The gatekeeper in a Medicaid managed care program is the specialist to whom the patient is referred

Delivering Care in Separate Silos

Oral health, behavioral health, and medical care developed and evolved separately, even though Medicaid managed care was applied to these health care delivery sectors since the institution of the Oregon Health Plan (OHP) in 1989. These separate delivery systems were reinforced by different (1) technical foci (mind, body, mouth), (2) types of trained professionals delivering the care, (3) funding streams, and (4) state organizational structures. House Bill 2009 created the Oregon Health Authority (OHA) to oversee all the state’s health-related programs, including addictions and mental health, public health, and the OHP (Medicaid). Separate departments were created to oversee operations for each sector, and each maintained different policies and standards for accountability and billing, many of which were influenced by federal regulations and restrictions. For example, mental health services were often paid through federal block grants; these funds were carefully monitored and separate from medical care funding, and the local counties administered funds in their region. This was not the model for medical care. General dentistry was separate from other health care sectors because of the lack of provisions for dental care in Medicare. In addition, professional participation in Medicaid was limited as a result of poor reimbursement rates for general dental practitioners and inconsistent eligibility criteria for dental services for adults in the OHP (Oregon Medicaid Advisory Committee: Oregon Health Work Group, 2016). Thus, the primary care practices, community mental health centers, dental practices, and substance use treatment programs that deliver medical, oral, and behavioral health services to patients developed quite separately.

State and national quality improvement also affected dental, behavioral, and medical professionals in different ways, creating an imbalance in resources and foci on improvement, which has further separated these service areas. For example, primary care practices in Oregon participated in state and federal programs directed at changing care delivery structures to become Patient-Centered Primary Care Homes (PCPCHs), which focused attention on better coordination and patient-centered care. Other federal initiatives for primary care (e.g., Comprehensive Primary Care Initiative and Meaningful Use) were encouraging practices to implement care coordination, care management and risk stratification, and practices were choosing to take federal incentives to implement electronic health records, and taking steps to “meaningfully use” these tools to promote better care quality. Some of this work included an emphasis on creating patient registries and tracking quality measures.

Dental and behavioral health organizations were not directly affected by these programs, and electronic information infrastructure in these organizations had less sophistication and capacity. Dental organizations had record systems, but they were largely designed for medico-legal and billing purposes, and rarely included diagnosis codes like those included in medical records systems, as dentists primarily use procedural, Current Dental Terminology (CDT) codes. Behavioral health organizations and dental clinics have been slower to adopt Electronic Health Record (EHR) systems. There are multiple reasons for this. First, there has been little incentive for smaller, single-owner behavioral and dental clinicians to adopt EHRs, as there simply was a less urgent need for these tools among independent practices. Second, EHRs are expensive. Even among larger behavioral health clinics who are connected to medical practices, and who see the value of these systems struggle to afford EHRs (Cifuentes et al., 2015). With the absence of federal funding to motivate the purchase and implementation of EHRs, few behavioral and oral health organizations use these tools. Where EHRs are used by these organizations, firewalls are in place to prevent or stringently restrict the flow of clinical information between medical, behavioral, and dental clinicians. Even those practicing within the same organization and using the same information system may have information sharing restrictions. Although there was a regulatory basis for many of these restrictions [e.g., 42 CFR Part 2, Health Insurance Portability and Accountability Act (HIPAA)] particularly related to substance use treatment, the flow of information between clinicians caring for the same patients has been, in our opinion, excessively restrictive.

