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Personalized Preventive Care Reduces Healthcare Expenditures Among Medicare Advantage Beneficiaries
Shirley Musich, PhD; Andrea Klemes, DO, FACE; Michael A. Kubica, MBA, MS; Sara Wang, PhD; and Kevin Hawkins, PhD
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Carrie H. Colla, PhD; William L. Schpero, MPH; Daniel J. Gottlieb, MS; Asha B. McClurg, BA; Peter G. Albert, MS; Nancy Baum, PhD; Karl Finison, MA; Luisa Franzini, PhD; Gary Kitching, BS; Sue Knudson, MA; Rohan Parikh, MS; Rebecca Symes, BS; and Elliott S. Fisher, MD
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Edwin S. Wong, PhD; Matthew L. Maciejewski, PhD; Paul L. Hebert, PhD; Christopher L. Bryson, MD, MS; and Chuan-Fen Liu, PhD, MPH
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Personalized Preventive Care Reduces Healthcare Expenditures Among Medicare Advantage Beneficiaries

Shirley Musich, PhD; Andrea Klemes, DO, FACE; Michael A. Kubica, MBA, MS; Sara Wang, PhD; and Kevin Hawkins, PhD
This study investigated the impact of an enhanced preventive care delivery system on healthcare expenditure and utilization trends among Medicare Advantage beneficiaries.
Health status was determined from medical claims diagnoses and place of service utilization coding and included the CCI score (0, 1+)25, PDG score (0, 1+)26, and the annual number of inpatient admissions. The CCI is a measure of the risk of 1-year all-cause mortality attributable to selected comorbidities. PDGs are validated psychiatric dignostic groups analogous to major diagnostic groups in the diagnostic-related group (DRG) system, but provide better classification of individuals with substance abuse and/or mental health disorders. Variables denoting missing data were included in the analyses; however, for brevity, results associated with missing data are not shown in the tables included with this manuscript but are available upon request.

Modeling. Two sets of analyses were conducted to: 1) describe the characteristics of the members, and 2) estimate the likelihood of enrollment and match nonmembers to member characteristics.

Descriptive. The first set of analyses categorized members by demographics, socioeconomics, supply of services, and clinical characteristics, and compared members with nonmembers, using univariate techniques without adjusting for case mix differences. This was done to determine if case mix differences needed to be adjusted for between the groups prior to comparing the outcome variables. Chi-square and Student t tests were used in these analyses to test for differences in categorical and continuous variables, respectively. All analyses were performed using SAS software (Version 9.2, Cary, North Carolina). Propensity Matching. Propensity score matching was used to minimize case mix differences between members and nonmembers. In this second set of analyses, a logistic model was used to estimate the likelihood of enrolling in the program. The variables used in the model were those previously described. The propensity score for each sample individual was defined as each member’s predicted probability of being in the program. This probability was then used to match members to similar nonmembers. Propensity score matching is a convenient and acceptable way to remove case mix differences when evaluating health and wellness programs.27 

Second-stage regressions are often warranted to remove any remaining case mix differences after matching, and to adjust for skewed medical expenditure distributions that are often common in healthcare. In a final set of medical expenditure statistical analyses, we used Exponential Conditional Mean regression models to estimate the impact of the program compared with nonmembers on medical expenditure trends.

Outcome Variables. Utilization of medical services was examined to provide a mechanism that could potentially drive changes in healthcare expenditure trends. Utilization metrics included annual rates of ED visits, inpatient admissions, and readmissions, as well as length (in days) of hospital stay and average inpatient expenditures comparing members with matched nonmembers. Healthcare expenditure cost trends were compared with pre-MDVIP enrollment to post enrollment for MDVIP members and nonmembers. Co-payments and membership fees paid by the members were not included in this evaluation because they were not relevant to the savings realized by the payer— the health plan, Medicare Advantage—and we are evaluating the program from the perspective of the payer. Healthcare expenditures are presented as total medical and pharmacy, medical only, and pharmacy only for preenrollment, year 1, and year 2.

