Quality improvement collaboratives (QICs) have emerged as an important strategy to systematically improve processes and outcomes of care through interorganizational learning. QICs are most commonly characterized as organized “group learning initiatives” in which clinicians—often from different departmental units or organizations—come together at regular intervals to learn how to improve the provision of care for focal clinical conditions (Nadeem, Olin, Hill, Hoagwood, & Horwitz, 2013, p. 359). Members of these collaboratives develop measurable targets, gather data at regular intervals as a means of assessing their progress toward those targets, and initiate process changes on a small scale “to advance reinvention and learning by doing” (Schouten, Hulscher, van Everdingen, Huijsman, & Grol, 2008, p. 2). Project leaders or experts in quality improvement (QI) and the clinical topic of interest often guide team members in these pursuits. QICs are conceptualized as a means by which to integrate QI efforts into the heart of systemic organizational transformation (Shaw et al., 2013).
Two systematic reviews evaluated the impact of QICs on improvements in quality of care and found mixed results with regards to participation in collaboratives and the attainment of clinical and QI goals (Nadeem et al., 2013; Schouten et al., 2008). Other work examining specific QICs suggests that they are capable of improving processes of care and exert positive influence on clinical outcomes (Strating, Broer, van Rooijen, Bal, & Nieboer, 2012; Young, Glade, Stoddard, & Norlin, 2006). Evidence also suggests that internal organizational capabilities may enable participation in interorganizational learning activities such as collaboratives, yet few studies have identified the organizational factors that support physician practice engagement in QICs (Deo et al., 2009). Such engagement typically requires substantial time and resources on the part of practices, both in terms of formal participation of clinicians in collaboratives as well as the effort needed to apply lessons from collaboratives to improving processes of care within the practice setting (Ovretveit et al., 2002). In one study, Nembhard found that use of interorganizational learning activities, such as conference calls and monthly reports, had positive impacts on performance improvement for participants across four Institute for Healthcare Improvement-sponsored collaboratives. Use of intraorganizational activities, such as plan-do-study-act (PDSA) cycles in which organizations plan, test, and evaluate new interventions, was also shown to increase the odds of performance improvement (Nembhard, 2012). These results suggest that internal practice capabilities in addition to interorganizational learning play a central role in QI team engagement in QICs and the success of these efforts.
Past work has tended to focus upon interorganizational learning among hospitals or large health systems (Nembhard, 2012; Ratnapalan & Uleryk, 2014), but there has been a dearth of research examining QIC participation among physician practices. Recent initiatives such as the Centers for Medicare and Medicaid Services’ State Innovation Model Initiative, Transforming Clinical Practice Initiative, the Medicaid Program, and the Child Health Insurance Program promote QICs for improving quality of patient care (Centers for Medicare and Medicaid Services, 2016a, 2016b; Maine Department of Health and Human Services, 2015). As more physician practices align with accountable care organizations (ACOs), an exploration of how ACO affiliation is associated with physician practice participation in QICs is especially timely (Muhlestein, 2015). Previous research indicates that structural attributes can have profound impacts on organizational capacity to engage in QI initiatives (Alexander & Hearld, 2011); we also examine the role of ownership in practices’ propensity to take part in collaborative. Finally, we explore the extent to which practice use of QI methods such as Lean, Six Sigma, and use of PDSA cycles mediates the relationship between ACO affiliation and QIC participation, as QI methods may provide a foundation for the activities practices take part in when engaging in QICs. The relation of use of these practice capabilities and QIC participation remains unexplored in the literature.
The Consolidated Framework for Implementation Research highlights the organizational factors that influence the effectiveness of QI interventions in health care delivery organizations (Damschroder et al., 2009). The Consolidated Framework for Implementation Research emphasizes that both the inner and outer settings relevant to a given intervention play significant roles in implementation effectiveness. The outer setting encompasses externally imposed incentives or policies and competitive pressures stemming from the organizational field, whereas the inner setting includes such characteristics as structure, leadership, and culture (Damschroder et al., 2009). We conceptualize the inner components as those existing at the organization level, whereas the outer component represents an aspect of the regulatory environment. In the case of QIC participation among physician practices, the inner setting includes practice ownership, practice size, ACO affiliation, health information technology (HIT) functionality, percentage of practice revenue derived from Medicaid and uninsured patients, and use of various QI methods, whereas the outer setting includes public reporting of quality metrics by external entities. The figure depicts the conceptual model guiding our inquiry (Figure 1).
