Reliability of Telemedicine in the Assessment of Seriously Ill Children

An evaluation of the telemedicine (vs. in-person) application of the Yale Observation Scale and the Respiratory Observation Checklist in febrile children age 2 to 36 months. ,as implemented via a telemedicine system. Strong agreement was found between bedside and telemedicine observers. Excellent agreement between bedside and telemedicine observers was found for the impression of respiratory distress.

Siew 2016 (Pediatrics) | PubMed 26908666 | Author Search

New Technologies as a Strategy to Decrease Medication Errors: How Do they Affect Adults and Children Differently?

A review of techniques used to reduce medication errors in pediatrics. Within the limitations of the heterogeneous system that makes up information technology for child health, the authors conclude that CPOE and accompanying decision support can help but also creates new types of error (World Journal of Pediatrics).

Ruano 2015 (Link) | PubMed 26684316 | Author Search

Anthropometric Charts for Infants Born Between 22 and 29 Weeks’ Gestation

Based on data from the Vermont Oxford Network from 183,243 racially diverse, singleton infants born in the US without congenital malformations. Tends to represent smaller infants compared to older charts; this is likely due to the increased survival of small infants over time. Provides norms for Asian, Black, and White infants.

Boghossian 2016 (Pediatrics) | PubMed 27940694 | Author Search

Growth Charts for Children With Down Syndrome in the United States

down_graphResults of the Down Syndrome Growing Up Study, which compiled 1520 measurements on 637 participants from 25 states (but mostly Pennsylvania) to create weight, height, head circumference, and body-mass index charts for children with Down syndrome through age 20 years. Comparisons to the old 1988 DS growth charts are illustrated. A supplement provides graphs and tables.

Zemel 2015 (Pediatrics) | PubMed 26504127 | Author Search

Ways to Identify Children with Medical Complexity and the Importance of Why

Comparison of 4 examples of diagnosis classification systems that have been used to identify the health problems in children with medical complexity: (1) Complex chronic conditions (CCCs), an open-source set of childhood conditions that are strongly associated with mortality, morbidity, functional limitations, high health resource utilization, and use of a complex care clinical program; (2) Clinical risk groups (CRGs), a proprietary system of hierarchical pediatric diagnosis groups ranging from healthy children without a chronic condition to unhealthy children with a catastrophic chronic condition that is associated with high morbidity and mortality; (3) Chronic condition indicators (CCIs), developed by the Agency for Healthcare Research and Quality, an open source diagnosis classification system that dichotomizes ∼14 000 ICD9 and ∼68 000 ICD10 diagnosis codes into chronic and non chronic conditions; and Patient medical complexity algorithm (PMCA), developed by Seattle Children’s Hospital, an open source, pediatric-specific, diagnosis classification system that uses ICD9 codes to group children into 1 of 3 categories: complex, chronic disease; noncomplex, chronic disease; and nonchronic disease.

Berry 2015 (J. Pediatrics) | PubMed 26028285 | Author Search

Autism Screening With Online Decision Support by Primary Care Pediatricians Aided by M-CHAT/F

An evaluation of the feasibility, validity, and reliability of the Modified Checklist for Autism in Toddlers (M-CHAT) with Follow-up Interview (M-CHAT/F) as administered via a secure web site before an 18- or 24-month well-child visit. Validation pf the primary-care process was evaluated by trained research assistants in an academic center via in-person or telephone interviews. Results were promising that this 2-step, primary-care based method was valid and had good positive predictive value.

Sturner 2016 (Pediatrics) | PubMed 27542847 | Author Search

Optimization of Drug-drug Interaction Alert Rules in a Pediatric Hospital’s Electronic Health Record System Using a Visual Analytics Dashboard

Describes an effort to reduce alert rates from drug-drug interactions, with some evidence that fewer alerts led to increased salience (lower override rates). Rates for pharmacists fell from 58.74 alerts per 100 orders to 25/100 orders. For providers, the drop in rates was less dramatic (~20 to 15/100 orders) but they were getting far fewer alerts in the first place. Pharmacists’ rate of alert overrides fell, but providers’ rates stayed the same. The basic methodology used was a visualization tool developed in a commercially available data-visualization application.

Simpao 2015 (JAMIA) | PubMed 25318641 | Author Search