Leah M. Marcotte, MD

As a broadly defined care delivery strategy, care management can improve outcomes but has yielded variable results, particularly within value-based payment models.

One contributing factor is that care management encompasses different types of activities, functions, and goals.

A framework categorizing care management as disease management, utilization management, and/or health care navigation interventions can help providers and payers invest in, implement, and reap benefits from care management programs.

Emphasis on care management has become ubiquitous in the era of value-based payment. At the vanguard of the movement, policy makers such as Medicare have emphasized care management via a variety of initiatives, ranging from payment models that encourage longitudinal care management

 to billing codes that reimburse clinicians for coordinating the care of patients who have chronic conditions.

 Hospitals and physician groups increasingly view care management as a key area for investment and as a strategy for succeeding in a value-based environment,

 and there have been isolated reports of success within value-based payment models.

However, despite widespread enthusiasm, complex care management and coordination activities are not associated with differences in quality, utilization, or cost outcomes in prominent value-based payment models such as the Medicare Shared Savings Program (MSSP).

 One potential driver is the variability in how care management programs are defined and implemented. For instance, a survey of individuals leading care management programs demonstrated such a wide range of roles, program characteristics, patient selection methods, and measurement strategies that researchers faced challenges when attempting to classify programs.

In light of this variation, it is important for policy makers and clinicians to clearly define care management in order to evaluate its impact on patient care. Doing so is vital because care management is not a monolithic concept, but rather one that encompasses several distinct types of activities: those related to clinical conditions, health care utilization, and patient experience and access. Although these types of activities need not be mutually exclusive or at odds with each other, failing to distinguish between them when implementing care management programs carries risks.

Organizations may invest in programs that emphasize one activity while expecting the benefits of another, creating misalignment among organizational goals, program investments, and eventual results. For example, care management programs that emphasize disease management may improve quality performance (eg, increasing adherence to guideline-concordant processes for a given disease) while worsening cost performance by increasing utilization

 (eg, if patients who were previously nonadherent to guideline-concordant care begin receiving more of it).

Consequently, organizations that build disease management—heavy programs expecting them to drive performance in total costs of care reforms, such as accountable care organizations, may fail to see return on investment and arrive at the conclusion that “care management” does not work. This dynamic may exist in the MSSP, in which clinical condition–focused management initiatives have failed to contribute to cost savings.

 For patients, the end result of confusing the functions, benefits, and risks of different care management activities is that some individuals may receive care management services they do not need, while others do not receive services that they do need.

To address this issue and support organizational efforts to engage and succeed in value-based care, we offer a care management taxonomy based on the distinct activities of managing clinical disease (hereafter, 

), containing health care utilization (hereafter, 

), and improving patients’ navigation through the health care system (hereafter, 

). We contrast these 3 activities by focusing on differences in function, target populations, requisite competencies, and key risks and limitations (

Care management programs focused on disease management predate the era of value-based care.

 These programs target patients with chronic conditions, using medical evidence to create processes that improve patients’ disease self-management via steps such as medication adherence and lifestyle or behavioral changes (eg, related to diet and exercise).

The main function of disease management initiatives is to drive greater use of evidence- or guideline-based care. In turn, the requisite competencies for this type of care management activity consist primarily of the ability to translate clinical knowledge into care processes and engage patients in their own care (eg, via motivational interviewing, self-management support). From the perspective of organizational performance goals, this type of program can align with value-based care delivery by improving disease-specific metric performance. However, there is a dearth of evidence that disease management programs lead to appreciable reductions in total costs of care.

 Because disease management programs emphasize evidence- or guideline-based medicine, they are also limited by the lack of medical evidence in many areas of clinical care.

Another limitation of disease management programs is that they are not designed to address nonclinical determinants of clinical, quality, and cost outcomes. For example, hyperglycemia and hypoglycemia in insulin-dependent patients with diabetes can result from food insecurity—an issue that cannot be addressed by clinical strategies such as insulin titration. The clinical focus of these care management programs excludes social and other determinants of health.

