Tag Archives: ICD

14 ICD-10 Codes That Defy Belief

For every medical diagnosis, disease, injury, symptom, complaint and procedure, healthcare professionals use a specific code to maintain consistent outcomes, data assessment and billing. These codes, formerly called the ICD-9, are a set of characters and numbers that have experienced a series of revisions since they were first put into place. As of Oct. 1, 2015, the ICD-10 (International Classification of Diseases, Revision 10) is in effect.

With such a wide range of known diseases, ailments, symptoms and more, ICD-10 codes can get a little wacky. The infographic below illustrates 14 of the most unusual codes that illustrate the totally bizarre ways in which people manage to injure themselves. From an ice skater’s initial collision with a stationary object to computer keyboarding injuries, the ICD-10 codes don’t miss a trick.

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Obesity as Disease: A Profound Change

The obesity rate in the U.S. has doubled in the past 15 years. More than 50% of the population is overweight, with a BMI (body mass index) between 25 and 30, and 30% have a BMI greater than 30 and are considered obese. Less than 20% of the population is at a healthy weight, with a BMI less than 25.

On June 16, 2013, the American Medical Association voted to declare obesity a disease rather than a comorbidity factor, a decision that will affect 78 million adults. The U.S. Department of Health and Human Services said the costs to U.S. businesses related to obesity exceed $13 billion each year. With the pending implementation of ICD (International Classification of Diseases) 10 codes, the reclassification of obesity is is fast becoming a reality and will dramatically affect workers’ compensation and cases related to the American Disability Act and amendments.

Before the AMA’s obesity reclassification, ICD-9 code 278 related to obesity-related medical complications rather than to obesity. The new ICD-10 coding system now identifies obesity as a disease, which needs to be addressed medically. Obesity can now become a secondary claim, and injured workers will be considered obese if they gain weight because of medications, cannot maintain a level of fitness because of a work-related injury or if their BMI exceeds 30. The conditions are all now considered work-related and must be treated as such.

The problem of obesity for employers is not confined to workers’ compensation. The Americans with Disability Act Amendment of 2008 allows for a broader scope of protection for disabilities. The classification of obesity as a disease now places an injured worker in a protected class pursuant to the ADA amendment. In fact, litigation in this area has already started. A federal district court ruled in April 2014 that obesity itself may be a disability and will be allowed to move forward under the ADA (Joseph Whittaker v. America’s Car-Mart, Eastern District of Missouri).

Obesity as an impairment

Severe obesity is a physical impairment. A sales manager of a used car dealership was terminated for requesting accommodation and won $128,000. He was considered disabled, and the essential function of the job was walking, so he was terminated without reasonable accommodation.

The judge ruled that obesity is an accepted disability and allowed him to pursue his claim against his employer. This could have substantial impact for employers as injured workers could more easily argue that their obesity is a permanent condition that impedes their ability to return to work, as opposed to a temporary life choice that can be reversed.

The Equal Employment Opportunities Commission (EEOC) has recently chimed in on obesity. According to the EEOC, severe [or morbid] obesity body weight, of more than 100% over the norm, qualifies as impairment under the ADA without proof of an underlying physiological disorder. In the last year, we have seen an increasing number of EEOC-driven obesity-related lawsuits. Federal district courts support the EEOC’s position that an employee does not have to prove an underlying condition, especially in cases where there is evidence that the employer perceived the employee’s obesity as a disability or otherwise expressed prejudice against the employee for being obese.

Workers’ compensation claims are automatically reported to CMS Medicare with a diagnosis. When the new ICD-10 codes take effect, an obesity diagnosis will be included in the claim and will require co-digital payments, future medical care or continued treatment by Medicare.

There is good news on the horizon. Reporting of a claim only happens if there is a change in condition not primarily for obesity. It is recommended that baseline testing for musculoskeletal conditions be conducted at the time of hiring and on the existing workforce. In the event of a work-related injury, if a second test is conducted that reveals no change in condition, it results in no reportable claim and no obesity issue. In the event of ADA issues, the baseline can serve to determine pre-injury condition or the need for accommodations.

What does this mean to employers?

Obesity is now considered a physical impairment that may affect an employees’ ability to perform their jobs and receive special accommodations pursuant to the ADA.

An increasingly unhealthy workforce will pose many challenges for employers in the next few years. Those that can effectively improve the health and well-being of their employee population will have a significant advantage in reducing work comp claim costs, health and welfare benefits and retaining skilled workers.

