We aimed to examine how interventions addressing social determinants of health (SDH) have been adopted in the context of Medicaid managed care organizations (MMCOs), which serve a large proportion of patients with social and economic barriers to good health. 

We designed a systematic literature review to examine how SDH interventions have been adopted in MMCOs.

The review included published articles from PubMed, Scopus, and Business Source databases, as well as review articles published in the gray literature and articles recommended by the study’s National Advisory Committee to identify interventions describing how MMCOs have invested in interventions that address patients’ SDH. To be included in the review, an article had to describe an intervention that was based in the United States, be supported financially by an MMCO, focus on at least 1 SDH, and be integrated into clinical care delivery. 

Twenty-five programs were identified in either commercial Medicaid or Medicaid-only MCOs that involved interventions integrated into clinical care and related to SDH. Interventions varied widely in terms of target populations and target SDH, and rarely included rigorous evaluations. The majority of programs described “case management services” that did not clearly distinguish between the delivery of medical and social interventions. 

Despite a growing interest in clinical interventions that address SDH, little information is available in the published literature about the extent to which these interventions have been adopted by MMCOs, where they are likely to have early traction based both on capitated funding structures and the low-income populations served. 

	Medicaid managed care organizations (MMCOs) have increasing incentives to support interventions addressing the social needs of the low-income patients they serve. 

		MMCO investments are focused on social needs interventions for high healthcare utilizers and on members anticipated to become high utilizers based on specific health conditions like hypertension and diabetes. 

		Few studies were identified to indicate that MMCOs are making organizational commitments to social screening or social interventions. 

		More information is needed on MMCOs’ organizational decision making around nonmedical health interventions, the funding streams supporting these interventions, and their impacts on health outcomes and health services use.

The World Health Organization’s (WHO) Commission on the Social Determinants of Health has defined social determinants of health (SDH) as “the conditions in which people are born, grow, live, work, and age. These circumstances are shaped by the distribution of money, power, and resources at global, national, and local levels.”

 These conditions include social, economic, and environmental factors, such as income, education, housing, employment, transportation, and the physical layout of neighborhoods.

There is increasing evidence that SDH play a major role in the onset and progression of disease. Addressing SDH may help achieve the healthcare “Triple Aim”—improved healthcare quality for individuals and populations, and decreased healthcare costs.

 Despite this, much of the field of social epidemiology—including that related to healthcare disparities&mdash;focuses on documenting the effects of SDH rather than on ways to change or intervene on exposures.

Existing social interventions integrated into clinical settings—which range from interventions offering housing to homeless patients to on-site food pantries and legal services clinics&mdash;can expand the traditional bounds of healthcare to address “upstream” determinants of health, including social, behavioral, and environmental conditions.

 Although the field of interventions related to nonmedical health determinants is growing in clinical settings around the country,

 little is known about the operational and design characteristics that define them—particularly in the context of differing payer and provider environments. Understanding how SDH interventions are designed, implemented, funded, and scaled within distinct payer environments is key to translating the growing interest around the role and replication of SDH interventions in healthcare settings into a substantive, actionable strategy.  

Based on distinct membership characteristics and financial incentives defining Medicaid managed care organizations (MMCOs), these payers may be particularly well-suited to support integrated SDH approaches.

 The populations served by MMCOs are disproportionately affected by poverty and associated material deprivation, including food and housing insecurity, poor habitability, unsafe drinking water, social exclusion, low education levels, and unemployment. These conditions are known to reduce opportunities, limit choices, and threaten health.

 Additionally, over the last 15 years, there has been a nationwide increase in patients enrolled in MMCOs,

 which already enroll about half of all Medicaid beneficiaries.

 This combination of population needs and the shift toward risk-based care together encourage upstream intervention and prevention as one potential way to limit costly healthcare utilization.

Despite the apparent alignment of these structural characteristics with low-income members’ unmet social needs, there are multiple challenges limiting MMCOs from expanding social services.

 New prevention services are not easily incorporated into MMCO-state capitation agreements, so MMCOs have to cover any additional benefits out of administrative or community benefit dollars. Coding practices and other administrative requirements for MMCOs can also make it difficult to adopt new prevention services. Furthermore, any financial return related to social service investments may take many years to realize, which can decrease the financial feasibility of adoption. Finally, MMCO care delivery models, financing contracts, and organizational structures (which may span several states) can make community collaborations and public partnerships—often critical to a comprehensive approach to social service delivery&mdash;more challenging.

A Systematic Review of Clinical Interventions Addressing Nonmedical Health Determinants

Reviews completed to-date on clinical SDH interventions have offered an important glimpse into the range of potential interventions and funding mechanisms in this emerging field, but have not answered key questions about the implementation of these interventions, their financial drivers, and other characteristics specific to the context of MMCOs.

 We conducted a systematic review to identify published literature on clinical SDH interventions supported by MMCOs, the design and integration of these programs into healthcare delivery systems, and the determinants addressed and the target populations served by these programs. Our aim was to inform health policy decision makers around incentivizing these programs for broader dissemination.

Based on previous research in the area of clinical SDH interventions, models of organizational readiness for change,

 and an assessment of existing MMCO structural incentives and barriers to incorporating these interventions into routine activities, we developed a theoretical model outlining the range of ways that MMCOs might engage in activities related to SDH (

). We hypothesized that most MMCOs would be making low-SDH investments, given the relatively recent increase in SDH research.

