3 MATERIAL Y MÉTODOS
2. Estudio descriptivo de la muestra a estudio
Budget development within the context of the ACO framework entails analyzing historical utilization and cost data for the purposes of identifying areas for performance improvements, and developing benchmark spending targets that will determine shared-savings eligibility. Essentially, this process is used to measure the financial performance – the ability to control cost growth – of the ACO. There are many different approaches to measuring the performance of an ACO. In the PGP and MHCQ shared-savings demonstrations, a quasi- experimental design has been used to determine shared savings. This approach compares spending and quality trends from the population served by intervention providers, ACOs, with similar trends in control populations, those not being treated by the ACO.
This cohort approach has several advantages. For instance, it more easily allows for the inclusion of trends, such as a sudden surge in utilization due to an unforeseen local disease outbreak, which would have been difficult to anticipate prior to the budget development process.
There are also several drawbacks with the control group approach. For example, there is often a lengthy lag between the end of the performance period and when the results can be analyzed due to the slow availability of claims information. As a result, providers often do not find out the results
of their intervention until months – even years – after the performance period ends. Consequently, they would not receive shared-savings until much after they have made initial investments. The PGP Demonstration, for example, was already at the end of performance year five before results were available for the first three years.
Another challenge with using the cohort approach is ensuring that the control and intervention groups share similar risk characteristics. For example, in the PGP demonstration, diagnoses and other health information indicated that claims data are used to control for relative risk differences between the intervention and control cohorts. However, there are incentives for intervention providers to improve diagnostic coding practices from how they coded prior to participating in the demonstration. Financially, there is incentive to more fully document diagnoses as higher-risk scores ultimately translate into higher payments. Participating providers may also improve documentation to better target quality improvement initiatives. This issue can make it difficult to distinguish cost and quality impacts based on improved performance in care delivery from coding changes.
Another major drawback of the cohort approach is that it will become less viable over time. More specifically, as interest in ACOs grows – as well as many other value-based payment initiatives – especially with the start of the Medicare Shared Savings program in 2012, it will be increasingly difficult to identify control populations without significant intervention activity. Therefore, a budget projection approach using historical data appears to be a more sustainable approach.
The budget projection model builds on historical spending and utilization data from the ACO specific population to project budget benchmarks for future performance periods. The ACOs’ actual spending in the performance period is compared to the spending benchmark to determine whether savings were achieved.
Aside from addressing the increasing difficulty in finding adequate external control populations, there are a number of other benefits to the budget projection approach. Having prospective budget benchmarks gives providers the ability to judge their ongoing performance and set course corrections throughout the performance period, rather than having to wait until after the period to learn about its performance relative to a control cohort about which they had no comparative information. As discussed later on in more detail, the budget projection
approach may also mitigate difficulties involved in controlling for relative risk characteristics, since the intervention population itself is used to develop the spending benchmarks.
While the budget projection approach has advantages, there are also several technical and theoretical problems. For example, budget projection models build historical trends into the projected benchmarks. This could unintentionally favor and reward ACOs with historically high spending growth from years of inefficient health care practices, as this trend extrapolates into higher benchmark spending targets that provide easier opportunities to achieve cost reductions. Setting benchmarks on a prospective basis under the budget projection framework also makes it more difficult to take into account unanticipated shifts in spending patterns from historical trends. Using a control group approach with retrospective spending benchmarks would better account for system-wide changes in health care delivery.
Besides the fact that the cohort approach will become less viable over time, another major factor for the budget projection model to become the predominate method for measuring financial performance in an ACO construct is that the Patient Protection and Affordable Care Act (ACA) has legislated such an approach for the Medicare shared-savings program. Furthermore, the cohort approach is not as feasible in the private sector due to limitations in the amount of data that would be available on non-ACO patients. Therefore, we focus on the budget projection in Part 3, which is also the
method of choice for the providers participating in the Brookings-Dartmouth ACO pilot program. The following provides a discussion of the key issues involved in developing a budget projection model in the ACO framework, while also providing an illustrative example.
