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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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Monday, March 30, 2015

The Best Data Might Be Your Own

by Karen Wolfe

Workers’ Compensation claims and medical managers are continually challenged by upper management to analyze their cost drivers. Moreover, upper management wants comparisons of the organization’s results to that of their peers.

The request is appropriate. Costs of doing business directly impact competitive performance of the organization. Understanding cost drivers is key to making adjustments to improve performance. Still, it’s not that simple.
Meaningful and relevant data
Executing the analysis is the lesser of the two demands. More challenging is finding industry or peer data that is similar enough to create an apples to apples study. In a recent article, Nick Parillo states “Regardless of the data source, whether it be peer-related or insurance industry-related, risk managers must be focused on aligning the data to their respective company and its operations.”[1] Parillo emphasizes the data should be meaningful and relevant to the organization.
Aligning the data to the situation can be challenging. Industry or peer data may not be situation-specific enough or granular enough to elicit accurate and illuminating information. State regulations vary, as do business products and practices, along with a multitude of other conditions that make truly accurate comparisons difficult.

Data variability
Variability in the data available for benchmarking can be especially disconcerting when considering medical cost drivers that now account for the majority of claim costs. Differences in state fee schedules and legislation such as required UR and the use of evidence-based guidelines can produce questionable comparative results. Additionally, whether the contributed data is from self-insured and/or self-administrated entities can skew the results.

Other variables that make comparing industry or peer data less valid are unionization, physical distribution of employees, employee age and gender, as well as industry type and local resources available. Potential differences are unlimited.

Cultural variables
External sources such as local cultural and professional mores, particularly among treating medical providers can play a significant role in disqualifying data for comparison. For instance, my company’s analysis of client data has uncovered consistent differences in medical practice patterns in one large state defined by geography. In one geographic sector, referrals to orthopedists with subsequent surgery and higher costs are far more frequent than in another sector of the state for the same type of injury.

Parillo continues, “Given the uncertainty and limitations on the kinds of peer group data a risk manager would need to perform a truly “apples to apples” comparison, the most “relevant and meaningful” data may be that which a risk manager already possesses: His own.”[2]

Internal data
Analyzing internal data can be highly productive. First, the conditions of meaningful and relevant are guaranteed, for obvious reasons. The differential across one state was found in one organization’s internal data which insures data variability is not a factor.

Analyses can be designed that dissect the data at hand. Follow up to the above example might include looking for other geographic variables in costs, in injury types, and in medical practice patterns. Compare physician performance for specific injury types in the same jurisdiction and then look for differences within. To gain this kind of specificity and relevance, drill down for other indicators.

Moving costs
Evaluate how costs move. Look at costs at intervals along the course of claims for specific injury types. In this case, utilizing ICD-9’s is more informative than the NCCI injury descriptors. One client found that injury claims which contained a mental health ICD-9 imbedded during the course of the claim, showed an upsurge in costs beginning the second year. Now further analysis can begin to discern earlier indicators of this outcome. In other words, dive further into the data to find leading indicators.

Imbedded indicators
Industry data is not likely to contain the detail necessary to evoke subtle mental health information during the course of the claim. Most analysis ignores the subtlety and sequence of diagnoses assigned. Few would uncover the mental health ICD-9 because few bother with ICD-9’s at all.

Drilling down, analyze claims that fall into this category for prescriptions, legal involvement, and other factors that might divulge prophetic signs. It is an investigative trail that relies on finite internal data analysis.

Undervalued data
Too often people disrespect their own data, thinking it is too poor in quality, therefore of little value. It’s true, much of the data collected over the years is of poorer quality, but it still has value. Begin by cleaning or enhancing the data and removing duplicates. Going forward, management emphasis should be on collecting accurate data.

Benchmarking data sourced from the industry may be useful, but should not necessarily be considered the most accurate or productive approach. Internal data analysis may be the best opportunity for cost driver discovery.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation medical analytics and technology services company. MedMetrics analyzes the data and offers online apps that super-charge medical management by linking analytics to operations, thereby making them actionable. karenwolfe@medmetrics.org

[1] Parillo, N. The Caveats of Qualitative Benchmarking. Risk and Insurance. March 3, 2015. http://www.riskandinsurance.com/the-caveats-of-qualitative-benchmarking/
[2] Ibid.

Tuesday, March 10, 2015

How to Make Data a Robust Medical Managment Tool

by Karen Wolfe

Data makes all the difference.” This is according to a White Paper published by LexisNexis®. Entitled, “More Data, Earlier: The Value of Incorporating Data and Analytics in Claims Handling” states that carriers can reduce severity payments by up to 25 percent.[1]

This is true for P&C carriers, but especially true for Workers’ Compensation payers where medical costs have steadily increased for decades. In Workers’ Compensation medical services are not limited by plan design. The costs for medical now amount to more than 60% of claim cost and they continue to climb. Nevertheless, data managed correctly, can make all the difference and save real dollars.

