scholarly journals The Australian Incident Monitoring Study: An Analysis of 2000 Incident Reports

1993 ◽  
Vol 21 (5) ◽  
pp. 520-528 ◽  
Author(s):  
R. K. Webb ◽  
M. Currie ◽  
C. A. Morgan ◽  
J. A. Williamson ◽  
P. Mackay ◽  
...  
Keyword(s):  
Patient Outcome ◽  
Human Error ◽  
Safety Margin ◽  
Monitoring Study ◽  
Set Up ◽  
Closed Claims ◽  
Voluntary Basis ◽  
Type Of Anaesthesia ◽  
Incident Monitoring

The Australian Patient Safety Foundation was formed in 1987; it was decided to set up and co-ordinate the Australian Incident Monitoring Study as a function of this Foundation; 90 hospitals and practices joined the study. Participating anaesthetists were invited to report, on an anonymous and voluntary basis, any unintended incident which reduced, or could have reduced, the safety margin for a patient. Any incident could be reported, not only those which were deemed “preventable” or were thought to involve human error. The Mark I AIMS form was developed which incorporated features and concepts from several other studies. All the incidents in this symposium were reported using this form, which contains general instructions to the reporter, key words and space for a narrative of the incident, structured sections for what happened (with subsections for circuitry incidents, circuitry involved, equipment involved, pharmacological incidents and airway incidents), why it happened (with subsections for factors contributing to the incident, factors minimising the incident and suggested corrective strategies), the type of anaesthesia and procedure, monitors in use, when and where the incident happened, the experience of the personnel involved, patient age and a classification of patient outcome. Enrolment, reporting and data-handling procedures are described. Data on patient outcome are presented; this is correlated with the stages at which the incident occurred and with the ASA status of the patients. The locations at which the incidents occurred and the types of procedures, the sets of incidents analysed in detail and a breakdown of the incidents due to drugs are also presented. The pattern and relative frequencies of the various categories of incidents are similar to those in “closed-claims” studies, suggesting that AIMS should provide information of relevance to those wishing to develop strategies to reduce the incidence and/or impact of incidents and accidents.

scholarly journals The Australian Incident Monitoring Study in Intensive Care: AIMS-ICU. An Analysis of the First Year of Reporting

1996 ◽  
Vol 24 (3) ◽  
pp. 320-329 ◽  
Author(s):  
U. Beckmann ◽  
I. Baldwin ◽  
G. K. Hart ◽  
W. B. Runciman ◽  
Keyword(s):  
Intensive Care ◽  
Safety Margin ◽  
National Study ◽  
First Year ◽  
Contributing Factors ◽  
Staff Members ◽  
Monitoring Study ◽  
Set Up ◽  
Incident Monitoring

The AIMS-ICU project is a national study set up to develop, introduce and evaluate an anonymous voluntary incident reporting system for intensive care. ICU staff members reported events which could have reduced, or did reduce, the safety margin for the patient. Seven ICUs contributed 536 reports, which identified 610 incidents involving the airway (20%), procedures (23%), drugs (28%), patient environment (21%), and ICU management (9%). Incidents were detected most frequently by rechecking the patient or the equipment, or by prior experience. No ill effects or only minor ones were experienced by most patients (short-term 76%, long-term 92%) as a result of the incident. Multiple contributing factors were identified, 33% system-based and 66% human factor-based. Incident monitoring promises to be a useful technique for improving patient safety in the ICU, when sufficient data have been collected to allow analysis of sets of incidents in defined “clinical situations”.

scholarly journals Problems Associated with Nursing Staff Shortage: An Analysis of the First 3600 Incident Reports Submitted to the Australian Incident Monitoring Study (AIMS-ICU)

1998 ◽  
Vol 26 (4) ◽  
pp. 396-400 ◽  
Author(s):  
U. Beckmann ◽  
I. Baldwin ◽  
M. Durie ◽  
A. Morrison ◽  
L. Shaw
Keyword(s):  
Nursing Staff ◽  
Contributing Factors ◽  
Staff Shortage ◽  
Patient Dependency ◽  
Monitoring Study ◽  
Minimal Data ◽  
Incident Reports ◽  
Set Up ◽  
Incident Monitoring