Engaging in QI initiatives necessitates both the proper infrastructure and adequate resources within the organizational setting. Past empirical work suggests that capital is more readily accessed by practices belonging to systems (Robinson & Casalino, 1996; Rodriguez, McClellan, et al., 2016). Such a conclusion aligns well with a resource dependence perspective, as ownership represents one basis for power among organizations, which in turn affects an organization’s ability to satisfy external environmental pressures (Aldrich & Pfeffer, 1976). Practices owned by systems may also benefit from “greater managerial and resource planning expertise” (Shortell et al., 2005, p. 417). In turn, system-owned practices may find it easier to absorb the time and resource requirements necessary to send teams of clinicians to participate in QICs. In addition, the structural alignment of multiple practices owned by a system is expected to contribute to greater uniformity both from a management and operational perspective. For example, the integration of physician practices into a large health system in Pennsylvania facilitated strong programmatic support of new clinical programs and initiatives (Levin & Gustave, 2013). Thus, practices within a system may have more support to engage in QICs, in contrast with independent practices that are physician-owned, which may lack the slack resources (Nohria & Gulati, 1996) to participate in QICs.
Hypothesis 1: Practices that are owned by health care systems are more likely than physician-owned practices to take part in QICs.
Since 1998, the Health Resources and Services Administration’s (HRSA) Bureau of Primary Health Care has sponsored health disparities collaboratives (HDCs). These collaboratives are often composed of 20 or more community health centers (CHCs) engaged in learning sessions in which best practices to more effectively manage chronic conditions are shared and formal instruction in QI techniques such as PDSA cycles is provided (Landon et al., 2007). Past HDCs have addressed topics that are of greatest relevance to the patient population served by CHCs, such as diabetes prevention and care, depression, asthma, cardiovascular disease, and cancer. Other health disparities-related topics, such as infant mortality, have also been a focus of QICs (Agency for Healthcare Research and Quality, 2008), as these health disparities are especially challenging for CHCs to address due to the vulnerable populations they serve.
HDCs differ from other types of QICs in that they are specifically designed for CHCs and they provide additional infrastructure in the form of regional coordinators and HIT support (Chin et al., 2007). Grossman et al. reported that as of 2008 over 90% of CHCs funded by HRSA had taken part in at least one HDC (Grossman et al. 2008), but more current data are not available. Such high participation rates in HDCs may be influenced by normative pressures experienced by CHCs as they strive to meet the needs of their patient populations and are subject to a shared set of quality metrics set by HRSA. Coupled with HRSA’s direct oversight of CHCs and the direct support provided for CHCs to engage in collaborative learning through HDCs, we expect that such factors will encourage CHCs’ participation in these types of QICs.
Hypothesis 2: CHCs are more likely than physician-owned practices to participate in QICs.
The rise of new organizational forms such as ACOs and the subsequent affiliation of physician practices with systems and networks are also expected to influence practices’ decisions to participate in QICs. The defining characteristics of ACOs include a distinct emphasis on primary care and preventive medicine, accountability for the achievement of quality and cost benchmarks, and the use of payment strategies such as global budgets and shared savings in lieu of fee-for-service (McClellan, McKethan, Lewis, Roski, & Fisher, 2010). ACOs are held accountable for quality and continuity of care at the same time that they are charged with generating shared savings through their relationships with large payer groups such as Medicare. An explicit emphasis is placed upon reducing care that does not add value to patients as well as reducing readmissions and unnecessary emergency department visits within a broader environment of advancing quality while constraining costs.
Although market forces serve as one stimulus for organizations to achieve efficiency, institutional forces such as the requirements placed on ACOs prompt conformity to environmental expectations and norms (D'Aunno, 1991). In addition to securing material and technical resources, attaining institutional credibility is equally critical to organizational survival. Influential organizations establish norms that lay the foundation for other legitimacy-seeking organizations within the field (Scott, 2000). Isomorphism, which can act through coercive, mimetic, or normative channels, describes the adoption of these norms as organizations vie for legitimacy (DiMaggio & Powell, 1983). Legitimacy functions as an outward signal to other organizations of alignment with shared values and norms, which in turn enables access to critical resources and support.
Researchers have previously highlighted the strong normative pressures to which ACOs are subject by virtue of their mission to deliver high-quality, cost-effective care (Shortell, Wu, Lewis, Colla, & Fisher, 2014). These same normative pressures may create the conditions under which ACOs enable practices to maintain legitimacy. QIC participation can signal a QI orientation to key stakeholders and all the physician organizations to remain abreast of best practices simultaneously, enabling the delivery of high-quality care for which ACOs are financially accountable.
