ACO Quality: Don't Forget the Processes

While the Centers for Medciare and Medicaid Services proposed regulations for Accountable Care Organizations (ACO) are not due until early 2011, there are clues to which Quality Measures will be used in the ACO Shared Savings program. CMS does not define the quality indicators, but they have outlined a roadmap for value-based purchasing. CMS places importance on outcomes, resource use, and transition measures, but plays down some process measures. In addition, MedPAC urged CMS to adopt population-based outcome measures and patient satisfaction surveys (CAHPS and H-CAHPS). 

Unfortunately, the CMS staff responsible regulations for the ACO Shared Savings bonus program have been reported to have an anti-managed care bias. This means that the process measures typically associated with managed care quality programs may be left out. This may be a mistake when developing a the ACO pay-for-performance/shared savings scheme.

Processes are the actions that are taken to deliver care including tests, treatments, medication adherence, and education. The most common process measures for quality used by the CMS managed care regulators are those “HEDIS” measures developed by the National Committee on Quality Assurance. Outcomes are the effects of the care provider on the patient’s health status. Outcomes may be the preferred metric of CMS fee-for-service (Parts A & B) regulatory staff because they reflect the end results of healthcare services. (Together with resource use measures, outcomes indicators reflect the value of healthcare).

However, most incentive-based programs place less importance on outcomes, and instead focus on process measures. Some researchers and practitioners prefer process measures in healthcare performance assessment. Specifically, Rubin et al. (2001) write that process measures provide information that is actionable, require less risk adjustment for benchmarking, take less time to measure (no need to follow up after years of illness), and marginal effort is needed because the information can be collected in administrative data. Besides, many studies show that process measures are significantly related to health status and quality of life outcomes (Bradley et al., 2006; Kahn et al., 2007; Werner & Bradlow, 2006).

Let’s hope that process measures are included in the upcoming CMS ACO regulations.

Risk-adjusted Capitation Primer

Managed care organizations (MCOs) are paid by Medicare, Medicaid and private companies to provide insurance functions (e.g., claims payment) and preventive healthcare interventions designed to improve patient health outcomes. MCOs, in turn, pay providers for the direct delivery of healthcare to members enrolled in their plans. Typically, MCOs receive per-member-per-month prospective payments for the health care management of their enrolled members, called capitation. To make a profit, a MCO must manage the health services of their population in ways that keep the actual healthcare costs below the capitation payment, on average.

While promoting cost-efficiency, capitation payment encourages MCOs to attract members whose likely costs are below the capitation rate. MCOs can behave in ways that lower the likelihood of attracting the unprofitable members - through benefit design, provider network composition, selective marketing, or other methods. These prohibited activities are called risk selection (“cherry picking”) and discriminatory disenrollment (“dumping”).

To mitigate cherry picking and dumping, payors have begun to adjust the payments made to MCOs based on patient diagnosis, so that more is paid to MCOs for the enrollment of sicker individuals than for healthier ones. Conventionally, these capitated payments can be “risk-adjusted” to account for certain factors that predict future healthcare costs, such as demographics (age, gender, geography), diagnoses, and pharmaceutical utilization.

With risk-adjusted capitation payments, more is paid to MCOs for the enrollment of sicker individuals than for healthier ones. The more accurately the risk adjustment payment model predicts the future healthcare costs of a member, the less incentive the MCO has to cherry pick or dump members. Sicker members mean higher capitation payments for the MCO.

Predictive power of risk adjustment methodologies continues to improve, but comparisons in the literature investigating Medicaid recipient populations show that the predictive power (R²) of the risk adjustment models ranges from 0.11 to 0.18 (Kronick, Gilmer, Dreyfus, & L. Lee, 2000) and from 0.15 to 0.23 (Gilmer, Kronick, Fishman, & Ganiats, 2001). While risk adjustment does not explain much of the variability in future healthcare expenditures, it is still sufficient for many government payors, such as Medicare, to pay MCOs for managing beneficiaries’ care. (It should be noted that perfect prediction of future health expenditures is not the goal. If future healthcare utilization was known, then insurance would not be necessary.)

Notwithstanding improvements risk adjustment accuracy, about 80% of the future health care costs are unexplained using risk adjustment. This remaining risk leads MCOs to engage in risk selection behaviors. While risk adjustment is a significant improvement over average cost capitation, the incentive to attract healthy enrollees and avoid the sick ones still exists. Are risk-adjusted payments better than nothing, though?

Where’s the Solidarity?

A recent New England Journal of Medicine article by Dr. Victor Fuchs pointed out some startling facts about the U.S. healthcare system. Fuchs states that the U.S. spends “50% more than the next-highest spender and twice as much as the average country in the Organization for Economic Cooperation and Development.” Unfortunately for U.S. citizens, the life expectancy at birth in European countries is higher than ours. Ouch.

Fuchs offers two explanations. First, he says that the structure of the U.S. political system confers too much power to health care “special interests” — manufacturers of drugs, devices, and equipment, as well as physicians and hospitals that want higher expenditures. This brings increased healthcare access for citizens. Unfortunately, this benefit is unfairly distributed. In the U.S., the poor are more likely to be unable to obtain or delay care due to the high costs of healthcare.

