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The Secret Power of the NPI

This is a David and Goliath story about how the seemingly insignificant NPI code can fight medical fraud and have a positive impact on workers’ comp medical management. Many in the industry consider the NPI irrelevant. Yet it is a powerful factor in medical management and medical fraud detection.

The NPI is the National Provider Identifier assigned by CMS (Centers for Medicare and Medicaid Services) to individual medical providers and organizations that deliver medical services. It is required on bills for Medicare and Medicaid. Individual medical providers and medical groups must include their NPI on all bills submitted.

If the NPI is required for Medicare and Medicaid reimbursement, it follows that probably all medical doctors have an NPI number from CMS that uniquely identifies them. The problem is that many workers’ compensation payers do not ask for the NPI, do not require it and, even when the NPI is available, do not record it or transfer it to the next level.

Some, but not all, states require the NPI on workers’ comp bills. However, even if it is added to the bill, the use often goes no further.

The value of the NPI is that it uniquely identifies individual medical doctors. It carves out individual treating physicians in groups, organizations and facilities. Without the NPI associated with individuals, all those in a group are lumped together under the organization’s NPI or, worse, the entity’s tax ID. This matters. The assumption is that all members of the group practice in exactly the same way. But they do not.

The ability to parse individuals from groups in the data is essential to fair performance analysis. Individual differences seen in the data can be distinguished, even when associated with a group with individual NPIs. This is essential to creating quality preferred provider networks and directories. It is also indispensable for leveraging the data to create a teaching platform for improving provider performance in workers’ comp.

See also: Easy Way to Spot Workers’ Comp Fraud  

Physicians should be given the opportunity to see themselves portrayed in graphic reports comparing their performance to others like them. By nature, they are high achievers, and they want to show well. The graphic presentations are targets or guides for improvement.

Simply paying attention to a treating doctor in this objective manner will result in behavior change! Using the comparative data is invaluable, but success depends on accurately identifying individuals in the data using the individual NPI.

Another valuable use of the NPI is to assign medical specialties to individuals. Professional specialties can be obtained electronically from CMS databases using the NPI. Specialty is yet another data element missing in much of the bill review and claim system data. If the NPI number is available, specialties can be derived.

Specialties are important so that treating doctors are grouped with other doctors who are similarly prepared and licensed. The argument from doctors that they only treat the more difficult cases is nullified when they are compared only with others in their specialty. The best example is pain management specialists, who really do treat the more difficult cases. Their performance should always be compared with other pain specialists.

Unfortunately, there are those who twist the positive aspects of the NPI for fraudulent purposes. Close examination of the data reveals that less reputable medical doctors and other providers obtain multiple NPI numbers, using them in different locations or situations to deliberately obfuscate the data.

When multiple NPI numbers are fraudulently used, the door is open to undetectable duplicate billing. Systems cannot recognize overall performance for the individual because their performance is fragmented across multiple NPIs. To accurately analyze performance for an individual, all treatment incidences should be combined for one practitioner, thereby creating a critical mass of data for that individual.

While some will think the focus on NPI is much ado about nothing, it is not. Individual NPI numbers on all medical bills is essential; payers should insist on it. In fact, reimbursement should be withheld until the correct information is included on the bill as is done in Medicare.

See also: States of Confusion: Workers Comp Extraterritorial Issues 

Treating doctors not only drive direct medical costs but also indemnity costs, return to work and disability ratings at the end of the claim. They can also influence legal involvement. Consequently, finding the best doctors and avoiding the bad ones is crucial.

The way to determine who should be included in quality medical provider networks is to analyze past performance based on the data. The only way to accurately analyze performance is to identify individual treating doctors in the data and evaluate their performance across multiple claims based on the relevant performance factors. Correct NPI numbers included on medical bills are essential.

Workers’ comp payers must require correct individual NPI numbers on all medical bills. This is not an outrageous demand and does not add to costs. However, it does require attention to the matter. The benefits are too great to miss this simple yet powerful opportunity.

The simple little NPI is a powerful element in workers’ compensation medical management. It is the David that can effectively and affordably fight the medical fraud Goliath.

