Larry R. Hearld, PhD

 To examine the relationship between participation in value-based programs and care coordination activities.

 Cross-sectional, observational study of 1648 US hospitals using the American Hospital Association (AHA)’s 2013 Survey of Care Systems. Value-based program participation included participation in either an accountable care organization (ACO) or a bundled payment program. We assessed adoption (whether a hospital was using any of a set of 12 care coordination activities in the AHA survey) and spread (in each hospital adopting care coordination activities, how extensively those activities were implemented throughout the hospital).

Ordinary least squares regression assessed associations between participation in an ACO or bundled payment program and the adoption and spread of 12 care coordination activities.

 Hospitals adopted nearly two-thirds of the possible care coordination activities (mean [SD] = 7.9 [4.4] of 12). Among those hospitals adopting care coordination activities, there was a relatively moderate spread of these activities (mean = 2.5; range, 1 [minimally used] to 4 [used hospitalwide]). Hospital participation in an ACO was associated with the adoption of 3.07 more care coordination activities (

 <.001), on average, and 0.16 more points on the scale of spread of care coordination activities (

 <.001) compared with hospitals that were not participating in an ACO. Hospital participation in a bundled payment program was associated with the adoption of 1.84 more care coordination activities (b = 1.84; 

 <.001) but not greater spread (b = —0.04; 

 Value-based programs such as ACOs appear to encourage the adoption and spread of care coordination activities by hospitals.

Overall, US hospitals adopted a relatively high number of care coordination activities (nearly two-thirds, on average, of those possible) but were less interested in or effective at spreading these activities throughout the hospital.

Opportunities to improve the use of care coordination activities are not evenly distributed, with hospitals reporting extensive use of some activities and minimal use of others.

Hospital participation in value-based programs, especially accountable care organizations, may provide a catalyst to adopt and spread care coordination activities.

Care coordination—defined as the deliberate organization of patient care activities between 2 or more participants (including the patient) involved in a patient’s care to facilitate appropriate delivery of healthcare services

—is an essential component of high-quality, high-value care. For example, research has found that care coordination activities that help patients transition out of the hospital and back into the community are effective at avoiding undesirable outcomes, such as unnecessary readmissions.

 However, research has also documented varying degrees of care coordination capabilities among providers,

 highlighting an important opportunity for improving the quality of care.

Considerable emphasis in recent years has been placed on incentive programs such as accountable care organizations (ACOs) and bundled payments in the hopes that they will improve the value of healthcare in the United States. One means by which these programs may do so is by improving care coordination activities by healthcare providers, including and especially hospitals.

 Research is beginning to emerge as to whether these programs are effective at promoting quality and lowering costs

; however, related but underresearched questions pertain to whether these programs are effective at promoting care coordination activities.

 Answers to such questions are important for understanding reasons these programs may or may not be effective at promoting value in the US healthcare system.

Therefore, the purpose of this paper was to examine the adoption and spread of care coordination activities among US hospitals following the introduction of value-based payment programs. Further, the paper assesses the degree to which the adoption and spread of care coordination activities varies as a function of participation in these programs.

Bundled payment and Medicare ACO programs were launched by CMS in 2012.

 We focused on 2013 to understand how well prepared early adopters of these programs were to coordinate care. Therefore, we consider our findings important in providing a baseline assessment of how hospitals may have responded to the incentives that these programs offer.

The data for this study were drawn from 3 sources: (1) the 2013 American Hospital Association (AHA) Annual Survey of Hospitals, (2) the 2013 AHA Survey of Care Systems, and (3) the Health Resources and Services Administration’s 2013 Area Health Resource File (AHRF). The AHA Annual Survey of Hospitals is an annual electronic survey of approximately 6300 hospitals in the United States that provides extensive data regarding hospital organizational characteristics.

 The AHA reports an 80% response rate to this survey each year. The Survey of Care Systems is a relatively new data collection effort by AHA, initiated in 2013, to monitor the evolution of new systems of care such as ACOs. The 2013 survey used in this analysis included 1648 hospitals, or approximately 34.4% of the general service acute care hospitals in the United States. The AHRF is a collection of data from different sources (eg, US Census Bureau, Bureau of Labor Statistics) that is used to construct the environmental characteristics considered in the study. The final analytic sample consisted of 1648 US acute care hospitals operating in 2013.

The analysis included 2 dependent variables related to hospital-initiated care coordination: adoption and spread. Adoption refers to the decision to use a new technology or process, whereas spread (also known as penetration or reach) pertains to the integration of that new technology or process within a setting.

 In our study, the adoption of care coordination activities reflected how many different care coordination activities were reported as being used by a hospital. The AHA survey included questions that asked hospitals about whether they were engaging in 12 care coordination activities (

). The use of each activity was measured on a 5-point scale (“not at all used,” “used minimally,” “used moderately,” “used widely,” or “used hospitalwide”). To construct our care coordination adoption variable, we first constructed a dichotomous indicator (“used” or “did not use”) for each care coordination activity (0 if hospitals reported these activities were not at all used and 1 if hospitals reported they used these activities minimally, moderately, widely, or hospitalwide). Next, we summed these dichotomous indicators for each hospital (range, 0-12), with greater values indicating greater adoption of care coordination activities. As a sensitivity analysis, we estimated these relationships using a more conservative estimate of adoption (0 if hospitals reported not using these activities at all or using them minimally and 1 if hospitals reported using these activities moderately, widely, or hospitalwide). The results using this definition of adoption were similar to those reported here (results available from authors upon request).

 Our second dependent variable—the spread of care coordination activities&mdash;reflected how extensively care coordination activities were implemented throughout a hospital. This variable was based on the same 12 care coordination items. In this case, we constructed the variable by quantifying the degree of spread for any of the 12 survey items for which a hospital indicated it had adopted the care coordination activity (range, 1 [“used minimally”] to 4 [“hospitalwide”]) and then averaging across the 12 items. We opted for this operationalization to make the adoption and spread processes more distinct and to reflect the fact that a hospital can spread only the care coordination activities that it has already adopted. An exploratory and confirmatory factor analysis of the care coordination activities indicated a single-factor solution and supported the decision to analyze these as a single composite.

Our primary interest was in hospital participation in value-based programs, which we measured with 2 dichotomous variables. One variable reflected hospital membership in an ACO (1, yes; 0, no). The second variable reflected whether a hospital participated in a bundled payment program (1, yes; 0, no). Bundled payment programs included those sponsored by Medicare, as well as by commercial insurers.

The analysis also controlled for a number of hospital organizational characteristics that previous research has found to be associated with care coordination

 and with participation in value-based programs,

 as well as the physical, socioeconomic, and healthcare delivery system characteristics of the surrounding community. 

 provides a list of these variables and how they were operationalized.

The unit of analysis was the hospital. Univariate statistics were used to describe the sample hospitals, including their overall level of adoption and spread, as well as hospital adoption and spread for individual activities. Ordinary least squares (OLS) regression was used to assess the relationship between hospital participation in value-based programs and care coordination adoption and spread. We considered alternative functional forms for these models by estimating the relationships using a Poisson regression model. The relationships were consistent with our OLS regression models in terms of direction and statistical significance (results available from authors upon request). We also assessed whether the results differed when the ACO and bundled payment program participation variables were estimated in separate regression models. Once again, the relationships were consistent in terms of magnitude with the OLS models that simultaneously included both participation variables (results available from authors upon request).