Next, Fuchs writes that other counties with national health insurance systems are more successful at redistributing money from the wealthy to the poor (and from the healthy to the sick). This is done through the tax system. (The U.S. government’s share of the total personal healthcare expenditures is about 50%, whereas European governments typically pay for 70-90%.) As Fuchs points out, to achieve the level of health services spending of the U.S. the counties would have to raise their taxes beyond responsible levels. Instead, these countries use cost control mechanisms such as limits on expensive technologies, price negotiations, and restraints on standardized physician fees.

The reason for our failure to implement a national health insurance scheme that uses tight cost controls, according to Fuchs, is American’s strong sense of individualism. In other words, we lack the need for solidarity in our society.

So, what if Americans had a sense solidarity? In other words, what if we were more like the Germans? As German health services researchers wrote, “Solidarity has been the governing principle of the German social health insurance (SHI) since its implementation under Bismarck in the late 1880s.”

Like the U.S., Germans have relatively good access to care. A recent study showed that, among seven industrialized nations, German and U.S. adults reported the most rapid access to elective surgeries (although Americans were most likely to have gone without care because of cost).

Similar to the U.S., Germans have insurance companies, called sickness funds. In the 1990s, the Germans introduced market reforms to reduce the social health insurance deficits. Some of the features of their solidarity-competition balancing act are reminiscent to our recent reforms found in the Accountable Care Act (ACA). Examples include a requirement to contract with all health insurance applicants and standardized insurance premiums with risk-adjusted subsidies to the sickness funds (health plans).

With health reform, are we becoming more like the German system? Or are the Germans viewing us with schadenfreude?

Arizona Medicaid Transplants and Comparative Effectiveness

With rising unemployment and increasing Medicaid enrollment, state Medicaid programs are facing significant coverage cuts. In Arizona, coverage for certain transplants of the heart, liver, lung, pancreas and bone marrow was eliminated on October 1, 2010.

Beyond the rhetoric, the debate boils down to comparative effectiveness research.

The Associated Press quotes Arizona Republican Rep. John Kavanagh, who leads the House appropriations committee, as saying "We need to fund what works and not fund what doesn't work and pay for what we can afford."

So transplants aren’t cost effective? Dr. Yeager, the director of the Blood and Marrow Transplantation Program at the Arizona Cancer Center, claims that they have the efficacy data to refute Arizona Governor Jan Brewer’s decision.

But it really is it that simple?

Terasaki, Ozawa and Castro (2003) calculated kidney dialysis at an annual cost of $67,506, but report the annual anti-rejection medication for kidney transplants at $13,749. Using Medicare as an example, they calculate the savings to the 128,000 functioning transplants in 2003 to be roughly $344 million dollars per year for the U.S. government. This article also supports the cost effectiveness of Medicare’s kidney transplant policies.

Kidney transplants are cost-effective, but the other types may not be. Heart and lung transplants remain expensive, with quality-adjusted life-year gains at about $50,000 per year. Some experts consider this within the cost-effective range.

The New York Times quotes Alan Weil, the executive director of the National Academy for State Health Policy, as saying that Arizona’s cuts are “a precursor to a much larger number of states having this discussion.” In my state, Florida, no changes in coverage for heart, liver, lung bone marrow, and retinas were made at the most recent Florida Organ Transplant Advisory Council October 2010 meeting.

However, the budget shortfall in Florida will continue to grow – with Medicaid acting as “the single largest driver in next year’s budget projections.” Hopefully, Florida will use the comparative effectiveness studies to inform their decisions.

Community Health Scorecard

This year, I was fortunate to participate in a project to develop a population-level community health and quality of life scorecard for the Tampa Bay region. The goal of the health scorecard was to produce a status of our community’s well-being in a variety of areas, such infant mortality, mammogram screenings, average commute to work and violent crime rates. No such community health report yet existed for the Tampa Bay region.

The scorecard was coordinated by Tampa Bay Partnership (TBP), a non-profit economic development organization that represents eight area counties including Citrus, Hernando, Pasco, Pinellas, Hillsborough, Polk, Manatee and Sarasota. The compilation of community health scores will act as a call to action for future region-based community health efforts. The scores provide a benchmark for various parties – employers, health departments, academics –  to set goals and collaborate to accomplish improvements. Also, Tampa Bay area legislative delegation - those state senators and representatives who comprise 25% of the Florida Legislature - are an important audience for the scorecard.

A Health Data Subcommittee developed a list of 75 indicators that were divided into five categories including Health Status/Outcomes, Environmental/Infrastructure/Transportation, Health Related Behaviors, and Health Systems/Access to Healthcare. My main function was to collect the data through secondary data sources including the Center for Disease Control and Prevention, Florida Vital Statistics, Florida Department of Health, Agency for Healthcare Research and Quality, etc.

Yesterday, I participated in a Healthy Communities visioning exercise with the TBP. The purpose was to develop the actions necessary to respond to deficiencies identified in the health scorecard. This is probably the most difficult task associated with health scorecards, according to researchers Fielding, J.E. Sutherland, C.E., Halfon, N. (1999). These visioning activities brought together various health leaders in the region to discuss the report and prioritize actions. The first of many steps, this meeting provided the groundwork for future projects aimed at improving the health of our community.

This project has been a real pleasure. If you are interested in the process, let me know, and I will put you in touch with the TBP leadership.