Using benchmarks for comparative analysis is essential (e.g., weighing the risk of a new reimbursement structure, determining performance targets, or measuring outcomes). In fact, when creating performance-based benchmarks for a population, one must consider how to account for the disease burden. The question becomes, how does one adjust for circumstances outside of the control of a health care system (e.g., genetics and environment) or where the health system has limited influence (e.g., life style choices)? The objective is to try and remove all uncontrollable factors, thereby identifying any variance from the benchmark as an opportunity that could be addressed by either medical management or contractual change. Variance from the benchmark would then illuminate potential calls to action.
In order to create this ideal condition benchmark adjustment, we have found the following characteristics to be essential in the adjustment factor development process:
- Broad vs. Narrow Disease Categories:
- Cohorts should present in credible amounts for a condition in order to reflect the overall disease prevalence.
- A broad definition captures all stages of a disease’s progression.
- Broad condition adjustments can be used for population predictive modeling.
- Condition definitions should not be weighted too strongly by bursts of health care due to short-term events.
- The methodology assumptions should not restrict the data sets used in benchmark construction. Traditional large medical and Rx claim data sets can be used, yet we can also leverage credible EMR or billing/charge master data sets as well if these are available
In MedInsight, we have created several condition-based benchmark adjustment factors, each targeted at providing a solution for a specific, often narrow, need, yet we feel the best overall method for condition adjustment (adj) is our Chronic Condition Hierarchical Groups (CCHG) product. The CCHGs assign all individuals to a single category (both the sick and the healthy; 100% of the population), the cohorts are clinically similar, the number of cohorts are small/manageable, and, most importantly, the hierarchy algorithm stems from the clinical point of view; physician decision making is at the heart of the system.
Traditionally, we combine the CCHG condition factor with our Milliman Health Cost Guidelines (HCGs) benchmarks as a means to improve our Trend Management benchmarks. As seen below, we have a traditional factor table for some of our Inpatient HCG level adjustments:
In the HCG benchmarks above, the CCHG condition factors are meant to resolve or tie out to the HCG benchmarks to adjust for uncontrollable disease-related factors. Later in 2012, we hope to apply the CCHG methodology as an optional benchmark factor unto itself and not a part of the HCG benchmark. We believe this will improve our risk adjustment process and better explain results in both clinical performance projects and Health Plan Marketing activities. Using the CCHG-based condition adjustment factors will allow Plans to better quantify their value proposition while identifying new ways to improve their overall efficiencies.