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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, November 30, 2015

How to Find the Best Doctors for Workers' Comp

by Karen Wolfe

As is the case in any professional group, individual medical provider's performance runs the gamut of good, bad, and iffy. The trick is to find good medical providers for treating injured workers, avoid the bad ones, and scrutinize those who are questionable. To qualify as best for injured workers, medical providers need proficiency in case handling as well as medical treatment.

High-value physician services 
The first step is to clarify the characteristics of the best providers, especially in context with Workers’ Compensation. One resource is an article published by the American College of Occupational and Environmental Medicine in association with the IAIABC (International Association of Industrial Accident Boards & Commissions) entitled, “A Guide to High-Value Physician Services in Workers’ Compensation  How to find the best available care for your injured workers”[1] It’s a place to begin.

The article notes, “Studies show that there is significant variability in quality of care, clinical outcomes and costs among physicians.”[2]  That may be obvious, but it also verifies the rationale for taking steps to identify and select treating doctors rather than pulling from a long list of providers to gain the discount. The question is what process should be used to select providers?

Although considerable effort from scores of industry experts contributed to this article, the approach they recommend is complex, time-consuming, and subjective. In other words, it is impractical. Few readers will have the expertise and resources to follow the guide. Moreover, one assertion made in the article is simply wrong. 

The article states it would be nice to have the data, but the data is not available. “Participants in the workers’ compensation system who want to direct workers to high-quality medical care rarely have sufficient data to quantify and compare the level of performance of physicians in a given geographic area.”[3] 

Actually, the data is available from most payers whether they are insurers, self-insured, self-administered employers, or TPA’s. However, collecting the data is the challenge. 

Data silos 
The primary reason data is difficult to collect is that it lives in discrete database silos. The industry has not seen fit to place value on integrating the data, but that is required for a broad view of claims from beginning and throughout their course.

At a minimum, claim data should be collected from medical billing or bill review, the claims system, and pharmacy (PBM). The data must be collected from all the sources, then integrated at the claim level to get a broad view of each claim. It takes effort, but it is doable. Yet, there remains another data challenge. 

Data quality 
Payers have traditionally collected billing data from providers, through their bill review vendor. The payer’s task has been paying the bill and sending a 1099 statement to providers at the end of the year. All that is needed is a provider name, address, and tax ID so the payment reaches its destination. It makes no difference to payers that providers are entered into their systems in multiple ways causing inaccurate and duplicate provider records. One payment is a payment. The provider might receive multiple 1099’s, but that causes little concern.

What is of concern is that when the same provider is entered into the payers’ computer system in multiple ways, it can be difficult to ascertain how many payments were made to an individual provider. Moreover, when the address collected by the payer is a PO Box rather than the rendering physician’s location, matters become more complicated. This needs to change. 

The new ask 
Now payers are being asked to accurately and comprehensively document individual providers, groups, and facilities so the data can be analyzed to measure medical provider performance. They need to collect the physical location where the service was provided and it should be accurately entered into the system in the same way every time. (Note: this is easily done using a drop-down list function rather than manual data entry.)

Most importantly, a unique identifier is needed for individual providers, such as their NPI (National Provider Identification). Many payers are now stepping up to improve their data so accurate provider performance assessments can be made.

High-value, quality medical providers can be identified by using the data. However, quality data produces better results. Selecting the best medical providers is not a do-it-yourself project. Others will do it for you.
Karen Wolfe is the founder and President of MedMetrics®, LLC, a Workers’ Compensation medical analytics and technology services company. MedMetrics analyzes the data to score medical provider performance and offers online apps that super-charge medical management by linking analytics to operations, thereby making them actionable. karenwolfe@medmetrics.org

[1] A Guide to High-Value Physician Services in Workers’ Compensation How to find the best available care for your injured workers
[2] Ibid.
[3] Ibid.

Tuesday, October 20, 2015

Big Data Drawn From Bad Data Makes Big Bad Data

by Karen Wolfe

One thing we know for sure is the Workers’ Compensation industry has created and stored huge amounts of data over the past 25 years. The copious amount of data available has led to a new phenomenon in our industry similar to most others—the concept of Big Data. The goal is to corral, manage, and query the industry’s Big Data for greater insight.

Big Data
Big Data is a general term used to describe voluminous amounts of unstructured or structured data.[1] It’s that simple.

Unstructured data
Unstructured data is a generic term used to describe data not contained in a database or some other type of prescribed data container. Examples of unstructured data are claim adjuster and medical case manager notes. It can also include emails, videos, social media, instant messaging and other free-form types of input. Gaining reliable information from unstructured data is significantly more difficult than from structured data.

Structured data
Structured data is that which is housed in a predefined container that can be mined for information. It has a specified format for organizing and storing data. Structured data is designed to organize data for a specific purpose so that it can be accessed and manipulated.

The Workers’ Compensation industry has both forms of data, structured and unstructured. However, structured data is more available for mining, analyzing, and interpreting. Structured and unstructured data both make up Big Data.

Integrate silos
To evolve ordinary data to Big Data in Workers’ Compensation, data from multiple silos must first be integrated. The industry uniquely maintains claim-related data in separate places such as bill review, claims systems, UR, medical case management, and pharmacy or PBM. While integrating data is an achievable task, other issues remain.

Poor quality data
Unfortunately, much of the existing data in this industry has quality issues. Data entry errors, omissions, and duplications occur frequently, and if left unchanged, will naturally become a part of Big Data. Poor data quality is amplified when it is promoted to Big Data.

Value of Big Data
The reason Big Data is so attractive is that it provides the quantity of data necessary for reliable analytics and predictive modeling. More data is better because analysis is statistically more valid when it is informed by more occurrences. Nevertheless, greater volumes of data cannot produce the desired information if it is wrong.

The obvious
Predicting that a devastating earthquake will occur in the next twenty-five years does not generate urgency. Likewise, knowing “clean” Big Data will be needed to remain competitive and viable in the future does not inspire aggressive corrective action now. But it should.

Correcting smaller data sets is easier than trying to fix huge data sets. It may not even be possible to adequately cleanse Big Data. Moreover, preventing erroneous data before it occurs is an even better approach. Data quality should be valued. Those responsible for collecting data, whether manually or electronically, should be held accountable for its accuracy. Existing data should be evaluated and corrected now to create complete and accurate data. Doing so will speed migration to Big Data without drowning in Big Bad Data.

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

[1] www.techtarget.com