We conducted a literature review examining how commercial and Medicaid-only MMCOs invest in interventions that address SDH for their patients or their network clinics’ patients. To be included in our review, an intervention or program had to meet our inclusion criteria: a) based in the United States, b) financially supported by an MMCO, c) address at least 1 social determinant of health (housing, employment, food, education, safety, legal services, or transportation), and d) be integrated into the healthcare services delivery system. The definition of SDH varies in different contexts, so we focused our search on SDH not typically addressed within the current healthcare delivery system. As a result, we excluded papers describing interventions related to health behaviors, including behaviors like tobacco use and physical activity. We also excluded papers describing interventions exclusively related to healthcare access. Clinical integration meant that a patient’s social need was identified within the clinical setting and then either referred to an external intervention program or to a social intervention conducted in the clinical setting.

We developed an electronic search strategy to scan for references in the following databases: PubMed, Scopus, and Business Source. We limited our search to publications dated 2000 to 2014 and we combined search terms using “AND” to capture at least 1 term from each of 3 major categories: SDH, healthcare settings, and intervention studies (see eAppendix for more detail [

]). Funding source was added as an element of the detailed data extraction process on references meeting other review criteria. References were also collected from national experts in the field, including the study’s national advisory group.

Our review methods included a hierarchical exclusion process. Titles and abstracts of references collected from the electronic search strategy and the national study advisory group were assessed initially based on whether or not they described an SDH program or other intervention. Where the title and abstract were insufficient to deduce if they referred to an intervention, the full-text article was reviewed. Those references that referred to SDH but did not describe an intervention were excluded; in other words, articles were excluded if they only described theories of SDH or studies on risk factors and disparities without describing any specific intervention or program addressing those risk factors or disparities.

Remaining references were excluded if they did not describe an intervention or program addressing at least 1 SDH. For example, references were excluded if interventions or programs focused exclusively on medical care and services, such as treatments, immunizations, or health behaviors; described quality improvement interventions without an SDH component, such as guidelines intended to improve clinical care coordination that did not address social needs; or exclusively described healthcare access interventions or outreach programs, such as mobile health services or Medicaid enrollment programs.

References describing an intervention addressing SDH underwent review of the full-text article to determine the degree of clinical integration and financial support from an MMCO. Those interventions or programs without any description of clinical integration or MMCO support were excluded from the final data set. For instance, interventions and programs that were community-based and had no integration with clinic-related work flows related to screening, intervention, referral, or tracking, were excluded. Two investigators reviewed each article included to determine if it met all inclusion criteria. A third investigator reviewed any articles where the reviewing investigators were in disagreement; in these cases, final decisions were made about inclusion after discussion between all reviewers. Interventions described in more than 1 reference were only counted once.

For those interventions and programs meeting inclusion criteria, we collected a detailed set of program data, including intervention name, organization, name/state of MMCO, HHS region, clinic setting, program start date, description of the intervention or program, target population, target SDH, level of intervention/prevention (primary, secondary, tertiary), model of intervention (social and/or medical approaches), level of clinical integration (extent to which clinical providers identify/address social need), study citation, study design, study findings, and an assessment of evidence quality based on the Community Guide to Preventive Services evidence rating guide (high, medium, low quality).

The electronic search strategy yielded 3975 unique references from PubMed, Scopus, and Business Source (

). References from our national experts included, but were not limited to, reports from the Association for Community Affiliated Health Plans, Alliance of Community Health Plans, Medicaid Health Plans of America, Manatt Health Solutions, and the Institute for Alternative Futures.

A total of 111 articles were identified that included SDH interventions. These 111 articles were screened to determine whether they described interventions integrated into healthcare delivery systems and whether they were funded by MMCOs. Although many described serving Medicaid populations, only 13 articles described Medicaid managed care—supported interventions addressing SDH integrated within a clinical setting. The final data set included 25 interventions and programs described within those 13 articles.

 Seven references described 1 primary SDH-focused intervention or program

 and 6 references described more than 1 intervention.

Targeted Populations, Targeted Social Determinants, and Intervention Models

Target populations included specific demographically defined groups—primarily low-income individuals, children, or families. Other population groups included seniors, minority groups (racial, ethnic), those experiencing homelessness, and those who were broadly eligible for Medicaid. Some interventions defined target populations based on healthcare utilization patterns (eg, high-cost, high-utilizer), while others focused on patients with specific health conditions (eg, asthma, hypertension, diabetes and other dietary-related chronic health issues, HIV, multiple sclerosis, mental illness).

Seven interventions specifically targeted a single social issue, such as housing (4),

 The remaining 18 interventions provided more comprehensive services to address multiple SDH. Interventions varied widely in how they addressed SDH within the clinical setting. Specific intervention components included variations on team-based approaches, including case managers, social workers, community health workers, and other nonprofessional staff integrated into clinical teams—although the degree of integration and communication with other clinical staff was rarely described. Intervention settings also differed across programs, including both training and education delivered on site, referrals to off-site programs, and home visits.

The majority of the programs identified were designed to address both social and medical needs of the patients being served. For example, asthma programs typically included elements focused on pharmacological management and specific housing risk factor reduction.