RESULTS

The study population included 2360 members and 5521 nonmembers. Outliers were excluded at the top 1 percentile of annual healthcare expenditures, resulting in 28 cases (N = 2332) being deleted from the member subgroup and 105 cases (N = 5416) being deleted from the nonmember subgroup. Members differed from nonmembers on most of the demographic, socioeconomic, supply of services, and health status variables, demon- strating the need for the propensity score matching to adjust for these case mix differences. After matching, 2320 members and 2320 nonmembers remained in the study population. Most of the significant differences in characteristics between members and nonmembers were eliminated with the matching methodology (Table 2). Utilization trends for years 1 and 2, investigated to document the potential source of healthcare savings, indicated significantly lower rates for ED visits and inpatient admissions (Table 3). While there were trends associated with members for lower readmission rates, lower lengths of stay, and lower average inpatient expenditures, none of these additional utilization measure comparisons were significant.

Medical and pharmacy expenditure trends for Years 1 and 2 indicated that members compared with nonmembers (matched, regression-adjusted and excluding outliers) saved $86.68 per member per month (PMPM) in Year 1 and $47.03 PMPM in Year 2 from their relative baseline expenditures prior to enrollment (ie, difference between member and nonmember expenditure trends over the time period; Table 3). Likely a result of the relatively small study population and the large variance of Medicare healthcare expenditures, only Year 1 medical and pharmacy savings amounts approached statistical significance. Medical and pharmacy expenditure trends were also analyzed separately, with results indicating that savings were realized primarily from moderated medical cost trends among members compared with nonmembers. In contrast, pharmacy expenditures increased significantly for members, as is expected with increased pharmaceutical compliance, but the magnitude of these increases was less than the savings in medical expenditures.

DISCUSSION

MDVIP provides a model of personalized preventive care that delivers a more individualized approach to its patients, focusing on disease prevention and wellness. MDVIP’s model is uniquely positioned to provide enhanced preventive care to those Medicare Advantage beneficiaries with or without chronic comorbidities enabling better medical management over time as well as providing expected diagnostic services for illness episodes. We found in a previous study that Medicare MDVIP members compared with nonmembers had significantly lower medical services utilization in a primary care setting.21 

This study similarly demonstrated reduced utilization rates for ED and inpatient admissions. Associated with these reduced utilization rates were savings of $87 PMPM in year 1 and $47 PMPM in year 2 after enrollment—savings primarily from reduced medical expenditures. For the 2320 members, the savings totaled about $3.7 million over the 2 years. These savings accrued directly to the Medicare Advantage health plans.

For members, there was a minimal annual savings in co-payments for medical services received (calculated separately) of about $160 in year 1 and $180 in year 2— savings not sufficient to offset the membership fee (about $1650 annually). However, members find value in the personalized preventive care programs for the advantages of individualized care with greater access to physicians and a focus on preventive care, care coordination, and disease prevention and management. 

This model of healthcare delivery integrates wellness into a personalized primary care model that focuses on provision of medical services but also the management of health, including lifestyle behaviors and chronic conditions. 21,22 The model leverages the influence of physicians30 with an augmented physician-patient relationship and the development of a personalized wellness plan based on the patient’s health status. The underlying premise is that delivery of preventive medicine can improve health status and reduce healthcare spending with decreased utilization of ED (since physicians are available to directly manage health concerns) and of inpatient admissions (with better management of chronic conditions, preventive screenings to promote early diagnoses, and lower levels of treatment). This current study is one of the first to demonstrate the cost-effectiveness of a personalized preventive care model within a Medicare population. The feasibility of this model of healthcare delivery in other Medicare populations (eg, fee-for-service) has yet to be determined.

To date, CMS has focused programmatic efforts on high-risk/high-cost Medicare patients. Its conclusion is that current disease/case management programs are what work best to reduce hospitalizations—but for only a subset of very sick patients, those generally with at least 1 hospitalization within the year prior to enrollment in the programs.8 While the efforts to mitigate spending and promote better medical management for the sickest patients are noteworthy, more attention needs to be given to the healthier Medicare beneficiaries who are much more numerous.