Slightly more than 18% of the sample hospitals were participating in an ACO compared with 8.9% participating in a bundled payment program (

). The ACO participation rate is comparable with other estimates that reported approximately 14% of all US hospitals participating in a Medicare ACO program in 2012-2013.

 Similarly, a CMS report indicated that 423 hospitals, or approximately 8.5% of all US acute care hospitals, participated in the Bundled Payments for Care Improvement program in the first 3 years.

 Less than 5% (n = 69) of the sample hospitals were participating in both programs. A majority of the sample hospitals were private not-for-profit hospitals (70.6%), followed by public not-for-profit (21.1%) and for-profit (8.4%) hospitals. More than 60% of the sample hospitals were affiliated with a system, and approximately one-third (34.1%) were teaching hospitals. Nearly two-thirds of the sample hospitals (63.9%) were located in urban areas. Notably, a comparison of the sample hospitals with other acute care, general service hospitals that did not respond to the survey indicates significant differences in many organizational and community characteristics (Table 3). In general, this comparison suggests that respondents to the survey tended to be larger, private, not-for-profit teaching hospitals located in urban areas. Given these differences and research demonstrating that these characteristics may affect participation in value-based programs,

 we control for these factors in the multivariate analyses. Nevertheless, these differences should be taken into consideration when interpreting the results presented below.

On average, sample hospitals had adopted two-thirds of the 12 potential care coordination activities (mean [SD] = 7.9 [4.4]) (Table 3). Among those hospitals adopting at least 1 care coordination activity, there was a moderate degree of care coordination spread (mean [SD] = 2.5 [0.6]). Of the individual care coordination activities, medication reconciliation was the most widely adopted, with 99% of the sample hospitals having adopted it. This was followed by telephonic outreach to discharged patients, with nearly 95% of the sample hospitals reporting some use. The least commonly adopted care coordination activity, on average, was arrangement of home visits for homebound and complex patients, with a little more than two-thirds (68.9%) of the sample hospitals reporting some use.

Among those hospitals adopting a specific care coordination activity, the most widely spread care coordination activity was the use of hospitalists for inpatients (mean [SD] = 3.6 [0.7]; range, 1-4), followed closely by medication reconciliation (mean [SD] = 3.5 [0.8]). The least widely spread care coordination activity was the use of predictive analytic tools to identify individual patients at high risk of poor outcomes (mean [SD] = 2.0 [1.0]). This was followed closely by the use of post—hospital discharge continuity of care programs (mean [SD] = 2.1 [1.0]) and nurse case managers (mean [SD] = 2.1 [1.1]).

On average, hospital participation in an ACO was associated with the adoption of 3.07 more care coordination activities compared with hospitals that were not participating in an ACO (

). Likewise, hospitals that were participating in an ACO reported more extensive spread of these care coordination activities (b = 0.16; 

Similar to ACO participation, hospital participation in a bundled payment program was associated with the adoption of 1.84 more care coordination activities, on average, compared with hospitals that were not participating in a bundled payment program (

 <.001). In contrast to ACO participation, however, hospital participation in a bundled payment program was not significantly associated with greater spread of these care coordination activities within a hospital (b = —0.04; 

As a supplementary analysis, we also tested whether the participation of a hospital in both programs was associated with greater adoption and spread of care coordination activities. We did so by reestimating the models and including a multiplicative interaction term between ACO participation and bundled payment program participation. The results of this analysis (available from the authors upon request) indicate that hospitals participating in both programs adopted numbers of care coordination activities (mean = 10.7) similar to those of hospitals participating in only an ACO (mean = 10.6) or only a bundled payment program (mean = 10.2), none of which were statistically significantly different. In contrast, hospitals participating in both programs reported less spread of care coordination activities (mean = 2.4) compared with hospitals participating in only an ACO (mean = 2.7; difference not significant [

 = .38]) or only a bundled payment program (mean = 2.6; 

 <.01). In this case, the average spread of care coordination activities for hospitals participating in both programs was comparable with that for hospitals participating in neither program (mean = 2.4).

Our analysis reveals that hospitals have adopted a relatively large number of the 12 coordination activities named in the AHA survey. However, certain activities, such as medication reconciliation and the use of hospitalists, were more widely spread throughout hospitals. One explanation for such variations is that the business case for some of these activities may be more salient for hospitals relative to other care coordination activities. For example, medication reconciliation may directly reduce adverse drug interactions that have the potential to increase length of stay and hospital costs. Likewise, it is possible that the evidence supporting the effectiveness of these activities at improving care coordination and quality is stronger,

 and this evidence has resulted in greater adoption by hospitals. Alternatively, it is possible that hospitals adopt these activities for normative rather than instrumental reasons.

 For example, the professionalization of hospitalists over the past 2 decades has created pressure for their use by hospitals,

 irrespective of whether they do, in fact, improve quality. Regardless of the underlying reason, our analysis shows that an opportunity exists to increase the use of effective care coordination activities by hospitals.

Our analysis also revealed that both ACO and bundled payment program participation were associated with greater adoption of care coordination activities. Both ACOs and bundled payment programs are approaches to integrating resources and efforts across the care delivery continuum by creating accountability for a defined patient population or episode of care.

 The premise of these programs is that global payments can alter fee-for-service reimbursement methods by incorporating shared savings arrangements. Under these arrangements, providers are eligible to share in savings generated by avoiding adverse outcomes. Improvements in care coordination are assumed to be a primary avenue providers can take to avoid these adverse outcomes, achieve cost reductions, and generate shared savings.

 Although our study does not definitively establish care coordination as the underlying mechanism by which these programs reduce adverse outcomes and achieve savings, it does suggest that these programs are associated with greater adoption of care coordination by hospitals and may be effective at combating the fragmentation and disincentives to coordination often created by the traditional fee-for-service reimbursement systems in the United States.

 It is possible that the financial incentives and structural changes enabled by ACO and bundled payment participation are directly responsible for increases in the adoption and spread of care coordination activities among participating hospitals. Alternatively, some hospitals may have characteristics that make them simultaneously more likely to engage in care coordination activities and more likely to engage in ACOs or bundled payment programs.

Our analysis also revealed, however, that ACO membership was significantly associated with greater spread of care coordination activities within hospitals, whereas participation in a bundled payment program was not. One explanation for the observed difference is that ACOs are more comprehensive programs that entail more pervasive changes in how providers relate to each other to coordinate care. In contrast, hospitals can enter into condition-specific bundled payment arrangements that may entail circumscribed changes to their care coordination activities. Put another way, more widespread implementation of some of the care coordination activities we examined may not be as valuable for succeeding in a bundled payment program. For instance, predictive analysis tools and prospective management of chronically ill patients are often used to identify patients at risk of poor care while they are in the community. Thus, they may have less utility for managing patients in a bundled payment program who have already sought out care. Future research might consider whether there are qualitative differences in the types of care coordination activities adopted by hospitals participating in these 2 programs.

Another explanation for this observed difference pertains to hospitals’ experience with these programs. Many organizations, especially complex ones like hospitals, do not simultaneously adopt and implement an innovation enterprisewide. Instead, they pilot an innovation, such as a new care coordination activity, in a limited capacity (eg, single unit) before attempting to spread it to the rest of the organization. This approach allows an organization to leverage its history and experience with an innovation to determine the feasibility and value of implementing it elsewhere in the organization, as well as to identify opportunities to refine the implementation process.

