Advanced Plan for Health founder and CEO, Neil Godbey, recently sat down with Benefits Advisors’ reporter, Matt Skoufalos to discuss the state of population health in relation to data management and care management performance. The following is a recap of the interview and the subsequent article.
As a result of health reform, the role of the broker and health benefits consultant has changed. Employers are demanding more strategic guidance and rigorous data analysis to help them better manage their health plan amid skyrocketing costs. Reform pushes brokers and consultants to provide self-funded employer clients with a clear view of their plan and actionable steps for improving plan performance.
For the modern broker, advanced data tools and predictive analytics may prove to be the difference-maker when it comes to closing the deal. But how do brokers differentiate themselves in a highly crowded market when seemingly every competitor has some sort of analytics upsell?
What does it look like to have true insight into a certain population’s health? The distinction between the two types of insight – technology-derived data sets and human expertise – is clear, both types are key to accurately assessing and improving the health of a population. It is important to understand the distinction and the application of each type of insight.
In this climate of runaway costs, self-insured companies lack the necessary analytical tools and data to target and control spending or anticipate future trends. In fact, many are blindly investing in programs they hope will improve outcomes and reduce claims. Yet the desire to get patients to better manage their own health remains strong. That’s why fully 72 percent of employers now offer programs and services to raise participants’ awareness of their health status and risks (pg. 8).
Like the war general, the best benefits brokers know that implementing a good benefits plan (or war plan) requires a mixture of science, art, technology and even psychology. When it comes to preempting costly diseases and health plan pitfalls, anticipation and an outside-of-the-box mentality is everything. To have a comprehensive understanding on how individuals function and behave within the health plan design is to be a step ahead of the curve.
The ability to predict and address costly healthcare patterns linked to an individual’s behavior is the holy grail of data analysis for health plan performance. Employers need the ability to determine whether a particular employee is highly likely to result in excessive costs that could otherwise be moderated by alerting the local health provider to take immediate actions.
For self-insured employers, one purpose of predictive analytics software is to tell a narrative – to expound on the history of an employee population’s health. Lab data, claims and biometrics data (to name just a few data-sets) are analyzed and structured in a cohesive, refined and chronological order to help predict risk and avoid substantial health plan costs.
While health care costs vary significantly from market to market, and some areas have higher operating expenses, price transparency is considered one of the ways to stabilize health spending and – in turn – long-term, macro financial stability in the United States. That’s why more than 30 states have passed or have proposed legislation to increase healthcare cost transparency.