June 05 2018 | 0 Comments | 133 reads Average Rating:

Analytics To The Rescue: Helping Brokers Answer Health Insurance ROI Queries

by David Hom in Health Analytics

Employers typically aim to hold health insurance investment costs down while also enhancing employee health programs to experience improved recruitment/retention, employee productivity and overall satisfaction.

The challenge is a formidable one – but one that brokers can now help employers deal with successfully. Indeed, as employers seek to make the best employee health insurance investments, brokers can provide the insight required to make the best choices by providing hard evidence, confirming that employers are setting proper spend and reserve levels. They also can provide guidance on how to lower the amount that employers and employees are spending on healthcare while achieving the outcomes they desire.

How exactly can brokers conquer this Herculean challenge? They can go beyond simply offering vague platitudes regarding the value of employee health insurance and deliver quantifiable answers about ROI. While the data needed to come up with such responses has typically been fragmented across a variety of systems from claims to electronic health records (EHRs) to finance to population health management and others, it is now possible to aggregate disparate data from various systems and apply predictive and prescriptive analytics to gain valuable insights. As such, brokers can work with employers to develop plan options that are a better fit for the organization’s health and financial needs.

Imagine 200 out of 3,000 employees have a chronic condition such as heart disease. Medications used to manage the condition are expensive and many of these employees often wind up visiting the emergency department or being admitted to the hospital. Taking de-identified data from the health plan and other sources, next-generation predictive analytics can create clinical and financial risk scores for each of those heart disease employees. If desired, employees can be stratified into personas, which account for factors such as where they live (Zip+4 data), income and education levels, co-morbid conditions, ethnicity, typical lifestyle considerations and other factors. This information is then used to determine the right care and prioritize that care based on their persona and their impactability (the likelihood of a positive outcome from closing care gaps). Once brokers deliver that information back to the employer and health plan, targeted programs can be implemented to improve employee health while lowering out-of-pocket costs for the employees and employers.

And, brokers can then rest easy, knowing that they provided the insight to make employers truly believe in the current and future value of their employee health insurance investment. This blog was adapted from “Using analytics to rationalize healthcare spending,” an article that was recently published in Employee Benefit Adviser.

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David Hom
Chief Evangelist

Dave is an internationally-recognized expert in the field of consumer engagement through programs such as Value Based Benefits and Employee Wellness.

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Arun Rangamani
VP, Payment Services Delivery Leader

Ben Steverman
Chief Technology Officer

David Hom
Chief Evangelist

Jodi Siegel, MSHI, BSN, RN (Guest Author)
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Vice President, Life Sciences

Lalithya Yerramilli
VP, Healthcare Solutions

Lesli Adams, MPA (Guest Author)
Director of Population Health Strategy, Oracle Corporation

Linda Pantovic (Guest Author)
Director of Compliance & Risk Adjustment, Scripps Health Plan Services

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