Hypothesis 3: ACO-affiliated practices are more likely to participate in QICs than practices not affiliated with ACOs.
Study Design and Sample
We utilize data from the third wave of the National Study of Physician Organizations (NSPO3) (2012–2013) for this analysis. NSPO3 is a national survey of physician organizations that includes information regarding size, ownership, specialty mix, and patient demographics of these practices. Internal organizational capabilities such as HIT functionality and use of care management processes to care for patients with chronic diseases such as asthma, congestive heart failure, depression, and diabetes are also included. This 40-minute phone or Website survey was fielded to physician leaders or practice managers, achieving an overall response rate of 50% (n = 1,398 organizations). Following exclusion of 39 practices for which data were missing, our analytic sample includes 1,359 practices (97% of respondents). An article by Wiley et al., which includes details about the administration of NSPO3, found only small differences between respondents and nonrespondents to NSPO3 (Wiley et al., 2015).
QIC participation among our sample of practices was determined through a binary (yes vs. no) response to the following question, “Does your practice use the following formal and systematic quality improvement system: quality improvement learning collaboratives?” Participation in a QIC was assessed separately from use of other common QI methods, such as PDSA cycles, Lean production techniques, and Six Sigma.
Practice ownership was determined based on a response to the following question: “Who owns the equipment and employs the nonphysician staff of your practice?” Practices are classified as system-owned when the response to the above question is a “hospital,” “hospital system,” “health care system that is not an academic medical center,” or an “HMO or other insurance entity.” Practices are categorized as physician-owned if the answer to the above question is “physicians in the practice” or “nonphysician managers.” To ensure that we classified CHCs correctly, we examined survey responses to three questions: where respondents reported their organization’s name, the ownership of their organization, and whether their organization identified as a “community clinic.” Respondents were able to provide open-ended answers for each of the three questions. Responses for each question were searched for the terms “community,” “health center,” “nonprofit,” and similar derivatives of those terms. Extensive research, including online searches, was conducted for all practices flagged on any of the search terms. Specifically, organizations were considered CHCs when (a) they identified as a federally qualified health center or a federally qualified health center look-a-like, (b) they were found in a national listing of “Health Centers and look-a-like Sites” published by the HRSA, or (3) an online search showed that practices held values consistent with being CHCs (offered financial assistance, serve patients “regardless of ability to pay,” or provide comprehensive care “for the community”).
ACO affiliation is a binary variable indicating whether a practice had joined a public or private ACO by 2012.
We include four control variables in our models, three at the organizational level and one at the environmental level. First, we include practice size, which is categorized according to the number of physicians across all practice locations (1–2 physicians, 3–9 physicians, 10–19 physicians, and 20 or more physicians). HIT functionality measured on a scale from 1 to 14 comprises such measures as the use of an electronic medical record with progress notes, medication lists, problem lists, and alerts for drug interactions and abnormal test results. Other capabilities included in this measure are the ability to communicate with patients via e-mail, e-prescribing, and registries for chronic diseases (Rodriguez, Henke, Bibi, Ramsay, & Shortell, 2016). HIT functionality is included as a control because HIT can impact health care organizations’ ability to continuously monitor progress toward QI goals (Li, 1997). Practices with a greater proportion of revenue from commercially ensured patients are expected to have relatively more resources to invest in activities such as QI when compared with practices serving more Medicaid or uninsured patients. For this reason, we also include the percentage of a practice’s revenue derived from Medicaid and uninsured patients as a control variable. At the external environmental level, we control for public reporting of quality metrics by external entities. Practices may be incentivized to participate in QICs to the extent that it contributes to reporting higher-quality scores on reported measures as part of these external initiatives.
Unweighted chi-square tests were used to examine differences in QIC participation across categories of ownership, ACO participation, and all key variables used in the study. Then, multivariate logistic regression with survey weights were estimated to explore the association of ownership, ACO participation, and physician practice propensity to participate in QICs, controlling for practice size, HIT functionality, percentage of practice revenue from Medicaid and uninsured patients, and public reporting of quality metrics by external entities. Alternative specifications of the regression model were examined to assess the sensitivity of our main results to consideration of other QI methods used by physician practices, including Lean, Six Sigma, and PDSA. We used the Sobel–Goodman Test to explore the mediating influence of these QI methods on the relationship between ACO affiliation and QIC participation. We calculated the mediating effects of Lean, Six Sigma, and PDSA cycle use, both independently and when one or more of the QI methods were used. We specified two mediation models to compare estimates that incorporated versus excluded control variables. We also computed the variance inflation factor for each independent variable to determine whether multicollinearity was present. All statistical analyses were completed using STATA 14.0 The research protocol was approved by the Institutional Review Board of the University of California, Berkeley.
