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Little is known about how practices reorganize when transitioning from traditional practice organization to team-based care. We compared practice-level (1) configuration as well as practice- and team-level (2) size and (3) composition, before and after establishing teams. We employed a pre-/poststudy using personnel lists of 1571 to 1711 staff (eg, job licenses, titles, and team assignment) and practice manager surveys. All personnel (physician and nonphysician) worked within 18 Massachusetts academic primary care practices participating in a 2-year learning collaborative aimed at establishing team-based care. We found that establishing team-based care can involve changing practice configurations and composition without substantially changing practice size.

Team-based care is a foundation of health care redesign models like the patient-centered medical home (PCMH). Yet few practices rigorously examine how the implementation of PCMH relates to teamwork. We identified factors associated with the perception of a practice operating as a real team. An online workforce survey was conducted with all staff of 12 primary care sites of Cambridge Health Alliance at different stages of PCMH transformation. Bivariate and multivariate analyses of factors associated with teamwork perceptions were conducted. In multivariate models, having effective leadership was the main factor associated with practice teamwork perceptions (odds ratio [OR], 10.49; 95% confidence interval [CI], 5.39-20.43); in addition, practicing at a site in an intermediate stage of PCMH transformation was also associated with enhanced team perceptions (OR, 2.44; 95% CI, 1.28-4.64). In a model excluding effective leadership, respondents at sites in an intermediate stage of PCMH transformation (OR, 1.95; 95% CI, 1.1-3.4) and who had higher care team behaviors (such as huddles and weekly meetings; OR, 3.41; 95% CI, 1.30-8.92), higher care team perceptions (OR, 2.65; 95% CI, 1.15-6.11), and higher job satisfaction (OR, 2.00; 95% CI, 1.02-3.92) had higher practice teamwork perceptions. This study highlights the strong association between effective leadership, care team behaviors and perceptions, and job satisfaction with perceptions that practices operate as real teams. Although we cannot infer causality with these cross-sectional data, this study raises the possibility that providing attention to these factors may be important in augmenting practice teamwork perceptions.

Objective: To develop and validate a survey instrument designed to measure team dynamics in primary care., Data Sources/study Setting: We studied 1,080 physician and nonphysician health care professionals working at 18 primary care practices participating in a learning collaborative aimed at improving team-based care., Study Design: We developed a conceptual model and administered a cross-sectional survey addressing team dynamics, and we assessed reliability and discriminant validity of survey factors and the overall survey's goodness-of-fit using structural equation modeling., Data Collection: We administered the survey between September 2012 and March 2013., Principal Findings: Overall response rate was 68 percent (732 respondents). Results support a seven-factor model of team dynamics, suggesting that conditions for team effectiveness, shared understanding, and three supportive processes are associated with acting and feeling like a team and, in turn, perceived team effectiveness. This model demonstrated adequate fit (goodness-of-fit index: 0.91), scale reliability (Cronbach's alphas: 0.71-0.91), and discriminant validity (average factor correlations: 0.49)., Conclusions: It is possible to measure primary care team dynamics reliably using a 29-item survey. This survey may be used in ambulatory settings to study teamwork and explore the effect of efforts to improve team-based care. Future studies should demonstrate the importance of team dynamics for markers of team effectiveness (e.g., work satisfaction, care quality, clinical outcomes)., (© Health Research and Educational Trust.)

Importance: Evidence of effective treatment of childhood obesity in primary care settings is limited., Objective: To examine the extent to which computerized clinical decision support (CDS) delivered to pediatric clinicians at the point of care of obese children, with or without individualized family coaching, improved body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) and quality of care., Design, Setting, and Participants: We conducted a cluster-randomized, 3-arm clinical trial. We enrolled 549 children aged 6 to 12 years with a BMI at the 95% percentile or higher from 14 primary care practices in Massachusetts from October 1, 2011, through June 30, 2012. Patients were followed up for 1 year (last follow-up, August 30, 2013). In intent-to-treat analyses, we used linear mixed-effects models to account for clustering by practice and within each person., Interventions: In 5 practices randomized to CDS, pediatric clinicians received decision support on obesity management, and patients and their families received an intervention for self-guided behavior change. In 5 practices randomized to CDS + coaching, decision support was augmented by individualized family coaching. The remaining 4 practices were randomized to usual care., Main Outcomes and Measures: Smaller age-associated change in BMI and the Healthcare Effectiveness Data and Information Set (HEDIS) performance measures for obesity during the 1-year follow-up., Results: At baseline, mean (SD) patient age and BMI were 9.8 (1.9) years and 25.8 (4.3), respectively. At 1 year, we obtained BMI from 518 children (94.4%) and HEDIS measures from 491 visits (89.4%). The 3 randomization arms had different effects on BMI over time (P = .04). Compared with the usual care arm, BMI increased less in children in the CDS arm during 1 year (-0.51 [95% CI, -0.91 to -0.11]). The CDS + coaching arm had a smaller magnitude of effect (-0.34 [95% CI, -0.75 to 0.07]). We found substantially greater achievement of childhood obesity HEDIS measures in the CDS arm (adjusted odds ratio, 2.28 [95% CI, 1.15-4.53]) and CDS + coaching arm (adjusted odds ratio, 2.60 [95% CI, 1.25-5.41]) and higher use of HEDIS codes for nutrition or physical activity counseling (CDS arm, 45%; CDS + coaching arm, 25%; P < .001 compared with usual care arm)., Conclusions and Relevance: An intervention that included computerized CDS for pediatric clinicians and support for self-guided behavior change for families resulted in improved childhood BMI. Both interventions improved the quality of care for childhood obesity., Trial Registration: clinicaltrials.gov Identifier: NCT01537510.