Blue Cross/Blue Shield of Tennessee

Blue Cross/Blue Shield of Tennessee requested that child health providers use standardized, validated screening at all EPSDT visits. To facilitate compliance, Blue Cross/Blue Shield of Tennessee piloted a program in 34 high-volume, Medicaid-managed care practices. Outreach nurses, called regional clinical network analysts, trained providers on site how to administer, score, interpret, and submit reimbursement for the Parents' Evaluation of Developmental Status questionnaire (the standardized developmental-behavioral surveillance and screening instrument that elicits parents' concerns about their children). After training, screening rates increased from 0% to 43.5% during the pilot phase. At the same time, the practices experienced a 16% increase in attendance at scheduled well-child visits, which suggests that focusing on parents' concerns may increase their adherence to visit schedules. Blue Cross/Blue Shield of Tennessee, together with the Tennessee Chapter of the American Academy of Pediatrics, is now providing training across the state.25 More information can be found through the Center for Health Care Strategies, “Best Clinical and Administrative Practices for Statewide” developmental and behavioral screening initiatives as established by the Center for Health Care Strategies [http://www.chcs.org/]

V. Medicaid and Managed Care

Managed care has special implications for low-income women and their families. Managed care can be an attractive system of care for low-income families. The services HMOs provide are comprehensive and out-of-pocket costs are often minimal; if any fees are charged beyond the capitated premium, they are usually very low. Disturbingly, however, both consumer surveys and health outcome studies show that low-income Americans may fare less well in managed care plans than in more traditional plans [12–14].

Despite these warning signs, and if current trends continue, five years from today the vast majority of Medicaid beneficiaries will receive their health care through managed care plans. State governments have moved aggressively in recent years to enroll Medicaid beneficiaries into managed care. Several states, such as Kentucky, Tennessee, and Oregon, have obtained federal waivers to convert their entire Medicaid programs to managed care. From 1991 to 1995, the proportion of Medicaid beneficiaries enrolled in managed care more than tripled, from 10 to 40% [15]. In 1995, 13.3 million Medicaid beneficiaries were enrolled in managed care plans.

Some states have been more successful than others in making the transition to Medicaid managed care. To help determine why, the Henry J. Kaiser Family Foundation and The Commonwealth Fund have supported in-depth case studies of selected states’ efforts [16]. Factors that correlated with success in making a smooth transition included gradual implementation, education of beneficiaries and providers about the new system's rules and procedures, broad use of managed care by working populations in advance of the Medicaid initiative, and state assistance to “safety net” providers, such as public hospitals and community health centers, to boost their ability to compete. On the other hand, states that failed to put adequate time and staffing into matching beneficiaries with appropriate plans, setting fair payment rates, and protecting the quality of care tended to experience problems in moving people into managed care. Low capitation rates were a particular problem, sometimes leading to poor care or threatening the financial viability of the plan.

Low-income families, disabled and frail elderly people, and other Medicaid enrollees pose special challenges for managed care, especially for plans that have mainly served younger, healthier, working families in the past. Even with insurance, many poor, near-poor, and minority patients lack resources, such as telephones and transportation, that make it possible to receive regular health care. Managed care can add to the burden by imposing complex preapproval and referral procedures, limited locations or hours of operation, and early hospital discharge. In addition, established managed care plans have not developed expertise in meeting the unique and complex health and social needs of Medicaid beneficiaries and the disenfranchised. Unless health care systems build outreach, education, and support services and develop links to the public health and social services sectors and traditional safety net providers, these less fortunate groups may be unable to comply with health plan rules and medical care instructions.

To help monitor the impact of managed care on Medicaid beneficiaries, the Commonwealth Fund and the Henry J. Kaiser Family Foundation have cosponsored a set of state-level surveys of patients. Initial analysis of the Kaiser/Commonwealth State Low Income Surveys finds that Medicaid managed care enrollees are more likely to rate their overall health care services as fair or poor than beneficiaries remaining in traditional Medicaid (21% vs 14%) and much more likely to rate their physicians as fair or poor (26% versus 9%). However, considerable variation exists across states, plans, and beneficiaries. In some states, managed care enrollees are more likely to receive preventive care than those covered by traditional Medicaid. In other states, Medicaid managed care enrollees continue to receive very episodic care, often from emergency rooms.

