Decision Support is good for Your Health

1. The system discussed in this case was a decision support system. However, other types of computer aided support are utilized in medicine. Can you think of ways that the medical profession could use AI systems? For example, how about pattern recognition? Could that help in diagnosing illness?

 

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.

Undoubtedly, telemonitoring is an effective follow-up care system as well as post operative care. Together with predictive analytics, follow-up care with telemonitoring could avert many situations that could become emergency room visit. It would provide an effective way to not only remind patients to take their medications but also retrieve vital information on that patient. When these patients arrive at the hospital, the doctor will already have a wealth of knowledge on the patient’s current status.

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.