Purpose: Although mental health screening is recommended for adolescents, little is known about the predictors of referral to mental health services or engagement in treatment. We examined predictors of mental health referral from primary care and service use for commercially insured youth who had been screened using the Pediatric Symptom Checklist or Youth-Pediatric Symptom Checklist., Methods: A retrospective chart review was conducted of commercially insured patients 14-17 years of age who were newly identified by the Pediatric Symptom Checklist or Youth-Pediatric Symptom Checklist at a well-child visit. Comparisons were made with propensity-matched negative adolescents meeting the same criteria. Bivariate analyses were conducted to examine differences between positives and negatives and between referred and nonreferred positives. Logistic regression analyses were performed to assess predictors of mental health referral for positive youth., Results: Medical records of 117 positive and 110 negative youth were examined. Compared with negative youth, positive youth were significantly more likely to be referred for mental health treatment (p < .0001) and receive specialty mental health services (p < .0001). Of the positives, 54% were referred for mental health care and 67% of them accepted. However, only 18% completed a face-to-face mental health visit in the next 180 days. Pediatric Symptom Checklist score (odds ratio, 1.21; confidence interval, 1.03-1.42), parental or personal concern (odds ratio, 10.87; confidence interval, 2.70-43.76), and having depressive symptoms (odds ratio, 9.18; confidence interval, 1.49-56.60) were predictive of referral., Conclusions: Despite identification after behavioral health screening, limited treatment engagement by referred patients persists. Primary care physicians and mental health specialists must enhance their efforts to engage and monitor identified patients., (Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.)

Objective: Universal mental health screening in pediatric primary care is recommended, but studies report slow uptake and low rates of patient follow-through after referral to specialized services. This review examined possible explanations related to the process of screening, focusing on how parents and youth are engaged, and how providers evaluate and use screening results., Method: A narrative synthesis was developed after a systematic review of 3 databases (plus follow-up of citations, expert recommendations, and checks for multiple publications about the same study). Searching identified 1,188 titles, and of these, 186 full-text articles were reviewed. Two authors extracted data from 45 articles meeting inclusion criteria., Results: Published studies report few details about how mental health screens were administered, including how clinicians explain their purpose or confidentiality, or whether help was provided for language, literacy, or disability problems. Although they were not addressed directly in the studies reviewed, uptake and detection rates appeared to vary with means of administration. Screening framed as universal, confidential, and intended to optimize attention to patient concerns increased acceptability. Studies said little about how providers were taught to explore screen results. Screening increased referrals, but many still followed negative screens, in some cases because of parent concerns apparently not reflected by screen results but possibly stemming from screen-prompted discussions., Conclusions: Little research has addressed the process of engaging patients in mental health screening in pediatric primary care or how clinicians can best use screening results. The literature does offer suggestions for better clinical practice and research that may lead to improvements in uptake and outcome., (Copyright © 2013 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.)

Objective: Validated behavioral health (BH) screens are recommended for use at well-child visits. This study aimed to explore how pediatricians experience and use these screens for subsequent care decisions in primary care., Methods: The study took place at 4 safety net health centers. Fourteen interviews were conducted with pediatricians who were mandated to use validated BH screens at well-child visits. Interview questions focused on key domains, including clinic BH context, screening processes, assessment of screening scores, and decision making about referral to mental health services. Qualitative analysis used the Framework Approach., Results: A variety of themes emerged: BH screens were well accepted and valued for the way they facilitated discussion of mental health issues. However, screening results were not always used in the way that instrument designers intended. Providers' beliefs about the face validity of the instruments, and their observations about performance of instruments, led to discounting scored results. As a result, clinical decisions were made based on a variety of evidence, including individual item responses, parent or patient concerns, and perceived readiness for treatment. Additionally, providers, although interested in expanding their mental health discussions, perceived a lack of time and of their own skills to be major obstacles in this pursuit., Conclusions: Screens act as important prompts to stimulate discussion of BH problems, but their actual scored results play a variable role in problem identification and treatment decisions. Modifications to scheduling policies, additional provider training, and enhanced collaboration with mental health professionals could support better BH integration in pediatric primary care.

