Busy employee benefits managers typically don’t have the bandwidth to identify their costliest “blind spots” when it comes health coverage, yet recent data shows that 5% of an organization’s employees can account for 50% or more of an employer’s healthcare expense.
Data analytics can provide an edge. Identifying and dashboarding workforce health trends is a way for benefits managers to improve healthcare outcomes, realize ROI on population health and disease management programs and better predict needed coverages and coinsurance.
New population health management tools can quickly aggregate and present data (e.g. which sub-segments of the workforce have suffered a heart attack in the last year, or are diabetic, or currently undergoing dialysis, etc.) that used to take weeks and cost tens of thousands of dollars to collect. With the right analytics system, you don’t have to be a data scientist to understand the power of predictive modeling to improve your health claims outlay:
· These tools can examine custom data sets to predict conditions, trends, gaps in care, admissions and readmissions 6-12 months in advance.
· Individuals within a population can be assessed for risks and presented with customized action plans.
· Granular data views and analysis can help health administrators understand costs, care delivery and quality at the individual level and company-wide
· Results can be adjusted for regional variances in care costs and then benchmarked against other companies in the same or similar industries.
Where to start
By examining individual and group trends, data analytics risk engines that are custom-built for population health management can provide dynamic insight into your population with just a few clicks. The result is the ability to predict conditions, trends, gaps in care, admissions and readmissions six to 12 months in advance. At minimum, benefits managers should leverage these tools to identify and address common patterns of behavior that significantly increase medical costs. These might include:
· Creating more granular medication lists. Are employees using non-formulary, compounded or specialty drugs when a cost-saving generic would have the same result? Custom reports catalog unique behaviors such as patients using multiple pharmacies.
· Reining in multiple specialists/multiple PCP scenarios. Some patients may use multiple specialists and multiple primary care docs. Coordinating care under a single PCP can reduce costs.
· Generating next-generation ER utilization reports. Encouraging patients to use urgent care—and avoid the ER copay—can boost a health plan’s bottom line. But you have to know more about ER utilization than you do now to learn when they’re being used inappropriately and predict when it might happen next.
· Improving chronic care. Wellness programs, preventive medicine and case managers all can help at risk populations take better care of themselves and avoid ER visits. But you will have to track the results.
With new tools that offer revolutionary predictive modeling capabilities, benefits managers can use at-a-glance dashboards to pinpoint and address unnecessary medical expenses that are inflating their healthcare costs and sapping their company’s bottom line.
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