dence of real-world success across differ-ent types of populations.
A Starting Point
Research on the wide variation in med-ical practice and treatment patterns has yielded a set of remarkable insights on the U.S. health care system.3 Some of this variation is linked to real differences in patient needs but a considerable portion is unwarranted—that is, cannot be ex-plained by illness, medical need or the dictates of evidence-based medicine. In 2003, researchers at Dartmouth produced a seminal research paper that suggested Care management is a part of nearly every major health plan, but the forms care management takes can vary widely. The author describes what employers should consider looking for in a well-designed program, including ways to help individuals so that they can make informed decisions with their doctors and basing programs on data from a variety of sources. He also explains why some programs fail to reduce medical costs.
Care Management:
What Works?
by David Veroff
Overview
With the administration focusing on health care reform and a continuing eco-nomic crisis, the need for more effective and less costly models of health care has never been more pressing. Traditional disease management has been seen as a way to improve care and slow rising health costs,1 but the value of these tradi-tional programs, particularly for Medicare fee-for-service beneficiaries, has been hotly debated.2 Despite this debate and often because of it, care management has evolved into an enhanced set of services that includes disease management as one
piece. Though the form varies widely, al-most every major health plan offers care management programs, and most em-ployers pay directly or indirectly for these services. Deciding which of these models is ideal is a key public policy and business issue.
A growing body of evidence indicates that well-designed, analytically driven programs that facilitate shared decision making between doctor and patient can deliver success, particularly in addressing health cost increases. This article outlines and describes what works and what to look out for when implementing care management programs and provides
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known as preference-sensitive care. An example is care for lumbar back pain caused by a herniated disc. Ev-idence shows that there is no overall difference in outcomes between pa-tients who receive surgery versus patients who receive nonoperative treatments, such as physical ther-apy and/or medications.6 And yet, the treatment selected may matter very much to the patient, depend-ing on their risk aversion, functional needs and ability to cope with dis-comfort. The figure shows how the average health care dollar is spent on different types of care. Disease management has typically ac-counted for just 12% of the average dollar as it focuses on getting ap-propriate tests and treatment for individuals with chronic conditions (effective care). Interestingly enough, it is a common misconcep-tion that increasing rates of effective care will reduce costs in the near term when, in fact, increasing rates of effective care will likely increase short-term costs somewhat.7 What about the other 88%? A care man-agement program should address all three sources of unwarranted variation with a special emphasis on care that is preference sensitive or supply sensitive.
What Works:
Empowering Individuals
Ideal care management provides a broad range of support to help individu-als participate in their care choices so they can make informed decisions with their physicians and lead healthier lives. that if unwarranted variation in theMedi-care system could be eliminated, the quality of care for Medicare participants would be dramatically improved and Medicare costs would be 30% lower.4 Un-warranted variation drives cost and qual-ity problems in three categories of care and can be addressed as follows:
• More effective care: All too often patients do not get enough effective care—care for which evidence is clear and uncontested to produce better outcomes. Examples include the use of beta blockers for patients who have had a heart attack, or hip replacements for individuals with hip fractures. While effective care services improve health they may actually increase costs in the short and medium term. Most disease management programs have fo-cused on promoting effective care.
• Less supply-sensitive care: Care that is driven by health system ca-pacity is known as supply-sensitive care. The volume of imaging stud-ies, for example, is tightly correlated with the number of imaging devices available in a particular geographic area and appears to be entirely un-related to differences in patient needs. Evidence suggests that re-ducing unnecessary health care ser-vices dramatically reduces costs and improves health outcomes.5
• The right preference-sensitive care: For ailments that do not have a clear, evidence-based, singular treatment choice, patients often re-ceive care that is not in line with their values and preferences. Health service choices that should be de-termined by patient preferences are
Unwarranted Variation in Health Care
What About the Other 88%?
Effective Care 12% Supply-Sensitive Care 63% Preference-Sensitive Care 25%
Figure
Such programs can make a systemwide impact on unwarranted variation and the myriad of avoidable cost and quality problems stemming from unwarranted variation in effective care as well as sup-ply-sensitive and preference-sensitive care. Individuals can influence their desti-nies when they
• Participate in key decisions at key junctions of care. Patients should have influence on decisions about treatments. Support to help them become well-informed and know how to discuss preferences with providers is critical. For conditions for which there is no indicated treatment choice, informed patients will often choose less costly and less risky courses of action.8
• Become more informed about health conditions to reduce uncer-tainty about how to respond to symptoms and warning signs. In-formed, more confident patients are much less likely to receive unneces-sary care.
• Learn self-care for instances when self-care is appropriate, thereby avoiding unnecessary exposure to the forces that drive supply-sensi-tive care.