Surveys show a high correlation between patients’ ability to choose their managed care provider and their satisfaction with that provider. For example, one study found that when New York City Medicaid beneficiaries voluntarily enrolled in managed care plans, they were more likely to give higher satisfaction ratings to their plans than people enrolled in traditional Medicaid FFS plans [17].

Low-income families experience frequent changes in insurance status, moving from Medicaid coverage to no coverage to employer coverage and back again to Medicaid, depending on their employment status. Nationally, among women aged 18–64 receiving Medicaid, 28% have been in the program for less than a year [18]. Many women lose coverage—and, under managed care, access to their previous doctor—after they give birth, when they get a job, or when their earnings increase. Two-thirds of those who lose Medicaid coverage become uninsured.

Frequent changes in insurance status not only undermine beneficiaries’ ability to see a particular physician, but they also make it more difficult for them to get continuous care. In addition, frequent turnover in patients means that managed care plans have less incentive to provide preventive care. Steps to assure continuation of Medicaid coverage as changes in employment, earnings, health status, and marital status occur are necessary to improve the health care of low income families.

4.3.2 Recent Utilization Analyses

In the late 1990s and early 2000s, the DD Health Home office location moved from the original hospital base to a number of integrated community-based office settings. At the same time, the State of New Jersey began to move the health care for Medicaid-eligible individuals, including those with I/DD, from fee-for-service Medicaid to Medicaid managed care. To assure continued funding for the model, contracts with Medicaid HMOs were developed and the challenges of serving I/DD patients through Medicaid managed care were addressed (Kastner, Walsh, & Criscione, 1997a, 1997b; Ronder, Kastner, Parker, & Walsh, 1999; Walsh & Kastner, 1999).

The movement of patients into Medicaid managed care provided additional opportunities to evaluate costs and utilization in the DD Health Home model. During the past decade, the model has been evaluated in relation to payment systems operating in managed care networks. A common measure of economic performance in managed care is the health benefits ratio or HBR. The HBR is the fraction of health care dollars (or premium payments) spent by HMOs on actual health care services. The industry standard for HMOs to devote to the HBR is approximately 88%. An economic study prepared by an HMO partner of the DD Health Home in 2004 of its New Jersey I/DD, Medicaid-only product line found an HBR of 75% for group members who received their health care through the DD Health Home—a substantial savings from the standard. In contrast, the overall average HBR for all of its SSI beneficiaries during the same year was 102% showing that there were actually losses associated with that population.

Another way to assess the impact of the savings is for the patient panel as a whole. Taken together, the annual health care cost savings for 1350 patients in the model at the time were, based on the HBR of 75%, approximately $3 million per year (assuming a premium of $700 per person per month and comparing the DD Health Home group to the 102% HBR for SSI beneficiaries).

These identified savings are often in the form of reduced emergency room use and fewer hospital admissions. To look at specific types of utilization, New Jersey State Medicaid claims data were recently examined to compare hospital utilization of patients enrolled in the DD Health Home model with patients enrolled in Medicaid managed care but not in the DD Health Home (Walsh, Kastner, & Dixon-Murriell, 2012). Using Medicaid claims data for individuals with I/DD derived from a single HMO, 185 patients receiving care through the DD Health Home who had either one or more hospital admissions or visited an emergency room during 2007 (n = 85), 2008 (n = 52), or 2009 (n = 48) were identified as the DD Health Home group. These patients were matched by age and gender to randomly selected patients who received Medicaid managed care, but were not patients of the DD Health Home (the usual-care group). Three measures of hospital utilization were analyzed: (1) the number of emergency room visits per year; (2) the number of inpatient hospital admissions per year; and (3) the average length of stay, in days, across all inpatient stays by each individual.

