Burden-of-Illness Comparisons: A Tool for Managing Risk Contracts. - Brief Article - book reviews

Healthcare Financial Management, Dec, 1999

Many provider organizations struggling to make their full-risk contracts successful hope that analyses of cost per member per month (PMPM), inpatient days per thousand members, and other broad indicators will offer some answers. To effectively manage risk contracts, however, provider organizations need to identify the root causes of cost variations and target specific interventions at these causes.

Claims data can be used to develop utilization and financial reports that compare one primary care physician practice with another. Variations in the mix and severity of illnesses--or burden of illness (BOI)--that each group must manage can affect utilization rates significantly and possibly reveal the root causes of cost and utilization variations.

BOI analysis begins by identifying the high-cost diagnoses for the entire at-risk population and then counting the number of patients with one of these high-cost diagnoses that each primary care practice is treating.

Exhibit 1 shows the distribution of high-cost-diagnosis patients being treated at 13 primary care practices. Practice 12 had the largest percentage of patients with one of the identified high-cost diagnoses, with an especially large number of cancer, cardiac, and diabetic patients. These patients were a major reason why Practice 12 incurred the highest PMPM costs and had the most inpatient admissions.

To remedy the situation, a case manager was assigned to the group to assist in managing its high-cost patients, especially its cancer, cardiac, and diabetic patients. The case manager and the practice physicians focused on inpatient management issues and reduced their length of stay by 1.5 days. The case manager also organized a practice-sponsored diabetes education program for both the office staff and for their diabetic patients, and started a cardiac patient support group and outreach program.

By understanding the variations in patient mix from a BOI perspective, groups can better understand variations in utilization rates and can target interventions to meet the needs of their most costly patients.