Objectives: Safety-net populations are underrepresented in research and quality improvement (QI) studies despite the fact that safety-net providers are uniquely positioned to engage in translational research. This study aimed to understand the current level of interest in, experience with, predicted career satisfaction associated with, and barriers experienced in conducting research and QI among primary care providers (PCPs) at 18 safety-net practices in the Boston, Massachusetts area., Methods: The Harvard Catalyst Safety-net Infrastructure Initiative partnered with staff at a large academic public hospital system, including 15 primary care sites, to develop and administer an online survey. This survey was then adapted and administered at three other academically affiliated community health centers., Results: Of the 260 providers surveyed, 136 (52%) responded. Nearly 80% reported interest in conducting either QI projects or clinical research and 95% of them believed it would enhance their career satisfaction. However, 63% did not report prior experience or training in research or QI and 93% reported at least one barrier to engagement., Conclusion: While supporting safety-net PCPs' engagement in research and/or QI may improve career satisfaction there are numerous barriers that must be addressed to achieve this goal., (© 2013 Wiley Periodicals, Inc.)

Objective: The purpose of this quasi-experimental study was to examine the effect of a computerized point-of-care alert with clinical decision support on the rates of diagnosis of childhood obesity in a multisite group practice in Massachusetts; Cambridge Health Alliance (CHA) which implemented an alert, relative to a separate group practice, Harvard Vanguard Medical Associates (HVMA), that did not., Design and Methods: Height and weight data from 19,466 children of 2-18 years with 34,908 well-child care visits in CHA and 123,446 children with 282,271 visits in HVMA between 2006 and 2008 were collected. The alert and decision support tool was activated for CHA patients with an age- and sex-specific body mass index of ≥95th percentile. The main outcome measure was documentation of an International Classification of Diseases, Ninth Revision [ICD-9] code for obesity before and after implementation of the alert at CHA in 2007., Results: Among obese children, the adjusted rate of an ICD-9 diagnosis of obesity increased from 2006-2007 to 2008 significantly more at CHA than at HVMA (P < 0.001 for time-by-provider group interaction). In 2006-2007, the rate of ICD-9 diagnosis of obesity was significantly lower at CHA than at HVMA (adjusted odds ratio [OR]: 0.57; 95% confidence interval [CI]: 0.52-0.62); but by 2008 was significantly higher at CHA than HVMA (adjusted OR: 1.25; 95% CI: 1.14-1.38)., Conclusion: A point-of-care alert was effective in improving obesity diagnosis in a multisite group practice, relative to a separate group practice that did not adopt an alert. Clinical decision support tools could help improve obesity diagnosis in pediatric primary care., (Copyright © 2013 The Obesity Society.)

Objective: The objective was to determine whether transitioning from paper to electronic health records affected behavioral health screening rates in a large Northeastern pediatric practice., Methods: The study setting was a pediatric practice with seven pediatricians, serving about 6,000 patients. The patient population was diverse (54% nonwhite, 40% publicly insured or self-paying, and 31% non-English speakers). An interrupted times series design was used to evaluate the impact of electronic record implementation on behavioral health screening rates. The main outcome measure was the rate of such screening 18 months before and 36 months after implementation., Results: The rate of behavioral health screening increased from 70% to 91% during the baseline period. The training period-six months before electronic record implementation-was associated with a 28% decline in adjusted screening rates (from 83.3% to 55.5%). Only 50% of eligible youths were screened in the first month after implementation. The screening rate took more than three years to recover to baseline levels, climbing to 82% by April 2008., Conclusions: Practice changes resulting from electronic record adoption were highly disruptive of care, and disruptions took several years to resolve completely. When medical assistants rather than physicians were tasked with transferring data from paper screening forms to the electronic record, reporting compliance improved. Compliance with Healthcare Effectiveness Data and Information Set standards and Medicaid performance measures will likely be similarly affected as electronic records are implemented nationwide. Although implementing a fully automated medical record has some benefits, the unintended effects on care after implementation must be acknowledged.