Our experiences studying exemplar primary care practices, and our work assisting other practices to become more patient centered, led to a formulation of the essential elements of primary care, which we call the 10 building blocks of highperforming primary care. The building blocks include 4 foundational elementsengaged leadership, data-driven improvement, empanelment, and team-based care-that assist the implementation of the other 6 building blocks-patient-team partnership, population management, continuity of care, prompt access to care, comprehensiveness and care coordination, and a template of the future. The building blocks, which represent a synthesis of the innovative thinking that is transforming primary care in the United States, are both a description of existing high-performing practices and a model for improvement. 2014;166-171. doi: 10.1370/afm.1616. Ann Fam Med INTRODUCTIONA chieving the triple aim of health reform-better health, improved patient experience, and more affordable costs-is dependent on a foundation of high-performing primary care. To this end, a vigorous movement is underway to re-engineer primary care practices. Both patients and care providers feel uncertain about how this new primary care model looks. Practices could benefit from a roadmap to help navigate the journey from old to new. In this article we describe a conceptual model that guides our work as practice improvement facilitators: the 10 building blocks of high-performing primary care. The model represents the synthesis of our thinking from a decade of observing and experiencing improvement work in primary care. METHODSOur development of the building blocks framework was based on case study methods using information from several sources: site visits by the authors and colleagues to 23 highly regarded practices, 1,2 our experiences as practice facilitators in more than 25 practices, and a review of existing models and research on primary care improvement. Seven site visits were performed by the authors; for the others, the authors reviewed site visit reports, looking for descriptions of building block implementation.Practices were selected for site visits on the basis of being known as innovators and having a reputation for high performance in 1 or more of the triple aims. The 23 practices included 8 hospital-based clinics, 7 integrated delivery system sites, 6 federally qualified health centers, and 2 independent private practices. Seven of the 23 practices had 5 or fewer physicians.2 Most of the 25 practices for which the authors have worked as practice facilitators are federally qualified health centers.From these case studies and coaching experiences, we used an iterative process to identify common attributes of high-performing primary care. By comparing and discussing field notes, we discerned a set of elementsbuilding blocks-that occurred with regularity among well-functioning practices. We cross-referenced emerging building block concepts with themes articulated in other published frameworks to look for shared...

We highlight primary care innovations gathered from high-functioning primary care practices, innovations we believe can facilitate joy in practice and mitigate physician burnout. To do so, we made site visits to 23 high-performing primary care practices and focused on how these practices distribute functions among the team, use technology to their advantage, improve outcomes with data, and make the job of primary care feasible and enjoyable as a life's vocation. Innovations identifi ed include (1) proactive planned care, with previsit planning and previsit laboratory tests; (2) sharing clinical care among a team, with expanded rooming protocols, standing orders, and panel management; (3) sharing clerical tasks with collaborative documentation (scribing), nonphysician order entry, and streamlined prescription management; (4) improving communication by verbal messaging and in-box management; and (5) improving team functioning through co-location, team meetings, and work fl ow mapping. Our observations suggest that a shift from a physician-centric model of work distribution and responsibility to a shared-care model, with a higher level of clinical support staff per physician and frequent forums for communication, can result in high-functioning teams, improved professional satisfaction, and greater joy in practice.

Objectives The study sought to determine whether objective measures of electronic health record (EHR) use—related to time, volume of work, and proficiency—are associated with either or both components of clinician burnout: exhaustion and cynicism. Materials and Methods We combined Maslach Burnout Inventory survey measures (94% response rate; 122 of 130 clinicians) with objective, vendor-defined EHR use measures from log files (time after hours on clinic days; time on nonclinic days; message volume; composite measures of efficiency and proficiency). Data were collected in early 2018 from all primary care clinics of a large, urban, academic medical center. Multivariate regression models measured the association between each burnout component and each EHR use measure. Results One-third (34%) of clinicians had high cynicism and 51% had high emotional exhaustion. Clinicians in the top 2 quartiles of EHR time after hours on scheduled clinic days (those above the sample median of 68 minutes per clinical full-time equivalent per week) had 4.78 (95% confidence interval [CI], 1.1-20.1; P = .04) and 12.52 (95% CI, 2.6-61; P = .002) greater odds of high exhaustion. Clinicians in the top quartile of message volume (>307 messages per clinical full-time equivalent per week) had 6.17 greater odds of high exhaustion (95% CI, 1.1-41; P = .04). No measures were associated with high cynicism. Discussion EHRs have been cited as a contributor to clinician burnout, and self-reported data suggest a relationship between EHR use and burnout. As organizations increasingly rely on objective, vendor-defined EHR measures to design and evaluate interventions to reduce burnout, our findings point to the measures that should be targeted. Conclusions Two specific EHR use measures were associated with exhaustion.