Lessons learned from employer-based health management programs indicate that managing risk across a population requires a 2-pronged approach: providing programs for high-risk subgroups and, as or more importantly, providing opportunities to assist currently healthy beneficiaries in managing their health as they age.16,28,29 Without attention to disease prevention and wellness, the migration of individuals to higher risk categories will continue as they age (ie, a natural progression of aging and disease), especially as Americans continue to live longer.14 Therefore, slowing down the upward transitions must be a priority if population health management is to be successful in this population.19,20

Limitations include a relatively small study population enrolled in a Medicare Advantage program provided by a single insurer, which may not be generalizable to all Medicare Advantage beneficiaries. The strengths of the study include a rigorous evaluation utilizing former patients of affiliated physicians (to control for differences in physicians’ medical delivery styles and quality) and using multivariate models to adjust for case mix differences between those in the program and comparison members.

The results demonstrated that a model of personalized preventive care delivery can reduce spending among Medicare Advantage beneficiaries. As CMS continues to explore potential models to enhance the quality of medical delivery to seniors, improve patient satisfaction with care, and reduce medical spending, an enhanced preventive care model should be considered. Population health management of senior populations requires not only programs to mitigate spending among high-risk/highcost patients, but also programs that serve to enhance health and improve quality of life, helping seniors stay as healthy as possible as they age.

Acknowledgments
       
The authors thank Frank G. Bottone Jr, PhD, for his critical review of this manuscript and his editorial assistance.

Author Affiliations: OptumInsight, Advanced Analytics, Ann Arbor, MI (SM, SW, KH); MDVIP, Boca Raton, FL (AK); Applied Quantitative Sciences, Pompano Beach, FL (MAK).

Funding Source: This study was funded by MDVIP.

Author Disclosures: AK is an officer of MDVIP. The other authors have no disclosures to report.

Authorship Information: Concept and design (SM, AK, MAK, SW, KH); acquisition of data (SW, KH); analysis and interpretation of data (SM, AK, MAK, KH); drafting of the manuscript (AK, KH); in critical revision of the manuscript for important intellectual content (SM, AK, MAK, KH); statistical analysis (SW, KH); and supervision (KH).

Address correspondence to: Andrea Klemes, DO, FACE, 1875 NW Corporate Blvd, Suite 300, Boca Raton, FL 33431. E-mail: aklemes@mdvip.com.
1. Congressional Budget Office. The budget and economic outlook: fiscal years 2008 to 2018. Washington, DC: Congressional Budget Office; 2008. http://www.cbo.gov/ftpdocs/89xx/doc8917/01-23-2008_Budget-Outlook.pdf. Accessed May 31, 2013.

2. CoinNews Media Group. US Inflation Calculator. http://www.usinflationcalculator.com/inflation/current-inflation-rates/. Accessed June 5, 2013.

3. Thorpe KE, Ogden LL, Galactionova K. Chronic conditions account for rise in Medicare spending from 1987 to 2006. Health Aff (Millwood). 2010;29(4):718-724.

4. Thorpe KE, Howard DH. The rise in spending among Medicare beneficiaries: the role of chronic disease prevalence and changes in treatment intensity. Health Aff (Millwood). 2006;25(5):w378-w388.

5. Lochner KA, Cox CS. Prevalence of multiple chronic conditions among Medicare beneficiaries, United States, 2010. Prev Chronic Dis.2013;10:E61:120-137.

6. Pham HH, Schrag D, O’Malley AS, Wu B, Bach PB. Care patterns in Medicare and their implications for pay for performance. N Engl J Med. 2007;356(11):1130-1139.

7. Bott DM, Kapp MC, Johnson LB, Magno LM. Disease management for chronically ill beneficiaries in traditional Medicare. Health Aff (Millwood). 2009;28(1):86-98.

8. Brown RS, Peikes D, Peterson G, Schore J, Razafindrakoto CM. Six features of Medicare coordinated care demonstration programs that cut hospital admissions of high-risk patients. Health Aff (Millwood). 2012;31(6):1156-1166.

9. Lyle Nelson; Congressional Budget Office. Lessons from Medicare’s Demonstration Projects on Disease Management, Care Coordination, and Value-Based Payment. http://www.cbo.gov/sites/default/files/cbofiles/attachments/01-18-12-MedicareDemoBrief.pdf. Published January 18, 2012. Accessed April 18, 2013.