 With respect to the value-based programs we examined, the ACO concept began to emerge more than a decade ago,

 and incentive programs tied to ACOs (eg, Advanced Payment ACO Model and Pioneer ACO Model) were introduced in 2011 and 2012. Bundled payment programs, in contrast, were introduced later, with the most prevalent program being the Bundled Payments for Care Improvement initiative that was introduced in 2013. It is plausible that limited experience with bundled payment programs has provided insufficient opportunities for hospitals to learn how to best spread these activities throughout the organization to succeed in these programs.

Our findings highlight the important role that value-based programs may play in improving care coordination by hospitals and in counteracting a fragmented delivery system. Given other research findings that demonstrate the positive effects of care coordination activities on quality,

 our findings also provide some preliminary evidence for the mediating role of care coordination in the value-based program—quality relationship.

 Collectively, these findings point to the importance of continuing to support and potentially expanding these programs.

Our results also add interesting context to prior research that suggests that observed cost savings among ACOs may not be the result of improvements in care coordination.

 On one hand, the failure of ACOs to achieve cost savings through improved care coordination is not surprising, given the sample hospitals’ above-average level of adoption but moderate level of spread of care coordination activities. This may suggest that ACO and bundled payment programs are sufficient for facilitating adoption but not on a scale that results in cost savings. It may be that policy makers need to alter the ACO or bundled payment programs to improve the spread of care coordination processes and achieve the maximum possible cost savings. On the other hand, our results do show an association between ACO participation and the adoption and spread of care coordination activities during the early years of these value-based payment programs. This suggests that hospitals participating in ACOs could be making efforts to improve care coordination. If the care coordination efforts we have observed are nascent attempts to respond to value-based programs, perhaps we can expect future increases in the adoption and spread of care coordination efforts that ultimately result in greater cost savings, even without changes to the ACO or bundled payment programs. Either way, we believe that a thorough understanding of the effects of value-based programs requires not only looking at outcomes but also understanding how program participation may be changing care coordination processes.

The findings of our analysis should be interpreted in light of several limitations. First, the sample of hospitals used in the analysis may not be representative of all US acute care hospitals. The data were based on a supplemental survey conducted by the AHA that included about 1700 hospitals, or approximately one-third of all US acute care hospitals. A comparison of the hospitals in our analysis with the overall US acute care hospital population indicated significant differences (Table 3). Despite this shortcoming, these data are, to our knowledge, the most comprehensive available with respect to care coordination by hospitals and are beneficial for providing a snapshot of these important activities.

A second potential limitation of the analysis pertains to our measures of care coordination. Accurate responses to these questions require knowledge of processes and practices across an entire enterprise, which may be more difficult in certain types of hospitals (eg, large, complex systems). The responses also assume that respondents from different hospitals have similar definitions of the care coordination activities; therefore, different forms (or degrees of implementation) of similar care coordination activities cannot be accounted for. Furthermore, it is possible that the responses reflect a level of social desirability, with respondents wanting their hospitals to appear more progressive in terms of their care coordination activities. Third, our measures of value-based program participation were based on self-report and included programs sponsored by multiple payer types (eg, CMS or private pay) and thus could not be verified with other data sources. Similarly, these measures were not able to capture some of the nuances of participation. Future research could build on our results, for example, by examining whether the use of care coordination activities, overall or for specific activities, differs among certain types of ACOs (eg, physician-led vs hospital-led).

Despite evidence of their positive effects on quality, our study demonstrates that there is room to improve the extent to which hospitals use care coordination activities. Notably, these opportunities are not evenly distributed, with hospitals reporting extensive use of some activities and minimal use of others. Value-based programs such as ACOs and bundled payment programs appear to have the potential of improving the use of care coordination activities by hospitals.

 Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham (LRH, NC, AH), Birmingham, AL.

 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 (LRH, NC, AH); acquisition of data (LRH); analysis and interpretation of data (LRH, NC, AH); drafting of the manuscript (LRH, NC, AH); critical revision of the manuscript for important intellectual content (LRH, NC, AH); statistical analysis (LRH); and provision of patients or study materials (LRH).

 Larry R. Hearld, PhD, Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham, 3201 1st Ave N, Birmingham, AL 35222. Email: lhearld@uab.edu.

1. McDonald KM, Sundaram V, Bravata DM, et al. 

Closing the Quality Gap: A Critical Analysis of Quality Improvement Strategies (Vol. 7: Care Coordination)

. Rockville, MD: Agency for Healthcare Research and Quality; 2007.

Articles

Value-based programs such as accountable care organizations appear to encourage the adoption and spread of care coordination activities by hospitals.

The Adoption and Spread of Hospital Care Coordination Activities Under Value-Based Programs

 A growing body of research has established the benefits of patient activation, which is defined as the knowledge, skills, confidence, and motivation to make effective decisions and take action to maintain or improve one’s health. Consequently, healthcare stakeholders of all types continue to seek ways to improve patient activation. The purpose of this study was to empirically examine whether enrollment in a health plan—sponsored care management (CM) program that included coaching for activation (CFA) was associated with utilization, medication adherence, and clinical outcomes.

Cross-sectional, quantitative study of commercially insured enrollees in a Midwest-based health plan.  

 Poisson, logistic, and ordinary least squares regression models were used to test the relationships between CM programs and outcomes.  

 The benefit of measuring patient activation and offering CFA was associated with reduced healthcare utilization and better clinical outcomes. Relative to respondents in the CFA CM group (ie, intervention), respondents in the usual CM group experienced 18.29% more emergency department visits, 97.78% more hospital admissions, a higher glycated hemoglobin level (β = 0.48; 

 <.001), and higher systolic blood pressure (β = 1.19; 

These findings suggest that coaching interventions based on activation level may help care managers engage in more effective interactions that strengthen a patient’s role in managing his or her healthcare. Programs that are more targeted in their application, rather than uniformly developed and implemented, may be an important factor in reducing utilization and improving clinical outcomes.

	This study investigates the benefits of a coaching for activation intervention and its associated outcomes.  

		Relative to respondents in the intervention group, the usual care group experienced 18.29% more emergency department visits, 97.78% more hospital admissions, higher glycated hemoglobin levels, and higher systolic blood pressure. 

		Health plan coaching interventions based on patient activation may increase effective interactions, thereby strengthening a patient’s role in managing his or her healthcare.

		Programs more targeted in their application, rather than uniformly administered, may be an important factor when implementing policy and healthcare reform, especially in caring for chronically ill populations.

Approximately 117 million—or half of American adults&mdash;currently live with a chronic disease, and 1 in 4 live with multiple chronic conditions.

 A major contributor to chronic diseases and their attendant costs is an individual’s poor health behaviors

; additionally, patients with chronic disease often do not adhere to the medication or treatment plans designed by their providers.

 This lack of adherence frequently leads to clinical crisis, unnecessary death, and expensive treatment services, such as preventable emergency department (ED) visits and hospital admissions.

 A growing body of research suggests that patient health behaviors that contribute to these problems can be improved by engaging patients so they will take a more active role in their health and healthcare

; consequently, an important question in healthcare today is how to engage patients to do so.

Given their financial risk and unique position to facilitate population health management, health plans have an important role to play in engaging patients and improving patient behaviors. One potential means that health plans may use to improve patient engagement entails the use of active care management (CM) programs. Despite the growing use of CM programs

 in general, there is surprisingly little research regarding specific interventions that attempt to promote patient engagement

 and their association with utilization metrics that reflect patient behaviors.

