data, doctors can
analyze certain lifestyle decisions and see the result of those decisions.
Coupling lifestyle information with other information like the demographics of
an individual can greatly increase the power behind the data. Doctors would
then be able to notify the public of certain lifestyle decisions’ consequences.
To sick people, analytics help to determine the origin of the illness so that
they can seek cure accurately. To healthy people, analytics help to forecast
the likelihood to get illness from the living style so that they can correct it
such as quit smoking and do more exercises.
4. A
clinical study has shown that telemonitoring, discussed briefly in this case,
helps in keeping down medical costs. In
fact, monitored patients were hospitalized about half as often as those with
the same illnesses who were not monitored.
Emergency room visits were five times more likely among those who were
unmonitored. What types of illnesses
could be monitored this way (think chronic diseases like high blood
pressure)? Would it make sense to use
the system as follow-up care? How could
this data be utilized to help those might become sick in the future? Into what part of Isabel would this data fit?
Using telemonitoring systems can
be very useful and cost effective for a hospital. They can be used to lower
costs of patient visits as well as avoiding revisits. The illnesses where telemonitoring systems
would be the most effective could include illnesses that recurring in nature.
For example, diabetes, high blood pressure, and even infections that require
antibiotics.
By
monitoring, Isabel helps the clinician to take into consideration of the
situations and incidents happened during the period so to more accurately
diagnose the patient’s real illness and make the latest medical information
readily available at the point of care.
5. Could
an automated medical diagnosis system ever replace live doctors? Why or why not? Would you trust an experienced doctor over a
database that you could query yourself?
Why or Why not?
Definitely
that an automated medical diagnosis system is useful and brings a lot of
benefits to the illness diagnoses process. However, we don’t think that the
system could replace real doctors. Even though system could be more accurate
than the doctors but it can’t bring comfortable and relaxing feeling to the
patients. Sick people are normally in anxious and stressful condition during
the diagnoses or treatment. System will only question them to get the answers the
system needs. It might causes the patients provide inaccurate information
during nervousness. On the contrast, a human doctor can calm the patients down
and make them speak their symptoms out. A complex amount of information that is
not tangible and cannot be spoken or inputted into an algorithm: Eye
contact; Subtle physical movements; How they respond to questions – does their
tone change when describing a particular symptom; How they smell; How they are
sitting; The reaction of family members when the patient responds to a
particular question; What they are wearing; Any signs of underlying trauma; and
much more. This information is crucial for the doctors to make accurate
decision on the diagnoses.
I
would rather to trust an experienced doctor over a database that I could query
myself. When I am in sick or going to be sick, I have unclear mindset that I
might not able to tell the real situation of my symptoms. Without correct data
the system might be making wrong diagnoses. However an experienced doctor could
tell my actual pain from my expression when they try to find the real spot of
illness. During sick time speaking much is truly not easy. This could be solved
by an experienced doctor.