Results indicate that during the 3-year study period, patients in the usual-care group averaged 6.59 emergency room visits per year compared to DD Health Home patients who averaged only 2.05, a reduction of 68.8% (see Table 3). Similar differences between the two groups were found for the number of hospital admissions with the usual-care group averaging three times as many admissions (1.29 per person per year) as the DD Health Home group (0.43 admissions per person) (Table 3). These are striking differences attesting to the importance of the health home approach. Group differences in the average length of hospital stays were not significant.

Table 3. Means and SDs for outcome measures (3 years)

Overall levels of emergency room use in this study are likely to be high in relation to national norms. New Jersey is unique in that long-term care staff members are required by a state law, Danielle's Law (2003), to call 9-1-1 when they believe that a person with I/DD is experiencing a life-threatening event or illness. Because the law includes individual civil penalties, it has emerged as a major driver of emergency room utilization for this population in the state. This fact, along with the large SDs in the usual-care group, once again suggested that there were disproportionate values, or outliers, in the dataset. To identify outliers an arbitrary, but generous, figure of more than 40 emergency room visits per year for an individual patient was adopted as the criterion for a case to be considered as an outlier in the emergency room data. Remarkably, a search of the data identified five individuals who exceeded this criterion—all of whom were found in the usual-care group. Together, these five subjects alone accounted for 457 visits to the emergency room over the 3-year study period! Individually, these five outliers accounted for 119, 62, 42, 118, and 116 emergency room visits, respectively.

As in one of the utilization studies reported earlier, the data were reanalyzed with the outliers removed. Without the five outliers, the group difference for emergency room visits was somewhat attenuated but remained statistically significant (see Table 3). Similarly, a single outlier with 50 hospital admissions in a single year was found in the usual-care group when the hospital admission criterion was set at more than 20 in a given year. An attenuated group difference was again found but remained statistically significant when the data for hospital admissions was reanalyzed without this outlier (see Table 3).

As intimated previously, searching for outliers in health care datasets for individuals with I/DD often leads to surprising cases of utilization. In fact, the criteria used here to define outliers (i.e. 40 emergency room visits or more than 20 hospital admissions per year) is far removed from what would be found in the general population (on the order of about 0.4 emergency room visits and 0.2 hospitalizations per person per year) (National Center for Health Statistics, 2012). Again, although removal of outliers from the data analysis may help to clarify the nature of the data, all of the outliers identified are actual cases and show how, without care coordination, health care utilization can skyrocket in the I/DD patient population. Although the DD Health Home is able to identify these cases and avoid such utilization, the significant group difference without the outliers suggests that the model also impacts cases that are not so extreme.

With regard to utilization costs, any approach that can reduce overutilization can be seen as positive. From the data described above, managed care alone did not serve as a control on excessive utilization because all of the cases in both groups were enrolled in managed care. In fact, there were striking cases of overutilization in the usual-care patients, despite their enrollment in managed care. Thus, these data show that the DD Health Home, with its critical care-coordination function, reduced both emergency room visits and hospital admissions.

These more recent utilization results are also interpretable within the triple aim of health care, specifically the third part of the triple aim—reducing the costs of health care for populations. Without doubt, health care cost savings are realized through the use of the DD Health Home. Consider the group difference found in average number of emergency room visits, excluding the outliers—2.05 visits for the DD Health Home group versus 4.24 for the usual-care group. Over the 3 years of the study, this mean difference would account for 186 visits in the first year, 114 in the second year, and 105 in the third year—or 405 emergency room visits in all. According to the Medical Expenditure Panel Survey (MEPS, 2009), the average cost of an emergency room visit in America in 2009 was $1318; therefore, for the 405 emergency room visits alone that the DD Health Home avoided there would be a savings of over $500,000. The same calculation for hospital admissions reveals that the DD Health Home would have avoided 109 hospital admissions. MEPS estimate of average hospital charges in 2009 for a single inpatient encounter was $17,089 resulting in a savings of more than $1.86 million. In short, with regard to the part of the triple aim having to do with reducing the population costs of health care, the DD Health Home clearly achieves such savings.

