The Centerpiece of Quality Practice Patterns

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

It is difficult to construct an adequate medical practice pattern profile without case-mix or risk adjustments. There needs to be an algorithm that adjusts for the medical severity of patient mix. 

For example, a tertiary care center in New York City cannot be compared using unadjusted data with a community hospital outside the city. The tertiary care center will use more resources, and thus cost more, than the community hospital no matter how exemplary the tertiary care center. 

And, a cardiologist cannot be compared to a family practitioner, since in general the cardiologist will see patients of greater severity. 

Algorithms for Case-Mix Adjustment

A wide variety of methodologies exist that are useful for case-mix, risk, and severity of illness adjustment. And, a number of third-party vendors exist that sell software groupers for case-mix categorization.

Since each methodology has different strengths, some MCOs have purchased more than one software package. There is no such thing as a “perfect” adjuster. Five examples of commonly used algorithms follow:

·         Diagnosis Related Groups (DRGs) and related adjusters: Originally put into use in the early 1980s, DRGs were intended for use mainly as a methodology for Medicare to determine reimbursement for hospital stays.  Nevertheless, DRGs and their more recent derivatives (Revised DRGs or RDRGs, and All Patient Refined DRGs or APR-DRGs, both of which subclassify each DRG category into three to five severity strata using various algorithms) are useful for inpatient case-mix adjustment.  An example of a DRG category is DRG 89, “Simple pneumonia & pleurisy, age > 17, with CC [complications]”. The same can be said adjusters related to the newest Medical Severity DRGs [MS-DRGs].

·         Episode Treatment Groups™ or ETGs (Symmetry Health Data Systems, Inc.): This data grouper classifies the claims records into episodes of care that track the progress of an acute illness from onset to resolution and includes related diagnoses and treatments.  For more chronic illness episodes, where there is really no defined “onset” or “resolution”, one usually profiles providers on a pre-defined time window, such as a year-long episode. To capture enough episodes for analysis, ETGs generally require a two-year reporting period. Since this case-mix adjuster depicts the longitudinal aspects of care, ETGs are a process-based adjuster, meaning that they emphasize the process of care and the treatment the patient receives over a time course. A member can, and often does, have more than one ETG during a reporting period. An example of an ETG is “Obesity, morbid, with surgery”. There exist over 600 ETG categories, which are granular enough to detect nuances in illness classes and severity but not so large as to lead to significant small cell size problems. ETGs also group pharmacy claims and attach them to the most relevant episode based on priority tables. Over 400 health plans have purchased the grouper as of May, 2003, and 700 by 2008. In addition, Episode Risk Groups™, a derivative of ETGs, can be used prospectively for predictive modeling of cost as well.

·         Adjusted Clinical Groups or ACGs (Johns Hopkins University): ACGs group illnesses into morbidity clusters rather than specific diseases as do ETGs. An example of an ACG is “Acute major and likely to recur”. Since ACGs are based on morbidity clusters, patients with multiple complex illness conditions can be readily identified.  Since each patient has only one ACG for an entire reporting period, such an adjuster is called population-based.  The process of care over time is not as important with such algorithms. In fact, ACGs do not require procedure or CPT codes at all – just ICD diagnoses, age, gender, and member and provider identification fields, which gives the methodology the advantage of input simplicity. There exist over 100 ACGs at present, and they are in use at nearly 200 organizations worldwide. In general there are fewer categories in population-based adjusters than in process-based adjusters, since process-based algorithms need to account for specific diseases.

·         Diagnosis Cost Groups™ or DCGs (DxCG, Inc.):  DCGs are also a population-based grouper.  Although the grouper begins with 184 Condition Categories (ex: “Benign neoplasm of skin”). These Condition Categories are also sorted into hierarchies and aggregated into broader categories. The combinations of Condition Categories that a member has can then be used to predict health care resource utilization based on an overall risk score for each member. This prediction can either be for the current year or for the subsequent year, depending on the model used. Over 100 organizations now use DCGs, and like ACGs they do not require procedure codes. One important feature of DCGs is its ability to be used in predictive modeling of prospective resource use, using a different model than that used for retrospective analysis

·         Age-gender:  In these models, various age and gender strata are used to account for risk.  Generally there are about 9 to 20 strata for age gender, depending on the needs of the health plan. Basically, resource use is moderate in the early years up until about age 5, then decreases through adolescence and the 20s, then slowly rises again in a non-linear fashion until it becomes quite high in the senior years. Females also tend to use more resources during their reproductive years. Of all the models described, age-gender has the least explanatory power for the prediction of resource utilization either retrospectively or prospectively. The ability of a case-mix adjuster to explain variation in resource utilization is determined by the “R-squared” (the square of the correlation coefficient), with the case-mix categories or risk score as the independent variables and a measure of resource use (such as cost) as the dependent variable. Age-gender models have an explanatory power of about 3 – 7% while publications on proprietary adjusters have generally shown that they explain about 30 – 50% of the variation for retrospective analysis. Prospective explanatory power is somewhat less, usually around 15 – 25%.

Assessment

Medical providers have the right to ask that reports dealing with health care resource utilization have proper case-mix or severity of illness adjustment, and that resources are available at the health plan or MCO to answer questions concerning the adjustment algorithm and to offer a complete explanation of the case-mix methodology used.

Conclusion

Many MCOs and HMOs now provide literature to physicians and medical providers that discuss the reporting and case-mix methods when the profile reports are distributed. Are you aware of them; please comment and opine on their use, or abuse? 

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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