Recent studies

In a four-year study conducted by Johns Hopkins with an N value of 7,690, 85% of the injured workers studied were classified as obese. In a Duke University study involving 11,728 participants, researchers revealed that employees with a BMI greater than 40 had 11.65 claims per 100 workers, and the average claim costs were $51,010. Employees with a BMI less than 25 had 5.8 claims per 100 workers, with average claim costs of $7,503. This study found that disability costs associated with obesity are seven times higher than for those with a BMI less than 30.

A National Institute of Health study with 42,000 participants found that work-related injuries for employees with a BMI between 25 and 30 had a 15% increase in injuries, and those with a BMI higher than 30 had an increase in work-related injuries of 48%.

The connection between obesity and on the job injuries is clear and extremely costly for employers. Many employers have struggled with justifying the cost of instituting wellness programs just on the basic ROI calculations. They were limiting the potential return on investment solely to the reduction in health insurance costs rather than including the costs on the workers’ comp side of the equation and the potential for lost business opportunities because of injury rates that do not meet customer performance expectations. Another key point is that many wellness programs do not include a focus on treating chronic disease that may cause workers to be more likely to be injured and prolong the recovery period.

Customer-driven safety expectations

There are many potential customers (governments, military, energy, construction) who require that their service providers, contractors and business partners meet specific safety performance requirements as measured by OSHA statistics (recordable incident rates) and National Council on Compensation Insurance (NCCI) rating (experience modifiers) and, in some cases, a full review by 3rd party organizations such as ISNet World.

Working for the best customers often requires that your company’s safety record be in the top 25th percentile to even qualify to bid. To be a world-class company with a world-class safety record requires an integrated approach to accident and injury prevention.

Challenges of an aging workforce

The Bureau of Labor Statistics projects that the labor force will increase by 12.8 million by 2020. The number of workers between ages 16 and 24 will decline 14%, and the number of workers ages 25 to 54 will increase by only 1.9%. The overall share of the labor force for 25- to 54-year-olds will decline from 68% to 65%. The number of workers 55 and older is projected to grow by 28%, or 5.5 times the rate of growth in the overall labor force.

Employers must recognize the challenge that an aging workforce will bring and begin to prepare their workforce for longer careers. A healthy and physically fit 55-year-old worker is more capable and less likely to be injured than a 35-year-old worker who is considered obese.

Treating chronic disease

Employers who want a healthy work force must recognize and treat chronic disease. Many companies have biometric testing programs (health risk assessments) and track healthcare expenditures through their various providers (brokers and insurance carriers).

The results are quite disappointing. On average, only 39% of employees participate in biometric screenings even when they are provided free of charge. For those employees who do participate and who are identified with high biometric risk (blood pressure, glucose, BMI, cholesterol), fewer than 20% treat or even manage these diseases.

This makes these employees much more susceptible to injury and significantly lengthens the disability period. The resulting financial impact on employers can be devastating.

Conclusion

Best-in-class safety results will require a combined approach to reduce injuries and to accommodate new classes of disability such as obesity. It is important that employers focus on improving the health and well-being of their workforce while creating well-developed job descriptions, identifying the essential functions, assessing physical assessments and designing job demands to fall within the declining capabilities of the American workers. It is important for an employer to only accept claims that arise out of the course and scope of employment. This is especially true with the reclassification of obesity as a disease. Baseline testing will play an essential role in separating work-related injuries from pre-existing conditions in this changing environment.

A Secret for Comparing Workers’ Comp Costs

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

The request is appropriate. Costs of doing business directly affect the competitive performance of the organization. Understanding drivers of workers’ comp costs is key to making adjustments to improve performance. Still, it’s not that simple.

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.” Parillo emphasizes that 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.

Variability in the data available for benchmarking can be especially disconcerting when considering medical cost drivers, which now account for the majority of claim costs. Differences in state fee schedules and legislation such as required utilization review (UR) and the use of evidence-based guidelines can produce questionable comparative results. Additionally, whether the contributed data is from self-insured 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.

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. 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.”

Analyzing internal data can be highly productive. First, the conditions of meaningful and relevant are guaranteed, for obvious reasons. The geographical differential across one state was found in one organization’s internal data, which ensures that 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.

Evaluate how costs move. Look at costs at intervals along the course of claims for specific injury types. In this case, utilizing ICD-9s is more informative than the National Council on Compensation Insurance (NCCI) injury descriptors. One client found that injury claims that contained a mental health ICD-9 showed a surge 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.

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-9s 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.

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 discovering cost drivers.