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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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Sunday, November 27, 2011

How to Convert Your Data to a Valuable Corporate Asset by Karen Wolfe

Data is the organizations’ most valuable asset
At the beginning of the personal computing era back in the 1980’s and early 1990’s a frequent motivation for computerizing was that data is the organization’s most valuable asset. The idea was a persuasive argument for investing big dollars in computerization. Nevertheless, though the value of data as an asset could be envisioned back then, it was actually far from it.

Rather than an asset, computers, software, and data were burdensome and expensive. Continuous capital investments were needed to improve the hardware as technology rapidly advanced. Software, limited by hardware memory and capacity resulted in incomplete data. Workers struggled to adapt to data entry discipline, an entirely new way to work. Moreover, data was often corrupted or lost by systems that were primitive by today’s standards. Managing networks was arduous. Data backup and storage required ever more hardware and new systems demanded data migration, along with the IT personnel to manage the all the processes. It was an all-encompassing and pricey undertaking with little benefit realized.

Now what?
Despite the challenges, organizations overcame the obstacles and committed to computerization, still chasing the data value proposition. Over the years, organizations have amassed boatloads of data, begging the question: Now what? Sitting in storage, the data certainly is not an asset! Instead, it must be gathered, integrated and analyzed to gain intelligence about how to proceed.

Walmart modeled the next step many years ago by implementing analytics. They began analyzing their data to derive intelligence about their own organization, the effectiveness of their processes, and their customers’ buying patterns. Walmart leveraged analytics to improve processes, optimize operations, trim costs and improve profitability. Following their lead, most other organizations in every industry have adopted initiatives for analyzing their data.

Workers’ Comp boards the bandwagon
The Workers’ Compensation industry is no exception. Of late, many organizations in the industry are executing analytics. Departments have been created with dedicated experts who analyze the data to derive intelligence. Nevertheless, people are now asking the question, “What should we do with the analytics?” They realize analytics alone cannot elevate the copious data to the level of an organizational asset.

Analytics alone do not an asset make
Having amassed so much data, people correctly think it should be made useful. Yet, analytics too often reside in attractive graphic reports made available to the top echelon, the board, shareholders and managers. However, to be useful, to be an asset that can change processes and outcomes based in the intelligence gained, analytics must made available to the people who do the organization’s work. Line personnel must have access to easy, actionable tools that cause them to act on the intelligence gained through analytics. Only those who do the organization’s work can change processes and create value. But they cannot achieve change without help. Analytics must be operationalized.

Operationalize the intelligence
Again using Walmart as an example, one piece of information gained from their analyzed data is exactly what people purchase in different geographic regions when a weather disaster is forecast. That information told them what to do in response, what action to take. Walmart systems were reengineered to automatically shift distribution channels in response to weather advisories, thereby insuring the right commodities arrive in time in the stores affected by the weather. The quantifiable benefits are that customers are satisfied by finding the products they need and Walmart profits are boosted because they have enough goods to sell.

Analytics tell Walmart what is needed and what processes must be changed to respond to a new set of conditions. But nothing will happen until their systems are changed to apply that information. That is linking analytics to operations to actualize the knowledge gained through analytics.

In the case of Workers’ Comp., opportunities to apply analytics to modify processes in real time are abundant. For example, analyze the data to find the best medical doctors for treating low back strain in specific geographic areas. Claimants can be directed to those doctors who have a record of excellence. Or, analyze provider prescription practices to identify those prescribing opioids and other potentially addictive drugs. But most importantly, do not stop there! Automatically alert line personnel so they can initialize appropriate action.

The critical final stage—simplicity
Whatever method is used to link analytics to operations must be elegantly simple for the user. If Walmart required store managers to follow a written procedure to rush-order products based on local forecasts, chances are good the results would be unsatisfactory. If the process adds to the workload or requires looking up directions, little will be gained. To be effective, systems should do the work and automatically direct or redirect actions.

