It’s no secret that U.S. healthcare costs have skyrocketed in recent decades. As a result, employers are demanding more strategic guidance and rigorous data analysis to help them better manage their health plans. For brokers and consultants to retain self-funded employers as customers, they need to provide them with a clear view of their plan as well as steps to improve performance.
Advanced data tools and predictive analytics can be the difference-maker as brokers vie for the business of health benefits for self-insured employers. Clear opportunities exist for brokers to help self-insured employers identify healthcare cost centers by creating their own custom population health management programs.
To many employers, population health management is a health-benefits catchphrase that represents a goal without means to achieve it. Data analysis, backed by common-sense human interactions to take action on the steps identified for plan performance, provides the edge when it comes to population health management.
Benchmarking for PMPM costs
Detailed and ongoing data analysis is central to dashboarding data, to uncover hidden healthcare cost centers. Looking at claims data and comparing costs to national benchmarks — adjusted for regional cost variation — can uncover hotspots where an employer may have opportunities to address with better policies that address patients’ needs.
Every employee population has its own opportunities for streamlining costs. But needs and opportunities can’t be addressed until the claims data have been analyzed and compared to benchmarks from an all-payer claims database — a service brokers now can offer as an added benefit.
Data trends should be keyed to per-member, per month cost to level-set the field, account for fluctuation in employee levels and the resulting change in the number of covered families. This will help determine if a few high-cost patients skewed one particular year, which in turn influences stop-loss and reinsurance decisions.
With analytics tools set to population health management objectives, services that used to take weeks and cost tens of thousands of dollars are less expensive in 2016. Using different views of these data sets can help plans quickly identify cost centers they previously didn’t know existed.
Analyzing the future
Brokers don’t have to be data scientists to understand the power of predictive modeling technology that can improve their customers’ health claims outlay. New analytics tools possess predictive modeling capacities that are able to look at the whole individual and group trends — by examining hard data in a practical format, comparing to cost benchmarks and factoring in rapid changes in healthcare.
What each health plan needs to view, measure or adjust is different, so assembling a backward look is difficult, let alone projecting the next year’s worth of claims; they can uncover individuals who can benefit from care management, compliance with preventive and condition-related best practices, and participation in health activities. New tools, when examining custom data sets, can predict conditions, trends, gaps in care, admissions and readmissions six to 12 months in advance.
Granular data views and analysis can help health plans better understand their costs, care delivery and care quality at the individual level as well as for the whole company — benchmarked against other companies or similar industries, and adjusted for regional variances in care costs.
In today’s healthcare world, benefit brokers are primed to gain a competitive edge through harnessing the power of the big data their employees generate daily in the healthcare system. With the right analytics engine, brokers can streamline risk analysis to provide their clients with insights — at micro and macro levels — that shed light on crucial aspects of their member population and generate actionable results. Data must be actionable to be successful.
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