“Why can’t we get useful data from our health plan?” benefit managers often bemoan. A common and persistent problem for practitioners is the scarcity of useful data, though pros often have reams of reports from their plan. However, finding real answers to the most pressing health data questions lies in the words you use.  
Imagine a benefit manager faced with this question from his/her CEO: How much did we spend last year on depression?  
Each part of the question is a data point.  The phrase “how much did we spend” could mean how much the plan actually paid in benefits (after copays or benefits from other plans), or how much the plan benefit or allowed amount was. It could include or exclude how much employees paid in copays and deductibles.  
“Last year” could mean calendar year, plan year, or fiscal year. It could mean the most recent 12 months. Further, it’s uncertain whether the CEO wants only the dollars that left the plan during the “year” (incurred and paid claims) or the dollars that were spent to cover all the services that occurred during the “year,” regardless of when the claim was actually paid.
“On depression” could mean medical bills that have “depression” as the principal (first) reason for the care. Or, it could mean medical bills that have depression as a diagnosis anywhere on the claim. Further, it could include or exclude prescription drugs that are typically used for depression.  
Comparatively, here is the same question translated into a data request: What was the net plan payment for medical claims with a principal diagnosis code of 311 (depressive disorder), where the claim date of service occurred during the 12-month period ending June 30, 2012, and the claim was paid during the 15-month period ending Sept. 30, 2012?  
If you wanted the figures for depression-related medications, you would ask: What was the net-plan payment for pharmacy claims for drugs with ATC code N06A* where the claim date of service occurred during the 12-month period ending June 30, 2012 and the claims was paid during the 15-month period ending Sept. 30, 2012?  
Somewhere in those stacks of reports, the data to answer the CEO’s question is hiding — but it’s cloaked in numbers, codes and jargon.  As a result, plan administrators throw up their hands, and say, “We’ve given them all the data!” And benefit managers say, “We have no data!”  
Health data is not intuitive. You can’t look at diagnosis codes and instantly know what they mean — it’s a specialized skill to make lists of numbers and codes useful for decision-making.  Without this skill, benefit managers will stay in the drought even while they are deluged by data.  
Guest blogger Linda K. Riddell is a principal at Health Economy, LLC. She can be contacted at LRiddell@HealthEconomy.net.

“Why can’t we get useful data from our health plan?” benefit managers often bemoan. A common and persistent problem for practitioners is the scarcity of useful data, though pros often have reams of reports from their plan. However, finding real answers to the most pressing health data questions lies in the words you use.  

Imagine a benefit manager faced with this question from his/her CEO: How much did we spend last year on depression?  

Each part of the question is a data point.  The phrase “how much did we spend” could mean how much the plan actually paid in benefits (after copays or benefits from other plans), or how much the plan benefit or allowed amount was. It could include or exclude how much employees paid in copays and deductibles.  

“Last year” could mean calendar year, plan year, or fiscal year. It could mean the most recent 12 months. Further, it’s uncertain whether the CEO wants only the dollars that left the plan during the “year” (incurred and paid claims) or the dollars that were spent to cover all the services that occurred during the “year,” regardless of when the claim was actually paid.

“On depression” could mean medical bills that have “depression” as the principal (first) reason for the care. Or, it could mean medical bills that have depression as a diagnosis anywhere on the claim. Further, it could include or exclude prescription drugs that are typically used for depression.  

Comparatively, here is the same question translated into a data request: What was the net plan payment for medical claims with a principal diagnosis code of 311 (depressive disorder), where the claim date of service occurred during the 12-month period ending June 30, 2012, and the claim was paid during the 15-month period ending Sept. 30, 2012?  

If you wanted the figures for depression-related medications, you would ask: What was the net-plan payment for pharmacy claims for drugs with ATC code N06A* where the claim date of service occurred during the 12-month period ending June 30, 2012 and the claims was paid during the 15-month period ending Sept. 30, 2012?  

Somewhere in those stacks of reports, the data to answer the CEO’s question is hiding — but it’s cloaked in numbers, codes and jargon.  As a result, plan administrators throw up their hands, and say, “We’ve given them all the data!” And benefit managers say, “We have no data!”  

Health data is not intuitive. You can’t look at diagnosis codes and instantly know what they mean — it’s a specialized skill to make lists of numbers and codes useful for decision-making.  Without this skill, benefit managers will stay in the drought even while they are deluged by data.  

Guest blogger Linda K. Riddell is a principal at Health Economy, LLC. She can be contacted at LRiddell@HealthEconomy.net.

 

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