New patient-centered care delivery models, such as the patient-centered medical home (PCMH), aim to improve patient health outcomes by enhancing the degree to which primary care providers can deliver comprehensive, coordinated, and patient-responsive services (Crabtree et al., 2011 ; Jackson et al., 2013). To achieve these goals, the models depend substantially on creating highly integrated interdisciplinary teams composed of physicians, nurses, and clerks, who provide continuity of care for designated patient panels. Other professionals, such as pharmacists and social workers, provide care across several patient panels. Teams aim to provide timely, accessible care as well as proactive care management, behavioral health integration, prevention-oriented population management, and patient engagement in self-management (Kearney, Post, Zeiss, Goldstein, & Dundon, 2011 ; Patel et al., 2013). Workflow efficiencies are expected to be achieved in large part by optimizing team skill mixes and thus enabling all team members to assume responsibilities at the top of their competencies.
Achieving the integrated teams called for by the PCMH model requires changes in both primary care structure (e.g., increased staffing) and in team process (e.g., team communication; Department of Veterans Affairs, 2014). The Veterans Health Administration (VHA) nationally implemented its PCMH model, termed Patient-Aligned Care Team (PACT), in 2010 across over 900 primary care sites, with a primary goal of using enhanced primary care team capabilities to minimize unnecessary hospitalizations (Department of Veterans Affairs, 2014). Prior to PACT, as in many non-VHA primary care practices, physicians and staff functioned as coworkers rather than integrated team members (Sullivan, Ibrahim, Ellner, & Giesen, 2016); VHA supported transition to the new team-based model through team training (Giannitrapani et al., 2016), new information technology, quality improvement initiatives, and support for new team roles and communication. One of the main interventions for improving team communication, for example, was the “huddle” or the requirement for teams to meet to touch base on the day’s upcoming activities (Rodriguez et al., 2014). Previous studies have assessed components of (Gale et al., 2015 ; Hysong et al., 2016) and barriers to (True, Stewart, Lampman, Pelak, & Solimeo, 2014) team functioning in PACT or in other large-scale PCMH implementations. Although studies of, for example, geriatric and rehabilitation care models have shown links between team functioning and outcomes (Mukamel et al., 2006 ; Schouten et al., 2008 ; Strasser et al., 2005), we know of no prior study that assesses team functioning in PACT or other PCMH models in relationship to patient health outcomes.
We used a modified version of the Consolidated Framework for Implementation Research (CFIR; Damschroder & Hagedorn, 2011) to show the relationships between team functioning and outcomes within a PCMH (Figure 1). CFIR categorizes the characteristics of the context that can affect the success of an implementation initiative as those pertaining to the intervention itself, those characterizing the inner and outer settings within which the intervention occurs, and those of the intervention participants. Our analysis explores what Wagner refers to in the Chronic Illness Care Model (Wagner, 1998) as the “prepared, proactive team” in relationship to health outcomes. In order to positively affect outcomes within the PCMH model, we postulated that the primary care practice inner setting must achieve high team functioning, measured here as (a) team cognitive traits (perception of knowledge and skill of team members) and (b) team processes (processes related to team knowledge and skills).
The success of PCMH model implementation (the intervention in our framework) is influenced by contextual factors such as those identified in CFIR as inner and outer setting characteristics. We used prior research on barriers and facilitators to PCMH team functioning to identify key factors measurable in our data, which impact team functioning. For inner primary care setting, in addition to structural characteristics such as descriptive practice information and what the practice’s patient sickness burden level was, we measured staffing sufficiency, the lack of which can create unfavorable working conditions that impact teamwork (Ladebue et al., 2016 ; Rodriguez et al., 2014 ; Rosland et al., 2013 ; True et al., 2014). We also assessed practice organizational readiness, in terms of leadership engagement and support, as well as implementation climate, in terms of each practice’s level of provider and staff burnout, because participant dissatisfaction or disaffection can affect the health of its work environment. Considering outer medical system setting factors, we did not have measures of system policies or incentives. We know, however, that a practice’s patients’ needs and resources, such as patient health status, severity, and homeless status, can affect both the demands on PCMH teams and the likelihood of unfavorable patient outcomes. Thus, although some characteristics relevant to CFIR components are not measured here, our model reflects key factors that we studied and are often not available in assessing impacts of team functioning on outcomes.
