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The American Journal of Managed Care May 2016
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Implementing a Hybrid Approach to Select Patients for Care Management: Variations Across Practices
Christine Vogeli, PhD; Jenna Spirt, MPH; Richard Brand, PhD; John Hsu, MD, MPH; Namita Mohta, MD; Clemens Hong, MD, MPH; Eric Weil, MD; and Timothy G. Ferris, MD, MPH
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Implementing a Hybrid Approach to Select Patients for Care Management: Variations Across Practices

Christine Vogeli, PhD; Jenna Spirt, MPH; Richard Brand, PhD; John Hsu, MD, MPH; Namita Mohta, MD; Clemens Hong, MD, MPH; Eric Weil, MD; and Timothy G. Ferris, MD, MPH
Hybrid approaches allow for clinician input into case finding for care management, but training and monitoring is required to protect against unintentional biases.


Objectives: Appropriate selection of patients is key to the success of care management programs (CMPs). Hybrid patient selection approaches, in which large data assets are culled to develop a list of patients for more targeted clinical review, are increasingly common. We sought to describe the patient and practice characteristics associated with high-risk patient identification and selection for a CMP during clinical review, and to explore variation across primary care practices.

Study Design: Retrospective cohort study.

Methods: Standardized estimates of Medicare beneficiaries identified as high risk for poor outcomes and high medical expense, and appropriate for a CMP within a large Pioneer Accountable Care Organization, were developed using mixed effects logistic models. Study subjects were 2685 Medicare beneficiaries aged over 18 (includes individuals eligible for Medicare due to a disability) aligned to 35 primary care practices in 2013.

Results: Independent predictors of patient identification as high risk include older age; higher risk score; recent increases in medical conditions; higher numbers of medical hospitalizations, skilled nursing facility days, and primary care physician visits; and shorter relationships with the primary care physician. Older age, and lower income, but no prior hospice use were independently associated with patient selection for a CMP among the subset of patients identified as being high risk. Adjusted predicted percents of high-risk patients varied significantly across practices overall and for 5 of the 6 patient characteristics that were independently associated with identification as high risk.

Conclusions: Inconsistency in high-risk patient identification and selection for a CMP may reflect differences in practice resources, but also highlight the need for continual training and feedback in order to protect against unintentional biases.

Am J Manag Care. 2016;22(5):358-365

Take-Away Points
Clinician review within hybrid case selection approaches includes decisions about which patients are high risk and, among high-risk patients, identification of the subset of patients most appropriate for a care management program (CMP). 
  • Primary care physicians were more likely to recommend high-risk patients who are poorer, older, and not using hospice services for CMP. 
  • Significant variations in adjusted proportions of patients selected for care management across practices may reflect differences in practice resources, but may also introduce bias into the process.
  • Continual training and feedback to practices on their review decisions may help identify missed patient opportunities.
The observation that health expenditures are concentrated within a relatively small subset of the population is well known by now, with 5% of the population accounting for 50% of medical expense in any given year. One-third of the individuals in this group—frequently those with poor health status and multiple chronic conditions—remain high-cost the following year,1,2 making them appropriate for primary care–based care management approaches.3,4 However, although effective care management targeted at the right individuals has been shown to moderate costs and utilization,5,6 inconsistent results have highlighted the need to appropriately select patients.7

There are a host of available case-finding tools and algorithms that can be used to identify individuals appropriate for care management; nevertheless, there is also increasing understanding that these tools have limitations.8,9 Many case-finding tools are proprietary “black boxes” that create a single score to rank patients according to risk.10 Additionally, these tools can vary considerably in terms of their input criteria, and there is no standardization of definitions across tools8—most importantly, the input criteria cannot be modified to meet specific program characteristics and operational realities.

Data timing and completeness are a challenge for all case-finding algorithms, especially those based on paid claims. Delays in data capture can result in the identification of patients who are no longer enrolled within the health plan or practice, as well as identification of patients who no longer require intervention.8,11,12 Incomplete data, such as removal of substance use, HIV/AIDS, and in some states, mental health claims,13 paint incomplete pictures of patient needs. In addition, demographic and psychosocial characteristics are often incomplete or missing from readily available administrative data (eg, poverty, education, living situation). The absence of patient psychosocial data is particularly challenging, as these data could help to identify cohorts of patients who would be most likely helped by care management programs (CMPs)5,14; for example, addressing cost-related medication underuse among low-income patients may improve diabetes control and reduce inappropriate emergency department utilization.15 Although many healthcare systems do collect psychosocial data on patients, system alignment issues often preclude use of more granular and descriptive data for case finding.

Because of these limitations, case finding based on administrative data is typically not the only method used.16 Patient health-risk assessments and clinician referrals are techniques frequently used to identify patients for case management and can provide more detailed and actionable data, but they require time and resources to implement.3 Often, multiple case-finding methodologies, including predicted risk scores, utilization triggers/markers, medication adherence and gaps in care, and clinician referrals, are used in concert.11,14

Case-finding approaches that rely on multiple methodologies are typically termed “hybrid approaches” and can vary widely, but generally refer to the use of multiple methodologies—often in sequence—to refine case finding. Within this evaluation, we studied a sequential approach to case finding that uses an algorithm to comb through administrative claims and identify a preliminary set of potentially high-risk patients. This preliminary set of patients is then reviewed by physicians and nurse care managers who make the final decision of whether to refer a patient for care management. This sequential hybrid approach is thought to be reliable3 and can offer a number of advantages.