10. Peikes D, Chen A, Schore J, Brown R. Effects of care coordination on hospitalization, quality of care, and health care expenditures among Medicare beneficiaries: 15 randomized trials. JAMA. 2009;301(6):603-618.

11. Brown R, Peikes D, Chen A, Schore J. 15-site randomized trial of coordinated care in Medicare FFS. Health Care Financ Rev. 2008;30(1):5-25.

12. Rula EY, Pope JE, Stone RE. A review of Healthways’ Medicare Health Support program and final results for two cohorts. Popul Health Manag. 2011;14(suppl 1):S3-S10.

13. Rula EY, Pope JE, Hoffman JC. Potential Medicare savings through prevention and risk reduction. Popul Health Manag. 2011;14(suppl 1):S35-S44.

14. Pope GC, Kautter J, Ellis RP, et al. Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Financ Rev. 2004;25(4):119-141.

15. Boult C, Green AF, Boult LB, Pacala JT, Snyder C, Leff B. Successful models of comprehensive care for older adults with chronic conditions: evidence for the Institute of Medicine’s “retooling for an aging America” report. J Am Geriatr Soc. 2009;57(12):2328-2337.

16. Musich S, McDonald T, Hirschland D, Edington DW. Examination of risk status transitions among active employees in a comprehensive worksite health promotion program. J Occup Environ Med. 2003;45(4):393-399.

17. Fries JF. Aging, natural death, and the compression of morbidity. New Engl J Med. 1980;303(3):130-135.

18. Musich S, McDonald T, Chapman L. Health promotion strategies for the “boomer” generation: wellness for the mature worker. Am J Health Promot. 2009;23(3):1-9.

19. Terry DF, Pencina MJ, Vasan RS, et al. Cardiovascular risk factors predictive for survival and morbidity-free survival in the oldest-old Framingham Heart study participants. J Am Geriatr Soc. 2005;53(11):1944-1950.

20. Wilcox BJ, He Q, Chen R, et al. Midlife risk factors and health survival in men. JAMA. 2006;296(19):2343-2350.

21. Klemes A, Seligmann RE, Allen L, Kubica MA, Warth K, KaminetskyB. Personalized preventive care leads to significant reductions in hospital utilization. Am J Manag Care. 2012;18(12):e453-e460.

22. Seligmann RE, Gassner LP, Stolzberg ND, Samarasekera NK, Warth K, Klemes A. A personalized preventive care model versus a traditional practice: comparison of HEDIS measures. Int J Pers Cent Med. 2012;2(4):775-779.

23. French MT, Homer JF, Klevay S, et al. Is the United States ready to embrace concierge medicine? Popul Health Manag. 2010;13(4):177-182.

24. Bogdan, GM, Green JL, Swanson D, Gabow P, Dart RC. Evaluating patient compliance with nurse advice line recommendations and the impact on healthcare costs. Am J Manag Care. 2004;10(8):534-542.

25. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383.

26. Ashcraft ML, Fries BE, Nerenz DR, et al. A psychiatric patient classification system: an alternaitve to diagnosis-related groups. Med Care. 1989;27(5):543-557.

27. Seeger JD, Williams PL, Walker AM. An application of propensity score matching using claims data. Pharmacoepidemiol Drug Saf. 2005;14(7):465-476.

28. Ozminkowski RJ, Goetzel RZ, Wang F, et al. The savings gained from participation in health promotion programs for Medicare beneficiaries. J Occup Environ Med. 2006;48(11):1125-1132.

29. United Health Foundation. America’s Health Rankings Senior Report: A Call to Action for Individuals and Their Communities, 2013 Edition. http://cdnfiles.americashealthrankings.org/SiteFiles/Reports/Americas_Health_Rankings_Senior_Edition_2013_final.pdf. Accessed May 31, 2013.

30. Subramanian U, Hopp F, Mitchinson A, Lowery J. Impact of provider self-management education, patient self-efficacy, and health status on patient adherence in heart failure in a Veterans Administration population. Congest Heart Fail. 2008;14(1):6-11.
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