This PHM program targeting the non-Medicare disabled segment of the Georgia Medicaid population demonstrated actual expenditures substantially lower than any projections based on relevant medical inflation rates. We found a parallel decrease in hospital admissions and hospital bed-days weighted for member months, with a slight absolute reduction in length of stay, consistent with an effect related to care coordination and chronic disease care management (CDCM). Unit costs per inpatient day, per admission, and per prescription increased 5%–6%, suggesting that reductions in provider payment rates do not explain the reduction in expenditure growth. Our mixed-effects growth models also demonstrated that the rate of increase in Medicaid expenditures declined as time increased, even though the population's risk profile measured by CDPS scores actually increased.
PHM targets clients for higher levels of contact based on risk stratification. For example, 57.9% of persons in the highest decile of Medicaid expenditures are still in the top 10% of enrollees for generating Medicaid expenditures 2 years later.16
The aged and disabled segment of the Medicaid population accounted for two thirds (68.7%) of all Medicaid spending in 2006.17
According to the Kaiser Commission on Medicaid and the Uninsured, the disabled accounted for three fourths (76%) of the $10.4 billion growth in US Medicaid expenditures from 2005–2006 (after adjustment for the shift to Medicare Part D drug spending for dual-eligibles).18
There is tremendous variability in Medicaid expenditures from state to state, with corresponding state-to-state variability in expenditure trends from year to year.19
Growth in overall Medicaid expenditures PMPM in Georgia from 1991 to 2004 averaged 6.4%, but expenditures in the 3-year period of 2002–2004 rose 15.7%.20
Any impact of this PHM program on moderating the trend toward rapidly rising Medicaid expenditures must be interpreted in the context of recent negative trials of DM in the Medicare demonstration program. For example, a recent meta-analysis of Medicare care management programs found only 3 of 20 that achieved some level of quality improvement “at or near budget neutrality.”7
Certainly Medicare and Medicaid differ in their benefits and in the clinical characteristics of their populations, but the disabled segment of the Medicaid population shares with the Medicare population a high prevalence of chronic disease and multimorbidity.
In addition, not all PHM/DM programs are the same. For example, the Medicare demonstration projects reported on a very “light-touch” or low-intensity telephone contact with patients. Similar findings from a randomized, intent-to-treat design study of the Florida LifeMasters Supported SelfCare demonstration program (also primarily telephonic contact) showed “virtually no overall impacts on hospital or emergency room (ER) use, Medicare expenditures, quality of care, or prescription drug use for the 33,000 enrollees,” although savings could be shown for specific diagnostic subgroups in a high-cost region of the state.21
Traditional MCOs may be similarly ill equipped. A survey of executive leaders of 95 California MCOs found that although there was substantial interest in increasing CDCM, “insufficient financial resources at the plan level, lack of organizational leadership and commitment in physician organizations, and limited information technology in physician offices were barriers to CDCM expansion.”22
It may also be that PHM shows a greater return on investment in populations in greater need of care coordination. The Medicaid disabled population is characterized by low-income, low health literacy, and high rates of multiple medical comorbidities, as well as high rates of co-occurring mental health disorders, favoring a program whose design and financial incentives target overall population health outcomes rather than one-disease-at-a-time metrics.