Confusion Reigns on Predictive Analytics

It seems everyone in workers’ compensation wants analytics. At the same time, a lot of confusion persists about what analytics is and what it can contribute. Expectations are sometimes unclear and often unrealistic. Part of the confusion is that analytics can exist in many forms.

Analytics is a term that encompasses a broad range of data mining and analysis activities. The most common form of analytics is straightforward data analysis and reporting. Other predominant forms are predictive modeling and predictive analytics.

Most people are already doing at least some form of analytics and portraying their results for their unique audiences. Analytics represented by graphic presentations are popular and often informative, but they do not change behavior and outcomes by themselves.

See Also: Analytics and Survival in the Data Age

Predictive modeling uses advanced mathematical tools such as various configurations of regression analysis or even more esoteric mathematical instruments. Predictive modeling looks for statistically valid probabilities about what the future holds within a given framework. In workers’ compensation, predictive modeling is used to forecast which claims will be the most problematic and costly from the outset of the claim. It is also the most sophisticated and usually the most costly predictive methodology.

Predictive analytics lies somewhere between data analysis and predictive modeling. It can be distinguished from predictive modeling in that it uses historic data to learn from experience what to expect in the future. It is based on the assumption that future behavior of an individual or situation will be similar to what has occurred in the past.

One of the best-known applications of predictive analytics is credit scoring, used throughout the financial services industry. Analysis of a customer’s credit history, payment history, loan application and other conditions is used to rank-order individuals by their likelihood of making future credit payments on time. Those with the highest scores are ranked highest and are the best risks. That is why a high credit risk score is important to purchasers and borrowers.

Similarly, workers’ compensation claim data can be collected, integrated and analyzed from bill review, claims system, utilization review, pharmacy (PBM) and claim outcome information to score and rank-order treating physicians’ performance. Those with the highest rank are the most likely to move the injured worker to recovery more quickly and at the lowest cost.

Both predictive modeling and predictive analytics deal in probabilities regarding future behavior. Predictive modeling uses statistical methods, and predictive analytics looks at what was, is and, therefore, probably will be. For predictive analytics, it is important to identify relevant variables that can be found in the data and take action when those conditions or events occur in claims.

One way to find critical variables is to review industry research. For instance, research has shown that, when there is a gap between the date of injury and reporting or the first medical treatment, something is not right. That gap is an outlier in the data that predicts claim complexity.

Another way to identify key variables is to search the data to find the most costly cases and then look for consistent variables among them. Each book of business may have unique characteristics that can be identified in that manner.

Importantly, predictive analytics can be used concurrently throughout the course of the claim. The data is monitored electronically to continually search for outlier variables. When predictive outliers occur in the data, alerts can be sent to the appropriate person so that interventions are timely and more effective.

For example, to evaluate medical provider future performance, select data elements that describe past behavior. Look at past return-to-work patterns and indemnity costs associated with providers. If a provider has not typically returned injured workers to work in the past, chances are pretty good that behavior will continue.

For organizations looking to implement analytics, those who have already made the plunge suggest starting by taking stock of your organization’s current state. “The first thing you need to know is what is happening in your population,” says Rishi Sikka, M.D., senior vice president of clinical transformation for Advocate Health Care in Illinois. “Everyone wants to do all the sexy models and advanced analytics, but just understanding that current state, what is happening, is the first and the most important challenge.”

The accuracy and usability of results will depend greatly on the quality of the data analyzed. To get the best and most satisfying results from predictive analytics, cleanse the data by removing duplicate entries, data omissions and inaccuracies.

For powerful medical management informed by analytics, identify the variables that are most problematic for the organization and continually scan the data to find claims that contain them. Then send an alert. Structuring the outliers, monitoring the data to uncover claims containing them, alerting the right person and taking the right action is a powerful medical management strategy.

How Work Comp Can Outdo Group Health

We all know the current healthcare system in the U.S. delivers erratic quality at unsustainable, yet ever-increasing, costs. Workers’ compensation medical care is affected by those costs. 

A major shift in the health industry, value-based healthcare, will benefit workers’ compensation. Embracing selected new medical management methodologies put forth in value-based healthcare has the potential to be powerful.

Value-based healthcare means restructuring how medical care is organized, measured and reimbursed. It moves away from a supply-driven system organized around what physicians do to a patient-centered system organized around what patients need. The focus is shifted from volume and profitability to patient outcomes (quality care). When fully implemented, the overall impact will be nothing less than staggering.

