Data analytics is the current and most effective means to achieving population health management, but let’s face it: To many employers, population health management is merely a health-benefits catchphrase, an empty expression that represents a goal but lacks the means to achieve it. However, in the right hands, data analysis is much more than that. Data analysis gives brokers the edge to fulfill population health management goals by providing common-sense human interactions to take action on the steps identified for plan performance.
Most employers with 500-10,000 insured patients neither employ a data scientist nor care to get too involved in the process. It’s those employers that need a simple solution to outsource population health management.
Finding a partner to identify and dashboard population health measures becomes a way for brokers to:
Help self-insured employers improve outcomes and make their employees healthier and more productive
Realize ROI on population health and disease management programs
Create value for customers and differentiate themselves from competition in crowded local markets.
The best analytics tools possess revolutionary predictive modeling capacities that look at the whole individual and group trends alike. What each health plan needs to view, measure or adjust is different, so assembling a backward look is difficult, let alone projecting the next years’ worth of claims. However, you do not have to be a data scientist to understand the power of predictive modeling technology to improve your customers’ health claims outlay.
Granular data views and analysis can help health plans understand costs, care delivery and quality at the individual level and as a company – benchmarked against other companies or similar industries, and adjusted for regional variances in care costs.
Analysis of individuals within a population ultimately identifies opportunities and risks; key information in the creation of customized action plans.
While analytics can be a powerful automation tool to identify opportunities to manage population health for a self-insured employer, the broker needs predictive modeling engine backup to turn these data points into action. The modern broker needs support from predictive modeling engines that enable revolutionary predictive modeling capabilities by analyzing a whole individual’s — as well as groups — trends and by examining hard data in a practical format, factoring in the impact of rapid changes in healthcare. The result is the ability to predict conditions, trends and gaps in care, as well as admissions and readmissions 6–12 months in advance.