Benchmarks and Performance Targets

CCO performance on each of the metrics is judged on a pass/fail basis, but in line with one of the key principles for the program, performance is rewarded on the basis of reaching an improvement target or on reaching an established benchmark. The Committee generally chose benchmarks of either the 75th or 90th percentile score for Medicaid managed care plans nationally based on the reasoning that the state should aim for being a top performer as well as the notion that if other Medicaid programs had attained a certain level of performance, it was by definition achievable. For those metrics where there were no national Medicaid data available, the Committee chose a benchmark based on what the state’s baseline score was and what they felt was reasonably achievable in a 10-year period. The benchmarks are the same for all CCOs, regardless of geographic region and patient mix.

One of the overarching principles of the Quality Pool Program was to keep it as simple and understandable as possible, so the Committee did not want to set performance improvement goals based on a set of algorithms that would be indiscernible to plans and providers. They chose to base improvement targets on the Minnesota Department of Health’s Quality Incentive Payment System referred to as the “Minnesota method” or “basic formula.” (Minnesota Department of Health, 2016). This method requires at least a 10% reduction in the gap between baseline and the benchmark to qualify for incentive payments. Including improvement targets has turned out to be a key component of the program’s success. CCOs are able to meet incremental improvements regardless of where they started in terms of initial performance. If the quality pool program relied on meeting aspirational benchmarks alone, the risk was that providers would be discouraged because for some metrics, the gaps were significant and we wanted to provide incentives for annual improvement. Because the annual performance improvement targets are attainable, the state has seen consistent year over year improvement.

Medicaid’s value-based purchasing: controlling costs and improving quality

Value-based purchasing initiatives are also an integral part of the Medicaid program.58,59 The Center for Medicaid and State Operations (CMSO) has infused incentives throughout the state-managed programs, including the State Children’s Health Insurance Program (SCHIP).60 The value-based purchasing programs are critical to identify appropriate utilization of limited program dollars.

The CMSO, in its role as an organization that transfers information from the federal government to the states, recognizes that each state may approach its value-based purchasing decisions differently. Managed care Medicaid model health plans have also been included in the program’s design for promotion of value-based purchasing. Despite state differences, the basic elements of the plan include attention to (1) evidence-based care and quality measurement, (2) pay-for-performance, (3) health information technology, (4) partnerships, (5) information and technical assistance, and (6) health care disparities.58

In 2007, the CMS announced its intent to develop a National Medicaid Quality Framework.61 The CMS has supported the ongoing “Medicaid and SCHIP Promising Practices” initiatives taking place in a number of states.62 For example, in Louisiana, a phone-based disease management program for asthma has effectively decreased unnecessary, expensive emergency room visits. Washington State has developed strict criteria for bariatric surgery after recognizing that the surgeries were costly and mortality rates were high. Other states have designed programs dealing with dental issues, health literacy, obesity, disease management, and care coordination.

As Medicaid programs increasingly look at costs and outcomes over an episode of care, it will be important for rehabilitation professionals to advocate for recognition that the services they provide are valuable in managing high-cost chronic conditions such as cardiovascular diseases, pulmonary disease, and diabetes. Participation by rehabilitation professionals in programs that are designed to address value-based purchasing initiatives will be critical. Ultimately, payment for services may depend on participation in the effective management of certain conditions by a team of skilled professionals.