 Other case management programs were designed to facilitate pharmacy and appointment access in addition to social service linkages.

Eleven program descriptions included some empirical evaluation indicating effects of the intervention on health system outcomes. Five program evaluations reported reductions in emergency department (ED) visits

 The impact of 1 home-based intervention targeting high-risk asthma patients on ED use and hospital admissions reported mixed results. Although the initial evaluation of enrollees’ pre- and post-utilization patterns showed significant reductions in hospital admissions and ED visits, a subsequent, more rigorous evaluation comparing an intervention group with an untreated control group found no overall differences in utilization patterns between the 2 groups.

 Several programs reported cost savings associated with changes in enrollee utilization patterns.

 Three studies described higher levels of patient satisfaction as a result of social intervention programs,

 and 1 study reported an increase in quality of care.

 The majority of program descriptions included no outcomes data or other return-on-investment information. The eAppendix includes a complete list of interventions and programs included in this review (eAppendix Table 1).

This study provides an overview of ways in which MMCOs help address patients’ nonmedical needs by using healthcare services as a venue for social needs screening and related social needs interventions. The 25 programs captured are geographically dispersed across the United States and across multiple MMCOs. The strong majority of programs identified target high healthcare—utilizing patients with specific chronic health conditions. Within target disease groups, programs frequently focus on specific racial or ethnic groups and low-income, homeless, or other specific sociodemographically defined populations. The existing literature provides no empirical data showing that MMCOs engage in universal social screening, needs assessments, or resource mobilization to address the social needs of all members. Based on our theoretical model, these findings suggest that MMCOs are making low, or at most, low to moderate investments in SDH interventions and are not yet systematically engaged in comprehensive SDH strategies to improve health or change healthcare utilization patterns of enrolled patients.

This review differs from previous reports examining SDH interventions in 2 important ways. First, it focuses exclusively on MMCOs because these organizations are relatively well-positioned and incentivized to address the social needs of their patient populations to improve healthcare outcomes and service utilization. Although we found many interventions and programs addressing SDH that serve Medicaid patients, this review focuses on the few that are financed or directly supported by MMCOs. Second, the review focuses on programs that have at least some degree of clinical integration, meaning that individual patients are being screened for social needs and connected with relevant services based on being patients in a clinical care delivery system. Understanding the degree to which SDH programs are clinically integrated is key for MMCOs, which must decide whether spending on SDH interventions will be linked to a plan’s patient care and quality improvement activities or to its community benefit activities. Previous reports describing and examining innovations in addressing SDH have either not been specific to MMCOs or have included both clinical and community-level interventions.

The 25 program descriptions we identified provide little detail on key program characteristics or MMCO decision-making processes that could help establish and disseminate best practices, such as the role of internal or external financial or other drivers or barriers to undertaking these interventions; any relevant community needs assessments on which interventions are based; or the role of executive sponsors, project owners, and key stakeholders in shaping the interventions. Similarly absent are descriptions of the return-on-investment calculations required to sustain these types of programs. This lack of information on organizational decision making, “readiness” assessments, and management processes, combined with the lack of rigorous evaluation of the impacts that these types of interventions have on health outcomes or health services, limits the capacity to understand and disseminate best practices in SDH-related interventions among MMCOs. 

Our review revealed several reports of case management programs for high-risk patient populations that include both social and medical components. This blended approach of social and medical case management may be an important target for scaling nonmedical health interventions within MMCOs. A recent report from John Snow, Inc (JSI) suggests that these services elicit a better response from clients if initiated at the provider level rather than at the payer level, which could incentivize MMCOs to fund provider-delivered programs.

 Both the JSI report and a related Commonwealth Fund issue brief authored by the Center for Health Care Strategies further suggest that the absence of assured flexible use of Medicaid managed care capitation rates may prevent MMCOs from transitioning from traditional case management of medical services to case management that includes behavioral and social needs coordination.

Ensuring flexible funding for managed care capitation rates could improve MMCO case management programs that address a combination of patient medical and social needs. This funding may be accessible via the Affordable Care Act’s Health Homes program, which does require both comprehensive care management and increased referrals to community and social support services

; the Health Homes program already supports intensive case management activities in 19 states,

 though only 1 was identified in this systematic review.

References meeting our inclusion criteria show wide variation in associated key words and terms, which made it difficult to develop a comprehensive, practical electronic search strategy. The WHO definition of SDH is very broad, and the process of translating that broad concept into meaningful search terms that capture specific social determinants interventions is complex. Furthermore, some MMCOs may choose not to publish information regarding successful programs in order to maintain advantage in a competitive marketplace. To minimize the challenges inherent to this search strategy, we supplemented the electronic search using references provided by national experts. Nevertheless, there could be programs that meet our inclusion criteria that were not identified by these methods.

Our methods included a hierarchical exclusion process: references were initially assessed based on whether or not they described an SDH program or other intervention. Those that referred to SDH but did not describe an intervention were excluded. Remaining articles were then reviewed to determine whether there was some degree of clinical integration for the intervention. In the 25 programs captured, there was a considerable range in the extent to which clinical integration was described. For example, one program simply said that a social worker was added to care teams (article not included), while another program more clearly described the integration, including information about how the social worker addressed patients’ social needs (article included). It is possible that some programs were clinically integrated, but the integration was insufficiently described in the reference article to justify inclusion. Future efforts should supplement available information via key informant interviews, organizations’ annual reports, or other data sources. The lack of existing information may negatively influence dissemination or quality improvement efforts.