In order to develop these spending benchmarks, it is necessary to determine the baseline spending amount and appropriately project spending for the contract period, assuming there are no behavioral changes in practice patterns (i.e., projecting what spending would have been without implementation of an ACO). Developing an accurate ACO budget requires the actual claims and exposure data, significant data analysis and interpretation, and an understanding of ACO operations. Additional factors that need to be taken into consideration include the projected time period, the type of data to be used, any data anomalies, changes in the population, and development of appropriate assumptions and adjustments to be used. Detailed below are the key steps involved in developing an ACO budget, organized into four broad sections: baseline data, trend estimates, adjustments, and performance monitoring.
Baseline Data
Ideally, the ACO budget will be developed using baseline data, including existing claims, utilization, and exposure data. Claims information is reported based on either a paid basis or an incurred and paid basis. For example, paid claims in calendar year (CY) 2008 represent all the claims that were paid during that year, regardless of when the services occurred. Incurred claims in CY 2008 represent the claims for services rendered in CY 2008, regardless of when the claims were paid. For budget development purposes, incurred claims are more appropriate. However, when using incurred claims, the significant lag time between when a claim is incurred and when it is actually reported by the provider to the insurance carrier or Medicare Administrative Contractor (MAC) must be considered.
Generally, insurance carriers report incurred claims for a 12-month period with a three-month lag (run- out), which is then adjusted for an incurred but not yet reported (IBNR) factor. For example, CY 2008 incurred claims paid through March 2009 would represent 12 months of data with three months of run-out. These incurred claims would then be increased by an IBNR factor to account for additional incurred claims in CY 2008 that have not yet been reported to the insurance carrier. Usually, the insurance carrier’s actuary will develop an IBNR factor to “complete” the claims. Using claims development models, actuaries review the claims payment patterns and the historical trends to determine the appropriate IBNR factor to apply to claims. The IBNR factor decreases as the number of months of run-out increases.
For budgeting purposes, incurred claims should be reported by broad service or expense categories. The categories may vary by payers since each payer has different reporting mechanisms. When determining these categories, special attention should be given to the plan designs chosen by the ACOs members. For example, if there were a specific copayment for CT-Scans, it would be helpful to track the CT-Scans as a separate category. This will help the ACO to account for the impact of plan designs (and future plan design changes) on their budget. Some payers may not include certain services in their ACO contracts; for example, Medicare fee-for-service (FFS) ACOs may not include the outpatient pharmacy service (since they are paid separately under Part D). Some services are more important for specific populations; for example, home health is an important service for the Medicare population, but this is not typically the case for commercial plans covering the non-elderly population. Suggested categories are shown below:
•
Hospital Inpatient•
Outpatient Pharmacy•
Hospital Outpatient•
Mental Health/Substance Abuse•
Lab/X-Ray•
Durable Medical Equipment (DME)•
Advanced Imaging•
Emergency Room•
Physicians (Primary Care and Specialty Care may be broken out separately)•
Other4The above discussion focused on a more traditional approach of developing the spending amount based on the service categories. As new payment methods evolve, such as the patient-centered medical home model, partial capitation payments, and bundled episode payments, additional analyses and modifications are needed. For example, with partial capitation payments, the capitation amount should be captured, and we would expect a cost and utilization reduction in the corresponding service categories.
The ACO needs to capture the total claims costs for each category. This includes the claims the payers are responsible for financially, and the members’ cost-share amount. The total costs are defined as the “allowed claims.” Note that these claim costs should also include the out-of-network claims, which will also count against the benchmark when determining the ACO’s eligibility for its financial performance payments.
In addition to the claims, developing the budget requires exposure data, which are typically reported in the form of member months. Exposure is
defined as the number of members enrolled in the ACO each month. For example, if there were 50 members enrolled in January, 49 in February, and 47 in March, the total member months for January through March would be 146. The time period of the exposure data must match the time period of the claims information. For CY 2008 incurred claims, one would need CY 2008 member month information. Exposure data are extremely important when there are significant fluctuations in enrollment. One cannot develop an accurate budget without it. For ACOs, member months are determined from the attributed members. As stated in the patient attribution section, patients are assigned once
a year. Thus, normally there will not be any new patients, unless there is a large group joining during the mid-year. However, there are deaths, so the number of members will typically only decrease. It is best to use the most recent 12-24 months of historical experience to develop the baseline data.