We used social and organizational psychology theory as the basis for our measure of team functioning. We assess two aspects of team functioning (team cognitive traits and team processes). Team cognitive traits refer to awareness of who knows what within the team and the degree of specialization or differentiation of knowledge within the team, promoting improved performance through implicit coordination (DeChurch & Mesmer-Magnus, 2010 ; Rico, Sánchez-Manzanares, Gil, & Gibson, 2008). Team processes refer to the nature of team member interaction in terms of communication and learning capacities. Effective teams agree on mental models for performing. Thus, it is both cognitive constructs and team processes that continue to reciprocally influence each other as they evolve and contribute to team outcomes (Kozlowski & Ilgen, 2006); both (not just process-related team constructs) should be considered in any model of teamwork.
Based on our model, we hypothesize that sites with higher compared to lower team functioning will have decreased unnecessary acute care (hospitalizations and emergency department [ED] care) and lower mortality rates over a 2-year period. In addition, we hypothesize that vulnerable patients or those who are more complex will be at higher risk for poor health outcomes and greater medical care needs. Based on prior research, these patients may be especially vulnerable to poor team functioning; therefore, we hypothesize that their outcomes will show stronger associations with team functioning levels compared to less vulnerable patients.
In the work presented here, we investigate the relationship between a primary care practice site’s self-reported team functioning and the outcomes (acute care use and mortality) experienced by a longitudinal cohort of 65,559 patients between 2012 and 2013 within a set of 15 primary care practices in one VHA administrative region that began PACT implementation in 2010. We also investigate effects of team functioning on a subgroup of vulnerable patients; these patients might be expected to be more sensitive to effects of poor team functioning on acute care outcomes based on higher illness levels and more complex needs for proactive primary care.
Study Design and Data Sources
We used administrative data on a longitudinal cohort analysis of all patients assigned to 1 of 15 primary care practices during early implementation of PACT to assess key patient and primary care practice demographic characteristics (practice type, size) and to assess patient outcomes. We used provider and staff survey data from the same set of practices to assess team functioning and additional primary care practice characteristics.
The original patient cohort was identified in 2009 as part of a larger study evaluating PACT implementation (Yoon, Chow, & Rubenstein, 2016) and included all patients from all VHA-staffed primary care practices with more than 5,000 patients in one VHA administrative region spanning Southern California and Nevada (Veterans Integrated Service Network or VISN 22). Practices belonged to one of the five medical center-based local health care systems in the region. We excluded practices with fewer than five survey responses each study year and excluded patients who did not receive any primary care in 2012. This analysis uses data from a 2-year period (fiscal years [FY] 2012 and 2013) for which both patient-level data and practice-level survey data were available.
We assessed annual patient outcomes (hospital admissions, ED visits, and mortality), patient demographic and health status characteristics, and practice type (community-based or hospital-based) from VHA Medical SAS files. We used data from a primary care provider and staff survey conducted as part of a larger evaluation of PACT implementation in VISN 22 during 2012 and 2013 to assess primary care practice-level team functioning, staffing sufficiency, readiness for PCMH, and implementation climate.
Survey Data Collection
The 2012 survey was conducted among 811 primary care clinicians and staff (354 and 457, respectively) for primary care practices as described above. It was fielded between November 30, 2011, and March 30, 2012, and the overall response rate was 64% (515/811, 54% for clinicians and 71% for staff). An earlier study has described the 2012 survey and data collection in detail (Meredith et al., 2015). The 2013 survey was fielded between August 1, 2013, and January 15, 2014. Responders included 136 providers and 348 staff (response rate = 40% and 52%, respectively) for an overall response rate of 48%.
The dependent variables included measures of desired outcomes from intervention: reduced unnecessary utilization (acute care hospitalization, ED visits) and improved health (lower mortality). We measured counts for each patient in each study year of all-cause and ambulatory care-sensitive condition (ACSC)-related admissions and ED visits. The measure of all-cause inpatient admissions included care for all diagnoses and all inpatient departments. ACSC-related admissions were identified from inpatient records and by primary diagnosis for an ACSC (Agency for Healthcare Research and Quality, n.d.a , n.d.b). ED visits were the number of visits in VHA EDs for any cause. All-cause mortality was also included as one of the dependent variables and was measured in each year from VHA vital status files. Patients were linked to the practice they attended most frequently or, in case of a tie, to the most recent practice they visited during a given study year.