Our results are consistent with similar recent studies. For example, a 2008 report of a randomized trial embedded in the Indiana Medicaid PHM/DM program found cost increases in both the intervention and control groups, but statistically significant cost avoidance (actual vs. projected expenditures), especially in the subset of congestive heart failure patients.23
A 2009 longitudinal study of the statewide impact of the program covering 44,213 Medicaid enrollees with diabetes or congestive heart failure concluded that “claims were increasing before ICDMP, flattened in the years around program initiation, and remained flat in the final year of follow-up.”24
In contrast to “light-touch” telephonic case management, the Indiana program involved more intensive case management, including home health nurse visits and direct provider engagement, with a conscious focus on the impact of multiple comorbidities (eg, chronic pain from osteoarthritis was associated with increased costs among those with diabetes).25
A study of more than 35,000 clients enrolled in a Virginia Medicaid PHM/DM program found statistically significant improvements “in patient's drug compliance and quality of life while reducing (ER), hospital, and physician office visits and adverse events,” with a specific cost savings related to reduced hospitalization rates.26
More focused disease-specific programs may be less effective. A pre-post assessment of utilization from 2001–2003 among 15,275 high-risk beneficiaries in Florida's Medicaid DM program (interventions targeting 4 specific disease categories) was able to demonstrate significant absolute reductions in emergency department visits, hospital admissions, and inpatient bed-days,27
but other costs, such as pharmaceutical expenditures, increased and no cost offsets were found.6
Even so, studies of this program have found additional benefits in clinical measures such as blood pressure, cholesterol control, and medication adherence.28
Are there uniquely effective elements of the Georgia Enhanced Care (GEC) program? The GEC program used RNs as health coaches as part of a larger team that also included pharmacists, psychologists, and social workers specifically trained to address multiple risks and comorbidities in a whole-person approach. In addition, the program attempted to engage the physicians who care for these complex patients, and also worked at the level of systems change (eg, negotiating a state policy change so that Georgia Medicaid would reimburse for influenza and pneumonia vaccines when clinically indicated). In order to facilitate interaction between the RN health coaches and patients or their providers, some of the health coaches were moved out of the call center to frontline clinical practice settings such as high-volume hospital emergency departments and federally qualified health centers.
A key element of the strategy was that the program's financial incentives were weighted heavily (80% of revenues at-risk) toward a whole-person, whole-population return on investment target, rather than targeting individual disease outcomes. Because there were no opportunities for the program to limit access to services as might be found in a utilization management program, cost savings could only be achieved by coordinating care to be more efficient and more effective in preventing costly complications, hospital admissions, or exacerbations of chronic disease.
While some evaluation studies have included a control group,6
this was an uncontrolled study of a real-world program implementation. However, even without a control group, we can rule out some alternative explanations for the gap between actual and projected expenditures. For example, a shift in the age-race-sex mix or duration of enrollment or diagnostic case-mix of the population could explain a moderation of cost increases per member, but appears to exclude these as possible explanations. In fact, despite a slight shift in age distribution, the case-mix actually appears to favor a trend toward higher costs. Because CDPS scores are used in part to predict a Medicaid population's future expenditures, these changes from baseline to program year indicate a population that increased its Medicaid expenditure risk by 21%-24%. This helps rule out regression to the mean or population changes in risk profile as explanations for the moderation in cost inflation. A study of patients with chronic obstructive pulmonary disease in the Colorado Medicaid program found virtually no regression to the mean or secular trend toward improvement in cost or utilization patterns.29
Neither was there any decline in unit cost (dollars paid per prescription or per office visit or per bed-day), suggesting a shift to lower cost providers or to deeply discounted medications was found to explain the moderation in Medicaid expenditure growth. A disproportionate impact on overall costs and average cost per member by a small number of costly outliers was also ruled out by calculating the expenditure variance both with and without the stop-loss cap.
In a certified actuarial report (unpublished data, Georgia Enhanced Care Disease Management Program Financial Results for Performance Year 1, APS Healthcare; Mercer Government Human Services Consulting; March 25, 2008), the state's actuarial consulting firm identified all potential program changes that might have had an impact on expenditures or on cost-growth trends. In so doing, they adjusted their annualized Medicaid cost growth trend assumption from 13.8% to 11.5%, based on program changes in 2004 and 2005 prior to program implementation, but then asserted that “No additional program changes that would impact the eligible population have occurred between the end of FY-2006 and the end of the Program Year 1 performance period.” This independent and certified actuarial report confirmed that the program “substantially exceeded” its bend-the-trend target savings guarantee even after adjusting for any other program changes that might have influenced expenditures.