Critical Errors Research Articles

Evaluating medical students’ ability to identify and report errors: finding gaps in patient safety education

ABSTRACT Background Although there are frequent complaints of medical students’ incompetence in reporting errors, few studies have examined their error-reporting abilities in the real world. Objectives Three sub-functions of self-regulation theory were used to evaluate medical students’ ability to identify errors (self-monitoring), analyse root causes (self-judgment), and devise improvement plans (self-reactions). In addition, whether students reacted differently to their errors and those of others (three sub-functions) was also examined. Methods A total of 952 patient safety reports were reviewed retrospectively, submitted by third-year medical students between 2016 and 2018. Data were quantitatively and qualitatively analysed to investigate who committed the error, to identify the type of error and its root causes, and to find suggested improvement plans. Chi-square and Fisher’s exact tests were used to compare students’ responses to error origins. Results Students reported other errors more frequently than their own (67.6% vs. 32.4%). They reported common critical medical errors, including errors related to engaging with patients (34.5%), invasive procedures (20.2%), and infection (18.5%). The root causes identified were more precise than the improvement plans by the students (75.5% vs. 18.4%, respectively). The students’ improvement plans were not practical, especially at the patient level (25.8%). When students committed errors, they considered human factors such as fatigue, scheduling, and training as the most common root cause, focussing on improvement plans at the individual level. Conclusions The results suggest that students were good at self-judgment, but not at self-monitoring and self-reactions. They reacted differently, based on who committed the error. To enhance self-regulated learning, Educators should encourage students to confront their errors, reflect on their self-reactions towards errors, develop well-being with time management, and think about the meaning of patient-centredness. Finally, active participation in clinical clerkship longitudinally may provide students with opportunities to learn from their errors.

The Review of New Scientific Developments in Drilling in Wood-Based Panels with Particular Emphasis on the Latest Research Trends in Drill Condition Monitoring

Drilling is still one of the basic cutting processes that are of particular interest to wood science and technology professionals. As a result, considerable (and very diverse thematically) research has been recently carried out on drilling. The article focuses on the new and quite spectacular approach to drill condition monitoring in wood-based panels machining. One of the most innovative elements in the analyzed research trend is the adoption of the new general methodological assumptions that allow one to define the drill condition monitoring problem as a standard three-class classification. The general effectiveness of the tested monitoring systems (accuracy of classification ACC [%]), ranged between 67% and 82%. The critical classification error (CCE [%]) ranged between 0% and 1.6%. These results seem very promising, yet are still not good enough to develop a commercial monitoring system. A more useful form of obtaining diagnostic data and more effective classification strategies (algorithms) are likely to be required.

Artificial Neural Network-Based Ionospheric Delay Correction Method for Satellite-Based Augmentation Systems

Ionospheric delay is a critical error source in Global Navigation Satellite Systems (GNSSs) and a principal aspect of Satellite Based Augmentation System (SBAS) corrections. Grid Ionospheric Vertical Delays (GIVDs) are derived from the delays on Ionosphere Pierce Points (IPPs), which are observed by SBAS reference stations. SBAS master stations calculate ionospheric delay corrections by several methods, such as planar fit or Kriging. However, when there are not enough IPPs around an Ionosphere Grid Point (IGP) or the IPPs are unevenly distributed, the fitting accuracy of planar fit or Kriging is unsatisfactory. Moreover, the integrity bounds of Grid Ionospheric Vertical Errors (GIVEs) are overly conservative. Since Artificial Neural Networks (ANNs) are widely used in ionospheric research due to their self-adaptation, parallelism, non-linearity, robustness, and learnability, the ANN method for GIVD and GIVE derivation is proposed in this article. Networks are separately trained for IGPs, and five years of historical data are applied on network training. Principal Component Analysis (PCA) is applied for dimensionality reduction of geomagnetic and solar indices, which is employed as a network input feature. Furthermore, the GIVE algorithm of the ANN method is derived based on the distribution of the residual random variable. Finally, experiments are conducted on 12 IGPs over the East China region. Under normal ionospheric conditions, compared with the planar fit and Kriging methods, the residual reduction of the ANN method is approximately 15%. The ANN method fits the ionospheric delay residual error better. The percentage of GIVE availability under 2.7 m increases at least 25 points in comparison to Kriging. Under disturbed conditions, due to a lack of training samples, the ANN method is incompetent compared with planar fit or Kriging.