PURPOSE Levels of burnout among primary care clinicians and staff are alarmingly high, and there is widespread belief that burnout and lack of employee engagement contribute to high turnover of the workforce. Scant research evidence exists to support this assertion, however. METHODSWe conducted a longitudinal cohort study using survey data on burnout and employee engagement collected in 2013 and 2014 from 740 primary care clinicians and staff in 2 San Francisco health systems, matched to employment roster data from 2016. RESULTSPrevalence of burnout, low engagement, and turnover were high, with 53% of both clinicians and staff reporting burnout, only 32% of clinicians and 35% of staff reporting high engagement, and 30% of clinicians and 41% of staff no longer working in primary care in the same system 2 to 3 years later. Burnout predicted clinician turnover (adjusted odds ratio = 1.57; 95% CI, 1.02-2.40); there was also a strong trend whereby low engagement predicted clinician turnover (adjusted odds ratio with high engagement = 0.58; 95% CI, 0.33-1.04). Neither measure significantly predicted turnover for staff.CONCLUSIONS High rates of burnout and turnover in primary care are compelling problems. Our findings provide evidence that burnout contributes to turnover among primary care clinicians, but not among staff. Although reducing clinician burnout may help to decrease rates of turnover, health care organizations and policymakers concerned about employee turnover in primary care need to understand the multifactorial causes of turnover to develop effective retention strategies for clinicians and staff.To read or post commentaries in response to this article, see it online at http://www.AnnFamMed.org/content/17/1/36.

Purpose: Burnout is a threat to the primary care workforce. We investigated the relationship between team structure, team culture, and emotional exhaustion of clinicians and staff in primary care practices.Methods: We surveyed 231 clinicians and 280 staff members of 10 public and 6 university-run primary care clinics in San Francisco in 2012. Predictor variables included team structure, such as working in a tight teamlet, and perception of team culture. The outcome variable was the Maslach emotional exhaustion scale. Generalized estimation equation models were used to account for clustering at the clinic level.Results: Working in a tight team structure and perceptions of a greater team culture were associated with less clinician exhaustion. Team structure and team culture interacted to predict exhaustion: among clinicians reporting low team culture, team structure seemed to have little effect on exhaustion, whereas among clinicians reporting high team culture, tighter team structure was associated with less exhaustion. Greater team culture was associated with less exhaustion among staff. However, unlike for clinicians, team structure failed to predict exhaustion among staff.Conclusions: Fostering team culture may be an important strategy to protect against exhaustion in primary care and enhance the benefit of tight team structures. (J Am Board Fam Med 2014;27: 229 -238.)

PURPOSE Health coaching by medical assistants could be a financially viable model for providing self-management support in primary care if its effectiveness were demonstrated. We investigated whether in-clinic health coaching by medical assistants improves control of cardiovascular and metabolic risk factors when compared with usual care. METHODSWe conducted a 12-month randomized controlled trial of 441 patients at 2 safety net primary care clinics in San Francisco, California. The primary outcome was a composite measure of being at or below goal at 12 months for at least 1 of 3 uncontrolled conditions at baseline as defined by hemoglobin A 1c , systolic blood pressure, and low-density lipoprotein (LDL) cholesterol. Secondary outcomes were meeting all 3 goals and meeting individual goals. Data were analyzed using χ 2 tests and linear regression models.RESULTS Participants in the coaching arm were more likely to achieve both the primary composite measure of 1 of the clinical goals (46.4% vs 34.3%, P = .02) and the secondary composite measure of reaching all clinical goals (34.0% vs 24.7%, P = .05). Almost twice as many coached patients achieved the hemoglobin A 1c goal (48.6% vs 27.6%, P = .01). At the larger study site, coached patients were more likely to achieve the LDL cholesterol goal (41.8% vs 25.4%, P = .04). The proportion of patients meeting the systolic blood pressure goal did not differ significantly.CONCLUSIONS Medical assistants serving as in-clinic health coaches improved control of hemoglobin A 1c and LDL levels, but not blood pressure, compared with usual care. Our results highlight the need to understand the relationship between patients' clinical conditions, interventions, and the contextual features of implementation. 2015;13:130-138. doi: 10.1370/afm.1768. Ann Fam Med INTRODUCTIONC ardiovascular disease is the leading cause of death in the United States, 1 and it accounts for more than $500 billion of health care spending annually.2 Despite the demonstrated effectiveness of pharmaceutical therapies and lifestyle modifications for improving cardiovascular disease outcomes, 50% of people with hypertension, 43% with diabetes, and 80% with hyperlipidemia have not reached their goals for blood pressure, glycemic control, or lipids, respectively. [3][4][5] One-half of patients do not take their chronic disease medications as prescribed, and only 1 in 10 patients follow recommended guidelines for lifestyle changes, such as smoking cessation or weight loss. 6 Minority and low-income communities bear a disproportionate burden of chronic disease and its complications, 7 and they are less likely to engage in effective self-management of their conditions. 8,9 Contributors to this gap in self-management include a lack of patient understanding of, and agreement with, the care plan, and low confidence and motivation to make healthy choices. or were not designed as randomized trials.17-19 They focused on a single condition such as diabetes or hypertension. [16][17][18][19] Medical assistant health coa...

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Point estimates of burnout notably differ between the self-defined and MBI measures. Compared to the MBI, the self-defined burnout measure misses half of high-burnout clinicians and more than 40% of high-burnout staff. The self-defined burnout measure has a low response burden, is free to administer, and yields similar associations across two burnout predictors from prior studies. However, the self-defined burnout and MBI measures are not interchangeable.