Additionally, many references in the original search did not include a description of the funding mechanism for the program. In other cases, funding was from a source other than Medicaid managed care entities. Interventions that met the other inclusion criteria but were not clearly funded by MMCOs are listed in eAppendix Table 2. To improve scaling and dissemination efforts, journals may consider requiring reporting of funding mechanisms.

This review is an important first step toward understanding how MMCOs are making investments in clinical nonmedical health determinants. Although risk-based capitated payment systems serving low-income populations provide incentives for incorporating models to address SDH, real and perceived local, state, and federal barriers can dis-incentivize adoption. In the context of increasing federal and state funding experimentation supporting Medicaid investments in SDH, MMCOs hoping to invest in these interventions will require detailed implementation, operations, scaling, and sustainability descriptions from other programs that have begun to make these investments.

The authors wish to thank Stephanie Chernitskiy for her editing and assistance with the paper’s figures and Beena Patel for her assistance in the literature review. Support for this study was provided by the Commonwealth Fund.

Articles

REVIEW ARTICLE

This review examines the breadth of published work on interventions addressing nonmedical determinants of health that are supported by Medicaid managed care organizations.

Clinical Interventions Addressing Nonmedical Health Determinants in Medicaid Managed Care

 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. 

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.

 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. 

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.

	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&mdash;remain high-cost the following year,

 making them appropriate for primary care—based care management approaches

 However, although effective care management targeted at the right individuals has been shown to moderate costs and utilization,

 inconsistent results have highlighted the need to appropriately select patients.

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.

 Many case-finding tools are proprietary “black boxes” that create a single score to rank patients according to risk.

 Additionally, these tools can vary considerably in terms of their input criteria, and there is no standardization of definitions across tools

—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.

 Incomplete data, such as removal of substance use, HIV/AIDS, and in some states, mental health claims,

 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)

; for example, addressing cost-related medication underuse among low-income patients may improve diabetes control and reduce inappropriate emergency department utilization.

 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.

 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.

 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.

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&mdash;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 reliable

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.

 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.

However, clinician referral may also introduce unanticipated variations in patient selection.

 Often, there are no specific criteria that physicians are asked to consider when identifying appropriate patients, leaving the door open to unintentional biases.

 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&mdash;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,

 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. 

 and include patient demographic characteristics, area-level poverty (American Community Survey 2012 5-Year Estimates

), 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 Model

), 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.

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 

 <.20, and used a similar approach to develop standardized estimates at the practice level.

We explored the extent to which practices consistently identified patients with specific characteristics as high risk for poor outcomes. Specifically, we examined 6 patient characteristics that were independently associated with being considered high risk for poor outcomes and that were present in at least 10% of patients in our sample; similar models were used for each, as described above, to develop standardized practice-level estimates of identification as high risk among patients with and without the characteristic. Full interaction models did not enhance predictive ability and decreased the number of practices with stable estimates; therefore, we only included interactions between PCP and the characteristic of interest in our final models.

Global tests were used to test for differences in overall practice marginal estimates and interaction marginal estimates. Chi-square statistics were used to describe the relationship between overall practice marginal estimates and practice estimates for individuals with specific characteristics.

The study was approved by the Partners Institutional Review Board.

Among Medicare ACO patients identified by the claims-based algorithm and who also passed an administrative review (roughly 5.9% of total Medicare ACO patients), 86.3% were thought to be high risk for poor outcomes by their primary care providers; 77.2% of these patients were referred to a nurse-led CMP within the PCP.

A number of patient characteristics were independently associated with patient identification as high risk during the clinician review. Specifically, independent predictors of high risk include advanced age, higher prospective risk score, a recent increase in diagnosed conditions, and higher numbers of medical hospitalizations and days in skilled nursing facility (SNF) care in the prior year. Patients identified as high risk also were more likely to have more visits with their PCP in the prior 12 months, but shorter relationships with their PCP than patients not identified as high risk. The patient characteristics with the strongest independent association with being identified as high risk include age 85 years or older (93.1%), 2 or more hospitalizations in the prior year (91.5%), and 30 or more SNF days during the prior year (92.7%) (Table 1).

Among high-risk patients, advanced age was also an independent predictor of patient referral to a CMP. However, when determining whether high-risk patients would benefit from care management, clinicians were also more likely to select individuals without prior hospice use and those living in lower-income areas. The adjusted proportions of high-risk patients 85 years or older selected for care management was 80.1%, and between 81.2% and 81.9% among patients residing in areas with more than 10% of individuals living in poverty (Table 1).