• Coordinate care directly or through a loved one or advocate. Individuals who understand how the mix of providers and treatments fit to-gether are less likely to have discon-nected care and complications that result in increased costs and worse outcomes.
• Understand how to handle transi-tions in care to reduce the chance of ending up in the hospital again. Patients who are moved from inpa-tient care to a home setting have a special set of needs and will be well-served by understanding their treat-ment plans, understanding signs and signals of concern, and know-ing with whom to communicate.
• Change behaviors that put health at risk such as poor nutrition, low physical activity, smoking, excessive alcohol use and other key behaviors.
• Advocate for clinically indicated care to ensure receipt of key health treatments and tests. Individuals who understand the importance of
basic savings methodology reduce systematic bias that inflates savings results. For example, a program should not take credit for large nat-ural cost reductions that occur in the brief period after a major claim indicates an individual has a chronic condition. Not adjusting methodologies for this “regression to mean” time period can dramati-cally inflate savings.
• Transparent: It is important to openly share details of methods and the supporting data and processes with the purchasers of services. Jointly managed validation processes give weight to the accuracy of the re-sults and ensure all parties have a full view of a program’s impact.
• Plausible: Validating measured im-pact with supporting data and con-ducting significant supplemental investigative analytics, called plausi-bility tests, to validate overall results and to understand the source of any variance between overall results and supporting evidence are key.
Positive results can be generated con-sistently across different population types, with some variation that largely re-flects underlying population characteris-tics and plan design. For example, take the case of several populations within a large regional health plan. In the first three years of a successful program, the focus of interventions was on individuals with specific chronic conditions and deci-sion support needs. In the past year, ser-vices have expanded to a much broader set of individuals with high risk of future costs (supply-sensitive and preference-sensitive care) across the spectrum of conditions. The services are available to enrollees in HMO and PPO plans, for both administrative services only and fully in-sured groups, and Medicare Advantage plans. This health plan has validated first- and second-year savings that greatly ex-ceed fees paid:
The first-year savings exceeded fees • For HMO enrollees: by 60% • For PPO enrollees: by 80%
• For Medicare Advantage enrollees: by 20%.
The second-year savings exceeded fees • For HMO enrollees: by 280% • For PPO enrollees: by 260%
• For Medicare Advantage enrollees: by 190%.
help identify the highest risk indi-viduals and can also reveal the channel and content most appro-priate for a particular individual. What gets shown (on paper, e-mail or the Web) or said (through per-sonal interaction with a health coach or with an interactive voice response system) can dramatically affect an individual’s reactions. Varying the form and content of outreach to match each individual’s profile and preferences demon-strates sensitivity to the unique situ-ation of each person and can con-siderably increase the likelihood of engagement.
• Individualized support: Interven-tions are far less likely to be effective with a “one-size-fits-all” approach. Individuals respond far better to support that meets them where they are rather than support that as-sumes a single, linear course of rea-soning with a narrow goal, such as taking a particular test or getting a prescription refilled. Key to lasting behavior change is enabling indi-viduals to discuss and get informed about their highest priorities first rather than priorities that have been predefined for them.
• Flexible information systems: In-formation systems should simplify opportunity assessment, enable tai-lored outreach, deliver rapid evalua-tion of program effectiveness and perhaps most importantly, facilitate understanding and prioritization for front-line clinicians who are supporting engaged individuals.
Evidence of Success
Repeated assessments, across a broad range of populations, have shown case management can reduce health care cost increases by $1.50-$2.50 per dollar spent in the first year of a program, with rapid escalation in savings in subsequent years. The methods used for assessing these savings are
• Credible: They use industry stan-dard principles9 and layer in impor-tant enhancements that improve validity. Informal and formal re-views of the methods and results have been highly favorable.
• Conservative: Adaptations to the clinically indicated care are more
likely to seek out this kind of care and remind providers of the specific care they need.
Care management must consider the spectrum of health care needs across in-dividuals and their families and take into consideration not only their conditions but their lives, values, preferences and ability to take an active role in their care. It must not look at singular conditions or issues, but must consider the total picture of health, including all conditions an indi-vidual faces as well as the social, cultural or economic factors at work in that per-son’s life. Large opportunities can be missed by not understanding all of a pop-ulation’s cost and health drivers. Solutions must be based on a total population view. For example, a singular focus on people with high-risk asthma may miss other key cost and quality drivers in the population and may, in fact, be money poorly spent if other individuals have higher risk.