Building the New Transformation Center

With funding from the CMMI State Innovation Model grant award to the state, the Transformation Center was created as a new office within the OHA, with a direct line of reporting to the agency director. Although the Division of Medical Assistance Programs (DMAP) was the part of the agency accountable for managing the Medicaid program and CCO contracts, a conscious decision was made to house the Transformation Center outside that division. This organizational placement was important because it (1) demonstrated to the rest of the agency, CCOs, and other stakeholders that support for the spread of innovation was an agency priority; and (2) allowed the Center to build a relationship with the CCOs outside of the traditionally rigid and bureaucratic interactions typical in Medicaid managed care plan contract management. Internally, this was often referred to as a “good cop, bad cop” setup. DMAP’s role was to hold the CCOs accountable to their contractual requirements, oversee the rate setting process, make payments, measure and manage quality performance, and ensure program integrity. The Transformation Center was accountable for providing support and resources, and for facilitating learning across the CCOs. If DMAP found a CCO to be underperforming in a certain area, the Center was there to help the CCO meet performance goals. If the Transformation Center was viewed as an oversight body, the CCOs would be less willing to share their challenges along with their successes, and engage with the Center in a trusting working relationship. Without this trust established, the Transformation Center could not be successful.

The idea that a state agency could be a partner in innovation and play a role in helping outside, private organizations innovate faster was not universally accepted with open arms. Although CMS and CMMI were very supportive of and invested in the approach, the CCOs themselves were initially skeptical. OHA took the concerns of the CCOs seriously and worked hard to address the skepticism head on. Oregon has a culture of seeking community input and working collaboratively with stakeholders. This collaborative approach is, in large part, why health system transformation and Medicaid expansion were passed nearly unanimously in an evenly divided Oregon State Legislature, despite the starkly partisan rhetoric surrounding President Obama’s Affordable Care Act at the national level. OHA maintained an open dialog with the CCOs about its plans for a Transformation Center from the start, seeking input and feedback. For example, OHA asked CCO representatives to participate in the interview panel and hiring decision for the Transformation Center Executive Director position. To succeed, the Transformation Center had to be created with the CCOs, rather than be something done to them. The CCOs did not all agree that a Transformation Center was needed nor that it would succeed, but engaging them directly in its formation helped make the Center’s success possible.

Medicaid

Medicaid, authorized by Title XIX of the Social Security Act, is a program jointly funded by federal and state governments that provides medical assistance for low-income individuals, families with dependent children, the aged, and the disabled. Although the federal government sets basic guidelines, establishes a basic set of core benefits, and generally pays 50% to 80% of the cost of Medicaid (depending on the state's per capita income), individual states actually administer the program. The Medicaid program, which began on January 1, 1966, covers some 40 million people.

In their Medicaid programs, states may cover medical and surgical services provided by PAs. The decision as to whether to cover PAs generally rests with the state, except with respect to federally certified rural health clinics. If a clinic is designated by the federal government as a certified RHC, the state's Medicaid program must cover PA-provided services in the clinic. Currently, all states cover PAs under their fee-for-service or managed care Medicaid plans.

As Medicaid costs rose in the late 1980s and early 1990s, states began to experiment with more cost-effective methods of providing care to beneficiaries; fee-for-service programs were shifted to managed care delivery systems. To make many of these changes, states were required to get permission from the federal government in the form of 1915 and 1115 waivers, which provided states with exemptions from the traditional guidelines of the Medicaid program.

One of the popular concepts that states have used to lower costs, and ideally to improve the quality of care, is to assign Medicaid beneficiaries to a specific health care provider, known as a primary care provider (PCP). The rationale is that beneficiaries will have better continuity of care and will be more likely to access the health care system at the appropriate time and place if one specific provider is responsible for directing their overall care. The PCP is able to refer the beneficiary to specialist and hospital inpatient care services as required. The federal government allows PAs to serve as PCPs, and some states allow PAs to assume that role.

Provisions of the Balanced Budget Act (BBA) of 1997 allow states greater flexibility in using managed care methods within their Medicaid programs without having to get formal approval (through the waiver process) from the federal government. The BBA of 1997 also contained language beneficial to PAs by giving states the authority to name PAs as primary care case managers (PCCMs) under the Medicaid program. A PCCM is typically paid a small monthly fee to act as a gatekeeper or coordinator of care for beneficiaries. States may cover PAs at the physician's rate of reimbursement or on a discounted fee basis. Coverage may apply in all practice settings and for all medical services, or there may be limitations. Table 5-2 shows the most current information on how states cover PAs under their Medicaid plans.