Like disease management, utilization management is not a new concept. However, interest in these programs has increased significantly as more clinicians and organizations have gained the ability to collect utilization information (eg, through advances in data capture via the electronic health record and interoperability via admission/discharge/transfer, or ADT, messaging) and the incentives to reduce utilization under value-based payment and delivery reforms. Rather than maximizing guideline-concordant care, the core main function of this care management activity is to reduce unnecessary health care utilization, particularly high-cost utilization associated with emergency department (ED), hospital, and postacute care facility care.

Given this focus, utilization management programs target patients at risk for high utilization (sometimes called “super-utilizers”) and emphasize core competencies such as data analytics and robust clinical triage capabilities, which can be used to identify and intervene in the care of super-utilizers. The specific strategy for triage can vary, with some organizations embedding care managers within their primary care infrastructure to reduce utilization related to ambulatory care—sensitive conditions,

 whereas others station utilization managers in EDs to more directly curb inappropriate hospitalization.

The major limitation of utilization management programs is the risk of inadvertently deterring appropriate care. No clinical triage processes are perfect, and it can be challenging to prospectively determine when ED or acute hospital care is medically necessary. Moreover, these programs do not inherently distinguish between clinical and social drivers of utilization. As organizations seek to discern that for themselves, underemphasis on social drivers can lead to disappointing results, while overemphasis on super-utilizer patients can create underinvestment in programs targeted to average- or low-risk individuals—interventions that have similar if not greater ability to curb unnecessary utilization.

 Finally, although patient experience is a key element of value-based care, organizations that heavily emphasize utilization management may be perceived as “gatekeepers” that lack patient-centered processes.

As a method for delivering patient-centered care, health care navigation has received increased attention in the era of value-based payment. Organizations have created new roles for “health navigators”

 or “health coaches” who focus on helping to remove structural barriers for patients who have difficulties in accessing and/or coordinating care.

 As another example of health care navigation, primary care clinics have enabled direct referrals to community-based programs to address adverse social determinants of health.

 By improving individuals’ experience and/or ability to access care, navigation-focused care management programs can support success in value-based payment arrangements via greater patient engagement and continuity.

However, because navigation seeks to address individualized barriers to care, there are also risks to broad implementation within value-based payment initiatives. In reducing barriers to care, organizations may spur utilization that unintentionally counteracts efforts to manage costs of care. The potential benefits from patient navigation and engagement may also be undercut by features of payment model design. For instance, most heavily emphasize quality and cost outcomes over patient experience outcomes, reducing the degree to which improved navigation is reflected in payment model performance.

As a broadly defined care delivery strategy, care management can improve outcomes. However, the results to date have been variable, particularly within value-based payment models. One way to improve the design, implementation, and potential benefits of care management programs is to utilize a framework that distinguishes among different types of activities, functions, and goals encompassed by the concept of care management.

 Department of Medicine (LMM, JML) and Value and Systems Science Lab (LMM, JML), University of Washington School of Medicine, Seattle, WA; Leonard Davis Institute of Health Economics, University of Pennsylvania (JML), Philadelphia, PA.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (LMM, JML); drafting of the manuscript (LMM); critical revision of the manuscript for important intellectual content (LMM, JML); administrative, technical, or logistic support (LMM); and supervision (JML).

Leah M. Marcotte, MD, Department of Medicine, University of Washington, 1959 NE Pacific St, Seattle, WA 98105. Email: leahmar@uw.edu.

1. Baron RJ. New pathways for primary care: an update on primary care programs from the innovation center at CMS. 

. 2012;10(2):152-155. doi:10.1370/afm.1366

2. Edwards ST, Landon BE. Medicare’s chronic care management payment—payment reform for primary care. 

. 2014;371(22):2049-2051. doi:10.1056/NEJMp1410790

3. Bindman AB, Blum JD, Kronick R. Medicare’s transitional care payment—a step toward the medical home. 

. 2013;368(8):692-694. doi:10.1056/NEJMp1214122

4. Lewis VA, Schoenherr K, Fraze T, Cunningham A. Clinical coordination in accountable care organizations: a qualitative study. 