The main independent variable was team functioning, obtained from provider and staff responses to the two-wave primary care survey and reflecting both team-level processes and cognitive traits. Six items were originally included; factor analysis results showed that one item did not load as well and was not included in the measure. The final measure comprises five questions adapted from the Team Diagnostic Survey (Wageman, Hackman, & Lehman, 2005; 5-point response scale) that ask about (a) team cognitive traits (the degree to which team members have the special skills that are needed for team work, possess the knowledge and skills that they need, possess more than enough talent and experience) and (b) team process (are skilled at capturing lessons that can be learned and actively share their special knowledge). We used a method adapted from the functional status questionnaire (Jette et al., 1986) to convert the five items into a single score ranging from 0 to 100; the index was validated in prior work, α = .81 (Giannitrapani, 2015) and intraclass correlation = 0.48. We used the median as the threshold for a low/high binary variable used in the analysis.
Other practice-level variables include whether the facility was a Veterans Affairs medical center (compared to a community-based outpatient clinic) and time-varying mean Charlson index reflecting the average severity of patients in each year. We also controlled for other time-varying measures of organizational context: leadership engagement and support, provider and staff burnout, and staffing sufficiency.
Leadership Engagement and Support (termed Leadership Norms in prior publications) reflects the culture of change within practices. The measure was captured during each survey period and includes the mean sum of responses for two survey questions (5-point response scale), capturing the degree to which leadership (a) provides measurable objectives for implementing the strategy and vision within the practice and (b) recognizes and rewards progress in implementing change with the practice.
The measure of provider and staff burnout includes the nine survey questions that constitute the emotional exhaustion subscale of the Maslach Burnout Inventory and describes feelings of being overextended and exhausted by one’s work (Maslach & Jackson, 1981). We used the exact item wording, which have been well validated and have undergone psychometric analyses (Maslach & Jackson, 1981). The sum of responses ranged from 0 to 54, and the aggregate scale was reported to have high reliability (α = .92) in a previous study of PACT provider burnout (Meredith et al., 2015).
Finally, staffing sufficiency data were obtained from administrative data collected for operational PACT metrics; the measure was categorized as a binary variable indicating whether the mean staff to provider ratio in a practice was equal to or above the VHA-mandated 3.0 ratio.
Other independent variables: A variable indicating study year (2012 or 2013) was included to control for time differences. For patient-level demographic characteristics, age, gender, means test category, and distance to VHA secondary care measured in the baseline year (2012) were used. For patient-level needs and resources, homeless status, prior year primary care visit volume, and three variables were used to control for time-varying patient severity measured in each year: poor health status indicator, prior year costs, and Charlson index score. Poor health status is an indicator of risk based on available lab results reported during the study years and has been described previously (Yoon et al., 2016). The indicator includes lab tests that could indicate a higher risk of morbidity and mortality: cholesterol tests (>100 mg/dl), hemoglobin A1C test (>8%), anemia hemoglobin (<13 g/dl), and kidney function (eGRF < 45). A patient severity variable indicated by the Charlson index using the Deyo–Quan approach (Quan et al., 2005) was also used (range 0–2).
Univariate analysis was conducted to examine various patient characteristics. Survey measures of team functioning, burnout, staffing, and leadership norms were aggregated to the practice level in each study year, and mean scores were compared between the two study years using one-way analysis of variance. Bivariate analyses examined the relationship of team functioning with outcomes by pooling all measures across both study years and conducting multivariate analysis of variance for the four outcomes. To test the relative associations of team functioning with measures of utilization and mortality over time, we first checked the correlations among the primary independent variable and covariates, verifying that there was no multicollinearity. Two-level multilevel regression models were used controlling for year, patient-level variables (patient age, gender, means test status, homeless indicator, distance to VHA secondary care, prior year primary care use [visit volume], Charlson score, prior year costs, and poor health status indicator), and practice-level variables (type, size, mean Charlson, leadership norms). Models for all outcomes included all patient- and practice-level predictors in addition to random effects at the person level to account for multiple observations per patient and robust variance estimators to account for clustering within practices. Because many patients had few or none of these services in any given year, we used negative binomial models to account for the distribution of rare outcomes. For mortality, we used a logistic regression model.
Subgroup analysis was performed for vulnerable patients—those reported being homeless or identified as having mental illness or dementia by International Classification of Diseases, Ninth Revision, Clinical Modification codes—for all outcomes in adjusted models. We also conducted separate sensitivity analyses where we separated the team functioning measure into two subindices (processes and cognitive traits) and of the effects of including community care—care not delivered by but still paid for by VHA—for inpatient admissions and ED visits. Community care data were available for FY2012 only.
The research was approved by the institutional review board committee at Stanford University.