The purpose of this study was to investigate whether enrollment in a health plan—sponsored CM program that included coaching for activation (CFA) was associated with patient outcomes. CFA is an intervention used by care managers that includes tailored coaching based on an individual’s level of patient activation—patient activation refers to the knowledge, skills, confidence, and motivation possessed by patients to not only make effective decisions, but also take action to maintain or improve one’s health.

Care management is a voluntary program available to patients who require additional clinical services for managing their chronic disease. Patients are enrolled through self-referral, provider-referral, plan-driven population segmentation, or transitions in care (eg, discharge from a hospital). Population segmentation is a health plan—based tool that uses historical claims data and other variables, such as age and gender, to identify at-risk populations based on the complexity of their chronic disease(s) and behavioral risk factors. Enrolled patients are assigned to care managers based on primary care providers.

In May 2013, the CM team at a nonprofit health plan in the Midwest implemented CFA via a program that featured coaching based on an enrollee's level of patient activation. Based on the evidence suggesting a correlation between activation levels and health outcomes, CFA was introduced as a new initiative to enhance patient engagement. Initial assessments were conducted in 1 of 2 ways: interactive voice recognition or directly by a care manager employed by the health plan. Both methods were completed via telephone. Subsequently, patients in CM were assigned to 1 of 2 groups based on the presence of a Patient Activation Measure (PAM) score (ie, if patients had a PAM score, they were assigned to the CFA CM group; if patients did not have a PAM score, they were assigned to the usual CM group).

For both groups, the same care managers worked with patients; however, in the CFA CM group, they tailored their interactions based on the patient’s level of activation. For example, a CFA approach for a patient with diabetes would be different for a level 1 patient compared with a highly activated level 4 patient. An appropriate coaching goal for a level 1 patient with diabetes might be to develop basic knowledge of good and bad foods. On the other hand, an appropriate coaching goal for a level 4 activated patient entails more complicated behaviors, such as replacing bad foods with good and replacing poor eating behaviors with better ones. In contrast, coaching and goal setting occurs in the usual CM program, but it is not conditional based on a PAM level. 

All care managers were either registered nurses or licensed practical nurses with additional PAM training through Insignia Health. The purpose of the training was to increase knowledge about patient activation and its implications for patient outcomes, orient each CM program to the CFA approach, and demonstrate skills through roleplaying. Ongoing in-service activities were used to reinforce this training during monthly team meetings.

The study sample was selected from the plan participants over a 19-month timeframe. The analytic sample was restricted to commercially insured enrollees in order to limit variations in utilization across different patient populations reflected in different insurance types (eg, Medicare, Medicaid). The analytic sample was further limited to an adult population (18 years or older). These steps resulted in the inclusion of 17,797 enrollees in the study, with 1520 enrolled in the CFA CM program and the remaining 16,277 patients enrolled in the usual CM group.

There were 4 primary data sources used in the study: an electronic charting tool, an administrative claims data system, demographic and socioeconomic data extracted from marketing data, and a proprietary data repository that links laboratory results to the patient data file.

The dependent variables included utilization, medication adherence, and clinical outcomes. The outcome variables were constructed based on the 6-month period following enrollment in the CM program. Utilization was quantified with 2 variables: the total number of ED visits and inpatient hospital admissions. Adherence to medication management was defined as ≥80% refill rate for prescribed medications and operationalized as a dichotomous outcome. The 4 clinical measures were: glycated hemoglobin (A1C), low-density lipoprotein (LDL), systolic blood pressure (SBP), and diastolic blood pressure (DBP). In cases where enrollees had multiple measurements, an average was constructed across all eligible values.

The primary independent variable, type of care management, was operationalized dichotomously comparing usual CM with CFA CM as the reference. The analysis controlled for several demographic and socioeconomic characteristics, including age, gender, education, annual household income, and family composition. 

 provides more details on how each variable was operationalized.

The unit of analysis was the enrollee. The multivariate analysis assessed whether outcomes varied as a function of the care management group and included 3 multivariable regression models. However, these models differed in their specification based on the different types of dependent variables. In the case of utilization outcomes, a Poisson model was used, as ED visits and hospital admissions were both measured as counts. For adherence, a logistic regression model was used due to the dichotomous nature of the prescription refill variable. Finally, for clinical outcomes, an ordinary least squares (OLS) model was used due to the continuous nature of these outcomes. For ease of interpretation, the incidence rate ratio was calculated for the ED visits and hospital admissions from the Poisson model and odds ratios were calculated for the adherence outcome. Beta coefficients were reported for the OLS models related to the clinical outcomes.

Of the 1520 patients/enrollees in the CFA group, the mean baseline patient activation score was 66.4 (range = 0-100), with a median of 64.9. The largest number of patients/enrollees reported PAM scores at level 4 (n = 695; 49%), followed by level 3 (n = 342; 23%) and level 2 (n = 339; 22%). The fewest number of patients/enrollees reported PAM scores in the level 1 range (n = 144; 9%). This distribution is consistent with the results of other studies that have examined patient activation.

Significant associations were detected between CM groups and utilization outcomes. Relative to enrollees in the CFA CM group, enrollees in the usual CM group experienced 18.29% more ED visits (

 <.001) and 97.78% more hospital admissions (

). CFA CM was not significantly associated with medication adherence. Relative to enrollees in the CFA CM, enrollees in the usual CM program had higher A1C levels (

). DBP was not significantly associated with the type of coaching received.

Additional analyses were conducted to evaluate the association of activation levels (1-4) on the study outcomes. PAM levels were not associated with better outcomes, with the exception of A1C (results not shown). Specifically, relative to enrollees in PAM level 4, A1C levels were higher for enrollees in PAM level 1 (

Our analysis found that CM with CFA was associated with lower healthcare utilization (ie, ED visits, hospital admissions) and better clinical values for 2 outcomes: A1C and SBP. These findings suggest that coaching interventions that are based on activation level may help care managers engage in more effective interactions that strengthen a patient’s role in managing their healthcare. For sponsors and developers of care management programs, these findings suggest that the programs that are more targeted in their application—rather than uniformly developed and implemented&mdash;may be more effective at producing desired outcomes.

Given the research showing an association between adherence to treatment care plans

 another question that arose from these findings is whether care management with CFA does, in fact, produce higher levels of patient activation, or whether patient activation alone is sufficient. Because the care management program with CFA considered in this study was only recently implemented, the number of enrollees with repeated patient activation scores was small and we were not able to adequately test changes in patient activation following participation in the program. This represents an important area for future research. As mentioned above, results from additional analyses indicated that patient activation by itself was not significantly associated with better outcomes in this patient population, with the exception of A1C levels. Together, these findings suggest that the care management program and patient activation may work synergistically to produce better outcomes; patients may achieve the best outcomes when efforts to improve patient activation are supported by health plan coaching.

In contrast, there was no significant difference between CFA CM and usual CM enrollees with respect to medication adherence. One explanation for this finding could be that the impact on medication adherence was counteracted by benefit designs that result in high co-pays, coinsurance, and/or deductibles. Consistent with this explanation, some research has found that newly diagnosed patients, or patients with newly prescribed medications, were at high risk for not obtaining their medication due to first-fill failures, higher co-payment, and type of insurance.

 Assuming benefit-design decisions influence some service types and behaviors more than others, it is possible that the effects of such decisions were felt more acutely in the area of prescription drugs. It is also possible that the impact of higher cost sharing may have inadvertently impacted prescription refill compliance sooner than other metrics, such as utilization and clinical outcomes.