. 2019;44(2):127-136. doi:10.1097/HMR.0000000000000141

5. Hsu J, Price M, Vogeli C, et al. Bending the spending curve by altering care delivery patterns: the role of care management within a Pioneer ACO. 

. 2017;36(5):876-884. doi:10.1377/hlthaff.2016.0922

6. Ouayogodé MH, Mainor AJ, Meara E, Bynum JPW, Colla CH. Association between care management and outcomes among patients with complex needs in Medicare accountable care organizations. 

. 2019;2(7):e196939. doi:10.1001/jamanetworkopen.2019.6939

7. Donelan K, Barreto EA, Michael CU, et al. Variability in care management programs in Medicare ACOs: a survey of medical directors. 

. 2018;33(12):2043-2045. doi:10.1007/s11606-018-4609-1

8. Aboumatar H, Naqibuddin M, Chung S, et al. Effect of a hospital-initiated program combining transitional care and long-term self-management support on outcomes of patients hospitalized with chronic obstructive pulmonary disease: a randomized clinical trial. 

 2019;322(14):1371-1380. doi:10.1001/jama.2019.11982

9. McWilliams JM, Chernew ME, Landon BE. Medicare ACO program savings not tied to preventable hospitalizations or concentrated among high-risk patients. 

. 2017;36(12):2085-2093. doi:10.1377/hlthaff.2017.0814

10. Piro L, Doctor J. Managed oncology care: the disease management model. 

11. Bertel O, Conen D. Individualized care in patients with chronic congestive heart failure. 

. 1987;9(suppl 2):S68-S72. doi:10.1097/00005344-198700002-00014

12. Barlow J, Wright C, Sheasby J, Turner A, Hainsworth J. Self-management approaches for people with chronic conditions: a review. 

. 2002;48(2):177-187. doi:10.1016/s0738-3991(02)00032-0

13. Taliani CA, Bricker PL, Adelman AM, Cronholm PF, Gabbay RA. Implementing effective care management in the patient-centered medical home. 

14. Shumway M, Boccellari A, O’Brien K, Okin RL. Cost-effectiveness of clinical case management for ED frequent users: results of a randomized trial. 

. 2008;26(2):155-164. doi:10.1016/j.ajem.2007.04.021

15. Marcotte LM, Reddy A, Liao J. Addressing avoidable healthcare costs: time to cool off on hotspotting in primary care? 

. 2019;34(11):2634-2636. doi:10.1007/s11606-019-05285-z

16. McGough P, Kline S, Simpson L. Team care approach to population health and care management. 

. 2017;22(2):93-103. doi:10.1108/IJHG-11-2016-0048

17. Lawson K. The four pillars of health coaching: preserving the heart of a movement. 

. 2013;2(3):6-8. doi:10.7453/gahmj.2013.038

18. Smith AM, Zallman L, Betts K, et al. Implementing an electronic system to screen and actively refer to community based agencies for food insecurity in primary care. 

. Published online October 12, 2019. doi:10.1016/j.hjdsi.2019.100385

Articles

To support effective care management programs in the context of value-based care, we propose a framework categorizing care management as disease management, utilization management, and care navigation interventions.

What We Talk About When We Talk About Care Management

The American Journal of Accountable Care. 2020;8(1):3-5

Over the past decade, accountable care organizations (ACOs) have expanded across payers, including Medicare, Medicaid, and commercial insurers. Participation among provider organizations has also increased over time, to the point where companies have emerged with the sole purpose of helping health systems succeed in prominent ACOs, such as the Medicare Shared Savings Program.

Although ACOs all possess a set of core features that define the payment model, differences across payer types and patient populations mean that no single strategy will be effective for all types of ACOs. Provider organizations would be best served considering and participating in ACOs after they have considered these differences and their implications for care delivery initiatives that can support ACO success. In this article, we leverage our perspective as physician leaders in an integrated delivery system that participates in multiple ACOs and discuss 3 factors—clinical complexity of ACO populations, social complexity of ACO populations, and ACO network configuration&mdash;to advance the discourse about payment model strategy and performance.

