Data analytics for decision making

Data analytics for decision making
“Big Data” alone provides no value to a decision maker. Data analytics must be employed to provide decision support and reveal actionable items in order to render data useful. Self-funded health plans administered by health insurers and many TPAs provide information in aggregate or summary form, often benchmarked against their block of business. But this type of reporting does not provide the plan sponsor with insight necessary to change behavior, improve health, or avoid costs. Aggregate data obscures individual circumstances. An aggregate report may show an increase in utilization of medical services. A much deeper analysis into the claims data will yield informed insights into the reasons for the medical services and whether they are warranted, preventable, or cost-effective.

We recently reviewed an insurer’s Medical Utilization report which indicated inpatient medical expenses increasing 7% over the previous year. This information is not actionable. Our deeper exploration of hospital expenses revealed supplies per claim skyrocketed by 34%. Substantial increases in lab-work costs per claim were also uncovered. These insights lend themselves to actions the plan sponsor can now take regarding consumer information, hospital recommendations, fee negotiations, and even plan re-design. Never settle for summary data.



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