Predictive Analytics Analysis
According to a study at Regis College, over 96% of hospitals in the United States have converted to electronic health records. Given that the majority of Americans visit the doctor at least once every year, this means today's health care systems are keeping track of an exceptional amount of data that is growing every year. Which a large amount of data, hospitals can use predictive analytic strategies to look at a large set of data to discover patterns and predict probability of outcomes of certain patient conditions. The large amount of data allows physicians to look back historically to make the best decision for a patients care. Historical medical records can also be cross referenced with real time information such as heart rate, body temperatures, and rate of breathing to anticipate how certain conditions are likely to affect a patient in the long run.
Real-time Patient Monitoring
Nurses and doctors are trained to monitor patients vital signs, and may visit a patient every few hours. However, a patients health may decline drastically in this time. Monitoring patients becomes even more difficult after a patient has been discharged. Patients may skip medication or ignore care instructions given by the doctor. Real-time monitoring and body sensors have become very valuable in providing more personalized treatment to patients. This monitors can communicate with clinical decision support software to analyze data in real time and provide insights to healthcare personnel almost instantly. This can help doctors spot patterns among individuals and act quickly. Doctor's can give a diagnosis and treatment can be prescribed more quickly. Let's say a patients heart rate rises exponentially while the doctor is away from a patient. A heart rate monitor can send information directly to the doctor in real-time, allowing a life-threatening situation to be averted.
Preventing Opioid Abuse
Now more than ever, opioid abuse cases are on the rise in the U.S., and are now responsible for more deaths than by car accidents. Big Data can help solve this problem. For the past few years, data scientists at Blue Cross Blue Shield have been analyzing insurance and pharmacy data to come up with algorithms to identity people at risk of opiod abuse. From their research, they have identified over 700 factors that they use to predict whether a patient runs the risk of abusing opioids. Although it is difficult to prevent these patients from abusing these drugs, it has helping create a foot in the door of reducing addiction related deaths and healthcare expenses.
Works Cited:
https://online.regiscollege.edu/blog/making-predictive-analytics-routine-part-patient-care-hospitals-2/
https://www.healthcareglobal.com/technology/how-big-data-aiding-patient-care
https://www.healthworkscollective.com/how-big-data-analytics-in-healthcare-saves-lives/
Comments
Post a Comment