In contrast to expectations, this study found a significant negative association between CFA CM and LDL. One possible reason for this finding can be linked to the initial program design and the timing of implementation. In February 2013, the initial focus was on enrollees with diabetes; therefore, the CFA CM program was first implemented for patients with diabetes and later expanded to enrollees with other chronic diseases. The phased implementation of this program for conditions like hypertension and congestive heart failure meant that members of the CM team may have had less experience delivering CFA services to patients with these conditions. Thus, the negative relationship between CFA and LDL levels—a measure of dietary and health compliance for patients with cardiovascular conditions&mdash;may reflect an experiential learning effect whereby things “get worse before they get better,” as CM team members engaged in trial-and-error efforts to identify effective techniques for working with enrollees.

 If so, patients with these conditions may not have exhibited the same levels of improvement in behaviors, and subsequent lab values, because the CM team members were at different points on the learning curve with respect to cardiovascular conditions compared with patients with diabetes.

For sponsors and administrators of care management programs, the study findings suggest that bundling CM with CFA might be beneficial for managing the health and healthcare of chronically ill populations. As such, administrators may want to consider obtaining patient activation scores online as part of a health risk assessment for new enrollees, and annually upon reenrollment. Doing so is likely important for identifying whether CM and CFA are needed and for maximizing the effectiveness of implemented programs (eg, PAM scores can change throughout a person’s life due to life stressors).

 The benefits of these data from a population health management perspective, however, must be balanced against the cost of data collection.

There are several limitations that should be considered when interpreting the study findings. First, the study compared 2 types of care management: one that included coaching based on patient activation level and the other based on usual care. Due to the phased implementation (ie, initially targeting patients with diabetes and later all patients with any chronic condition) and retrospective nature of this study, it was not feasible to randomly assign enrollees to these 2 groups, so selection bias may exist. Further, although the multivariable models control for some differences between the 2 groups, not all potentially confounding variables (eg, disease severity) were available, and therefore, could not be included as covariates. Second, the analysis focused on commercial patients, and the study population was primarily male and younger (ie, aged 50-55 years). Focusing on this population ensured greater homogeneity among study participants and, thus, helped account for other confounders; however, it also limited the generalizability of the study findings. Third, because the same nurses provided CM services to both the CFA and the usual CM groups, it is possible that nurses’ interactions with and coaching of patients in the usual CM group could be influenced by their CFA training, despite the fact that those patients did not receive the CFA intervention. Lastly, there may be other programmatic factors that influenced the outcomes. For example, the consistency of coaching may have varied among care managers, thus allowing for variations in intervention implementation in ways that systematically affected the outcomes.

The aforementioned limitations highlight several opportunities for future research. This study focused on utilization, medication adherence, and clinical outcomes; however, there are other outcomes that may be of interest, including total cost of care per PAM level, enrollment in health programs (eg, nutrition counselling, smoking cessation), or more subjective assessments (eg, patient satisfaction, quality of life). Other researchers may also consider the care management intervention itself, such as the interaction style between the care manager and enrollees (eg, trust, mutual respect) or other patient populations, such as Medicare and Medicaid, in addition to different healthcare settings (eg, hospital, ambulatory) to assess the study’s generalizability.

These findings suggest that coaching interventions that are based on activation level may help care managers engage in more effective interactions that strengthen a patient’s role in managing his or her healthcare. For sponsors and developers of care management programs, these findings suggest that the programs that are more targeted in their application—rather than uniformly developed and implemented&mdash;may be an important factor in reducing utilization and improving clinical outcomes. 

Priority Health (CR), Grand Rapids, MI; University of Alabama Birmingham (LRH, JMS), Birmingham, AL.

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 (LRH, CR); acquisition of data (CR); analysis and interpretation of data (LRH, CR, JMS); drafting of the manuscript (LRH, CR); critical revision of the manuscript for important intellectual content (LRH, CR, JMS); statistical analysis (LRH, CR, JMS); provision of patients or study materials (CR); and supervision (CR). 

A health plan—sponsored care management program that included a coaching for activation intervention was associated with reduced emergency department visits and hospital admissions, and better clinical outcomes.

An Examination of the Relationship Between Care Management With Coaching for Activation and Patient Outcomes

 Multi-stakeholder healthcare alliances in the Robert Wood Johnson Foundation’s Aligning Forces for Quality (AF4Q) program brought together diverse stakeholders to work collaboratively to improve healthcare in their local communities. This article evaluates how well the AF4Q alliances were collectively positioned to sustain themselves as AF4Q program support ended.

 This analysis relied on a mixed-methods design using data from a survey of more than 700 participants in 15 of the 16 AF4Q alliances (1 alliance was unable to participate because it was in the process of closing down operations at the time of survey implementation), qualitative interviews with leaders in all 16 of the alliances, and secondary sources. Qualitative analysis of interview data and secondary sources were used to develop a classification of alliance strategic directions after the AF4Q program relative to their strategies during the AF4Q initiative. Descriptive analyses of survey data

were conducted in the following areas: (1) alliance priorities for sustainability, (2) alliance positioning for sustainability, and (3) alliance challenges to sustainability.

 The likelihood of sustainability and the strategic direction of the former AF4Q alliances are both decidedly mixed. A substantial number of alliances are at risk because of an unclear strategic direction following the AF4Q program, poor financial support, and a lack of relevant community leadership. Some have a clear plan to continue on the path they set during the program. Others appear likely to continue to operate, but they plan to do so in a form that differs from the neutral convener multi-stakeholder model emphasized during the AF4Q program as they specialize, make a major shift in focus, develop fee-for-service products, or focus on particular stakeholder groups (ie, employers and providers). In most cases, preserving the organization itself, rather than its programmatic activities from the AF4Q program era, appeared to receive the greatest emphasis in sustainability efforts.

 As their core strategy, most alliances will not perpetuate the original AF4Q program vision of diverse local stakeholders coming together to implement a prescribed set of aligned interventions centered on healthcare improvement.

Multi-stakeholder healthcare alliances—also known as collaboratives, partnerships, or coalitions&mdash;are organizations that bring together diverse stakeholders to work collaboratively to improve healthcare in local communities.

 Many of these organizations have been created in response to misaligned incentives and persistent poor quality of care, and are intended to promote local collaboration among stakeholders that have traditionally operated independently and sometimes in a conflicting fashion on health-related issues.

 An alliance’s ability to accomplish its goal of improving the value of healthcare is predicated on sustaining the interest and participation of community stakeholders over extended periods.

The Aligning Forces for Quality (AF4Q) program was the signature effort of the Robert Wood Johnson Foundation (RWJF) to improve the overall quality of healthcare in targeted communities, reduce racial and ethnic disparities, and provide models for national reform through activities rooted in the efforts of local multi-stakeholder alliances.

 An article by Scanlon et al in this supplement provides a detailed description of the specific components and development of the AF4Q program.

 As the program entered its final years, AF4Q alliances were asked by RWJF to consider their respective futures and how to build on the progress made during the program. Alliances were provided with technical support for that effort. Questions of sustainability were particularly germane because RWJF’s vision was not to permanently support these alliances but to “prime the pump” by giving alliances a chance to establish themselves and get new programs off the ground, or to strengthen existing multi-stakeholder approaches to launch or enhance programs.

