By David Hom, Chief Evangelist, SCIO Health Analytics
Twitter: @SCIOanalytics
Employers offer health insurance to recruit and maintain talent as well as to keep employees healthy and productive. While these benefits might be “priceless,” employers still want to get the best return on their health benefits investments.
Brokers, in turn, are hard pressed to offer employers proof of this return on investment (ROI) – and even more pressured when it comes to helping employers actually reduce healthcare costs. Why is this so? To start, data is dispersed in a variety of systems such as electronic health records, population health management, finance and others — making it difficult to get a handle on outcomes. Even when brokers can access all this data, the volume is so massive that it is hard to make heads or tails of it. What’s more, when brokers do overcome these hurdles and make sense of the data, they typically see a retrospective view and don’t really squeeze any actionable insight out of their analysis.
However, by using more sophisticated predictive and prescriptive analytics, brokers can not only measure the effectiveness of health benefit program participation and demonstrate the ROI employers are generating, but also guide employers on opportunities to reduce costs across their workforce benefits, by:
1: More effectively serving high risk members. By working with de-identified data from the health plan and other sources, next-generation analytics can create and predict prospective financial risk scores for members. However, to go beyond this basic risk stratification tactic, much more precise insight can be identified in order to hone in on members where you can make the most impact. Impactability analytics is a prescriptive way to stratify members and populations in order to find high value opportunities to improve health outcomes and avoid unnecessary costs. By looking at recent historical costs and utilization of members, the impactability model can predict likelihood of future inpatient and emergency department visits within the next 12 months. Significant impact can be made if focus is placed on closing care gaps for members with the greatest impactability. Another way to look at impactability is at a condition level. By looking at disease prevalence across the workforce, one can identify which conditions are driving the greatest costs, and which conditions have members who are most impactable. By focusing disease management programs on conditions where you can generate the greatest savings, both the members and employers win in the end.
2: Develop effective care programs. Predictive analytical models require robust data sets to yield accurate insights. Common issues from relying on medical and pharmacy claims data alone, include using less than 12 months of claims data (e.g., new members), missing claim data (e.g., members who receive out-of-network care) and insufficient volume of claims data (e.g., members with low utilization rates). By blending data types of their own with outside sources, healthcare organizations are able to segment membership into unique consumer segments. Viewing membership this way helps organizations develop, evaluate, and market care management programs to the most appropriate members.
3: Keeping care in the network. Advanced analytics can be leveraged to optimize network utilization. By identifying employee’s utilization patterns and trends around employees going out-of-network for services, such as going to an ophthalmologist for diabetic eye screenings, guidance can be provided and education can be delivered to employees as needed. Another key area critical to ensuring network optimization is pharmacy spend and utilization. Guiding, informing and educating both employees and providers on drug utilization spend as well as less expensive options is instrumental to managing costs.
4: Create pricing transparency. Currently, when a primary care physician refers a patient to a specialist, the patient typically doesn’t think to ask what the cost is or whether there is a less-expensive option. However, having a procedure such as a cataract surgery can be much more expensive in the hospital, compared to an outpatient surgery setting. Brokers can add considerable value by demonstrating these differences and educating employees (and employers) about their options.
These are just a few of the ways that advanced analytics can be used to help employers get the most out of their health insurance investments. Can you think of any others? This blog was adapted from “Demonstrating the Value of Investing in Employee Health Benefits,” an article that was recently published in America’s Benefit Specialist.
This article was originally published on SCIO Health Analytics and is republished here with permission.