scholarly journals Interruptions and nursing workload during medication administration process

2019 ◽  
Vol 72 (4) ◽  
pp. 1001-1006 ◽  
Author(s):  
Renata Longhi Sassaki ◽  
Danielle Fabiana Cucolo ◽  
Marcia Galan Perroca
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Neonatal Intensive Care ◽  
Medication Administration ◽  
Nursing Workload ◽  
Medication Process ◽  
Nursing Team ◽  
Preparation Phase ◽  
The Mean ◽  
Mean Time

ABSTRACT Objective: To investigate the sources and causes of interruptions during the medication administration process performed by a nursing team and measure its frequency, duration and impact on the team’s workload. Métodos: This is an observational study that timed 121 medication rounds (preparation, administration and documentation) performed by 15 nurses and nine nursing technicians in a Neonatal Intensive Care Unit in the countryside of the state of São Paulo. Resultados: 63 (52.1%) interruptions were observed. In each round, the number of interruptions that happened ranged from 1-7, for 127 in total; these occurred mainly during the preparation phase, 97 (76.4%). The main interruption sources were: nursing staff – 48 (37.8%) − and self-interruptions – 29 (22.8%). The main causes were: information exchanges – 54 (42.5%) − and parallel conversations – 28 (22%). The increase in the mean time ranged from 53.7 to 64.3% (preparation) and from 18.3 to 19.2% (administration) – p≤0.05. Conclusão: Interruptions in the medication process are frequent, interfere in the workload of the nursing team and may reflect on the safety of care.

scholarly journals CARGA DE TRABALHO E DIMENSIONAMENTO DE PESSOAL DE ENFERMAGEM EM TERAPIA INTENSIVA NEONATAL

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Ana Tamara Kolecha Giordani Grebinski ◽  
Francislene Aparecida Biederman ◽  
Caroline Berte ◽  
Grasiely Masotti Scalabrin Barreto ◽  
João Lucas Campos De Oliveira ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Quantitative Study ◽  
Neonatal Intensive Care ◽  
Intensive Therapy ◽  
Nursing Workload ◽  
Cross Sectional ◽  
Nursing Team ◽  
Terapia Intensiva ◽  
The Mean

Objetivo: Mensurar a carga de trabalho da equipe de enfermagem de uma Unidade de Terapia Intensiva Neonatal (UTIN) e dimensionar o quadro de pessoal necessário para o suprimento desta demanda. Metodologia: Estudo transversal, documental e quantitativo. Foram coletadas variáveis de caracterização clínica e demográfica da amostra (n=105) de recém-nascidos e da carga de trabalho da enfermagem por meio do Nursing Activities Score (NAS). O dimensionamento foi calculado com base em equação para terapia intensiva e ajustado à Resolução nº 543/2017 do Conselho Federal de Enfermagem. Resultados: A média do NAS da UTIN foi de 749,9. Obteve-se quadro dimensionado de 43 profissionais, com déficit de 17 enfermeiros em comparação ao quadro disponível. Conclusão: O quadro de enfermeiros da UTIN é insuficiente.Descritores: Carga de trabalho; Dimensionamento; Equipe de enfermagem; Unidades de terapia intensiva neonatal.WORKLOAD AND SIZING OF THERAPY IN NURSING STAFF INTENSIVE NEWBORNObjective: To measure the workload of the nursing team of a Neonatal Intensive Care Unit (NICU) and to size the personnel needed to supply this demand. Method: Cross-sectional, documentary and quantitative study. Clinical and demographic characterization variables of the sample (n = 105) of newborns and the nursing workload were collected through the Nursing Activities Score (NAS). The design was calculated based on a formula for intensive therapy and adjusted to Resolution 543/2017 of the Federal Nursing Council. Results: The mean of the NICU NAS was 749.9. It was obtained a dimensioned picture of 43 professionals, with a deficit of 17 nurses in comparison to the available picture. Conclusion: Nurses from the NICU are insufficient.Descriptors: Workload; Sizing; Nursing team; Neonatal intensive care units.CARGA DE TRABAJO Y DIMENSIONAMIENTO DE PERSONAL DE ENFERMERÍA EN TERAPIA INTENSIVA NEONATALObjetivo: Medir la carga de trabajo del equipo de enfermería de una Unidad de Terapia Intensiva Neonatal (UTIN) y dimensionar el cuadro de personal necesario para el aprovisionamiento de esta demanda. Metodologia: Estudio transversal, documental y cuantitativo. Se recogieron variables de caracterización clínica y demográfica de la muestra (n = 105) de recién nacidos y de la carga de trabajo de la enfermería por medio del Nursing Activities Score (NAS). El dimensionamiento fue calculado con base en fórmula para terapia intensiva y ajustado a la Resolución 543/2017 del Consejo Federal de Enfermería. Resultados: El promedio del NAS de la UTIN fue de 749,9. Se obtuvo un cuadro dimensionado de 43 profesionales, con déficit de 17 enfermeros en comparación al cuadro disponible. Conclusión: El cuadro de enfermeros de la UTIN es insuficiente.Descriptores: Carga de trabajo; Dimensionamiento; Equipo de enfermería; Unidades de terapia intensiva neonatal.

