Would you like to predict the future of your health?
Then you are in the majority. Health and wellness weigh heavily on the minds of Americans, according to the 2015 Food & Health Survey: Consumer Attitudes toward Food Safety, Nutrition & Health. More than 1,000 Americans ages 10 to 80 responded to the survey. Results found that 91 percent of Americans have given a great deal of thought to the healthfulness of their diet, and 94 percent have thought about the amount of physical activity they get on a daily basis. And they are taking action, with 55 percent improving their diet and an equal percentage of respondents increasing their level of physical activity.
Health insurance payers are wise to consider members’ health as well, given the business impact of chronic disease on insurance payers today. According to Centers for Disease Control, chronic diseases are responsible for 7 of 10 deaths each year, and they account for 84% of the nation’s health care costs. To substantiate that cost, another source states that Americans spend $432 billion per year to treat heart disease and stroke, $174 billion per year on diabetes, $154 billion per year on lung disease and $148 billion per year on Alzheimer’s disease.
Healthy people, however, can directly benefit insurance payers’ bottom line. A mere 1 percent increase in savings across the aforementioned four chronic diseases would free up more than $9 billion in healthcare resources that the healthcare industry could use for research, facilities and profits. If these conditions could be identified in advance, how could they be addressed and managed more effectively?
Predictive analytics can help pave the way.
Insurers and healthcare providers are gaining new insights from the influx of member data driven by the Affordable Care Act. The legislation has spurred an increase of Americans with health insurance coverage over the past two years, thereby generating a greater amount of member data to generate analytical insights. Assessing the widespread impact of health ailments today can lead to better diagnoses and early treatment while reducing costs across the board.
Most importantly, this information can help improve members’ quality of life. Equipped with knowledge from predictive analytics, insurers can better engage with those members on wellness and prevention early in the customer lifecycle. They can build a stronger relationship from the get-go, fostering loyalty and improving member retention.
And using advanced analytics, reshaping members’ future might not be as far-fetched as it may seem. Innovative health insurance plans are aggregating member data, analyzing it, and discovering trends that empower members with information to make better choices down the road.
While it may take another one to three years for this data to mature, it has already started to yield insights that insurers could not access previously. This insight is valuable because it allows them to predict segments of the population who are most susceptible to chronic diseases, as well as those who are at the greatest risk of developing them.
Managing the member experience by gaining analytical insights is critical for payers as the health insurance marketplace becomes more competitive every year. Adding value with personalization helps insurance providers create meaningful relationships with members, fostering an environment where customer loyalty can thrive. As a win/win for members and insurance providers, it pays to be proactive with wellness programs spurring long-term health, informed by better insight into member health across a number of corresponding factors.
Predictive analytics are so on-point, compared to typical analytics, for two reasons: they make predictions for individuals rather than groups, and they do not rely on the typical bell-shaped curve. Using prediction modeling techniques such as artificial intelligence, predictive analytics create a profile or algorithm based on historical data from other individuals.
Predictive analytics allow insurance payers to identify at-risk members well before the onset of disease and recommend a course of action. With this insight and guidance, members can make lifestyle changes to prevent diseases, rather than ending up burdened with the stress and expense of disease once it is too late.
And it is highly likely this will cause a desirable ripple effect. As lifestyles change, the percentage of the population afflicted with chronic diseases could drop, resulting in medical cost savings.
Payers, hospitals and employers stand to benefit in partnerships, as well. Working in tandem with insurance providers, hospitals and employers can synchronize databases to build health insurance plans tailored to realistic needs. Employers could also use predictive analytics to review insurance providers with laser focus on which plans offer products most well suited to their needs. The models for both of the aforementioned types of plans would incorporate specific business characteristics. Metrics such as the number of times the average employee makes an appointment with their primary physician would play a critical role in these models.
While this all may seem far-fetched and futuristic, it is already a reality for some organizations. Predictive analytics gives insurance payers a very real, very tangible crystal ball to predict the future of members’ health issues and arm members with prevention methods to drive healthy business.
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