Big data
Big Data is currently in vogue. Everyone is talking about Big Data as though it will deliver a panacea of some sort. The notion is that organizing and analyzing copious amounts of data will produce new and improved insights, thereby gaining desired results. That may be true however, this outcomes is a function of complete, consistent, and accurate data. Unfortunately, data purity is rare, regardless of the size of the data set.

Bad data
The gains promised by Big Data are dependent upon data quality. Whether Big Data is comprised of large data sets or made up of many small data sets, quality may be the elusive factor. In order to achieve positive results using any data set, it must be complete and accurate. Duplicate records must be cleansed and merged for starters. More importantly, bad data input processes must be altered upstream where data is created. Standards for data quality must be set and enforced.

One reason data is of such poor quality is that little value has been placed on its veracity. That is changing as the vision for improved outcomes based on analytics is increasingly clear. Nevertheless, data input from the outset should be set to rigorous standards with accountability checks along the way. Automated imaging systems must be regularly calibrated to insure accuracy while individuals who input data along with their managers must be held responsible for the quality of the data.

Voluminous data
In Worker’ Compensation as with all insurance lines, comprehensive data is a fete accompli. Data has been collected digitally for decades, driven by claims payment requirements. In Workers’ Compensation, the claim is set up in the payer’s system and continually fed by incoming data. Mandatory reports of injury are submitted by employers and treating physicians. Bills from medical providers and others are streamed through bill review systems, then to claims systems throughout the course of the claim. Events such as litigation, court dates, and bills paid are documented in the claims system. Pharmacy is managed by the PBM (Pharmacy Benefit Management), thereby setting up an additional unique database related to the claim. Most payers also collect medical utilization review and medical case management data. The question is not the amount of data, but its quality and what can done with it. How is it applied?

Disparate data
Unfortunately, in Workers’ Compensation much of the data remains in separate silos. The focus has been on collecting the data. Now the question is, how to make data an operational tool that achieves the kind of positive savings results reported in the LexisNexis study. A different approach is needed.

Integrated data
Making data a useful work-in-progress tool is a matter of first integrating the data across multiple data sets relating to claims. This is sometimes a tedious process, but invaluable. The request and funding must come from the business units where anything related to data is not usually a priority. Business managers must begin to value the process of collecting good data and converting it to actionable information.

Analyzed data
Once the data is collected and integrated, analyzing it to gain the business knowledge is the task. Business managers can learn to articulate for IT what they want and need for decision support and other initiatives. IT has a role in assisting business managers in understanding how to ask more effectively for what they need. Cost drivers and trends can be uncovered in the analyzed data. Raw data is not a usable claims management tool, but analyzed and logically portrayed information can be powerful.

Current data
The power of data is best exploited when it is analyzed and made available to the business units as concurrently as possible. Intervention is far more effective when it is mobilized early.[2] Damage control is best achieved before it is irretrievable. Moreover, the analyzed data must be linked to operations, thereby making it actionable.

Knowledge derived from analytics is useless
until it is acted upon.

Linked data
Regardless of how impressive the analyzed data, it is useless unless acted upon. To actualize the data for useful application, it must be analyzed and re-presented to the business units in ways that can be easily accessed, understood, and applied. Through analytics, the data is transformed to knowledge: knowledge about conditions in claims, events, costs, and performance of vendors. Knowledge should not only be current, but should reach the operational front lines and portrayed in ways that promote action.

Actionable knowledge
Actionable knowledge is derived from analysis of the data that is presented to the business units in a functional form. To achieve measureable cost savings, continuously monitor the data, integrate, and analyze it, then re-present it to the business units in the form of easily interpreted knowledge and action tools. Individuals can be prompted by the system to take specific initiatives based on the knowledge, thereby creating a structured and powerfully enhanced approach to medical management with measurably positive results.

Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation medical analytics and technology services company. MedMetrics analyzes the data and offers online apps that super-charge medical management by linking analytics to operations, thereby making them actionable. karenwolfe@medmetrics.org

[1] A.Hassib. T.Fannin. More Data, Earlier: The Value of Incorporating Data and Analytics in Claims Handling LexisNexis® Risk Solutions. June, 2014
[2] K. Wolfe. Early Intervention Drives Better Outcomes, But is Not Really Pursued. http://medmetrics.blogspot.com/2014_10_01_archive.htmlenh