Lists of patients identified by the claims-based algorithm were reviewed within 35 practices where patients received their primary care. On average, practices reviewed 76 algorithm-identified Medicare ACO beneficiaries (range = 5-248) in 2013. Adjusted proportions of patients identified as high risk and referred to a CMP varied according to practice characteristics. Specifically, after adjusting for patient characteristics and other practice characteristics, practices with 6 or more physicians (88.8%), non—community health centers (87.3%), and practices without the capacity to provide care management to all patients (93.5%) had the largest adjusted proportions of algorithm-identified ACO patients identified as high risk during clinical review. Among high-risk patients, adjusted proportions selected for care management were highest within practices employed by the health delivery organization (80.8%) and non–community health center practices (78.4%) (

After adjusting for patient and primary care physician characteristics and controlling for the clustering of patients within PCPs, practices varied significantly in terms of both the adjusted proportion of patients identified as high risk (36% to 99%; 

 <.001) and the proportion of patients referred to the CMP (18% to 96% ; 

We further explored the extent to which patients with specific characteristics were consistently categorized as high risk across practices. For this exploratory analysis, we selected the 6 patient characteristics that were both independently associated with patient identification as high risk and were present in at least 10% of the patient population. Practices varied significantly in how they categorized patients for 5 of 6 patient characteristics studied (

Hybrid patient identification approaches, that use algorithms to identify a subset of patients who warrant more careful clinical review, hold promise as a feasible method for incorporating clinician judgment and knowledge of patient psychosocial needs into case findings. However, we know little about the clinical review process, including the criteria used by PCPs to identify patients as high risk, how practices identify which high-risk patients would be appropriate for care management, and how these decisions differ across PCPs. 

Consistent with findings from our interviews of primary care physicians and care managers,

 we found that primary care teams used additional information about their patients’ clinical profiles to decide whether a patient on an algorithm-generated list was high risk. Patients identified as high risk tended to be older, sicker, and with greater healthcare utilization, including primary care visits, than patients not identified as such. Patients with shorter relationships with their primary care physician were more likely to be identified as high risk, suggesting that PCPs might either have different thresholds for identifying patients who are new to their practice as high risk or may not have had the opportunity to establish necessary supports for their newer patients. Lack of a long-term patient—PCP relationship may compromise effective communication with patients and their caregivers, which is particularly important in older, complex patients.

As a second step, primary care teams were asked to identify which high-risk patients were appropriate for care management, and when selecting, patient clinical need was far less of a consideration. In addition to patient age and no prior hospice use, patient area—level poverty was independently associated with selection of high-risk individuals for CMP services. Thus, although predicted risk and prior utilization are important for identifying patients who are high risk, they are not the primary factors associated with the decision to refer patients to a CMP.

Our study extends prior work by clearly differentiating clinical assessments of whether patients are high risk from determinations of whether high-risk patients would be appropriate for CMP services. Additionally, our findings support other studies that have found that one of the benefits of including clinician input is the ability to consider nonclinical factors, such as modifiable health risks, patient health literacy, social and home environment challenges, social support, or inadequate coping skills and financial resources.

 Patients appropriate for care management often have issues with medical decision making and care coordination in addition to poorly controlled disease.

 Physicians may also preferentially select patients who they feel are more willing to participate in a CMP.

We found considerable variation in the overall adjusted proportions of patients identified as high risk across practices, as well as significant variations in practice-level identification rates for specific cohorts of patients, suggesting that practices may not use the same criteria to identify high-risk patients. Although practices were provided with central guidance on how to perform the clinical review, patient-by-patient decisions are left to the practices.  Some of this diversity in identification of high-risk patients may appropriately reflect unmeasured differences in practice resources and access to specialty care and services, such as behavioral health specialists and addiction counselors, for their patient population. Differences could also reflect variations in program implementation, such as nurse skill levels or comfort with different patient populations. Care management, by nature, is a diverse intervention for individuals with complex healthcare needs.

 The multifaceted nature of care management not only complicates the identification of patients, but may bias selection toward patients whose needs can be met by the skills of the care manager available to the practice. Training for care managers and feedback to practices regarding the benefits and limitations of CMP may help minimize unintentional disparities in patient selection.

METHODS

Hybrid approaches allow for clinician input into case finding for care management, but training and monitoring is required to protect against unintentional biases.

Implementing a Hybrid Approach to Select Patients for Care Management: Variations Across Practices

Little is known regarding physicians’ views on health service overuse or their awareness of the American Board of Internal Medicine Foundation’s Choosing Wisely campaign. Through the Survey on Overuse and Knowledge of Choosing Wisely, we assessed physician views on hypothesized drivers of overuse and Choosing Wisely.

We designed the survey to investigate physicians’ knowledge of, awareness of, and feelings toward Choosing Wisely, along with their concerns about malpractice, perception of patient demand, discomfort with uncertainty, and cost-consciousness. Where possible, we used pre-validated survey instruments.

We distributed the survey to clinicians practicing at Atrius Health, the largest ambulatory care provider in Massachusetts. We analyzed 584 responses (72% response rate) and calculated 3 previously validated scales.

Primary care physicians reported significantly greater awareness of Choosing Wisely (47.2%) than medical specialists (37.4%) and surgical specialists (27%). A majority (62%) of all respondents reported they found uncertainty involved in providing care disconcerting. Approximately one-third felt it unfair to ask physicians to be both cost-conscious and concerned with welfare, thought too much emphasis was placed on costs, and thought doctors were too busy to worry about costs. Surgical specialists were more concerned about malpractice, whereas primary care physicians reported feeling significantly more pressure from patients for tests and procedures.

Knowledge of Choosing Wisely is limited, but primary care physicians are more aware of the campaign than specialists. Although hypothesized drivers of overuse are prevalent, most physicians support cost-consciousness in medicine and embrace their responsibility in reducing costs.