Although many studies have attempted to define appropriate nursing staff levels, allocation and patient dependency, minimal data is available on the effect of nursing staff shortage (NSS) on quality of care provided in intensive care. This study aimed to identify incidents associated with staff shortage as reported to the Australian Incident Monitoring Study-ICU (AIMS-ICU) project and to assess their estimated effect on patient outcome. A search of narrative keywords and contributing factors identified 89 nursing staff shortage incidents (NSS-INCIDENTS) and 373 incidents involving nursing staff shortage contributing factors (NSS-CF). NSS resulted from inappropriate rostering for current patient load (81%) and inability to respond to increased unit activity (19%). Most frequent associated incidents included problems with: drug administration/documentation (47), patient supervision (20), set-up of ventilators/ equipment (16), and accidental extubation (14). Undesirable patient outcomes included: major physiological change (22%), patient/relative dissatisfaction (12%), and physical injury (3%). This study suggests that inadequate staffing results in incidents and compromised patient safety.

scholarly journals Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Gomesh Nair ◽  
Pradeep Isawasan ◽  
Abdul Hafiz Ab Majid
Keyword(s):  
Deep Learning ◽  
Aedes Aegypti ◽  
Real Time ◽  
Aedes Albopictus ◽  
Automated Classification ◽  
Expert Performance ◽  
Deep Convolutional Neural Networks ◽  
Significant Difference ◽  
Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

A Comparison of Rule-Based and Machine Learning Models for Classification of Human Factors Aviation Safety Event Reports

2020 ◽  
Vol 64 (1) ◽  
pp. 129-133
Author(s):  
Katherine Darveau ◽  
Daniel Hannon ◽  
Chad Foster
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Human Error ◽  
Data Science ◽  
Aircraft Engine ◽  
Rule Based ◽  
Root Cause ◽  
Textual Data ◽  
Safety Event

There is growing interest in the study and practice of applying data science (DS) and machine learning (ML) to automate decision making in safety-critical industries. As an alternative or augmentation to human review, there are opportunities to explore these methods for classifying aviation operational events by root cause. This study seeks to apply a thoughtful approach to design, compare, and combine rule-based and ML techniques to classify events caused by human error in aircraft/engine assembly, maintenance or operation. Event reports contain a combination of continuous parameters, unstructured text entries, and categorical selections. A Human Factors approach to classifier development prioritizes the evaluation of distinct data features and entry methods to improve modeling. Findings, including the performance of tested models, led to recommendations for the design of textual data collection systems and classification approaches.

scholarly journals Rapid Classification of Clostridioides difficile Strains Using MALDI-TOF MS Peak-Based Assay in Comparison with PCR-Ribotyping

2021 ◽  
Vol 9 (3) ◽  
pp. 661
Author(s):  
Adriana Calderaro ◽  
Mirko Buttrini ◽  
Monica Martinelli ◽  
Benedetta Farina ◽  
Tiziano Moro ◽  
...  
Keyword(s):  
Ionization Time ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Flight Mass Spectrometry ◽  
Clostridioides Difficile ◽  
Pcr Ribotyping ◽  
Typing Methods ◽  
Set Up ◽  
Tof Ms

Typing methods are needed for epidemiological tracking of new emerging and hypervirulent strains because of the growing incidence, severity and mortality of Clostridioides difficile infections (CDI). The aim of this study was the evaluation of a typing Matrix-Assisted Desorption/Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS (T-MALDI)) method for the rapid classification of the circulating C. difficile strains in comparison with polymerase chain reaction (PCR)-ribotyping results. Among 95 C. difficile strains, 10 ribotypes (PR1–PR10) were identified by PCR-ribotyping. In particular, 93.7% of the isolates (89/95) were grouped in five ribotypes (PR1–PR5). For T-MALDI, two classifying algorithm models (CAM) were tested: the first CAM involved all 10 ribotypes whereas the second one only the PR1–PR5 ribotypes. Better performance was obtained using the second CAM: recognition capability of 100%, cross-validation of 96.6% and agreement of 98.4% (60 correctly typed strains, limited to PR1–PR5 classification, out of 61 examined strains) with PCR-ribotyping results. T-MALDI seems to represent an alternative to PCR-ribotyping in terms of reproducibility, set up time and costs, as well as a useful tool in epidemiological investigation for the detection of C. difficile clusters (either among CAM included ribotypes or out-of-CAM ribotypes) involved in outbreaks.

