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Measures and Benchmarks

By Brent A. Metfessel MD, MS, CMP™ (Hon)

In a previous Executive-Post, we asked readers if they knew of any case-mix severity measures other than those utilizing expected values.

The Black-Box

When an MCO or HMO analyzes provider practice patterns, it is imperative that the organization educate providers on the methodology and validation of the adjuster, since provider buy-in to the adjuster cannot be obtained otherwise. 

Such education may consist of readings provided with the distributed performance reports that explain the algorithm as well as evidence for the algorithm’s validity.  The MCO needs to be open to questions from providers and show willingness to open the “black box” as much as possible.

Further Considerations

There are further considerations that are relevant to providers when dealing with case-mix adjusted reports:

1. Are the reported performance measures adjusted by specialty? 

The rationale for the additional adjustment comes from the fact that even though a number of specialties may treat congestive heart failure, for example, an internist or family practitioner generally treats less severe cases than would a cardiologist. 

Thus, even if a report is case-mix adjusted by illness class, the adjuster may not fully account for the differences in patient acuity within the illness class.  Adjusting by specialty will enable a more “apples to apples” comparison and achieve greater provider buy-in to the process.

However, for less common illnesses the additional specialty adjustment may cause the cell sizes to become too small, causing the adjustment to lose meaning since there would not be enough patients in some cells for meaningful comparisons.

Overall, whether or not specialty should be added as an additional adjustment is an individual decision made by the health plan.  The larger the health plan, the less chance that cell group sizes may become too small and the greater the advantage of the additional specialty adjustment.

2. What are the exclusion criteria? 

After the case-mix adjustment is performed, it is important that prior to reporting there exists an outlier exclusion criteria.  Without such criteria, there is a much greater chance that a good provider may perform poorly on a performance report since a few high-cost outliers, which may occur due to no fault of the provider, can strongly skew the case-mix indices and lead to artificially high cost variances and performance ratios. 

Some methodologies exclude general catastrophic cases, such as members with costs above $25,000, or there may be a truncation calculation where catastrophic members are included in the reporting information but are truncated to the criteria amount.

Thus, if a patient has costs of $50,000, the costs will be truncated to $25,000 prior to reporting.  This has the advantage of including all patients but the disadvantage of not knowing the actual cost of the patient panel. 

What about Outliers?

Another way to exclude medical outliers involves excluding them at the case-mix class level.  This means that illnesses that generally use less resources will have different criteria – in this case a lower high outlier exclusion boundary – that would an illness class that typically has high resource use. 

If cost is used as the measure of interest, the distribution curve of cost for a particular illness is skewed to the high side and thus does not look like the bell-shaped normal distribution.  This makes developing proper exclusion criteria more complex. 

For greater accuracy, a “non-parametric” or “distribution-free” test is useful.  One such test was developed in 1993 by Sprent and consists of the following equation:

                                 (| X_i – M | / MAD) > Max                                   

Where X_i represents any value being evaluated for outlier status, M represents the median (the value for which 50% of sample values are above, 50% below) of the sample (such as all cases in a disease class) and MAD is the median absolute deviation.

To calculate the MAD value, first obtain the absolute value of the difference between each value and the sample median. Then, sort the difference scores in ascending order. The median of the difference scores is the MAD value. Max is then the criteria point for excluding outliers.

A reasonable value of MAD would be 5.  Both low and high outliers would be excluded based on this equation.

Assessment

Ironically, medical outliers may contain very useful information in themselves. Yet, even more ironically, they are often rejected.

Conclusion

Do you still report outliers separately since such patients, particularly high outliers, may in some cases be steered to case management protocols?

Related Information Sources:

Practice Management: http://www.springerpub.com/prod.aspx?prod_id=23759

Financial Planning: http://www.jbpub.com/catalog/0763745790

Risk Management: http://www.jbpub.com/catalog/9780763733421

Healthcare Organizations: www.HealthcareFinancials.com

Administrative Terms: www.HealthDictionarySeries.com

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Filed under: Managed Care, Quality Initiatives |