A simple yet effective approach for log based critical errors prediction

With the advent of Industry 4.0, failure anticipation is becoming one of the key objectives in industrial research. In this context, predictive maintenance is an active research area for various applications. This paper presents an approach to predict high importance errors using log data emitted by machine tools. It uses the concept of bag to summarize events (or errors) provided by remote machines, available within log files. The idea of bag is inspired by the Multiple Instance Learning paradigm introduced by Dietterich et al. However, our proposal follows a different strategy to label bags, that we wanted as simple as possible. Three main setting parameters are defined to build the training set allowing the model to fine-tune the trade-off between early warning, historical data informativeness and time accuracy. The effectiveness of the approach is demonstrated using a real industrial application where failures can be predicted up to seven days in advance thanks to a classification model.

First study of the RuPatient health information system with optical character recognition of medical records based on machine learning

RuPatient health information system (HIS) is a computer program consisting of a doctor-patient web user interface, which includes algorithms for recognizing medical record text and entering it into the corresponding fields of the system.Aim. To evaluate the effectiveness of RuPatient HIS in actual clinical practice.Material and methods. The study involved 10 cardiologists and intensivists of the department of cardiology and сardiovascular intensive care unit of the L. A. Vorokhobov City Clinical Hospital 67 We analyzed images (scanned copies, photos) of discharge reports from patients admitted to the relevant departments in 2021. The following fields of medical documentation was recognized: Name, Complaints, Anamnesis of life and illness, Examination, Recommendations. The correctness and accuracy of recognition of entered information were analyzed. We compared the recognition quality of RuPatient HIS and a popular optical character recognition application (FineReader for Mac).Results. The study included 77 pages of discharge reports of patients from various hospitals in Russia from 50 patients (men, 52%). The mean age of patients was 57,7±7,9 years. The number of reports with correctly recognized fields in various categories using the program algorithms was distributed as follows: Name — 14 (28%), Diagnosis — 13 (26%), Complaints — 40 (80%), Anamnesis — 14 (28%), Examination — 24 (48%), Recommendations — 46 (92%). Data that was not included in the category was also recognized and entered in the comments field. The number of recognized words was 549±174,9 vs 522,4±215,6 (p=0,5), critical errors in words — 2,1±1,6 vs 4,4±2,8 (p<0,001), non-critical errors — 10,3±4,3 vs 5,6±3,3 (p<0,001) for RuPatient HIS and optical character recognition application for a personal computer, respectively.Conclusion. The developed RuPatient HIS, which includes a module for recognizing medical records and entering data into the corresponding fields, significantly increases the document management efficiency with high quality of optical character recognition based on neural network technologies and the automation of filling process.

Critical geometric errors identification of a five-axis machine tool based on global quantitative sensitivity analysis

Critical geometric errors identification of a five-axis machine tool based on global quantitative sensitivity analysis

Does quality assured eLearning provide adequate preparation for robotic surgical skills; a prospective, randomized and multi-center study.

PurposeIn particular after the onset of the COVID-19 pandemic, there was a precipitous rush to implement virtual and online learning strategies in surgery and medicine. It is essential to understand whether this approach is sufficient and adequate to allow the development of robotic basic surgical skills. The main aim of the authors was to verify if the quality assured eLearning is sufficient to prepare individuals to perform a basic surgical robotic task. MethodsA prospective, randomized and multi-center study was conducted in September 2020 in the ORSI Academy, International surgical robotic training center. Forty-seven participants, with no experience but a special interest in robotic surgery, were matched and randomized into four groups who underwent a didactic preparation with different formats before carrying out a robotic suturing and anastomosis task. Didactic preparation methods ranged from a complete eLearning path to peer-reviewed published manuscripts describing the suturing, knot tying and task assessment metrics.ResultsThe primary outcome was the percentage of trainees who demonstrated the quantitatively defined proficiency benchmark after learning to complete an assisted but unaided robotic vesico-urethral anastomosis task. The quantitatively defined benchmark was based on the objectively assessed performance (i.e., procedure steps completed, errors and critical errors) of experienced robotic surgeons for a proficiency-based progression (PBP) training course. None of the trainees in this study demonstrated the proficiency benchmarks in completing the robotic surgery task.ConclusionsPBP-based e-learning methodology is an effective training method avoiding critical errors in the suturing and knotting task. Quality assured online learning is insufficient preparation for robotic suturing and knot tying anastomosis skills.Trial registrationClinicalTrials.gov Identifier: NCT04541615.