The goal of this article is to provide an initial examination of how the AF4Q alliances were collectively positioned for the future as the AF4Q program ended. Using data gathered through an online survey, key informant interviews, and alliance documentation, the following were examined: (1) the strategic directions of the alliances after the AF4Q program, (2) priorities for sustainability for the alliances after the AF4Q initiative, (3) how well the alliances were positioned to accomplish their goals at the conclusion of the AF4Q program, and (4) perspectives on the likely challenges the alliances will face in pursing their goals. This article ends with a general conclusion about the state of AF4Q alliances and their collective future. Our assessment is intended to provide a window into how community-based, multi-stakeholder alliances transition from participating in a long-term program—characterized by direct funding, ample technical assistance, and prescriptive programmatic requirements&mdash;to a more uncertain, but open, future.

Conceptual Framework: the Central Importance of Value

A recent review of the literature on collaborative sustainability concluded that most studies emphasized operational and management aspects of collaboratives thought to promote sustainability but largely neglected first-order issues, such as the value provided by the collaborative.

 Research in this area typically assumes that collaboratives and their activities should be sustained, even in the absence of evidence that they provide value. Such assumptions are problematic because the absence of a clear value proposition that stakeholders agree on is likely to be a deterrent to stakeholders joining or maintaining membership in a collaborative, regardless of how well the alliance is managed. Conversely, if participants see value or have a hand in shaping the priorities and direction of the alliance, the chances of commitment and ongoing support by stakeholders increase.

Given these issues, the future of the AF4Q alliances was by no means clear or certain at the conclusion of the AF4Q program. Alliances faced the challenge of maintaining commitment and investment from diverse, sometimes disparate, partners while attempting to identify priorities and a strategic direction that create value for their stakeholders. To gauge how well AF4Q alliances were positioned to sustain themselves and their activities after the AF4Q program, we evaluated how alliance leaders and participants viewed their alliance and its likely future direction. We focused specifically on issues related to creating value for stakeholders and the community, notably the strategic direction of the alliance, opinions on necessary alliance priorities, assessments of current alliance positioning to sustain itself or its activities, and likely future challenges to alliance sustainability. Collectively, these areas provide insights into the potential value that may (or may not) accrue to the alliance as a result of different ways of using its resources in pursuit of a particular goal. In contrast to previous research that emphasized the specific actions of the alliance as an independent agent, we emphasize how alliance participants perceive value through their priorities and concerns about their alliance’s direction and likelihood of success.

This mixed-methods study utilized both qualitative and quantitative data collected from the AF4Q alliances. A brief description of each AF4Q alliance is located in the 

 of the article by Scanlon et al in this supplement (

 Qualitative data focused on the strategic direction of the 16 alliances after the AF4Q program relative to their strategic direction during the program. Strategic direction is defined as the alliance’s stated goals and the major course(s) of action the alliance has chosen to pursue them. Data on the strategic direction of the alliances following the AF4Q program were collected via a multi-stage process over the 6 months following the April 2015 end of the program. Specific qualitative data sources included key informant interviews with alliance staff and board leaders, alliance strategic plans and related process documents, and alliance website captures. Data on alliance strategies during the AF4Q program were collected through key informant interviews with alliance leaders and alliance documentation, including periodic progress reports that were submitted to the AF4Q National Program Office.

Quantitative data were drawn from an internet-based survey (alliance survey) of alliance participants in 15 of the 16 AF4Q alliances administered at the end of the AF4Q program (1 alliance was unable to

participate because it was in the process of closing down operations at the time of survey implementation). Survey content was based largely on the results of a systematic critical review of the literature on healthcare alliance sustainability.

 The survey sampling frame was developed from a comprehensive list of alliance participants provided by each alliance (ie, staff and consultants, board and leadership team members, committee and work group members, advisory group members, and members-at-large). The survey was conducted from June 2015 to September 2015.

The alliance leadership first sent a recruitment e-mail to all alliance participants. This e-mail alerted potential respondents to the survey request and served as an introduction to the purpose and administration of the survey. After the leadership’s introductory e-mail, the Penn State Survey Research Center sent a second e-mail containing the survey link to targeted respondents and subsequently followed up with 3 e-mail reminders, sent approximately 1 week apart. A total number of 638 individuals (38.6%) completed the whole survey (range across alliances, 21.8%-92.9%). In addition, 77 (4.7%) provided responses for a portion of the survey. Survey respondents included representatives from a broad range of stakeholder groups including insurers (9.9%), providers such as physicians and hospitals (26.1%), government agencies (11.2%), employers (5.1%), consumers (4.5%), alliance staff (17.5%), and unaffiliated participants (2.1%).

A classification of alliance strategic directions was developed to group alliances into distinct categories based on a comparison of their strategies and focal activities during the AF4Q program against their anticipated strategic direction after the program ended. A primary research associate developed a table for each alliance that summarized its programmatic focus during the AF4Q program and its plans following the AF4Q initiative. A second research associate and the lead investigator reviewed each table. When information gaps were identified, additional existing data were consulted (ie, past key informant interview transcripts and alliance documents on file) to verify or clarify the table. Using a predefined set of mutually exclusive categories developed by the authors and the summary tables, the lead investigator and the primary research associate independently assigned each alliance to 1 of the 3 classification system categories and then compared their assignments. While there was agreement across the classification for nearly all of the alliances, any classification decisions that differed for the 2 team members were discussed by the full set of authors before final assignment to a category.

The quantitative analysis utilized 2 univariate statistics for 12 survey items grouped into 3 categories: (1) alliance priorities for sustainability, (2) alliance positioning for sustainability, and (3) alliance challenges to sustainability. The first univariate statistic was the top-box score,

 which reflected the percentage of respondents who chose either of the 2 highest scale responses for each of the items (ie, “high priority” or “essential”; “agree” or strongly agree”; and “very likely” or “completely likely”). The second univariate statistic was the coefficient of variation (CV), a standardized measure of dispersion calculated as the standard deviation divided by the mean. A larger CV indicates less agreement or consensus regarding an item. Both statistics were constructed as an overall mean across respondents from all alliances and separately for respondents from each alliance.

Because absolute standards regarding magnitude for top-box and CV do not exist, interpretations of these statistics focused on relative values across survey items (ie, 1 item relative to another). Given the aim of providing a summative description of how well all AF4Q alliances were collectively positioned to sustain their efforts after the AF4Q program, the discussion of survey results emphasizes relative patterns across all alliances rather than specific results for individual alliances. Data for all 15 alliances are provided to allow more nuanced assessments of any general patterns or exceptions, when appropriate. Select responses to the open-ended question in the survey (“Please let us know what other thoughts you have about the effectiveness, future, or sustainability of the alliance”) provide specific examples of respondents’ opinions about alliance priorities, positioning, and challenges to sustainability. Descriptive findings and categorization of the alliances’ strategic direction following the AF4Q program, based on the strategic direction classification system results, are presented first and are then followed by key differences and similarities among responses to survey items within categories, based on top-box scores and degree of agreement (ie, CV).