scholarly journals 1412. The utility of respiratory viral panel testing for nosocomial fever in the neonatal intensive care unit

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S713-S713
Author(s):  
Carlo Fopiano Palacios ◽  
Eric Lemmon ◽  
James Campbell
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Linear Regression ◽  
Length Of Stay ◽  
Neonatal Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
P Value ◽  
Fisher Exact Test ◽  
The Mean ◽  
Pcr Test

Abstract Background Patients in the neonatal intensive care unit (NICU) often develop fevers during their inpatient stay. Many neonates are empirically started on antibiotics due to their fragile clinical status. We sought to evaluate whether the respiratory viral panel (RVP) PCR test is associated with use of antibiotics in patients who develop a fever in the NICU. Methods We conducted a retrospective chart review on patients admitted to the Level 4 NICU of the University of Maryland Medical Center from November 2015 to June 2018. We included all neonates who developed a fever 48 hours into their admission. We collected demographic information and data on length of stay, fever work-up and diagnostics (including labs, cultures, RVP), and antibiotic use. Descriptive statistics, Fisher exact test, linear regression, and Welch’s ANOVA were performed. Results Among 347 fever episodes, the mean age of neonates was 72.8 ± 21.6 days, and 45.2% were female. Out of 30 total RVP samples analyzed, 2 were positive (6.7%). The most common causes of fever were post-procedural (5.7%), pneumonia (4.8%), urinary tract infection (3.5%), meningitis (2.6%), bacteremia (2.3%), or due to a viral infection (2.0%). Antibiotics were started in 208 patients (60%), while 61 neonates (17.6%) were already on antibiotics. The mean length of antibiotics was 7.5 ± 0.5 days. Neonates were more likely to get started on antibiotics if they had a negative RVP compared to those without a negative RVP (89% vs. 11%, p-value < 0.0001). Patients with a positive RVP had a decreased length of stay compared to those without a positive RVP (30.3 ± 8.7 vs. 96.8 ± 71.3, p-value 0.01). On multivariate linear regression, a positive RVP was not associated with length of stay. Conclusion Neonates with a negative respiratory viral PCR test were more likely to be started on antibiotics for fevers. Respiratory viral PCR testing can be used as a tool to promote antibiotic stewardship in the NICU. Disclosures All Authors: No reported disclosures

scholarly journals The use of score for neonatal acute physiology perinatal extention II (SNAPPE II) in predicting neonatal outcome in neonatal intensive care unit

2016 ◽  
Vol 45 (6) ◽  
pp. 241
Author(s):  
Mia R A ◽  
Risa Etika ◽  
Agus Harianto ◽  
Fatimah Indarso ◽  
Sylviati M Damanik
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Neonatal Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
Characteristic Curve ◽  
Newborn Care ◽  
Scoring Systems ◽  
Laboratory Observation ◽  
The Mean ◽  
Overall Mortality