For data to become a valuable corporate asset, it must be gathered from all the appropriate sources, integrated, and analyzed, with the results automatically linked to operations, thereby mobilizing appropriate and timely action.

Learn how MedMetrics will transform your data to valuable corporate assets for you. Finally, the promise is fulfilled.

Sunday, November 6, 2011

How to Find “Best in Class” Doctors

It’s a safe bet that claims will not have a happy ending if the treating physician has a history of being associated with poor claim outcomes. In fact, physicians rated poorly in analytic studies based on past performance are 100% predictive of high costs and inferior outcomes in future claims where they are involved. The question is, how can those providers be identified?

Applying analytics
Evaluating physician and other provider performance is a matter of scrutinizing the data. The data offers a clear picture of actual provider performance. Whether the cause of poor performance is misunderstanding Workers’ Compensation or deliberate fraud, the claim results will be dismal. Nevertheless, in order to analyze provider performance, one must know where to look for the data, what to look for, and how to apply the knowledge gained from analysis to achieve improved results.

Where to find the data
Billing data tells the story of diagnoses, treatments and the billed amounts. However, billing data is never broad enough in scope to evaluate providers because it tells only a part of the story. Claims level data tells another part of the story. It describes the actual paid amounts, the amount of indemnity paid, whether legal was involved, and the final disability rating, the ultimate outcome indicator. But there is more.

Investigating PBM (Pharmacy Benefit Management) data has become imperative in recent years. Overuse and abuse of prescribed narcotic pain relievers is now a major concern in Workers’ Compensation medical management. Prescribing excessive opioids is unconscionable, but the guilty are often not identified and avoided as they should be.

Provider performance should be weighted by outcome combined with costs and other factors. Unless the initial injury was catastrophic, return to work following a workplace injury is often a function of medical management. Analyzing multiple data indicators from disparate data sources can describe individual physician performance.

Integrating the data for analysis
Any one Workers’ Compensation data source by itself is inadequate for the purpose of evaluating providers. Only the broad scope of data concerning a claim can provide a clear picture of the claim and provider culpability in outcome. Therefore, collecting the data from its various sources and integrating current and historical data are the first two crucial steps in provider performance analytics. The next steps are identifying, evaluating, and monitoring the data elements that are indicators of performance both from the medical and Workers’ Compensation viewpoints.

Industry research tells what to look for
Exposing substandard providers is a matter of integrating and analyzing the data to understand the course of the claim and the providers who contributed to poor claim results. Selecting the data items to monitor can be guided in the first instance by industry research. Organizations such as NCCI (National Council on Compensation Insurance), CWCI (California Workers’ Compensation Institute), WCRI (Workers’ Compensation Research Institute) continually publish their research based on data they collect from members. These organizations offer research regarding medical issues causing cost escalation in the industry, and usually make results available from their individual websites.

Academia and other organizations produce and publish research, as well. The best way to access other research is to use Google to find research studies regarding specific issues and interest areas. For instance, if the concern is low back pain management, simply use Google to find research and scholarly articles on the topic as it relates to Workers’ Compensation. Google is an extraordinary resource in that regard.

When the indicators of performance are identified, they can be applied to analyze providers. Providers tagged with a preponderance of negative indicators will not fall into the best in class category. On the other hand, those whose results are exemplary will rise to the top—best in class.

Link analytics to operations
Analytics results of any variety that remain in graphic form, in a fancy brochure, or pinned to a wall are useless in the effort of containing costs. The findings must be functionally applied to operations to make them actionable. Information regarding best (and worst) in class doctors identified through the methods discussed here must be made available to network managers in a usable form. Moreover, the information should be specific, current, dynamic, easily accessible, and contain objective supportive detail. The work of analytics is not complete until its results are operationalized, thereby linking analytics to implementation.

Learn more about MedMetrics analytics or contact karenwolfe@medmetrics.org.