Neonatal ICU System Safety: A Pilot Test for Medication Error by Using Fuzzy Grey Relational Analysis

Content validity of a Safe Nursing Care Checklist for a neonatal unit

BACKGROUND

Nursing checklists have been shown to improve communication, reduce the occurrence of adverse events, and promote safe, quality care in different care settings. However, to date, there is no validated patient care safety checklist for nurses caring for infants in Neonatal Intensive Care Units (NICU).

AIM

To describe development and content validation of the "Safe Nursing Care Checklist for Infants Hospitalized in the Neonatal Intensive Care Unit".

STUDY DESIGN

Online Survey.

METHODS

Based upon an integrative literature review, we developed a checklist focused on safe nursing care for infants in the NICU. Nursing experts participated in three rounds of a content validation process where they rated the items online. An agreement level ≥0.90 was required for inclusion in the checklist. Forty- three expert nurses with experience working in the NICU and who were certified in neonatal nursing or had a master's or doctoratal degree in child health provided content validation of the patient care checklist.

RESULTS

The final checklist contained 45 items with content validation index scores greater than 90%. The instrument was structured into six dimensions including patient identification, effective communication, medication safety, infection prevention, fall prevention, and pressure injuries/skin injuries prevention.

CONCLUSION

Content validity was established for the "Safe Nursing Care Checklist for Infants Hospitalized in the Neonatal Intensive Care Unit" which can identify strengths and weaknesses in safe nursing care for infants in the NICU as well as direct educational interventions to promote nursing care based on scientific evidence.

RELEVANCE TO CLINICAL PRACTICE

This checklist can potentially be used by bedside nurses to promote provision of safe care to infants in the NICU and to guide corrective strategies and encourage evidence-based decision-making. Validation in the clinical setting is needed.

Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit

Aim: To develop models that predict the presence of medication errors (MEs) (prescription, preparation, administration, and monitoring) using machine learning in NICU patients. Design: Prospective, observational cohort study randomized with machine learning (ML) algorithms. Setting: A 22-bed capacity NICU in Ankara, Turkey, between February 2020 and July 2021. Results: A total of 11,908 medication orders (28.9 orders/patient) for 412 NICU patients (5.53 drugs/patient/day) who received 2,280 prescriptions over 32,925 patient days were analyzed. At least one physician-related ME and nurse-related ME were found in 174 (42.2%) and 235 (57.0%) of the patients, respectively. The parameters that had the highest correlation with ME occurrence and subsequently included in the model were: total number of drugs, anti-infective drugs, nervous system drugs, 5-min APGAR score, postnatal age, alimentary tract and metabolism drugs, and respiratory system drugs as patient-related parameters, and weekly working hours of nurses, weekly working hours of physicians, and number of nurses' monthly shifts as care provider-related parameters. The obtained model showed high performance to predict ME (AUC: 0.920; 95% CI: 0.876-0.970) presence and is accessible online (http://softmed.hacettepe.edu.tr/NEO-DEER_Medication_Error/). Conclusion: This is the first developed and validated model to predict the presence of ME using work environment and pharmacotherapy parameters with high-performance ML algorithms in NICU patients. This approach and the current model hold the promise of implementation of targeted/precision screening to prevent MEs in neonates. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT04899960.

Exploring the relationship between the dielectric properties and viability of human normal hepatic tissues from 10 Hz to 100 MHz based on grey relational analysis and BP neural network

Liver is an important parenchyma organ, and its tissue viability plays an important role in liver transplantation and liver ischemic injury assessment. Dielectric property is a useful biophysical feature that provides insights into the structure and composition of biological tissues. This work aims to establish the relationship between the dielectric properties and viability of human normal hepatic tissues and explore the possibility of evaluating tissue viability by using dielectric properties. First, data on dielectric properties and tissue viability (including cell morphology and enzyme indicators) were collected from human liver tissues at 0.25-24 h after isolation. Grey relational analysis was conducted to select dielectric property and tissue viability indices that were highly correlated with prolonged ex vivo time as the inputs and outputs, respectively, of back-propagation (BP) neural network analysis. Finally, a BP neural network was developed with the Levenberg-Marquardt algorithm to explore the possibility of using dielectric properties as the basis for tissue viability evaluation. Results showed that the mean relative error for prediction was 2.40%, indicating that the model showed potential in forecasting liver tissue viability by applying dielectric properties.

What causes prescribing errors in children? Scoping review

OBJECTIVES

(1) Systematically assemble, analyse and synthesise published evidence on causes of prescribing error in children. (2) Present results to a multidisciplinary group of paediatric prescribing stakeholders to validate findings and establish how causative factors lead to errors in practice.

DESIGN

Scoping review using Arksey and O'Malley's framework, including stakeholder consultation; qualitative evidence synthesis.

METHODS

We followed the six scoping review stages. (1) Research question-the research question was 'What is known about causes of prescribing error in children?' (2) Search strategy-we searched MEDLINE, EMBASE, CINAHL (from inception to February 2018), grey literature and reference lists of included studies. (3) Article selection-all published evidence contributing information on the causes of prescribing error in children was eligible for inclusion. We included review articles as secondary evidence to broaden understanding. (4) Charting data-results were collated in a custom data charting form. (5) Reporting results-we summarised article characteristics, extracted causal evidence and thematically synthesised findings. (6) Stakeholder consultation-results were presented to a multidisciplinary focus group of six prescribing stakeholders to establish validity, relevance and mechanisms by which causes lead to errors in practice.

RESULTS

68 articles were included. We identified six main causes of prescribing errors: children's fundamental differences led to individualised dosing and calculations; off-licence prescribing; medication formulations; communication with children; and experience working with children. Primary evidence clarifying causes was lacking.

CONCLUSIONS

Specific factors complicate prescribing for children and increase risk of errors. Primary research is needed to confirm and elaborate these causes of error. In the meantime, this review uses existing evidence to make provisional paediatric-specific recommendations for policy, practice and education.