	Our analysis presents findings that can be used to inform practice and policy:

		Initiatives aimed at improving financial stewardship may benefit from efforts targeted at supporting clinicians in dealing with the uncertainty that comes with conservative management. Physicians may be more willing to forgo or delay low-value care if they have appropriate support that addresses patient demand, malpractice concerns, and other drivers of overuse. 

		There is receptivity to more support for physicians to consider costs when recommending testing or treatment regimens. Ideally, decision support would be combined with information on price to address the significant knowledge gap around costs expressed in our survey.

The overuse of healthcare services is receiving attention as physicians are increasingly asked to embrace financial stewardship and payment reform pushes provider groups to consider value. In the past, there was little agreement on classification of services as low-value and no consensus on which mechanisms were most effective in reducing the use of these low-value services.

 Aimed at filling this gap, “Choosing Wisely” is an effort of the American Board of Internal Medicine (ABIM) Foundation to help physicians be better stewards of finite healthcare resources.

Beginning in 2012, specialty societies partnered with the ABIM Foundation to create and publish lists of “Five Things Physicians and Patients Should Question”—evidence-based recommendations that should be discussed to help make wise decisions about appropriate care based on a patient’s individual situation. More than 70 specialty societies have joined the campaign and published lists, but the services on the lists are chosen by representatives from specialty societies, and wide participation is often not sought in their creation.

 This approach makes the level of awareness, acceptance, and adoption of the recommendations uncertain.

The impact of the Choosing Wisely initiative on low-value care depends on effective dissemination and uptake of the lists; understanding the drivers of overuse is also needed to inform interventions targeting these services. A suite of communication education modules is available through the ABIM Foundation to help providers engage in conversations with patients,

 and a small library of video resources is available on the Choosing Wisely website. State medical societies, specialty societies, and regional health collaboratives are helping extend the reach of the campaign in communities, and 

 is developing and disseminating materials to educate patients regarding the services targeted by Choosing Wisely.

Hypothesized drivers of health services overuse include discomfort with uncertainty (around diagnosis, for example), patient demand for services, fear of malpractice, and, perhaps most importantly, financial incentives that do not support cost-consciousness.

 Each of these requires a unique set of policy interventions to reduce overuse, and each may affect physicians and services in different ways. Understanding which of these factors physicians consider influential to their behavior regarding low-value services is key to the development of effective interventions.

The Choosing Wisely measures were developed by professional societies and appear to have gained credibility among a variety of stakeholders, but little is known about how widely the initiative is recognized among physicians. To maximize the influence of Choosing Wisely on the behavior of physicians, we must understand more about factors that lead to overuse of medical tests and procedures and how receptive physicians are to incorporating value-based decision making into their practice. In this paper, we report results from a survey of physicians at a large physician group practice regarding their awareness of, and feelings toward, the Choosing Wisely campaign and other hypothesized predictors of overuse, as well as their receptiveness to value-based decision making.

Atrius Health is the largest ambulatory care provider in Massachusetts, providing outpatient primary and specialty care for nearly 1 million patients. They are also a Pioneer Medicare accountable care organization with a mix of fee-for-service and capitated contracts. In January 2014, we sent a cover letter detailing the purpose of the survey and the survey instrument to all clinicians (MD or DO) practicing at Atrius Health from Atrius staff and study team members. Second and third mailings, coupled with reminder e-mails, were sent to nonresponders at 3-week intervals. We distributed the survey to 808 clinicians and received 584 responses (72% response rate) by April 2014.

The 29-item Survey on Overuse and Knowledge of Choosing Wisely was designed to investigate whether overuse is impacted by a physician’s: 1) level of cost-consciousness, 2) comfort with clinical uncertainty, 3) concerns regarding malpractice, and 4) perceived pressure from patients and the healthcare system. The survey questions used for each scale are noted in 

. Where possible, we used validated survey instruments to examine potential determinants of overuse.

The Cost-Consciousness Scale, developed by Tilburt et al,

 was designed to assess how cost-conscious physicians are in their day-to-day care. It was calculated as a summation of 11 survey questions, with an answer of “strongly agree” equaling 4 and an answer of “strongly disagree” equaling 1. The composite score was on a scale of 11 to 44, with 44 representing the highest reported consciousness of cost.

The Discomfort With Uncertainty Scale, originally developed by Gerrity et al,

 sought to assess how uncomfortable physicians were in providing care for their patients when a diagnosis or follow-up care is uncertain. Previous studies have shortened the original 13-item Discomfort With Uncertainty Scale and demonstrated predictive validity with the abbreviated versions.

 we included 1 item from this tool to scale discomfort while decreasing redundancy for survey respondents. The response scale ranges from 1 to 4, with 4 indicating the highest level of discomfort.

The 5-item Malpractice Concerns Scale, a validated set of questions demonstrated to be associated with resource utilization,

 aimed to assess day-to-day malpractice concerns of physicians using 5 survey questions. We computed the percentage of responses either “strongly” or “moderately” agreeing with the provided statement, and the percentage for each question amounted to 20% of a final Malpractice Concerns composite score on a scale of 0 to 100.