Background Scoring systems which quantify initial risks have animportant role in aiding execution of optimum health services by pre-dicting morbidity and mortality. One of these is the score for neonatalacute physiology perinatal extention (SNAPPE), developed byRichardson in 1993 and simplified in 2001. It is derived of 6 variablesfrom the physical and laboratory observation within the first 12 hoursof admission, and 3 variables of perinatal risks of mortality.Objectives To assess the validity of SNAPPE II in predicting mor-tality at neonatal intensive care unit (NICU), Soetomo Hospital,Surabaya. The study was also undertaken to evolve the best cut-offscore for predicting mortality.Methods Eighty newborns were admitted during a four-month periodand were evaluated with the investigations as required for the specifi-cations of SNAPPE II. Neonates admitted >48 hours of age or afterhaving been discharged, who were moved to lower newborn care <24hours and those who were discharged on request were excluded. Re-ceiver operating characteristic curve (ROC) were constructed to derivethe best cut-off score with Kappa and McNemar Test.Results Twenty eight (35%) neonates died during the study, 22(82%) of them died within the first six days. The mean SNAPPE IIscore was 26.3+19.84 (range 0-81). SNAPPE II score of thenonsurvivors was significantly higher than the survivors(42.75+18.59 vs 17.4+14.05; P=0.0001). SNAPPE II had a goodperformance in predicting overall mortality and the first-6-daysmortality, with area under the ROC 0.863 and 0.889. The best cut-off score for predicting mortality was 30 with sensitivity 81.8%,specificity 76.9%, positive predictive value 60.0% and negativepredictive value 90.0%.Conclusions SNAPPE II is a measurement of illness severity whichcorrelates well with neonatal mortality at NICU, Soetomo Hospital.The score of more than 30 is associated with higher mortality

scholarly journals Catheter-associated urinary tract infection: why do not we control this adverse event?

2019 ◽  
Vol 53 ◽  
Author(s):  
Écila Campos Mota ◽  
Adriana Cristina Oliveira
Keyword(s):  
Intensive Care Unit ◽  
Urinary Tract Infection ◽  
Intensive Care ◽  
Urinary Tract ◽  
Tract Infection ◽  
Urinary Catheter ◽  
Incidence Density ◽  
The Mean ◽  
Mean Time ◽  
Significant Factors

ABSTRACT Objective: To identify factors related to the occurrence of urinary tract infection associated with urinary catheter use. Method: A longitudinal, retrospective cohort study carried out by analyzing the electronic medical records of patients admitted to an intensive care unit of a high-complexity hospital from July 2016 to June 2017. Demographic and clinical data were analyzed by descriptive and analytical analysis. Results: The incidence density of urinary tract infection related to urinary catheter use was 4.8 per 1000 catheters/day, the majority (80.6%) with no indication for catheter use, and there was no prescription for insertion and/or maintenance in 86.7%. The mean time between catheter insertion and infection diagnosis was 11.3 ± 6.3 days (6 to 28 days). Statistically significant factors (p < 0.001) related to urinary infection linked to catheter use were hospitalization time in the unit (16.7 ± 9 days), catheter permanence time (12.7 ± 6.9 days), and the use of antimicrobials in the intensive care unit (8.6 ± 6.3 days). Conclusion: The association of indication absence and the record of the need for maintenance possibly potentiated the occurrence of urinary tract infection associated to catheter use.

scholarly journals A equipe de enfermagem frente aos acionamentos de alarmes em unidade de terapia intensiva neonatal The nursing team before alarm triggering in the neonatal intensive care unit

2016 ◽  
Vol 8 (3) ◽  
pp. 4773
Author(s):  
Cristiano Bertolossi Marta ◽  
Helio Casemiro Seabra Junior ◽  
Dayane José da Costa ◽  
Gicilene Moreira Martins ◽  
Roberto Carlos Lyra Silva ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Neonatal Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
Nursing Team ◽  
Terapia Intensiva

