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The MedMetrics blog provides comments and insights regarding the world of Workers’ Compensation, principally, issues that are medically-related. The blog offers viewpoints regarding issues affecting the industry written by persons who have long experience in the industry. Our intent is to offer additional fabric, perspective, and hopefully, inspiration to our readers.

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Tuesday, March 25, 2014

Make Your Data a Work-in-Process Knowledge Tool

by Karen Wolfe

Heard recently, “Our organization has lots of analytics, but we really don’t know what to do with them.” This is a common dilemma. Analytics (data analysis) are abundant, they are presented in annual reports, and published in colorful graphics. But too often the effort ends there. Nice information, but what can be done with it? How does it change operations and outcomes?

Obviously, the basic ingredient for analytics is data. After that comes skill, ingenuity, and creativity. Nevertheless, without data, there is no business knowledge, business intelligence, or much in the way of analytics.  Fortunately, the last thirty years have been primarily devoted to data gathering.

Data evolution
Over that time, all industries have evolved through several phases in data collection and management. Main frame and mini-computers produced data and with the inception of the PC in the 80’s, data gathering was the business of everyone. DOS systems were clumsy in the early PC years and there were significant restrictions to screen real estate and data volume. Recall the Y2K debacle caused by limiting year data to two characters.

Happily for the data gathering effort, progress in technology has been rapid. Advancement was enhanced first by local and wide area networks, then by the Internet along with ever more powerful hardware. Amazingly, wireless smart phones today are more powerful computers than were the PC’s of the 80’s and 90’s. Data gathering has been successful.

Big data
Now we have truckloads of data, often referred to as Big Data. People are trying to figure out how to handle it. In fact, a whole new industry is developing around managing the huge volumes of data. Once Big Data is corralled, analytic possibilities are endless.

The Workers’ Compensation industry has also collected enormous volumes of data. Yet, little has been done in the industry to actualize the analytics to reduce costs and improve outcomes.

Imbed analytic intelligence
The best way to apply analytics in Workers’ Compensation is to create ways to translate and deliver the intelligence to the operational front lines, to those who make critical decisions daily. Knowledge derived from analytics cannot change processes or outcomes unless it is imbedded into the work  of adjusters, medical case managers, and others who make claims decisions.

Consulting graphics for guidance is cumbersome, interpretation is uneven or unreliable, and the effects cannot be verified.  Therefore, the intelligence must be made easily accessible and specific to individual workers.

Front line decision-makers need online tools designed to easily access interpreted analytics that can direct decisions and actions. Such tools must be designed to target only the issues pertinent to individuals. Information should be specific.

Electronic monitoring
To effectively imbed analytic intelligence into operations, all claims data must be continuously electronically monitored. To link analytic findings to claims, all data must be monitored so the system can identify claims that contain conditions warned by analytics. Then that interpreted information is linked to operations.

Reverse strategy
When predictive modeling is employed as the analytic methodology, certain claims are identified as risky. Instead, all claims should be monitored continuously. By monitoring all claims for events and conditions pre-determined by analytics, no high risk claims can slip through the cracks.

Personnel can be alerted of all claims with risky conditions identified through analytics. At the same time, the analytic delivery system should automatically document itself.

Self-documenting
The system that is developed to deliver analytics to operations should automatically self-document, that is, keep its own audit trail to continually document to whom the intelligence was sent, when, and why. Furthermore, the system can then be expanded to document what action is taken based on the information delivered.

Without self-documentation, the analytic delivery system has no authenticity. Moreover, those who receive the information cannot be held accountable for whether or how they acted on it. When the system automatically self-documents, those who have received the information can be held accountable or commended for their part. Self-documenting systems also create Additionality.

Additionality
Additionality is the extent to which a new input adds to the existing inputs without replacing them and results in something greater. When the analytic delivery system automatically self-documents guidance and actions, a new layer of information is created. Analytic intelligence is linked to claims data and layered with directed action documentation.

Self-verifying
A system that is self-documenting can also self-verify, meaning results of delivering analytics to operations can be measured. Claim conditions and costs can be measured with and without the impact of the analytic delivery system. Further analyses can be executed to measure what analytic intelligence is most effective, in what form, and importantly, what action responses generate best results.

The analytic delivery system monitors all claims data, identifies claims that match analytic intelligence, and imbeds the interpreted information in operations. The data has become a work-in-process knowledge tool while analytics are linked directly to outcomes.

Karen Wolfe is the founder and President of MedMetrics, LLC, a Workers’ Compensation analytics company. MedMetrics offers online apps that link analytics to operations, thereby making them self-documenting, verifiable, and actionable. karenwolfe@medmetrics.org

 

 

 

 

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