Strategic Direction Classification Results

To provide a broad description of where AF4Q alliances appeared to be headed at the conclusion of the program, we identified 3 major categories and 3 subcategories of strategic directions pursued by the 16 AF4Q alliances. The main categories were: (1) no clear strategic direction, (2) pursuing the same strategic direction following the AF4Q program, and (3) pursuing a change in strategic direction after the AF4Q program. This final category was further broken down into 3 subcategories: expansion, retrenchment, and reinvention. Definitions of each category, and the frequency with which they were obtained among the 16 alliances, are presented in 

Perhaps the most striking finding is that the largest category (7 alliances) was represented by alliances that did not indicate a clear strategic direction following the AF4Q program. Although all alliances in this group engaged in a strategic planning process with their stakeholders or boards of directors, the process did not result in a clear direction or value proposition by the end of the summative data collection period. For example, 1 alliance in this group underwent a leadership change at the end of the AF4Q program, which stimulated a systematic reassessment of the alliance’s strategic direction. Although the alliance director was able to articulate a new strategy for the alliance, it had yet to be vetted by the board. Further impeding the potential change in strategy was the fact that the alliance’s current sources of funding emphasized different priorities and therefore did not provide strong fiscal incentives for setting a new direction. In another instance, an alliance restructured to emphasize a purchaser-led approach to its work following the AF4Q program. However, it was not clear how this change in focus would affect the alliance’s strategic direction and how the alliance would organize its work to accomplish its priorities. Also included in this category were several alliances that struggled to clearly define their focus after the end of the program and 2 alliances that ceased to exist as organizational entities at the conclusion of the AF4Q initiative.

The second major category consisted of 4 alliances whose strategic directions were essentially unchanged from the approaches taken under the AF4Q program. One alliance in this category defined its strategic direction early on in its history (prior to joining the program) and has not deviated from that direction. This alliance routinely prepares a master 5-year strategic plan and annual plans based on the master plan. During its participation in the AF4Q program, it did not fully embrace all aspects of the initiative; at times, the alliance negotiated with RWJF to maintain its traditional mission. In contrast, a second alliance in this category was created de novo for the AF4Q program and attained nonprofit status during the program. Following the AF4Q initiative, this alliance continues to work within the strategic framework it established during the program and is expanding its activities and reach within this framework.

The final category, change in strategic direction, comprises 5 alliances. Although each signaled a change in strategic direction relative to the strategies pursued under AF4Q, the nature of their changes was markedly different. The largest subcategory (ie, expansion) included 3 alliances that adopted a strategy of adding 1 or more new major goals and related activities while continuing the strategies undertaken during the AF4Q program. As an example, one alliance’s expansion was driven by community leaders’ recommendation to formally merge the alliance and 2 other health-related organizations. The strategic direction and programs of the resulting organization extend well beyond the original alliance’s direction and programs, including activities such as disaster preparedness education and training, healthcare workforce assessments, group purchasing, and physician recruitment.

A single alliance made up the subcategory of retrenchment. Alliance leaders in this community conducted a comprehensive review of their alliance’s work and concluded that the alliance should return to its “roots” as a provider-driven, dues-paying organization that provides a service/product to its members. As a result, the alliance has concentrated its efforts in a few areas perceived to be its core strengths.

The final subcategory was represented by one alliance that adopted an entirely new strategic direction with limited connection to the activities pursued under the AF4Q program. This alliance is pursuing an integrated, whole community approach to health through a purposeful alignment of healthcare system, social service, and community resources.

) are displayed for 15 AF4Q alliances. Because partnership alliances (3 or more distinct organizations collaborating to guide the implementation of the AF4Q program) differ structurally from the majority of AF4Q alliances, survey results for these alliances are provided separately.

On average, alliance participants assigned differential priority to the 4 listed strategic objectives: preserving the alliance as a viable organization, maintaining alliance status as a neutral convener, maintaining current programs/initiatives, and starting new programs and expanding reach (

). Alliance participants assigned the highest priorities to goals related to maintaining the alliance as a neutral forum (grand mean [GM] = 0.85) and preserving the viability of the organization (GM = 0.79). These priorities also received the most agreement among survey respondents (CV = 19.6 and 22.1, respectively). Sustaining existing initiatives received lower priority (GM = 0.59) and somewhat more disagreement relative to the other listed priorities (CV = 24.5). By contrast, there was relatively higher priority and agreement given to starting new initiatives or serving new populations (GM = 0.64, CV = 23.2). When examining the survey findings across alliances, it is notable that participants in all 3 partnership alliances gave substantially less priority to preserving the alliance as a viable organization relative to other alliances, but assigned somewhat higher priority to preserving or sustaining existing alliance programs.

Four indicators of how well participants viewed their alliance’s current positioning for sustainability were assessed: having a clear set of future goals, a coherent strategy to achieve those goals, having the appropriate community partners, and having sufficient financial resources (

). Respondents were more positive about alliance positioning with respect to clarity of alliance goals (GM = 0.74) and were generally less positive about whether the alliance had appropriate community partners (GM = 0.37) or sufficient financial resources to achieve its future goals (GM = 0.11).

Of the 4 indicators, the most negative perceptions were expressed about the sufficiency of financial resources. Participants in the 3 partnership alliances expressed somewhat more positive assessments of having the right community partners/leaders to accomplish their future goals than those in the other alliances, but were less positive about the adequacy of financial resources than those in the other alliances. Finally, none of the 15 alliances displayed high positive assessments across all 4 elements of sustainability positioning, indicating that all of the pieces may not currently be in place to ensure sustainability for alliances, generally or specifically. In general, alliance participants showed less agreement (ie, a higher CV) about whether the alliance had the right community partners (CV = 31.1) or sufficient financial resources (CV = 39.8) than they did on clarity of alliance vision (CV = 24.1) and having the right strategies to achieve future goals (CV = 23.6).

Alliance participants were asked to assess the likelihood of 4 major potential challenges to their alliance’s future sustainability: attracting or keeping the “right” people (defined as people with the skills, talents, or political connections needed) on the alliance board/key leadership group, getting broad-based support from the community, securing financial resources, and competition from other entities/organizations doing similar work (

). Overall, alliance participants expressed only modest concerns about the likelihood of future challenges (range of grand means, 0.10-0.29). Of the 4 listed challenges, alliance participants expressed relatively more concern about potential competition from other organizations (GM = 0.29) and the ability to secure financial resources (GM = 0.24). Considerably less concern was expressed about meeting challenges related to the commitment and participation from leadership and the community at large.

The level of agreement about the likelihood of these challenges varied across respondents. The least agreement regarding likelihood of alliance challenges appeared to be in the areas of competition from other organizations (CV = 44.9) and attracting or keeping the “right” people on the alliance board (CV = 46.2), suggesting that these challenges were not viewed by all alliance participants with the same level of concern.

Five major themes emerged from classification of the alliances’ strategic directions and analysis of the survey data. These are discussed below and include illustrative examples from the survey respondents’ open-ended comments.

Many Alliances Are Still Working to Identify a Clear Strategic Direction for Their Future Following the AF4Q Program

Sustainability planning requirements and technical assistance for the design of community-specific sustainable business models were built into the final 2-year phase of the AF4Q program. Despite these requirements and support, many alliances were not able to finish the program with a clear strategic direction or value proposition. These alliances were confronting a host of issues, such as developing a clear identity in the community, balancing a pressing need for resources against the risk of diluting their core mission, creating programs of value for key stakeholders, and managing the potential conflict between developing fee-for-service, revenue-generating products and maintaining their role as a neutral convener. As noted by one respondent, “The alliance is at an inflection point—moving from grant dependence to services and program dependence, but without losing the role of neutral convener.”

Additionally, prior research on alliances indicates that capacity building often conflicts with other priorities early on in their development as organizations.

 For example, funding agencies that stress accountability create pressure to produce and measure short-term outcomes, possibly at the expense of processes that support and sustain long-term capacity.

 Paradoxically, programs that focus more on capacity-building activities have been shown to obtain less external funding than programs that predominantly focus on measuring short-term results.