Certain ACO strategies are more or less likely to be successful based on the clinical complexity of the covered population. For instance, approximately two-thirds of Medicare beneficiaries have at least 2 chronic conditions,

 whereas the majority of beneficiaries in many commercial patient populations have no chronic medical diagnoses. This difference poses significant implications for how an ACO participant organization might approach 2 crucial types of activities: assessing patients’ clinical risk (ie, risk capture) and using risk information to implement care management services (ie, risk-stratified care management).

For Medicare beneficiaries, many high-cost conditions as defined by CMS’ Hierarchical Condition Categories (HCC) model are chronic and will remain stable year over year. As a result, providers can implement risk-capture activities within Medicare ACOs via approaches such as clinical decision support within the electronic health record to populate past diagnoses and prompt clinicians to affirm or update them.

In contrast, the lower chronic disease burden among beneficiaries in most commercial insurance plans undercuts the potential benefit of such approaches in commercial ACOs. In particular, the high-cost conditions for these patients, as defined by the HHS HCC model, will be acute (eg, trauma) and therefore less likely to be stable over time (ie, less likely to be relevant in even the next calendar year). Therefore, using clinical decision support prompts based on historical diagnoses can create more “noise” than “signal,” thus causing workflow inefficiencies for clinicians.

This logic also carries implications for risk-stratified care management within ACOs and, in particular, activities directed at high-cost patients. Differences in the clinical complexity of different ACO populations exacerbate a well-established issue with implementing risk-stratified care management: that there is inherent instability in high-cost populations, with individuals cycling in and out of these populations year to year.

Given this dynamic, care management interventions will adopt a different form within Medicare populations (eg, heavy focus on self-management of multiple chronic conditions) versus commercial populations (eg, preventive services to avoid catastrophic events such as motor vehicle accidents). Without these distinctions, provider organizations in ACOs may not achieve anticipated benefits from care management programs because of variation across different ACO populations.

Social factors play an outsized role in determining many health outcomes, serving as a larger driver than even healthcare factors.

 In turn, the extent of an ACO population’s social complexity carries major implications for provider organizations’ payment model strategy and performance.

For instance, because of their predictable coverage over an extended period, Medicare fee-for-service populations tend to be relatively stable in composition. In turn, condition-based care management can be used within Medicare ACOs to deliver guideline-concordant services that not only improve outcomes but also address utilization and costs as core ACO metrics. In contrast, this approach may be less effective in populations who are less stable over time and/or marked by higher social complexity, such as those in Medicaid. Many Medicaid beneficiaries have high costs as a result of social determinants (eg, housing and/or food insecurity) rather than or in addition to clinical determinants (eg, suboptimal medication titration). Delivering clinical interventions aimed at disease management fails to address nonhealthcare root causes of utilization and costs.

The extent of a population’s social complexity may determine how patients access care and, in turn, how organizations implement interventions to perform in ACOs. For example, comprehensive primary care—which focuses on activities such as chronic and acute care management, care coordination, and care continuity&mdash;has been cited as a critical strategy to cost containment within ACOs.

 However, the value of this approach is based on the assumption that patients’ primary healthcare access point is via a comprehensive and often brick-and-mortar primary care infrastructure. Although this may be true for certain groups, such as older Medicare populations, it may not be for others.

For instance, it may not be the norm for employed, less socially complex patients in commercial populations to access care in this way, raising the potential for organizations in commercial ACOs to better meet patient needs via technological approaches such as telemedicine, which can expand access while circumventing some costs of physical locations altogether. As another example, populations with high social complexity frequently access nonemergent care via the emergency department. Consequently, ACOs may be better off investing in fast-track triage and colocated urgent care and primary care facilities rather than comprehensive primary care or telemedicine infrastructure.