Patient and practice characteristics are summarized in Table 1. The patient cohort was predominantly male (95%), and more than half (56%) were between 55 and 74 years. Patients lived, on average, 33.8 miles from a VHA hospital, and 7% of the patients were homeless. In terms of patient clinical severity, about a third (32%) of patients had more than one comorbidity, and over half (63%) had a poor health indicator. Baseline FY2012 measures of practice-level team functioning (mean = 72.1, SD = 4.4), staffing sufficiency (mean = 3.1, SD = 0.4), and burnout (mean = 19.9, SD = 3.6) did not significantly change over the 2-year period; the measure of leadership norms increased significantly from 19.9 to 20.9 over the 2-year period (p = .05; Table 2).
In bivariate analysis (data not shown), patients in practices with high team functioning had significantly lower utilization for all acute care measures (all-cause admissions, ACSC admissions, ED visits). There was no overall difference in the practice’s patient death rate in relationship to its level of team functioning. Using multivariate analysis of variance, there was a statistically significant difference between high and low team functioning on the combined effects of the dependent variables (Wilks’ lambda = 0.999, F(4, 131161) = 30.80, p < 0.001).
Multivariate regression results are reported in Table 3. In analyses controlling for patient demographics, patient health status characteristics, and other practice characteristics, practices with higher team functioning had lower, though not significantly so, all-cause inpatient admissions (incidence rate ratio [IRR] = 0.95, p = .06), ACSC admissions (IRR = 0.97, p = .37), and ED visits (IRR = 0.94, p = .17). Patients in practices with higher team functioning had significantly lower odds of dying compared to patients in practices with lower team functioning (OR = 0.92, p = .04).
For vulnerable patients, better team functioning was independently significantly associated with lower all-cause admissions (IRR = 0.90, p < 0.01), ACSC admissions (IRR = 0.91, p = .04), and ED visits (IRR = 0.91, p = .03; Table 4). Mortality rates, though overall lower in practices with higher team functioning, were not significantly associated with team functioning levels (OR = 0.91, p = .21).
Other practice-level factors were also associated with outcomes. ACSC admissions were significantly lower in practices with higher measures of provider/staff burnout for the general population (IRR = 0.98, p = .01) as well as in the vulnerable population (IRR = 0.98, p = .01). Finally, practices with staffing sufficiency (higher staff to provider ratio) had significantly higher all-cause admissions (IRR = 1.11, p = .04) in the general population.
When we separated the team functioning measure into two subscales related to processes and cognitive traits, we found that the effect is not due largely to one relative to the other; the relationship of the combined team functioning measure to outcomes, however, was substantially stronger. Additional sensitivity analysis predicting community care in combination with VHA-based ED and in-hospital care showed similar results to our VHA-based analyses (data not shown).
Given ongoing PCMH implementation and its reliance on high-functioning interdisciplinary teams, we evaluated the relationship between team functioning and patient acute care outcomes, controlling for relevant organizational and patient factors. We found a small, though significant, relationship between better team functioning and reduced mortality. Although we did not find significant associations between higher team functioning and lower acute care use in the general population, we did find a significant association between high team functioning and lower all-cause admissions, ACSC admissions, and ED visits among vulnerable patients.
Our findings are consistent with our hypothesis that high-functioning teams would particularly reduce hospitalization among vulnerable patients through proactive primary care, potentially avoiding their otherwise high levels of acute care use. In addition, the substantial needs of these patients for care coordination and communication may align with the emphasis on these in our team functioning measure. Mortality, as a rarer outcome and one that is particularly likely to be strongly determined by underlying illnesses among vulnerable patients, may be hard to affect through team care and sometimes, as for patients needing palliative care, not even appropriate to target. Among the general population, in contrast, proactive high-functioning team care could have mixed effects on hospitalization while reducing mortality. For example, if high-functioning teams provide prompt telephone or in-person care to patients ill enough to need hospitalization and appropriately hospitalize them, they may avoid deaths but raise hospitalization rates. Because these high-functioning teams also avoid some hospitalizations, we would not expect significantly higher hospitalization rates across all patients.