Clinician referral, within the boundaries of a pre-selected list, focuses the referral process and reduces the burden on clinicians, while at the same time ensuring primary care practice and physician (PCP) involvement. This involvement may increase buy-in of the program, which is especially important for programs embedded within physician practices. Clinician input can also help to identify patients with more modifiable health risks, including those with sub-optimal health literacy and health insight, social and home environment challenges, and/or inadequate coping skills and financial resources.11,17,18 For example, primary care physicians are more likely to describe patients with poorly controlled diabetes, inadequate insurance, or with mental health or substance use disorders, as complex.19 

However, clinician referral may also introduce unanticipated variations in patient selection.8 Often, there are no specific criteria that physicians are asked to consider when identifying appropriate patients, leaving the door open to unintentional biases.14 Appropriate clinician referral into CMPs requires that clinicians have a clear understanding of the characteristics that place patients at higher risk for poor health outcomes, as well as the benefits the CMP could provide.

In this study, we sought to understand the unique patient characteristics associated with both patient identification as high risk for poor outcomes and subsequent clinician referral to care management. We also sought to describe variations in patient identification and selection for care management across PCPs overall, and for important patient characteristics. We examined the selection decisions during the second year of the CMP to avoid biases associated with the ramp-up of the program.


The Partners Healthcare Pioneer Accountable Care Organization (ACO)—1 of 4 Pioneer ACOs operating in eastern Massachusetts—is among the largest nationwide, and the only Pioneer ACO that exclusively aligns patients to primary care physicians. One of the signature initiatives within the Partners Pioneer ACO is the CMP. The CMP is a longitudinal program that relies on nurse care managers working in conjunction with primary care physicians to improve care quality and control healthcare utilization for referred patients. After a successful trial within the Medicare High Cost Beneficiary Demonstration Program,20 the CMP was implemented within all Partners Healthcare PCPs under the Medicare Pioneer ACO contract, starting in January 2012. Partners Healthcare uses a hybrid approach for patient selection, combining a claims-based algorithm with clinician review to identify high-risk patients who could benefit from the CMP.


The population for this study included traditional Medicare beneficiaries within the Partners Healthcare Pioneer ACO who were identified using a claims-based algorithm as potentially high risk for poor outcomes in the second year of the program (2013). By the second year, all practices had experience performing the clinical review and had 1 or more ACO patients who were actively engaged in CMP. In total, 7651 (12%) of 63,629 Medicare patients were identified by the Partners algorithm. Among individuals with a predictive risk score for future total medical expense via Optum ImpactPro software version 6.0 (Optum, Inc, Eden Prairie, Minnesota) that exceeds a minimum threshold, the Partners algorithm uses combinations of chronic conditions and patterns of healthcare utilization to identify individuals potentially at high risk and appropriate for subsequent clinical review. The Partners algorithm was developed with extensive input from an advisory committee of operational and clinical experts within the organization. During the clinical review process, PCPs receive detailed information on chronic conditions, utilization patterns, and predictive scores that triggered identification by the algorithm. PCPs had considerable latitude when selecting patients for care management, but were provided additional guidance through reference guides and regional medical directors who received orientation and training on the list review process.

In order to study clinician review decisions during the second year, this analysis excluded 2605 patients who had been identified and reviewed in the prior year, as well as 1267 patients who had moved, died before the review process, were not community-dwelling (eg, residing in long-term care), switched to a Medicare Advantage plan, or did not have a relationship with a Partners primary care clinician. Lists of the remaining 3779 patients (5.9% of 63,629 total Medicare ACO patients) were distributed to practice management organizations and, eventually, to PCPs and clinicians for final identification of individuals appropriate for the primary care–based CMP. We then further excluded 788 patients aligned to 44 PCPs that did not make differential review decisions (eg, identifying none or all patients as high risk for poor outcomes or requiring care management), as well as 306 patients who were not classified by the practice prior to the release of the 2014 list. Thus, the final study population consisted of 2685 patients who received primary care within 35 practices.


The dependent variables are the decisions made by primary care clinicians and their practices regarding whether patients were identified as high risk for poor outcomes and whether patients could benefit from CMP. 

The independent variables are listed in Table 1 and include patient demographic characteristics, area-level poverty (American Community Survey 2012 5-Year Estimates21), prospective risk score and changes in the number of conditions from calendar year 2012 to the year directly preceding the first list review (May 1, 2012-April 30, 2013) (CMS-Hierarchical Condition Categories Risk Adjustment Model22), healthcare utilization (ED visits, medical hospitalizations, skilled nursing and hospice service use) in the 12 months prior, and characteristics of the patient’s relationship with their PCP. Practice characteristics include practice size, level of affiliation with the healthcare organization, whether the practice is a community healthcare center, years of experience with care management, CMP capacity, and level of practice readiness for patient-centered medical home transformation. Models were also adjusted for primary care physician characteristics, including primary care physician age, full-/part-time status, and duration of employment within the healthcare organization.

Data Analysis

Descriptive analyses of the patient- and practice-level characteristics associated with patient identification as high risk for poor outcomes and selection for care management were explored using bivariate analyses. Standardized estimates of patient identification as high risk and selection for CMP were developed using logistic regression models with robust standard errors clustering at the practice. For regression models, we used the subset of explanatory variables that were independently predictive of either patient identification as high risk for poor outcomes or selection for care management at P <.20, and used a similar approach to develop standardized estimates at the practice level.

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