From Ideas to Action:
Implementing What Works
Care management programs need sev-eral key components to be effective in de-veloping the right skills in individuals:• Tailored analytics: Care manage-ment programs need innovative analytics that uncover cost drivers at a population level and then drill down to individual interventions. Opportunities for impact can be identified by feeds of hospital dis-charge notices or specialty referrals, but truly effective analytics must use more data sources, including medical, pharmacy and disability claims; biometric and laboratory data; and self-reported information such as health risk assessments. An-alytics should support rapid assess-ment and improveassess-ment of program components. Structured and timely assessment of the effect of interven-tions—measured in formal studies as well as quality monitoring sys-tems—should enable learning, in-novation and sensible use of re-sources.
• Innovative engagement tactics: So-phisticated and tailored outreach practices, which are fundamentally dependent on insightful analytics, are critical. The right set of analytics
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tion causes them to interact fre-quently with the medical system. Thus, they are more at risk to be drawn into the “other 88%” than are healthy individuals.
• Single or limited condition pro-grams: The largest opportunities for cost savings can be discovered when the entire population is available for potential intervention. A focus on one or two diseases constrains the overall impact significantly.
• Data sources that are inconsistent or untimely: Data are the lifeblood of a care management program. Without reliable and timely data sources, programmatic focus is off, front-line support processes are ill-advised and course corrections are difficult. While small inconsisten-cies and lags in data are expected, most are predictable and can be factored into analytic approaches. In some instances including the Medicare fee-for-service popula-tion, data problems are tremendous and workarounds complex. Exam-ples of potential problems that re-strict program performance include massive, unstructured errors in member identification data, large amounts of missing claims data and significant claims lag problems.
• Lack of real-time data feeds for ur-gent needs: Care management pro-grams need rapid response data sources, such as notifications when group that received less health
coaching.
What Doesn’t Work
Certainly there are care management programs that do not work, which has been most widely discussed around the Medicare fee-for-service population, but can occur in other populations as well. Problems that care management pro-grams can face include program design constraints and systemic data limitations. Careful consideration must be given to avoid these demonstrated missteps. Health Dialog’s analysis shows that pro-grams that do not reduce medical costs are subject to one or more of the follow-ing constraints:
• Enrollment or opt-in require-ments: Individuals with progressive chronic conditions are poor at pre-dicting future need. Enrollment or opt-in requirements bar access to initially unreceptive people who might pose tremendous opportuni-ties for impact in the future. Fur-ther, most opt-in or enrollment processes are complex and expen-sive and draw resources from active support for individuals in need.
• Limited or no provider touch points: While full-scale provider in-tegration is extraordinarily complex and largely still unrealized in most programs, high provider awareness and the opportunity for provider in-teraction with a care management program is a critical program de-sign feature.
• Narrow focus on clinical quality improvement: Improving clinical quality as currently measured may have little to no impact on short-term medical costs because it fo-cuses on effective care, where just 12% of dollars are spent, and be-cause its initial goal is to increase use of this effective care. A com-mon mistake is to overemphasize these types of measures at the ex-pense of ensuring that people with chronic and other high-risk condi-tions avoid excess supply-sensitive care and get the right preference-sensitive care—where 88% of the money is spent. The main reason that people with chronic conditions can be so costly is that their condi-The expanded services have higher
fees associated with them, and yet early demonstrable savings continue to be well in excess of fees. These results are sup-ported by rigorous plausibility testing that uses formal study processes with ran-domization. The services demonstrate the powerful impact on medical costs of ro-bust support for individuals with key risks:
• Effect of preference-sensitive deci-sion support: A series of tests of dif-ferent types of outreach for individ-uals at risk for back and joint surgery demonstrated that populations with much higher levels of health coach-ing had significantly lower surgical rates and significantly lower overall health costs. The study randomly as-signed one group of individuals to have outreach through outbound in-teractive voice response (IVR) calls and the other received postcards or no outreach. The IVR process pro-duced a health coaching rate of 7.9% while individuals with postcard or no outreach had a health coaching rate of 1.1%. Health coaching in combination with decision aids pro-duced 14% lower surgery rates (as members participated in shared de-cision making with providers about their treatment choices), with the 20% lower overall costs in the IVR group than in the mail-only or no-outreach group.
• Effect of engagement on overall costs: A second test of different out-reach approaches for individuals with chronic conditions, who had high likelihood of future costs but had not yet been reached by tradi-tional outreach processes, demon-strated that populations with much higher levels of health coaching had significantly lower overall health costs. The study randomly assigned one group of individuals to have outreach through outbound IVR calls; another group received tai-lored letters encouraging them to call a health coach. Of the group called through IVR, 11.2% spoke to a health coach. Only 1.3% of the indi-viduals who were sent a letter spoke with a health coach. The group that received more health coaching ex-perienced $41 per member per month less in overall costs than the
Care management must
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tion with care.” Annals of Internal Medicine, Feb-ruary 18, 2003, 138(4): 288-298.