Porter and Lee, healthcare industry strategists at Harvard, have described six value strategies necessary to achieve healthcare industry transformation. Many of the changes are now underway in ACOs (accountable care organizations) such as the Cleveland Clinic, proving the concept. These defined initiatives produce desired results—quality care at less cost. 

Six components of value-based healthcare

The following briefly describes the methodologies necessary to transform healthcare, according to Porter and Lee.

  1. Integrated practice units (IPUs)—meaning multiple specialists practice together, resulting in comprehensive and integrated medical care rather than fragmented, duplicated services
  1. Measure true outcomes and costs for every patientWhen outcomes are measured and reported publicly, providers are under pressure to improve. Fraud and self-dealing are reduced.
  1. Bundled paymentsPayment bundles are capitated single payments for all the patient’s needs during defined episodes of care, such as specific surgical procedures. Providers are rewarded for delivering quality while spending less.
  1. Integrate care delivery systemsServices are concentrated and integrated to eliminate fragmentation and to optimize the quality of care delivered at any given location.
  1.  Expand geographic reachCenters of excellence are developed where expertise is gained through higher volume of similar procedures.
  1.   Information technologyData mining powerfully enables the first five initiatives and informs services and decisions.

As Porter and Lee say, “Whether providers like it or not, healthcare is evolving from a proficiency-based art to a data-driven science, from freelance physicians to hospital-employed physicians, from one-size-fits-all community hospitals to vast hospital networks organized around centers of excellence.”

Value-based medical management in workers’ comp

The goal of value-based medical care is to enhance quality outcomes for patients (injured workers) while reducing costs. Focusing on quality (what the patient needs) actually reduces costs.

For group health, the measures are physical and philosophical, requiring widespread disruption in how services are organized, delivered and reimbursed. However, workers’ compensation payers can benefit by incorporating three of the six value measures into their medical management process now.

  1. Measure true outcomes and costs for every patient (the injured worker)

Physician performance is scored based on injured workers’ experience and outcomes along with cost. Providers who score poorly can be avoided.

  1. Bundle payments

Bundling is capitating payments for all the services required for procedures such as specific surgical procedures, including all associated pre-op and post-op care. The costs are kept in line because providers need to stay under the cap to be profitable. They also focus on quality, because re-dos, redundancy and complications add cost to the service bundle, thereby diminishing profits. Prepare to see bundled payment options available to workers’ compensation sooner rather than later.

  1. Information technology

The data in workers’ compensation, while in silos, is all organized around individual claims and injured workers. When the data is integrated at the claim level, patient experience, provider performance, outcome and cost analysis opportunities are unlimited. The more comprehensive and accurate the data, the greater the opportunity for gain.

Those who cling to traditional seat-of-the-pants medical management will be left behind. Those in group health may be hampered by slow regulatory change, organizational upheaval and resistant providers, while workers’ compensation payers are free to adopt transformative value measures now. Organizations that progress rapidly to implement the value agenda will reap huge benefits.

How to Find Best Work Comp Doctors?

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) titled, “A Guide to High-Value Physician Services in Workers’ Compensation How to find the best available care for your injured workers” 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.” 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 that it would be nice to have the data, but that 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.”

Actually, the data is available from most payers whether they are insurers, self-insured, self-administered employers or third-party administrators (TPAs). 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 1099s, 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 P.O. box rather than the rendering physician’s location, matters become more complicated. This needs to change.

The new request

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.

The Rise of Big (Bad) Data

The workers’ compensation industry has created and stored huge amounts of data over the past 25 years. The copious amount of data 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 is a general term used to describe voluminous amounts of data, whether unstructured or structured. It’s that simple.

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. The data 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 is that which is housed in a specified format in a predefined container that can be mined for information. Structured data is designed for a specific purpose so that it can be accessed and manipulated.

The workers’ compensation industry has both forms of data. However, structured data is more available for mining, analyzing and interpreting.

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, utilization review, medical case management and pharmacy or pharmacy benefits management (PBM).

While integrating data is an achievable task, other issues remain. 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.

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.

Predicting that a devastating earthquake will occur in the next 25 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.