Table 1 includes the survey weighted means and standard deviations for all variables in this analysis. Among the 1,359 physician practices, 13% (n = 185) participated in a QIC. Nearly 20% of all practices were affiliated with an ACO in 2012. Most practices (82%) were physician-owned, whereas nearly 14% were hospital- or health system-owned and the remaining practices (4%) were CHCs. Approximately 38% of QIC participating practices were affiliated with an ACO. Thirty-two percent of system-owned practices participate in QICs. Approximately 37% of CHCs took part in QICs. Tests examining the association of ACO affiliation and QIC participation and the association of ownership categories and QIC participation were both statistically significant (p < .05): X(1, N = 1,359) = 28.61, p < .01 and X(2, N = 1,359) = 114.19, p < .01.
As reported in Table 2 (Model 1), system-owned practices had three times the odds of QIC participation compared to physician-owned practices. This result did not, however, reach statistical significance (odds ratio [OR] = 3.05, p = .08). Thus, we found partial support for Hypothesis 1, which suggested that system-owned practices would be more likely to participate in a QIC relative to physician-owned practices. CHCs had six and a half times the odds (OR = 6.57, p < .001) of QIC participation compared to physician-owned practices, providing strong support for Hypothesis 2. We posited that ACO affiliation would be associated with practice propensity to participate in a QIC (Hypothesis 3). Model 1 results indicate that ACO-affiliated practices had greater odds of QIC participation compared to practices not affiliated with an ACO (OR = 1.51, p < .05), thus providing support for Hypothesis 3.
Results of multivariate analyses indicate statistically significant relationships between several control variables and practice participation in QICs (Table 2). Larger practice size was associated with greater odds of participation in QICs, where practices composed of 20 or more physicians had 14 times the odds (OR = 14.72, p < .005) of participating in a QIC relative to practices with one to two physicians. For each unit increase in the HIT capability of a practice, the likelihood of engaging in a QIC increased by 15% (OR = 1.15, p < .01). Finally, those practices whose clinical quality data were publicly reported by external entities had nearly three times the odds of QIC participation (OR = 2.90, p < .01) compared to practices not publicly reporting such information.
Other QI methods such as use of Six Sigma, Lean, and PDSA cycles appear to partially explain the association between ACO affiliation and QIC participation (Table 3). Results of the Sobel–Goodman tests of mediation that excluded control variables indicate that use of PDSA cycles and Lean mediates 35.2% and 36.0% of the effect of ACO affiliation upon QIC participation, respectively. Use of Six Sigma also mediates the effect of ACO affiliation upon QIC participation, but to a lesser degree (by approximately 28.6%). Use of one or more of the QI methods accounts for just over 58.2% of the ACO affiliation and QIC participation relationship. When control variables are included in the mediation analyses, use of one or more of the three QI methods still mediated a substantial proportion of the ACO affiliation–QIC participation relationship (46.7%).
Variance inflation factor results indicate that no variables attained a VIF value of above 3.9. On the basis of a conservative threshold of 5.0 (Acock, 2014), we conclude that multicollinearity was not a concern for our analyses.
This study aimed to better understand the relation of ACO affiliation and practice ownership on the propensity of physician practices to participate in QICs. Our findings provide important insights into the organizational and contextual factors associated with physician practice engagement in QICs. Our first hypothesis concerning the influence of system ownership on QIC participation was partially supported. Larger practice size, rather than system ownership, appears to have a greater influence on practice participation in QICs. Given that larger practices were more likely than small practices to participate in QICs, it may be that larger physician organizations, irrespective of ownership, have the scale and slack resources to participate in QICs. The ability to spread costs over a large number of practice sites and to disseminate best practices from one site to other sites may make QIC participation a more worthwhile investment for large physician organizations compared to small physician organizations.
Our second hypothesis posited that CHCs would be more likely than physician-owned practices to take part in QICs. Our results indicate that HRSA’s administrative and financial support to many CHCs as well as their role in organizing QICs may enable CHCs to participate in QICs. In support of our third hypothesis, ACO affiliation was positively associated with practice participation in QICs. For example, Oregon’s coordinated care organizations, which provide care to the state’s Medicaid population, participate in a statewide QIC. The Oregon Transformation Center integrates data and analytic tools for the entire community of coordinated care organizations while facilitating collaborative learning activities to advance evidence-based care and to reduce unnecessary utilization (McConnell et al., 2014). To the extent that normative and financial pressures encourage practices affiliated with ACOs to achieve quality benchmarks, the sharing of best practices via participation in QICs may serve as one important avenue by which ACOs support the provision of high-quality care to their patient populations. Whether normative institutional pressures underlie ACO-affiliated practices’ greater propensity to participate in a QIC deserves further empirical attention, especially in light of the growing number of practices joining ACOs (Muhlestein, 2015).
