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.
But that can't always be the case.
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. There was no gene treatment available, but evidence based research indicated to the PCP conditions that may be helpful for many early Alzheimer's patients.
Ever since, the physician has had the patient engaging in exercise, good nutrition, and brain games apps that the patient downloaded on his smart phone and which automatically upload to the patient's portal. Memory tests are given on a regular basis and are entered into the electronic medical record (EMR), which also links to the patient portal. The patient himself adds data weekly onto his patient portal to keep track of time and kinds of exercises, what he is eating, how he has slept, and any other variable that his doctor wishes to keep track of.
Because the PCP has a number of Alzheimer's patients, the PCP has initiated an ongoing predictive study with the hope of developing a predictive model for individual likelihood of memory maintenance and uses, with permission, the data thus entered through the patients' portals. At this visit, the physician shares the good news that a gene therapy been discovered for the patient's specific gene and recommends that the patient receive such therapy.
2. Predictive analytics will help preventive medicine and public health.
With early intervention, many diseases can be prevented or ameliorated. Predictive analytics, particularly within the realm of genomics, will allow primary care physicians to identify at-risk patients within their practice. With that knowledge, patients can make lifestyle changes to avoid risks (An interview with Dr. Tim Armstrong on this WHO podcast explores the question: Do lifestyle changes improve health?)
As lifestyles change, population disease patterns may dramatically change with resulting savings in medical costs. As Dr. Daniel Kraft, Medicine and Neuroscience Chair at Stanford University, points out in his video Medicine 2064:
During the history of medicine, we have not been involved in healthcare; no, we've been consumed by sick care. We wait until someone is sick and then try to treat that person. Instead, we need to learn how to avoid illness and learn what will make us healthy. Genomics will play a huge part in the shift toward well-living.
3. Predictive analytics provides physicians with answers they are seeking for individual patients.
Evidence-based medicine (EBM) is a step in the right direction and provides more help than simple hunches for physicians. However, what works best for the middle of a normal distribution of people may not work best for an individual patient seeking treatment. PA can help doctors decide the exact treatments for those individuals. It is wasteful and potentially dangerous to give treatments that are not needed or that won't work specifically for an individual. (This topic is covered in a paper by the Personalized Medicine Coalition.) Better diagnoses and more targeted treatments will naturally lead to increases in good outcomes and fewer resources used, including the doctor's time.
4. Predictive analytics can provide employers and hospitals with predictions concerning insurance product costs.
Employers providing healthcare benefits for employees can input characteristics of their workforce into a predictive analytic algorithm to obtain predictions of future medical costs. Predictions can be based upon the company's own data or the company may work with insurance providers who also have their own databases in order to generate the prediction algorithms. Companies and hospitals, working with insurance providers, can synchronize databases and actuarial tables to build models and subsequent health plans. Employers might also use predictive analytics to determine which providers may give them the most effective products for their particular needs. Built into the models would be the specific business characteristics. For example, if it is discovered that the average employee visits a primary care physician six times a year, those metrics can be included in the model.
Hospitals will also work with insurance providers as they seek to increase optimum outcomes and quality assurance for accreditation. In tailoring treatments that produce better outcomes, accreditation standards are both documented and increasingly met. (Likewise, predictive analytics can support the Accountable Care Organization (ACO) model in that the primary goal of ACO is the reduction of costs by treating specific patient populations successfully. Supply chain management (SCM) for model hospitals and insurance providers will change as needs for resources change; in fact when using PA, those organizations may see otherwise hidden opportunities for savings and increasing efficiency. PA has a way of bringing our attention to that which may not have been seen before. View More