Feeding Behaviors in Infants and Toddlers Later Diagnosed with Autism Spectrum Disorder: A Systematic Review.

Children with autism spectrum disorder (ASD) are five times more likely to have feeding difficulties than neurotypical peers, although the majority of evidence describes feeding difficulty in children age 2 years and older. The purpose of this study is to systematically review the literature on feeding characteristics of children age 0-24 months who were later diagnosed with ASD with an emphasis on the measurement tools used to assess these feeding behaviors. We conducted a systematic review of the literature using PRISMA guidelines. Using selected keywords, a search was conducted using PubMed, PsycINFO, and CINAHL databases for relevant articles to identify feeding characteristics in infants and toddlers (age 0-24 months) later diagnosed with ASD. Sixteen studies were selected for this review by two independent reviewers. Among the selected studies, feeding difficulties were reported in all infant oral feeding modalities (breastfeeding, bottle feeding, and complementary feeding) by infants later diagnosed with ASD. However, the evidence was conflicting among studies regarding feeding characteristics, such as sucking differences while breastfeeding, use of the spoon with feedings, and preference of solid food texture, that presented in infants later diagnosed with ASD. A lack of consistent measurement of feeding behaviors in infants later diagnosed with ASD contributes to the difficulty in comparison across studies. Future research should focus on developing targeted, validated instruments for measuring feeding difficulty in this population with emphasis on breastfeeding and bottle feeding difficulty.

Critical Care Nurses' Experiences of Caring for Patients With COVID-19: Results of a Thematic Analysis.

BACKGROUND: The COVID-19 pandemic has challenged health care professionals, especially those working in intensive care units (ICUs). OBJECTIVES: To explore critical care nurses' experiences with and perceptions of the COVID-19 pandemic during the early phases of the pandemic. METHODS: Data were from national surveys conducted during March and April 2020 to assess ICU providers' perceptions of the initial phases of the pandemic. A total of 831 responses from nurses to open-ended questions were examined by using thematic analysis. The questions assessed potentially limited resources in the ICU, adequacy of staffing, and measures used to reduce the possibility of spreading COVID-19 to family members. RESULTS: Overarching themes concerned access to equipment and preventive measures taken to reduce exposure to the virus. These themes included "sheltering the patient when I don't have enough" and "protecting those I love when I am a vector of transmission." Subthemes for the first overarching theme included not having enough personal protective equipment, not enough staff and not enough properly trained staff, and not enough institutional support. Subthemes for the second overarching theme included "isolating myself from everyone I care about" and "isolating everything I touch from everyone I care about." CONCLUSIONS: This thematic analysis identified several concerns of ICU nurses related to caring for patients in the initial phases of the COVID-19 pandemic. Ensuring adequate supplies, staffing, and administrative and emotional support are provided to frontline health care providers during the ongoing pandemic remains essential.

Nurse's Achilles Heel: Using Big Data to Determine Workload Factors That Impact Near Misses.

PURPOSE: To explore how big data can be used to identify the contribution or influence of six specific workload variables: patient count, medication count, task count call lights, patient sepsis score, and hours worked on the occurrence of a near miss (NM) by individual nurses. DESIGN: A correlational and cross-section research design was used to collect over 82,000 useable data points of historical workload data from the three unique systems on a medical-surgical unit in a midsized hospital in the southeast United States over a 60-day period. Data were collected prior to the start of the Covid-19 pandemic in the United States. METHODS: Combined data were analyzed using JMP Pro version 12. Mean responses from two groups were compared using a t-test and those from more than two groups using analysis of variance. Logistic regression was used to determine the significance of impact each workload variable had on individual nurses' ability to administer medications successfully as measured by occurrence of NMs. FINDINGS: The mean outcome of each of the six workload factors measured differed significantly (p < .0001) among nurses. The mean outcome for all workload factors except the hours worked was found to be significantly higher (p < .0001) for those who committed an NM compared to those who did not. At least one workload variable was observed to be significantly associated (p < .05) with the occurrence or nonoccurrence of NMs in 82.6% of the nurses in the study. CONCLUSIONS: For the majority of the nurses in our study, the occurrence of an NM was significantly impacted by at least one workload variable. Because the specific variables that impact performance are different for each individual nurse, decreasing only one variable, such as patient load, will not adequately address the risk for NMs. Other variables not studied here, such as education and experience, might be associated with the occurrence of NMs. CLINICAL RELEVANCE: In the majority of nurses, different workload variables increase their risk for an NM, suggesting that interventions addressing medication errors should be implemented based on the individual's risk profile.

Using Real-Time Data to Warn Nurses of Medication Administration Errors Using a Nurse Situational Awareness Dashboard.

Electronic Health Records (EHR) are constantly gathering an exponential amount healthcare data. Historical data is often studied to identify trends and determine the effectiveness of interventions, but rarely is Real-time data utilized to positively influence the nurse at the Point of Care. A dashboard allowing nurses to visualize their individual Near-Miss (MN) medication error risk as the needs and subsequent workload of the patients they served changed was created and piloted for 30- days. Implementation of the dashboard resulted in a 15.6% reduction of NMs.