The Sobel–Goodman Test results (Table 3) suggest that practice use of other QI methods, such as PDSA cycles, Lean, and Six Sigma, partially mediates the effect of ACO affiliation upon QIC participation. Use of PDSA and Lean have the strongest mediating effects, with each method mediating over one third of the total effect of ACO affiliation on increased odds of practice participation in QICs. Because the use of any of the three QI methods has a sizable mediating effect, the promotion of QI methods by ACOs may enable practices to be better positioned to take advantage of interorganizational QI opportunities.
A number of limitations should be noted. For example, the 50% response rate to the NSPO3 survey suggests that we cannot rule out the possibility of differential nonresponse. Also, the NSPO3 data are based on a single respondent and, therefore, subject to potential single informant bias. But this is mitigated to some extent by identifying the most knowledgeable physician or administrative leader to respond to the factual questions. Furthermore, although NSPO3 provides information about whether or not a physician organization participated in a QIC, it does not include specific details such as whether practices participated in multiple QICs, when QIC participation occurred, the clinical foci of QICs, or whether QIC participation resulted in improved quality performance metrics. Also, because NSPO3 is a cross-sectional survey, we cannot establish causal relationships between ACO participation, ownership, and QI methods. In addition, these analyses cannot rule out the possibility that participation in a QIC might have preceded use of other internal organizational tools such as Lean and PDSA. The use of longitudinal data could help clarify the temporal ordering of these factors on practice participation in QICs. It will be important to revisit these relationships when such data are available. Finally, although the findings provide evidence that practices affiliated with ACOs are more likely to participate in QICs, the processes that underlie such engagement remain unclear. Qualitative research of frontline clinicians, staff, and managers in ACO-affiliated practices could lend further insight into how and why QI methods and QIC participation are used by ACOs.
Organizational and contextual influences are critical to understanding the participation of physician practices in interorganizational learning activities such as QICs (Kaplan et al., 2010; Versteeg, Laurant, Franx, Jacobs, & Wensing, 2012). Internal practice capabilities such as HIT functionality and use of QI methods are often promoted by health care systems, CHCs, and ACOs to stimulate improved organizational outcomes and also appear to foster interorganizational learning through QIC participation. Our findings suggest a strong mediating role of practice use of QI methods such as use of PDSA cycles and Lean in explaining the greater propensity of ACO-affiliated practices to participate in QICs (Hung, Gray, Martinez, Schmittdiel, & Harrison, 2016). To support systematic improvement in processes and outcomes of care through QICs, organizational leaders should focus attention upon improving internal practice capabilities that are often emphasized by systems and ACOs. For example, practices that intend to engage in interorganizational learning activities such as QICs may benefit from expanding HIT capabilities to support the continuous monitoring and improvement activities that accompany such participation. As Nembhard and Tucker point out, the process of organizational learning that underlies engagement in a QIC requires both the “processes and infrastructure that enable the creation, storage, and dissemination of information that helps the organization perform better” (Nembhard & Tucker, 2016, p. 6).
QICs are one strategy physician practices can use to learn about best practices for managing chronic care and other conditions, so continued exploration of how to engage small, rural, and physician-owned practices in collaboratives and other QI activities should be a high priority for research and policy, as their internal capabilities currently limit their ability to effectively participate (Kilo, 1998). More specifically, clarifying the role of organizational and legitimacy factors in influencing QIC participation may inform the development of policies and interventions that enable small- and medium-sized practices to participate in QICs and other collaborative learning activities. Various initiatives currently occurring across the country such as the Clinical Practice Transformation Initiative provide important opportunities to gain insight into strategies to improve engagement in QICs by small practices and organizations. The perspectives of frontline clinical and administrative staff and leaders may also provide insights as to how internal learning orientation impacts participation in QICs and, ultimately, improved quality performance. Future research clarifying the cultural, organizational, and contextual influences on practice engagement in interoganizational learning activities would be especially valuable for practices affiliated with ACOs and related networks with the triple aim goals of high quality of care, improved population health, and lower costs of care.
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