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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.

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.

Despite the availability of national evidenced-based guidelines related to pediatric obesity screening and prevention, multiple studies have shown that primary care physicians find it difficult to adhere to them or are unfamiliar with them altogether. This article presents physicians' perspectives on the use of electronic decision support tools, an alert and Smart Set, to accelerate the adoption of obesity-related recommendations into their practice. The authors interviewed providers using a test encounter walk-through technique that revealed a number of barriers to using electronic decision supports for obesity care in primary care settings. Providers' suggestions for improving their use of obesity-related decision supports are presented. Careful consideration must be given to both the development of electronic decision support tools and a multilayered educational outreach strategy if providers are going to be persuaded to use such supports to help them implement pediatric obesity prevention and management best practices.

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.