Getting Quality Measurement Right: Exclusion Codes to Reach For

Value-based care systems largely rely on quality metrics and benchmarks to measure physician performance and determine payment. Understanding how insurers or other entities calculate performance — including how they attribute your patients to quality metrics and what factors would exclude patients from certain metrics — is vital for your success within these payment models.

Quality metric calculations include the following:

• Numerator (the subset of patients in the denominator for whom a particular service has been provided or a particular outcome has been achieved),
• Denominator (the total eligible patient population),
• Exclusions (patients with characteristics that remove them from the calculation).

For example, a common quality metric is the percentage of women age 50–74 who had a mammogram to screen for breast cancer in the 27 months prior to the end of the measurement period. In this case, the numerator is the number of women age 50–74 who had a mammogram in the measurement period. The denominator is the total eligible population (women 50–74 years of age). The exclusions are patients who should be removed from the calculation (women who have had a bilateral mastectomy, use hospice services, etc.).

For each metric, every payer contract may specify different requirements regarding which patients are included in the denominator. Adding to this confusion, every electronic health record (EHR) system has different methods for counting and attributing patients to various metric lists. Some EHRs offer tools to assist practices in identifying patients who may require specific screenings, but a basic understanding of metric parameters is necessary in order to accurately run these reports.

To ensure that quality metrics accurately reflect a patient's need for a screening, or lack thereof, physicians need to understand how to accurately apply exclusion criteria. Exclusion codes remove patients from the denominator of certain metrics. It is essential to apply these codes accurately so the right patients are counted — and the wrong patients aren't.