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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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Wednesday, May 28, 2014

Predict Claim Risk with Diagnostic Severity Scores

by Karen Wolfe

Predicting and measuring claim risk is an important effort in managing Workers’ Compensation claim costs. Huge sums of money are allotted to sophisticated predictive modeling initiatives hoping to tag the claims that will be the most costly. Scores of analytic professionals are put to the task and when high risk claims are identified, additional resources are applied to mitigate impending damage. Yet, one very powerful measure of claim risk remains virtually untapped and ignored by the legions of analysts.

Diagnostic codes
Every medical bill submitted for payment contains diagnostic information in the form of standard codes. The codes, assigned by the treating physician, describe the injury or illness that triggered the claim.

The codes, ICD-9 codes, are intended to justify treatment rendered and the fees charged. ICD-9’s codes are the International Classification of Diseases published by the World Health Organization (WHO). On Workers’ Compensation bills, they can tell us what the medical problem is.

Codes ignored
ICD-9 codes are part of the collected information from the bill, however, they are not well understood or used in claim management. That ICD-9 codes are ignored by claims professionals is perfectly reasonable.

ICD-9 codes on the claim are just codes. They do not contain the description of the injury and there are thousands of them. Adjusters do not have the time or inclination to search for code descriptions. Instead, they rely on the NCCI classifications of type of injury, body part, and cause. Nevertheless, neither ICD-9 codes nor NCCI classifications by themselves can define the seriousness of the medical condition.

Coding the codes
To define injury severity, individual ICD-9 codes must be graded for medical severity using a simple scoring methodology. For instance, multiple codes are used by physicians to describe back injuries and they have very different severity scores. A low back strain will not be scored as high for seriousness as a spinal cord injury. Likewise, a fracture of the tibia in a healthy young adult will have a lower severity total score than a fracture of a tibia of a 60 year old who also has diabetes.

Multiple codes on a claim
Rarely is only one ICD-9 code assigned to a claim. In fact, when a claim is complex or when recovery is slow or compromised, multiple ICD-9 codes accrue to the claim. Older claims involving many treatments over time can literally contain pages of ICD-9 codes. Each time an injured worker is referred to a new specialist, new codes are added.

Comorbidities such as diabetes, heart disease, or obesity that add complexity, delayed recovery, and cost to a claim can also be tracked through ICD-9 codes. When the treating physician notes such a condition, the code will be on the bill along with the injury codes. Treating physicians should be encouraged to include comorbidity diagnostic codes because they impact recovery.

Migrating claims
Claim diagnostic scores accumulate as the claim progresses. Total diagnostic scores are tallied and monitored by the computer system. As claim diagnostic scores accrue, automatic alerts are sent when the total reaches a pre-determined set point.

Importantly, migrating claims can never go unnoticed!

Moving indicators
Claim diagnostic scores are dynamic moving indicators of risk and exposure in a claim. Electronic monitoring claim ICD-9’s continuously offers critical information about current claim risk status. The claim diagnostic risk tally remains in the system background, interfering with nothing and no one. However, when the set-point is reached, an alert is sent to the appropriate person so that action can be mobilized.

As new medical bills arrive and new diagnoses are accrued, the diagnostic risk score for a claim mounts. While not the only indicator of claim risk, diagnostic severity scoring is powerful, current information. A high diagnostic severity score absolutely predicts high claim risk and cost.

Karen Wolfe is the founder and President of MedMetrics, LLC, a Workers’ Compensation analytics company. MedMetrics offers online apps that super-charge medical management by linking analytics to operations. MedMetrics apps include Diagnostic Severity Predictive Scoring with Alerts. karenwolfe@medmetrics.org


Thursday, May 15, 2014

The Callenge of Doing New Things is to Keep Doing Them

by Karen Wolfe

The Workers’ Compensation industry is often accused of resisting change. Moreover, simple observation bears this out, such as how long it has taken the industry to address analytics. Having now put its toe in the water, the industry is still light years behind other industries in implementing analytics. What is it in the industry culture that causes resistance to change?

An opportunity can be rather simple, yet taking the necessary steps to achieve it is daunting to many. An executive was once overheard saying in response to a proposal, “It all makes sense, but we would need to change the way we do things to make it happen”. Assuming change will be daunting seems to be a widespread condition of the culture. However, adopting new methods, can be very much worth the effort.

“There are risks and costs to action, but they are far less than the long range risks of inaction” J.F. Kennedy

Small change
Still, it may not be the risk of change that is the deterrent to action, but rather the effort required. An example of a small change that could significantly impact claim cost and outcome is improving data quality. Analytics are totally dependent on data quality so if it is inaccurate or incomplete, the analytics are of less value—a poor trade.

Don’t depend on IT
One thing that makes improving data quality intimidating is that it is not an IT responsibility. Even when IT can play a part in improving data quality, it is management that must demand it. Refer to Why Poor Data Quality is Not an IT Problem.

Data quality is a management challenge. Changing data quality is a management leadership issue and a fairly simple one. Data fields must be filled completely and accurately. Duplicates must be avoided. Moreover, individuals and processes must be held accountable for quality by management.
Source data
Sometimes the problem of data quality is the source of the data. Bill review data may not contain all the data fields needed, for instance. Again, only management can address the problem with the vendor organization.

Sustaining change
A significant amount of the effort needed for change is not so much mobilizing the action, but sustaining the initiatives. Change directives must become an integral part of the organization’s process. Management must continually check to see that mandates are carried out and which have slipped. Performance accountability is key.

Management owns data quality
The degree to which a change initiative is successful is positively correlated with management oversight. It is not difficult, but it can be tedious. In this regard, a definition of management is:

Good management is continually making sure what you did stays done.

Initiate the change, then follow up to insure continued practice. The real challenge is to keep doing it. As for accurate and complete data, it is the only affordable and practical resource on the horizon to advance to the next levels of medical management and measureable cost control through analytics. Only management can change data quality.

Karen Wolfe is the founder and President of MedMetrics, LLC, an Internet-based Workers’ Compensation analytics company. MedMetrics offers online apps that super-charge medical management by linking analytics to operations.  karenwolfe@medmetrics.org