 Similarly, research has found that the demands of implementing programs can undermine long-term capacity-building goals, as the operational and monitoring requirements of implementing these programs can dominate staff time and effort, resulting in a loss of focus on long-term program goals.

 All of this suggests that long-term programs that focus on externally driven interventions may distract communities from building internal capacity and decision-making processes.

The Desired Future Goals for Alliances Are Often Clearer Than the Means to Get There 

In relative terms, participants were more positive about the alliance having established a clear and important direction heading into the future than they were about whether their alliance currently has the capacity to achieve its vision. Doubts about current positioning for sustainability in the areas of a clear strategic plan, financial support, and a lack of involvement of community leadership created uncertainty about the future of the AF4Q alliances, despite relatively stronger participant support for the stated goals of their alliance. As one respondent expressed, “[The alliance] should be a viable organization and should play a lead role in communitywide health initiatives. Funding and sustainability are serious concerns. Pilot programs with grant funding are difficult to transition into scalable and sustainable models that move the needle.” This is not altogether surprising given the end of a major funding program and the inherent fragility of alliances. To their credit, however, most alliances have recognized the significance of this critical juncture and have employed resources and technical assistance from the AF4Q program to try to map a vision for the future and a strategy for realizing that vision.

The relatively lower confidence expressed in appropriate community leadership and finances is consistent with other research suggesting that alliances often face greater difficulty establishing consensus among participants when trying to operationalize their broad-based vision into more tangible actionable plans. Such transitions often require more sustained negotiation to resolve conflicts and tensions that emerge among stakeholders during this process.

 Greater disagreement about these specific issues may also be a reflection of the changing environment that has created more uncertainty for many participants about their future in a more competitive landscape. Healthcare reform, in particular, has resulted in consolidation among providers in many states and communities, as well as the incubation of other multi-stakeholder efforts in some of the AF4Q program regions. Under these conditions, participants may share similar opinions about the alliance’s goals but have greater doubts and more divergent beliefs about recruiting and retaining the requisite partners and the resources they can provide for alliance activities.

Sustainability Priorities Emphasize More Focus and More Flexibility

As noted above, alliance participants assign high priority to the preservation of the alliance as a neutral convener and to preserving the alliance as a viable organization. As one respondent stated: “[The alliance] is an organization uniquely chartered to help keep the marketplace honest and accountable (proprietary interests of the stakeholders notwithstanding) and needs to be preserved.” These priorities reflect infrastructure support and development, which many alliances have found difficult to fund. By contrast, alliance participants placed considerably less emphasis on maintaining the current initiatives of the alliance going forward. Alliance participants appear to want their alliances to move in new directions programmatically and are willing to abandon or substantially change their existing initiatives as they focus on sustainability planning. This suggests that current programs are too costly to maintain (individually and collectively) or lack stakeholder support, or that other programs may simply be a better fit for the alliance and the current needs of the community.

Survey respondents provided support for these diverse explanations: “[The alliance] used to be able to do 2 or 3 programs/objectives well and now it seems to have diversified into many programs/objectives, but none of them seem to be done with the same excellence. A company that intends to be around for a long time needs to make the difficult decision of choosing a few programs, do them consistently well, and allow them to morph as necessary.” Or, as another respondent stated: “With the end of RWJF funding, I believe it will be difficult to raise the funds to maintain the status quo. A reinvention of their [AF4Q] objectives will be required to become more efficient.” This suggests that first-order priorities are the infrastructure and role of the alliance in the community, not the specific initiatives that funders have supported in the past.

This may also speak to the desire of alliance participants to have the alliance be a vehicle for sustaining collaboration in the future and for maintaining the flexibility to respond to community health challenges as they arise. The notable exception to this pattern was partnership alliances, which typically lack the formal structure and governance arrangements of other AF4Q alliances. Because of these differences and their possible implications for sustainability, future research should contrast this more informal type of collaboration with alliances that are organized as more traditional stand-alone entities to assess which form engenders greater stakeholder commitment and more responsiveness to changing community needs.

Financial Support and Community Leadership Are Missing Pieces in the Sustainability Puzzle

If there was one major area of current concern that alliance participants consistently expressed, it would be the lack of sufficient financial support to accomplish the goals laid out in the AF4Q strategic plan. This was echoed repeatedly by the comments of survey respondents who emphasized that grant funds would not be a sustainable source of support for the alliance and its initiatives going forward. As one respondent noted, “Much of the work has been grant-funded, but now the community needs to decide the relative value of the services and impact and fund appropriately.” This suggests that if the alliance provides services or programs that are valued by the community, then community stakeholders will be more likely to provide support through membership dues, sponsorship, or in-kind contributions.

There was also concern expressed about having the right community leadership and support, which was somewhat surprising because the AF4Q program placed considerable emphasis on the importance of having key stakeholders involved. This may stem from several factors. First, alliance participants may feel that a leadership change is necessary to move the alliance in new directions after the AF4Q program concludes and that these new strategies might require a set of experiences and skills different from those needed to manage programs under a large grant like the AF4Q initiative.

 In a related vein, it may also suggest that the current composition of the alliance or its governing board is missing key players in the community to successfully take it in a new direction. As one respondent noted, “Sustainability is tied to developing linkages with organizations/entities beyond those currently involved in the alliance.” It could also signal that the alliance needs to focus on getting key decision makers from participating organizations directly involved in their work rather than lower-level staff or representatives who are not empowered to make decisions about resource expenditures and strategic direction for their home organizations.

Overall, alliances that facilitate more congruence between alliance and participant goals can provide a foundation for more effective coordination of effort and stimulate internalization of alliance goals and those of participating organizations.

 This may engender more deeply rooted institutionalized change that extends beyond the work of the alliance and results in sustainable communitywide improvements in health. Obviously, this is a greater challenge if key leaders are not involved in alliance decision making, are unaware of the alliance’s initiatives, or see participation in the alliance as a “community service” obligation rather than a core activity. Indeed, participation itself may be a catalyst for change, as prior research shows that the level of alliance participation is significantly associated with a participant’s level of engagement and perception of benefits and costs.

Stakeholder Optimism About Meeting Future Sustainability Challenges is High

Taken together, alliance participants did not view the future challenges to alliance sustainability as particularly significant. These findings stand in contrast to alliance participants’ views of current strategic positioning where there was much more concern expressed about several of the foundations for sustainability. Divergence between current realities and perceptions of future challenges may not be surprising given that responses to questions about future states may require more conjecture on the part of respondents as opposed to, say, how well the alliance is currently positioned for the future.

 In this case, such biases may have resulted in participants being more optimistic about an alliance’s ability to overcome its challenges, despite the recognition of its current deficiencies.

Additionally, the general lack of significant concern about the likelihood of sustainability challenges does not mean that these future challenges do not exist, only that they are seen by many as surmountable. Further, this optimism is not universal. For example, some voiced strong opinions that financial support was going to be critical for alliance sustainability and success going forward: “Admirable mission, but inadequate community leadership participation and funding sources are challenges to be fully successful.” Similarly, some open-ended comments reflected concerns about the inherent competing roles and interests of participants in a multi-stakeholder alliance: “Sustainability challenges are heightened by the unusual fact that essential alliance members can act in several or all of the following capacities: key supplier, valued customer, direct competitor, substitute service provider, or [a] barrier to pursuit of new activities. It is difficult to think of another industry with such role fluidity.”

Larry R. Hearld, PhD

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