Just as ACO strategy should be dictated in part by differences in patients’ clinical and social complexity, it will also likely differ as a result of which providers are delivering care to patients (ie, the ACO network configuration). One size does not fit all because of differences in the networks that are needed in different ACOs.

For instance, network configuration can influence provider organizations’ approaches to managing clinical referrals among different types of clinicians—a core ACO strategy for maintaining a stable, engaged patient population and addressing their utilization, quality, and costs.

 For Medicare patients with multiple chronic conditions, ACOs may require strong networks of primary and subspecialty care within which patients can be referred and comanaged.

In contrast, for commercial populations with less need for tight primary-subspecialty coordination, it may be more strategic for ACO networks to ensure inclusion of providers who are delivering occupation-aligned services (eg, physical therapy providers for labor workers, on-site services that can be accessed at the site of work). Given the extent to which some Medicaid patients receive care from emergency departments, organizations interested in Medicaid ACOs would be well served by carefully considering whether and how to include freestanding and hospital-related sites within their networks.

Differences in network configuration can also affect organizational care standardization activities within ACOs. As methods for reducing unwarranted care variation, clinical pathways can be a promising strategy to implement within ACOs.

 However, whereas ACOs with networks emphasizing primary care providers may elect to develop and implement pathways around prevalent chronic conditions encountered in primary care (eg, diabetes, hypertension), ACOs with networks encompassing quaternary care sites may opt to instead prioritize subspecialty pathways.

Although several core features define ACOs as a payment model, several differences (eg, clinical and social complexity of patient populations, ACO network configuration) highlight why specific strategies may have variable effectiveness in different types of ACOs. Provider organizations would be well served by accounting for these issues as they consider ACO participation.

Department of Medicine, University of Washington School of Medicine (LMM, JML), Seattle, WA; Value and System Science Lab (LMM, JML), Seattle, WA; Leonard Davis Institute of Health Economics, University of Pennsylvania (JML), Philadelphia, PA.

Concept and design (LMM, JML); drafting of the manuscript (LMM); critical revision of the manuscript for important intellectual content (LMM, JML); administrative, technical, or logistic support (LMM); and supervision (JML).

Leah M. Marcotte, MD, Department of Medicine, University of Washington School of Medicine, 1107 NE 45th St, Ste 355, Seattle, WA 98105. Email: leahmar@uw.edu.

1. Powers BW, Mostashari F, Maxson E, Lynch K, Navathe AS. Engaging small independent practices in value-based payment: building Aledade’s Medicare ACOs. 

. 2018;6(1):79-87. doi: 10.1016/j.hjdsi.2017.06.003.

2. Multiple chronic conditions. CMS website. cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Chronic-Conditions/MCC_Main. Updated April 5, 2019. Accessed November 24, 2019.

3. Marcotte LM, Reddy A, Liao J. Addressing avoidable healthcare costs: time to cool off on hotspotting in primary care? 

. 2019;34(11):2634-2636. doi: 10.1007/s11606-019-05285-z.

4. Schroeder SA. We can do better — improving the health of the American people. 

. 2007;357(12):1221-1228. doi: 10.1056/NEJMsa073350.

5. McClellan M, McKethan AN, Lewis JL, Roski J, Fisher ES. A national strategy to put accountable care into practice. 

. 2010;29(5):982-990. doi: 10.1377/hlthaff.2010.0194.

6. Song Z, Sequist TD, Barnett ML. Patient referrals: a linchpin for increasing the value of care. 

. 2014;312(6):597-598. doi: 10.1001/jama.2014.7878.

7. Marcotte LM, Chen A, Hwang CS, Liao JM. Primary care—oriented pathways to support population-based payment models. 

. 2019;5(7):39-42. doi: 10.25270/jcp.2019.09.00092.

Differences in patients’ clinical and social complexity and accountable care organization (ACO) network configuration highlight why specific strategies may have variable effectiveness in different types of ACOs.

Author | Leah M. Marcotte, MD

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