Objetivos: Descrever e Discutir as condutas dos profissionais de enfermagem diante dos alarmes disparados pelos equipamentos eletromédicos. Método: Trata-se de um estudo descritivo, exploratório e com análise qualitativa. Resultados: Os profissionais apresentam uma boa qualificação, porém atuam num cenário complexo que exige um dimensionamento de pessoal diferenciado e que não foi respeitado, fato que pode ter interferido no atendimento ao alarme acionado. Conclusão: Diante dos fatos mencionados, percebe-se que a capacitação dos profissionais de enfermagem e o estímulo a atualização de conhecimentos e ao cumprimento das normas técnicas e operacionais da profissão, apresentam-se como solução bem adequada às necessidades do individuo e da empresa e/ou unidade hospitalar, visto que o objetivo final é a prestação de uma assistência de qualidade e a segurança do paciente.

scholarly journals Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit

2018 ◽  
Vol 25 (5) ◽  
pp. 555-563 ◽  
Author(s):  
Yizhao Ni ◽  
Todd Lingren ◽  
Eric S Hall ◽  
Matthew Leonard ◽  
Kristin Melton ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Real Time ◽  
Medication Errors ◽  
Neonatal Intensive Care ◽  
Current Practice ◽  
Medication Administration ◽  
Automated System ◽  
Patient Exposure ◽  
Potential Harm

Abstract Background Timely identification of medication administration errors (MAEs) promises great benefits for mitigating medication errors and associated harm. Despite previous efforts utilizing computerized methods to monitor medication errors, sustaining effective and accurate detection of MAEs remains challenging. In this study, we developed a real-time MAE detection system and evaluated its performance prior to system integration into institutional workflows. Methods Our prospective observational study included automated MAE detection of 10 high-risk medications and fluids for patients admitted to the neonatal intensive care unit at Cincinnati Children’s Hospital Medical Center during a 4-month period. The automated system extracted real-time medication use information from the institutional electronic health records and identified MAEs using logic-based rules and natural language processing techniques. The MAE summary was delivered via a real-time messaging platform to promote reduction of patient exposure to potential harm. System performance was validated using a physician-generated gold standard of MAE events, and results were compared with those of current practice (incident reporting and trigger tools). Results Physicians identified 116 MAEs from 10 104 medication administrations during the study period. Compared to current practice, the sensitivity with automated MAE detection was improved significantly from 4.3% to 85.3% (P = .009), with a positive predictive value of 78.0%. Furthermore, the system showed potential to reduce patient exposure to harm, from 256 min to 35 min (P &lt; .001). Conclusions The automated system demonstrated improved capacity for identifying MAEs while guarding against alert fatigue. It also showed promise for reducing patient exposure to potential harm following MAE events.

scholarly journals Tracheostomy in the coronavirus disease 2019 patient: evaluating feasibility, challenges and early outcomes of the 14-day guidance

2020 ◽  
Vol 134 (8) ◽  
pp. 688-695 ◽  
Author(s):  
N Glibbery ◽  
K Karamali ◽  
C Walker ◽  
I Fitzgerald O'Connor ◽  
B Fish ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Standard Deviation ◽  
Intensive Care Unit Discharge ◽  
Organ Support ◽  
Unit Discharge ◽  
Surgical Tracheostomy ◽  
Ventilatory Assistance ◽  
The Mean ◽  
Mean Time

AbstractObjectivesTo report feasibility, early outcomes and challenges of implementing a 14-day threshold for undertaking surgical tracheostomy in the critically ill coronavirus disease 2019 patient.MethodsTwenty-eight coronavirus disease 2019 patients underwent tracheostomy. Demographics, risk factors, ventilatory assistance, organ support and logistics were assessed.ResultsThe mean time from intubation to tracheostomy formation was 17.0 days (standard deviation = 4.4, range 8–26 days). Mean time to decannulation was 15.8 days (standard deviation = 9.4) and mean time to intensive care unit stepdown to a ward was 19.2 days (standard deviation = 6.8). The time from intubation to tracheostomy was strongly positively correlated with: duration of mechanical ventilation (r(23) = 0.66; p < 0.001), time from intubation to decannulation (r(23) = 0.66; p < 0.001) and time from intubation to intensive care unit discharge (r(23) = 0.71; p < 0.001).ConclusionPerforming a tracheostomy in coronavirus disease 2019 positive patients at 8–14 days following intubation is compatible with favourable outcomes. Multidisciplinary team input is crucial to patient selection.

Sign in / Sign up

Export Citation Format

Share Document