Predictive analytics is the use of data, statistics, AI, and machine learning programs to sift through and analyze historical data and determine the likelihood of future outcomes. For the healthcare market, predictive analytics will not only improve care, it will also cut patient care costs.
Currently there’s a massive opportunity for predictive analytics to improve care and dramatically reduce waste in the healthcare system, addressing systematic issues in chronic diseases, over-treatment, and care coordination. So how does predictive analytics cut patient care costs? Read on to find out!
1. Better preventative care
A tremendous portion of what we spend on healthcare is for chronic disease, and a lot of chronic diseases can be prevented with proper lifestyle changes.
It's no secret that a large portion of the US population suffers from chronic illnesses like heart disease, stroke, lung and kidney disease, cancer, and diabetes; these illnesses also account for the majority of healthcare spending in the United States. According to the Centers for Disease Control (CDC), 6 in 10 adults in the U.S. have a chronic disease, and 4 in 10 have two or more. However, many of these diseases are preventable – stemming from excessive alcohol and tobacco use, poor nutrition, and lack of physical activity.
Right now, many providers are participating in new value-based care models, which aim to keep the patient out of the hospital, eliminating expensive procedures. More lifestyle medicine doctors are developing technologies to help prevent these chronic diseases. Using AI, healthcare professionals can get a complete picture of a patients’ health history, quickly compare it against that of patients with similar symptoms, and make more effective, and forward-thinking, care decisions.
2. Reduce healthcare over-treatment
Over-treatment in healthcare is increasingly being recognized as a cause of patient harm and excess costs. In 2010, the Institute of Medicine (IOM) called attention to the problem, suggesting that “unnecessary services” are the largest contributor to waste in US health care, accounting for approximately $210 billion of the estimated $750 billion in excess spending each year.
Over-treatment costs can include unnecessary prescription medications, tests, and procedures, and oftentimes these extra expenditures are a result of fear of malpractice, patient pressure or requests, or a general confusion with accessing electronic medical records (EHR/EMR technologies). These extra expenditures can also stem from our current fee-for-service system wherein physicians may be more likely to perform unnecessary procedures when they profit from them. Therefore, de-emphasizing fee-for-service physician compensation in favor of value-based care might reduce healthcare utilization and costs.
Many VBC programs promote use of new technologies that incorporate predictive analytics – harnessing troves of patient data in order to assess risk and treatment plans. With superior knowledge of the patient history, and AI helping to analyze entire patient populations as well as individual patient history trends, providers can be better prepared to prescribe the right medications, order the proper tests, and make the right calls when it comes to patient care.
3. Improved care coordination
It's a wonderful boon that consumers are increasingly adopting wearable technologies like FitBits, Apple Watches, and heart monitoring armbands. With a growing percentage of the population utilizing these wearable technologies, providers are able to use this data to track medical and health-related information from their patients, otherwise known as remote patient monitoring. Predictive analytics can help sift through a massive amount of patient data in real time for a more holistic approach to patient health.
And, AI technologiescan help in settings like radiology departments, where technologies like Google’s DeepMind AI technology can read 3D retinal OCT scans and diagnose ophthalmic conditions, or ICAD’s “ProFound AI” solution can allow radiologists to view each breast tissue layer independently, which helps detect cancer earlier (by 8 percent).
Even infants are receiving the benefits of predictive analytics in the form of genetic illness screenings. With AI technology, computers can scan through thousands of photos to detect genetic abnormalities like "white eye" (indicating a serious eye disease like retinoblastoma, cataracts, and Coats' disease) or even diagnose genetic disorders in children. This technology is only made possible through artificial intelligence and its ability to cover immense amounts of data and narrow down the options at lightning speeds.
Ultimately, these early detections and diagnoses can result in cost savings because it can catch the issue before high-cost procedures are required. And, for the patients with the highest risks of costly medical procedures, predictive analytics can also ensure that those patients are identified, monitored, and cared for between visits and following hospitalization (which also reduces the chance of needing high-cost follow-up procedures and long inpatient hospital stays). The earlier a disease or disorder is caught, the better – for both patient mortality rates and cost savings. A true win-win.
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