Our finding that higher practice-level burnout and lower staffing sufficiency were independently associated with lower avoidable hospitalizations is contrary to our prior hypotheses. This finding—especially in terms of potentially avoidable ACSC admissions—appears contrary to prior research showing higher burnout can have negative consequences for patients’ care (Fahrenkopf et al., 2008 ; Shanafelt, Bradley, Wipf, & Back, 2002 ; West et al., 2006 ; West, Tan, Habermann, Sloan, & Shanafelt, 2009). One interpretation of our findings is that burnout may not only manifest itself in providers providing poor quality of care, but that providers and staff who address patient concerns and provide more appropriate care may also be more likely to experience burnout. For example, an earlier study showed adverse physician outcomes (such as burnout or job dissatisfaction) can serve as a buffer between organizational factors and patient outcomes in the primary care setting (Linzer et al., 2009).
We found that staffing sufficiency (higher staffing ratios) were related to higher inpatient admissions. Both earlier quantitative and qualitative work suggest that higher staff-to-provider ratios are necessary for effectiveness of the PCMH model and are associated with lower ACSC admissions and ED use (Ladebue et al., 2016 ; Nelson et al., 2014). Because team-based models of care are thought to improve access, one potential explanation is that the patients had substantial health care needs, so increased care access and communication with members of the primary care team led to increased use of inpatient services, which was found to be true in an earlier VHA study (Weinberger, Oddone, & Henderson 1996). Future work might also explore the relationships between high team functioning, staffing sufficiency, and acute care use. For example, high team functioning might mediate the relationship between staffing levels and acute care use.
This study uses a measure of team functioning that combines team-level processes and cognitive traits. The measure reflects processes related to how a team is able to learn from and share individuals’ skills and knowledge as well as shared cognitive structures about team members’ skills and knowledge, both of which are thought to reciprocally influence implicit coordination processes. Our findings relate to prior research on explicit coordination as a team process and contribute to the modest evidence linking better coordination to lower cost and utilization as well as lower mortality (Bosch et al., 2009).
In addition, our findings give some support to previous research showing team processes to be a primary contributor to improved patient care within VHA’s PCMH model (Helfrich et al., 2014) and contribute by examining objective outcomes of acute care and mortality. The results highlight the potential importance of team functioning in primary care models based on interdisciplinary teams. Future work should examine more closely the interrelationships between specific group-level phenomena, such as team processes and cognitive traits. In addition, we did not find that clinics with higher team functioning differed by type, rurality, measures of burnout, staffing sufficiency, or leadership norms compared to clinics with lower team functioning. However, higher team functioning clinics may be different in critical ways; understanding these relationships is important to improving patients’ outcomes.
Our conclusions should be understood in the context of our limitations. Overall, our study was observational in design and cannot address causality. Also, though we adjusted for many patient- and practice-level characteristics, including multiple measures of patient severity, we cannot account for all relevant factors and are subject to omitted variables bias. We have, however, included a larger set of relevant organizational variables than most prior quantitative studies of team functioning. The data in our study were collected within the VHA network and used to assess the VHA-specific PCMH implementation, limiting generalizability outside the VHA. In addition, we could not link patients to the survey responses provided by their assigned team members; our data are at the primary care practice level. Given the known strong influence of context (e.g., inner and outer setting characteristics) on team functioning, however, practice-level analysis may be particularly meaningful. Also of note, we did not include care provided to patients outside the VHA system that was not paid for by VHA. Our sensitivity analyses of VHA-paid community care provide some protection in this regard. Finally, the study spanned 2 years and is relatively short in the context of large organizational change. However, inclusion of 2 years of survey data increases the reliability of our estimates of key organizational variables, compared to assessing them based on a single year’s results.
In summary, this study uses unique primary data collected on team functioning and other practice characteristics to examine the relationship with patient acute care use and mortality outcomes. These early findings give some support to the importance of team functioning within PCMH models and suggest that inner setting group-level constructs may directly impact patient outcomes. Future work should confirm these findings, ideally linking patients to individual care teams and clarifying interrelationships between specific team processes and traits as they exist in diverse primary care contexts. As a basis for future improvement efforts, team functioning—including both team processes and cognitive traits—is likely an important focus of team-based primary care models and likely may be enhanced through both leadership and training. There may be strategies that team leads or middle managers can employ such as through routine in-person exchange, team training including task and role clarification, or rewards and incentives; research examining the role of these strategies in forming both cognitive and process-related team functioning would help administrators and practitioners understand how to maximize the value of primary care investments in team-based care.
The authors thank Christian Helfrich, Lisa Meredith, and Susan Stockdale for their valuable feedback and contributions and Karleen Giannitrapani for her helpful comments on an earlier version of this manuscript. The authors acknowledge Adam Chow for programming work and Andrew Lanto for data preparation.
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Keywords:Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved
delivery of care; medical home; primary care teams; veterans