Medicaid

Unlike the Medicare Part D drug benefit, which is operated by private sector entities that use similar tiered formularies and negotiated discount strategies to private PBMs, the federal-state Medicaid program uses mandatory rebates. Because Medicaid beneficiaries are low income families with children, seniors and the disabled, even modest patient cost-sharing may lead to noncompliance. Rather than use tiered cost-sharing, since 1990 Medicaid has required manufacturers to give a mandatory rebate equal to the greater of 15.1% off the AMP (which is the manufacturer's average price charged to the private sector, including discounts) or the ‘best price’ (largest rebate) given to any private payer. For generics, the mandatory Medicaid rebate was a flat 11%, unrelated to discounts to other payers. When Medicare Part D was established in 2003, drug coverage for ‘dual eligible’ seniors (who are eligible for both Medicaid and Medicare) was exempted from these Medicaid rebates, and rebates to Medicare PDPs were exempted from the definition of ‘best price.’ Under the Affordable Care Act of 2010 (ACA), the minimum Medicaid rebate on brand drugs was increased to 23.1% (13% for generics) and Medicaid Managed Care Organizations are required to pay this rebate on Medicaid-eligible enrollees.

By requiring that manufacturers of brand drugs give to Medicaid the largest discount they give to any private purchaser, Medicaid's ‘best price’ rule effectively raised the cost of giving discounts that exceed the mandatory minimum Medicaid rebate (15.1% before 2010, now 23.1%) to private payers. Manufacturers rationally give discounts to customers who use formularies to create elastic demand. But paying the government a rebate for Medicaid usage has no effect on drug utilization by Medicaid patients, as the rebate is unrelated to preferred formulary status or incentives of patients or prescribers. Therefore, from the perspective of manufacturers, tying a mandatory rebate to Medicaid to a discount given to private payers reduces the overall elasticity of response to private rebates beyond the mandatory minimum, which is now the weighted average of the (presumably elastic) response of the private enrollees and the totally inelastic response of Medicaid enrollees. Thus, the Medicaid best price requirement reduced manufacturer willingness to give discounts to private payers in excess of the mandatory minimum Medicaid rebate, particularly for drugs with relatively high usage by Medicaid patients.

Empirical studies have confirmed that private sector rebates declined in response to this Medicaid best price. When Congress established Medicare Part D, discounts given to Medicare PDPs were explicitly excluded from the Medicaid best price calculations, in order to encourage manufacturers to give deep discounts to PDPs. The 2010 increase in the mandatory minimum Medicaid rebate to 23.1% means that the ‘best price tax’ now only applies to private sector discounts larger than 23.1%, hence increased discounting up to this 23.1% threshold is expected, ceteris paribus.

Mandatory Medicaid rebates may also create an incentive for manufacturers to raise the price from which the rebate is calculated. Anticipating this effect, Medicaid requires an additional rebate equal to the cumulative excess increase in a drug's price over the consumer price index (CPI), since the drug's launch. This ‘excess-CPI rebate’ has not been sufficient to eliminate increases in manufacturer prices for onpatent drugs faster than the CPI in recent years. Thus, the Medicaid mandatory rebate provisions have probably contributed to both higher list prices and smaller discounts for private sector payers. Consistent with this, Duggan and Scott Morton (2006) found that drugs with a higher Medicaid share experienced larger increases in prices (including discounts) to private payers.

History of Managed Care

Managed care has a long history. The earliest mention of arrangements in which individuals (often employers) contracted with a number of physicians to provide services for a preset fee to a defined population dates back to 1849. The Kaiser Health plan, and other large prepaid group practices, emerged in the 1930s. For many years, the plans faced considerable opposition from organized medicine.