Using transfer learning for diabetic retinopathy stage classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Enas M.F. El Houby
Keyword(s):  
Diabetic Retinopathy ◽  
Transfer Learning ◽  
Grade Level ◽  
Design Methodology ◽  
Content Type ◽  
Stage Classification ◽  
Set Up ◽  
Retinal Fundus ◽  
Automated Methods

PurposeDiabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers.Design/methodology/approachIn this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection.FindingsBy conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively.Originality/valueIn this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

scholarly journals Laboratory-based surveillance for Cryptosporidium in France, 2006–2009

2010 ◽  
Vol 15 (33) ◽  
Author(s):  
Collective The ANOFEL Cryptosporidium National Network
Keyword(s):  
Seasonal Pattern ◽  
Late Summer ◽  
Epidemiological Data ◽  
University Hospitals ◽  
National Network ◽  
Lack Of Information ◽  
Health Authorities ◽  
Public Health Authorities ◽  
Set Up ◽  
Voluntary Basis

In 2002, the French Food Safety Agency drew attention to the lack of information on the prevalence of human cryptosporidiosis in the country. Two years later, the ANOFEL Cryptosporidium National Network (ACNN) was set up to provide public health authorities with data on the incidence and epidemiology of human cryptosporidiosis in France. Constituted on a voluntary basis, ACNN includes 38 hospital parasitology laboratories (mainly in university hospitals). Each laboratory is engaged to notify new cases of confirmed human cryptosporidiosis, store specimens (e.g. stools, duodenal aspirates or biopsies) and related clinical and epidemiological data, using datasheet forms. From January 2006 to December 2009, 407 cryptosporidiosis cases were notified in France and 364 specimens were collected. Of the notified cases, 74 were children under four years of age, accounting for 18.2%. HIV-infected and immunocompetent patients represented 38.6% (n=157) and 28% (n=114) of cases, respectively. A marked seasonal pattern was observed each year, with increased number of cases in mid to late summer and the beginning of autumn. Genotyping of 345 isolates from 310 patients identified C. parvum in 168 (54.2%) cases, C. hominis in 113 (36.4%) and other species in 29 (9.4%), including C. felis (n=15), C. meleagridis (n=4), C. canis (n=4), Cryptosporidium chipmunk genotype (n=1), Cryptosporidium rabbit genotype (n=1) and new Cryptosporidium genotypes (n=4). These data represent the first multisite report of laboratory-confirmed cases of cryptosporidiosis in France.

scholarly journals Automated Negative Lightning Return Strokes Classification System

2021 ◽  
Vol 2107 (1) ◽  
pp. 012022
Author(s):  
F. Abdul Haris ◽  
M.Z.A. Ab Kadir ◽  
S. Sudin ◽  
D. Johari ◽  
J. Jasni ◽  
...  
Keyword(s):  
Classification System ◽  
Automatic System ◽  
Human Error ◽  
Processing Time ◽  
Visual Inspection ◽  
Automated System ◽  
Graphic User Interface ◽  
Data Comparison ◽  
Good Agreement

Abstract Over the years, many studies have been conducted to measure and classify the lightning-generated electric field waveform for a better understanding of the lightning physics phenomenon. Through measurement and classification, the features of the negative lightning return strokes can be accessed and analysed. In most studies, the classification of negative lightning return strokes was performed using a conventional approach based on manual visual inspection. Nevertheless, this traditional method could compromise the accuracy of data analysis due to human error, which also required a longer processing time. Hence, this study developed an automated negative lightning return strokes classification system using MATLAB software. In this study, a total of 115 return strokes was recorded and classified automatically by using the developed system. The data comparison with the Tenaga Nasional Berhad Research (TNBR) lightning report showed a good agreement between the lightning signal detected from this study with those signals recorded from the report. Apart from that, the developed automated system was successfully classified the negative lightning return strokes which this parameter was also illustrated on Graphic User Interface (GUI). Thus, the proposed automatic system could offer a practical and reliable approach by reducing human error and the processing time while classifying the negative lightning return strokes.

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