Finally, we developed several new items, including questions addressing physicians’ perceived pressure from patients and the healthcare system to order tests and procedures, physicians’ attitudes toward making referrals, and physicians’ awareness of and views on the Choosing Wisely campaign. All questions—except for the 3 directly addressing Choosing Wisely&mdash;were presented as statements with a 4-point Likert scale, where possible responses ranged from “strongly agree” to “strongly disagree”; there was no neutral “neither agree nor disagree” option. Finally, 2 questions on years of practice and weekly clinical effort were included at the end of the survey (our sample included gender and specialty). Our survey instrument (see eAppendix 1 [

]) was approved by the Partners Health Care Human Studies Committee.

We calculated summary statistics for the Atrius population of physicians, the survey sample, and the US population of physicians. We used 

 tests of proportion to test if our sample differed significantly from national physician characteristic proportions and from that of Atrius as a whole.

We calculated summary statistics for each survey item and each scale. We adjusted the means of each calculated scale for age, gender, and clinical effort (half-sessions worked per week) using a linear regression model and average marginal effects. For the questions about knowledge of the Choosing Wisely program, we adjusted the proportions of responses for age, gender, and half-sessions worked per week using logistic and ordinal logistic models and average marginal effects. These covariates were included because the literature has demonstrated an association between age, gender, and clinical effort with hypothesized drivers of overuse, and we wanted to control for the potential impact of these differences on perceptions of Choosing Wisely.

We used analysis of variance (ANOVA) to determine if there were significant differences in adjusted scale results between respondents in primary care, in medical specialties, and in surgical specialties.

 We used a logistic model and average marginal effects to calculate the predicted percent that agreed or strongly agreed, adjusted for years in practice, gender, and clinical effort. We tested the difference across specialties from these adjusted percentages using 

 tests. We used the same method to additionally determine if there was: a) a significant difference across specialties in adjusted awareness of the Choosing Wisely campaign, b) if physicians viewed the Choosing Wisely campaign as a legitimate source of guidance, and c) if the campaign had empowered respondents to reduce the use of unnecessary tests and procedures. Finally, we reported correlations between the calculated scales and awareness of Choosing Wisely and attitudes toward being a steward of resources.

Overall, 56% of respondents were female. Respondents reported being in practice a mean of 18.5 years and working an average of 6.6 half-day sessions of patient care (approximately 30 hours) per week; 56% were primary care physicians and the remainder practiced in medical specialties (25%) and surgical specialties (19%) (eAppendix 2). Our sample had significantly more female respondents than the national physician population (

 <.001), yet did not differ significantly from the overall Atrius physician population (

Primary care physicians reported significantly greater awareness of the Choosing Wisely campaign (47.2%) compared with medical specialists (37.4%) and surgical specialists (27.0%) (

) after adjusting for age, gender, and clinical effort. When asked if they viewed the Choosing Wisely campaign as a legitimate source of guidance, similar proportions across the 3 physician specialty categories reported they agreed or somewhat agreed that Choosing Wisely was a legitimate source of guidance (97.1%, 95%, and 92.2% in primary care, medical specialties, and surgical specialties, respectively) after adjusting for the covariates. Three-fourths (75.1%) of primary care physicians reported they agreed or somewhat agreed that Choosing Wisely empowered them to reduce use of unnecessary tests and procedures compared with 64.4% of medical specialists and 54% of surgical specialists; this was statistically significant (

The adjusted mean score on the Cost-Consciousness Scale was 29.2 out of a possible 44—similar to the mean of 31 found for physician populations in other studies (Table 1).

 Awareness of the Choosing Wisely campaign correlated with greater cost-consciousness, but scoring higher on the Cost-Consciousness Scale correlated with a less positive view of the campaign as a legitimate source of guidance regarding the use of unnecessary tests and procedures (eAppendix 3).

The adjusted mean score on the Malpractice Concerns Scale across all respondents was 58.1 (on a scale of 0 to 100), which is slightly lower than the mean 65.4 found by Carrier et al (Table 1).

 Surgeons reported the highest adjusted mean of 63.0, while primary care physicians reported a mean of 57.7 and medical specialties reported a mean of 50.7 (

= .016) (Table 1). Although our recorded scores are lower than those published by others, the trend of surgeons reporting the greatest malpractice concern is consistent with prior studies.

For the Discomfort With Uncertainty Scale, the mean of all respondents was a 2.7 out of 4. A majority (62%) of all respondents reported that uncertainty involved in providing care was disconcerting; this compares with 56% reported by others.

There were no significant differences in this score between physician specialties.

After adjusting for covariates, almost all physicians agreed that doctors need to limit unnecessary tests (96.8%), have a responsibility to control costs (92.2%), and should be aware of and adhere to clinical guidelines (97.9%) (

). Approximately one-third felt it unfair to ask physicians to be both cost-conscious and concerned with welfare (33.0%), thought there is too much emphasis on costs (30.7%), try not to think about costs (33.9%), and thought that doctors are too busy to worry about costs (27.8%) (Table 3); these proportions did not differ across specialties. Less than half of respondents (36.9%) reported having a firm understanding of the costs of tests and procedures to the healthcare system. Primary care physicians reported feeling significantly more pressure from patients to order tests and procedures than medical and surgical specialties (68.3%, 58.0%, 55.8%, respectively; 

= .024) (Table 3). Primary care physicians were also significantly more likely to report feeling pressure to refer patients to consultants (65.3% vs 34.7% in the medical specialties and 33.7% in the surgical specialties; 

<.001) and to feel that the risk of unnecessary tests is important to consider when requesting referrals (58.1% vs 27.9% in the medical specialties and 38.8% in the surgical specialties; 

Overall, we found knowledge of the Choosing Wisely campaign to be limited—but the campaign had been in place for less than 2 years at the time of the survey. Prior work on awareness of clinical practice guidelines showed a broad range in awareness across specialties and services in line with these estimates.

