Question

Predictive analytics is an emerging field in knowledge management. Discuss the pros and cons of the use of predictive analytics in the health care field.

Answer 1

Advantages are:

Higher quality treatment Since big data comes from a number of sources, including past doctor and pharmacy visits, social media, and other external sources, a more complete picture of a patient can be generated. Physicians only had access to a limited amount of patient information, such as a few charts and some personal information, using conventional charts loaded with medical transcription learning online. Healthcare providers can see what actually makes a person tick and use that knowledge to provide better quality care with the large amount of data now available. If a person enjoys skiing, as shown in their social media accounts, a doctor may communicate with that or use that data to understand what could cause a recurring problem with the leg. Likewise, with only basic health data, a physician may be able to see underlying causes for a health issue that would not be easily visible.

Early intervention Big data's overall goal in healthcare is to use predictive analysis to recognize and fix medical issues before they become bigger issues. Big data definitely makes the whole process more effective. Of example, medication may be prescribed to treat high cholesterol to a person who is seeing a doctor trying to lose weight. If that patient comments on social media about changes in their life causing stress, this information could be evaluated by the big data system and the patient could be flagged as at risk of heart attack. Then the doctor can change the medication to reduce the risk of a heart attack and thus eliminate the problem before it becomes life-threatening. Big data can also access archives of DNA to see if a person is at risk for a disease that has passed through the family line.

Fraud identification A growing problem in healthcare and insurance spaces is fraud, and patients in hopes of being charged file false claims. Big data is useful in combating this because for further analysis it can access a huge amount of data to identify contradictions in submitted statements and potentially fraudulent claims flag. Big data could sift through thousands of reports using its advanced algorithms to identify errors far faster than any group of humans can. Over the last two years, Medicare has saved in $1 billion by using big data to verify patient claims.

Disadvantages are:.

Privacy The lack of privacy, especially when it comes to sensitive medical records, is one of the most negative aspects of big data. Big data must have access to everything, including private records and social media posts, to be successful and get a full, comprehensive look at a person. The technology takes away personal privacy for the greater good, according to many big data experts. Although big data allows doctors to track the health of a patient from just about anywhere, it also does not give freedom to the patient.

Although some people see the ability to predict future medical conditions as beneficial, big data often raises the risk of doctors being replaced. Big data is clearly not yet at the stage where it can be used alone, and it certainly lacks a human doctor's personal touch. Many experts fear that growing big data could weaken doctors and leave patients turning to technology for answers rather than using a licensed doctor. Big data in healthcare can not be ignored, particularly as more companies and providers are increasing their investments in the region. Nonetheless, in order to create an environment that is effective and secure for patients and doctors, the drawbacks need to be taken into account as the technology grows.