Everyone is a patient at some time or another, and we all want good medical care.
We assume that doctors are all medical experts and that there is good research behind all their decisions. Physicians are smart, well trained and do their best to stay up to date with the latest research. But they can’t possibly commit to memory all the knowledge they need for every situation, and they probably don’t have it all at their fingertips.
Even if they did have access to the massive amounts of data needed to compare treatment outcomes for all the diseases they encounter, they would still need time and expertise to analyze that information and integrate it with the patient’s own medical profile.
But this kind of in-depth research and statistical analysis is beyond the scope of a physician’s work. That’s why more and more physicians – as well as insurance companies – are using predictive analytics.
Predictive analytics (PA) uses technology and statistical methods to search through massive amounts of information, analyzing it to predict outcomes for individual patients. That information can include data from past treatment outcomes as well as the latest medical research published in peer-reviewed journals and databases.
Not only can PA help with predictions, but it can also reveal surprising associations in data that our human brains would never suspect. In medicine, predictions can range from responses to medications to hospital readmission rates.
Examples are predicting infections from methods of suturing, determining the likelihood of disease, helping a physician with a diagnosis, and even predicting future wellness. The statistical methods are called learning models because they can grow in precision with additional cases.
There are two major ways in which PA differs from traditional statistics (and from evidence-based medicine): First, predictions are made for individuals and not for groups Second PA does not rely upon a normal (bell-shaped) curve. Prediction modelling uses techniques such as artificial intelligence to create a prediction profile (algorithm) from past individuals.
The model is then “deployed” so that a new individual can get a prediction instantly for whatever the need is, whether a bank loan or an accurate diagnosis. In this post, I discuss the top seven benefits of PA to medicine – or at least how they will be beneficial once PA techniques are known and widely used. In the United States, many physicians are just beginning to hear about predictive analytics and are realizing that they have to make changes as the government regulations and demands have changed.
For example, under the Affordable Care Act, one of the first mandates within Meaningful Use demands that patients not be readmitted before 30 days of being dismissed from the hospital. Hospitals will need predictive models to accurately assess when a patient can safely be released. 1. Predictive analytics increase the accuracy of diagnoses. Physicians can use predictive algorithms to help them make more accurate diagnoses.
For example, when patients come to the ER with chest pain, it is often difficult to know whether the patient should be hospitalized. If the doctors were able to answers questions about the patient and his condition into a system with a tested and accurate predictive algorithm that would assess the likelihood that the patient could be sent home safely, then their own clinical judgments would be aided. The prediction would not replace their judgments but rather would assist.
In a visit to one’s primary care physician, the following might occur: The doctor has been following the patient for many years. The patient’s genome includes a gene marker for early onset Alzheimer’s disease, determined by researchers using predictive analytics. This gene is rare and runs in the patient’s family on one side. Several years ago, when it was first discovered, the patient agreed to have his blood taken to see if he had the gene. He did.