 Primary care physicians were more aware of the campaign than specialists. Further, buy-in to the campaign was mixed, but compared with others, more primary care physicians report “absolutely” viewing the campaign as a legitimate source of guidance—this proportion was still less than half, however. One in 5 primary care physicians reported the campaign has “absolutely” empowered them to reduce their use of unnecessary tests and procedures; however, only a small proportion of specialists expressed this sentiment.

Policy makers, practitioners, and researchers have hypothesized drivers of overuse,

 and many of these factors were prevalent among our physician sample, with more than half of respondents reporting the following: pressure from patients to order tests, ordering tests to reduce risk of malpractice, finding the uncertainty involved in patient care disconcerting, and not understanding of the costs of tests and procedures to the health system. The finding that primary care physicians feel more pressure from patients for tests and procedures suggests that future interventions may need to be specifically oriented toward primary care physicians to equip them with strategies for resisting patient pressure and helping patients to understand that more is not necessarily better. Physicians in this group were remarkably open to the concept of cost-consciousness in medicine and their responsibility in reducing costs. Moreover, most felt decision-support tools would be useful. Working toward e-consults and telemedicine should also aid primary care physicians in gaining confidence at addressing overutilization caused by feeling pressure to complete a consultation.

 Risk of unnecessary testing as a consideration in referral decisions is in line with prior research showing that the most prevalent reason for overriding suggestions to forgo low-value care is recommendation by a specialist.

Our survey is strengthened by the combination of 3 distinct validated survey tools to measure discomfort with uncertainty, cost-consciousness, and malpractice concerns with other hypothesized drivers of overuse and knowledge of a financial stewardship campaign. Although recent studies have analyzed physician awareness of and attitudes toward Choosing Wisely, as well as physician views regarding healthcare costs,

 ours is the first, to our knowledge, to combine pre-validated survey instruments with questions specifically addressing the Choosing Wisely campaign. Our findings link prior literature on physician views of decision making and cost with potential cost-containment strategies informed by the Choosing Wisely effort. 

Our analysis of physician attitudes toward overuse is limited, however, by the fact that we relied on providers’ self-reported perceptions of Choosing Wisely and their attitudes toward overuse. We were thus unable to account for potential social desirability bias or other response biases. Atrius Health physicians also may not be representative of physicians nationwide, they may be more accustomed to initiatives that address healthcare costs. Compared with the ABIM Foundation’s survey of physicians nationwide

; our survey respondents reported being aware of the Choosing Wisely campaign at a significantly higher rate (41% vs 21%). Our study, however, provides insights into the views of physicians practicing within a system at the frontier of payment reform and large-scale integration of care. 

Although we cannot know exactly how representative our respondents are of physicians nationwide, outside of gender and specialty, most of our findings are consistent with previous physician survey studies. We found surgical specialists reported higher malpractice concern than primary care physicians, as previously demonstrated by Carrier et al.

 Also, in our survey, women reported greater discomfort with uncertainty than men—a common trend in the literature.

 These findings support the generalizable nature of our sample.

Our analysis also complements the existing literature on physician attitudes and behaviors regarding cost and low-value care while providing new insight. Prior studies identified physicians’ knowledge of clinical guidelines and awareness of costs of tests and procedures as factors affecting the delivery of appropriate, cost-effective care. In these studies, the majority of primary care physicians reported either a lack of knowledge regarding guidelines or forgetting guidelines during patient encounters as barriers to adherence.

Similarly, clinicians report having a very limited knowledge of costs despite their insistence that cost is an important factor in their decision making.

 Other research has uncovered the complicated trade-offs that physicians face as they strive to care for patients in a thorough yet cost-conscious manner.

By measuring potential causes of overuse using validated scales, our study quantifies these complex trade-offs and creates a replicable measurement tool that might be applied to a variety of clinicians and tracked over time to measure the impact of efforts aimed at behavior change.

Although widely discussed, the healthcare system has been slow to effectively discourage overuse. Patient and physician knowledge and understanding of which tests are low-value is a necessary first step to reduce overuse of services. This survey provides information on physicians’ knowledge of an informational campaign to identify low-value care and finds communication to primary care physicians has been more successful than communication to specialists. Perhaps most encouragingly, physicians report being ready to consider costs in treatment decisions. Ideally, clinical decision support would be combined with readily available information on price and relative price of tests and procedures to address the significant knowledge gap around healthcare costs expressed by physicians in our survey. This may be facilitated by legislation such as the Massachusetts Medical Price Transparency Law, enacted in January 2014, which guarantees that healthcare cost information be made available to patients and physicians.

The authors surveyed physicians regarding “Choosing Wisely” and hypothesized drivers of overuse, finding high reported prevalence of hypothesized drivers of overuse and widespread support for cost-consciousness.

May 2016

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