Clinical Decision Supports in Electronic Health Records to Promote Childhood Obesity-Related Care: Results from a 2015 Survey of Healthcare Providers

Obesity-related clinical decision support tools in electronic health records (EHRs) can improve pediatric care, but the degree of adoption of these tools is unknown. DocStyles 2015 survey data from US pediatric healthcare providers (n = 1,156) were analyzed. Multivariable logistic regression identified provider characteristics associated with three EHR functionalities: automatically calculating body mass index (BMI) percentile (AUTO), displaying BMI trajectory (DISPLAY), and flagging abnormal BMIs (FLAG). Most providers had EHRs (88%). Of those with EHRs, 90% reporting having AUTO, 62% DISPLAY, and 54% FLAG functionalities. Only provider age was associated with all three functionalities. Compared to providers aged > 54 years, providers < 40 years had greater odds for: AUTO (adjusted odds ratio [aOR], 3.0; 95% confidence interval [CI], 1.58–5.70), DISPLAY (aOR, 2.07; 95% CI, 1.38–3.12), and FLAG (aOR, 1.67; 95% CI, 1.14–2.44). Future investigations can elucidate causes of lower adoption of EHR functions that display growth trajectories and flag abnormal BMIs.

Parietal bone defect: differential diagnosis and neurologic associations.

Parietal bone defects are rare and exhibit variable etiologies. We report on a 16-year-old girl with an isolated, giant parietal bone defect with encephalomalacia, and an asymptomatic Rathke's cleft cyst. The patient presented with epilepsy. We discuss the differential diagnosis and pertinent neurologic associations. Irrespective of cause, parietal bone defects remain a benign clinical entity. However, it is important to define the extent of the bone defect and associated intracranial abnormalities, and if needed, to take early preventive steps, medical as well as surgical, against potential brain damage.