5. E. S. Fisher, D. E. Wennberg, T. A. Stukel, D. J. Gottlieb, F. L. Lucas and E.L. Pinder. “The implications of regional variations in Medicare spending: Part 1. Utilization of services and the quality of care.” Annals of Internal Medicine, 2003, 138: 273-287.
E. S. Fisher, D. E. Wennberg, T. A. Stukel, D. J. Gottlieb, F. L. Lucas and E. L. Pinder. “The impli-cations of regional variations in Medicare spend-ing. Part 2: Health outcomes and satisfaction with care.” Annals of Internal Medicine, February 18, 2003, 138(4): 288-298.
6. J. N. Weinstein, T. D. Tosteson, J. D. Lurie, A. N. A. Tosteson, B. Hanscom, J. S. Skinner, W. A. Abdu, A. S. Hilibrand, S. D. Boden and R. A. Deyo. “Surgical vs. Nonoperative Treatment for Lumbar Disk Herniation.” Journal of the
Ameri-can Medical Association, 2006, 296: 2441-2450.
7. J . C o h e n , P. N e u m a n n a n d M . Weinstein.“Does Preventive Care Save Money? Health Economics and the Presidential Candi-dates.” New England Journal of Medicine, 2008, 358(7): 661-663.
8. A. M. O’Connor, H. A. Llewellyn-Thomas and A. B. Flood. “Modifying unwarranted varia-tions in health care: shared decision making us-ing patient decision aids.” Health Affairs (Mill-wood) 2004; suppl. Web exclusive: VAR 63-72.
9. As expressed by the series of DMAA Out-comes Measurement standards.
For information on ordering reprints of this article, call (888) 334-3327, option 4.
Endnotes
1. “Realizing the Promise of Disease Man-agement: Payer Trends and Opportunities in the United States.” The Boston Consulting Group, February 2006.
A. Short et al. “Disease Management: A Leap of Faith to Lower-Cost, Higher-Quality Health Care.” Issue Brief No. 69, Center for Studying Health System Change, Washington, D.C. Octo-ber 2003.
2. D. M. Bott, M. C. Kapp, L. B. Johnson and L. M. Magno. “Disease Management for Chroni-cally Ill Beneficiaries in Traditional Medicare.”
Health Affairs, January 1, 2009; 28(1): 86-98.
S. M. Foote. “Next Steps: How Can Medicare Accelerate The Pace Of Improving Chronic Care?”
Health Affairs, January 1, 2009; 28(1): 99-102.
Congressional Budget Office (September 19, 2002), Disease Management in Medicare: Data Analysis and Benefit Design Issues, testimony be-fore the Special Committee on Aging, U. S. Senate. R. Z. Goetzel, R. J. Ozminkowski, V. G. Villagra and J. Duffy. “Return on investment in disease management: a review.” Health Care Financing
Review, 2005 Summer; 26(4): 1-19.
S. Mattke, M. Seid, S. Ma. “Evidence for the effect of disease management: is $1 billion a year a good investment?” American Journal of
Man-aged Care, December 2007, 13(12): 670-6.
3. www.dartmouthatlas.org.
4. E. S. Fisher, D. E. Wennberg, T. A. Stukel, D. J. Gottlieb, F. L. Lucas and E. L. Pinder. “The implications of regional variations in Medicare spending. Part 2: Health outcomes and
satisfac-individuals are discharged from the hospitals. Such data enables care management support for the critical transition back home, so readmis-sions can be avoided. Without rapid response data sources there is usu-ally deep reliance on claims data which, while rich, are rarely avail-able on a frequent basis.
• Limited or poor pharmacy infor-mation: Pharmacy data are ex-tremely useful for identifying indi-v i d u a l s w i t h c o n d i t i o n s , f o r developing appropriate targeting strategies and for enabling coaches to support individuals in need. With poor pharmacy data, program effec-tiveness is constrained.
Summing It All Up
The evidence shows that medical costs can be curbed by well-designed and ana-lytically driven care management pro-grams. There is also a growing body of evidence indicating that typical levels of interventions can be expanded dramati-cally to have a greater effect. Successful models move beyond typical chronic con-dition management of effective care and address all drivers of excess cost so that individuals receive better and less costly care and are happier with their care. As the administration’s health reform plan continues to unfold, sophisticated and well-planned care management repre-sents a model of health care delivery that should be leveraged in the national effort to make health care better for all.
B&C
David Veroff serves as Health Dialog’s vice president of eval-uation services responsible for developing new evaleval-uation, measurement and reporting approaches and for conduct-ing studies of program effectiveness. Veroff has 24 years of experience in the health care analytics and research field, including work for research, consulting and governmental organizations. He holds a B.A. degree from Michigan State University and a master of public policy degree from Har-vard University.