Fundamental thresholds of realistic quantum error correction circuits from classical spin models

Mapping the decoding of quantum error correcting (QEC) codes to classical disordered statistical mechanics models allows one to determine critical error thresholds of QEC codes under phenomenological noise models. Here, we extend this mapping to admit realistic, multi-parameter noise models of faulty QEC circuits, derive the associated strongly correlated classical spin models, and illustrate this approach for a quantum repetition code with faulty stabilizer readout circuits. We use Monte-Carlo simulations to study the resulting phase diagram and benchmark our results against a minimum-weight perfect matching decoder. The presented method provides an avenue to assess fundamental thresholds of QEC circuits, independent of specific decoding strategies, and can thereby help guiding the development of near-term QEC hardware.

POSA55 Optimizing Inhaler Technique in COPD with Digital Health Technology: An Economic Evaluation

More than 70% of patients with chronic obstructive pulmonary disease (COPD) make at least one critical error in using their inhaler. Critical handling errors limit effectiveness of pharmaceutical treatment with considerable negative clinical and economic consequences. Identifying errors and optimizing inhaler technique in routine care is resource intensive and training effects are usually not sustained beyond three months. Digital technologies for self-management of regular device training are a promising approach to optimize inhalation technique.

Ризики впровадження штучного інтелекту в комп’ютерні системи

Since the absolute reliability of computer systems and the results of information processes that run in them can not be guaranteed, the task of research is to identify critical areas where such errors and failures are unacceptable. The main problems with the introduction of artificial intelligence in computer systems are the inability to predict all real situations and program the behavior of the machine adequately to them, lack of reliability and software errors. The input on which artificial intelligence is taught may be incorrect. In addition, artificial intelligence systems are influenced by the way of thinking and values of its developers, who are not always familiar with psychology, sociology and other humanities. These shortcomings during the use of artificial intelligence systems have led to many incidents, including fatal. The analysis of the sample of artificial intelligence error messages allowed us to determine which areas are critical errors, ie where the use of artificial intelligence systems is associated with significant risk. In particular, these are such areas as medicine, military affairs, transport, manufacturing, where people and robotic systems cooperate, hazardous industries, energy, social management, legal institutions and more. Currently, there is no regulatory and legal framework for the use of artificial intelligence, so its implementation is spontaneous, which leads to unpredictable results and accidents. Artificial intelligence used in critical infrastructures, in areas related to human health and life, belongs to the category of high risk. Based on the analysis and due to the impossibility of ensuring the absolute reliability of computer systems and their software, the authors do not recommend the use of artificial intelligence in areas related to safety, health and human life, especially large human teams. Devices using artificial intelligence systems should be marked with messages about its use with a clear warning about the partial reliability of the device in terms of safety and consumer responsibility for the use of such a device. The authors strongly discourage the use of artificial intelligence in responsible decision-making in areas related to the security of large groups of people.

Recreating the Clinical Competency in the Zoom World of the COVID-19 Pandemic

This virtual Clinic Competency allowed students to demonstrate their ability to meet the objectives outlined in the established rubric. The students exhibited their ability to recognize, diagnose, and discuss appropriate clinical interventions to most adequately address the proper diagnosis of the patient presented in their clinical scenario. The faculty-student interaction in this virtual assessment was successful in mirroring the interaction that would have occurred in a live patient competency setting. Of the 73 virtual assessments administered, four students had a critical error and needed to repeat the assessment. They were all successful in attaining competency in performing a second clinical scenario. Different learning styles may have had an impact on the students' performance on a virtual exam. Further study and modifications of our initial endeavor may prove this type of assessment plays a positive role within the teaching framework by expanding our ability to establish an additional methodology for case presentation and student evaluation. The pandemic highlighted the need for a more diverse range of assessment techniques.

A Randomized, Cross-Over Study Comparing Critical and Overall Errors, Learning Time, and Preference of the ELLIPTA Versus BREEZHALER Dry Powder Inhalers in Patients with Asthma

A Randomized, Cross-Over Study Comparing Critical and Overall Errors, Learning Time, and Preference of the ELLIPTA Versus BREEZHALER Dry Powder Inhalers in Patients with Asthma

POSC51 Optimizing Inhaler Technique in Asthma with Digital Health Technology: An Economic Evaluation

Up to 80% of patients with asthma do not use their inhaler device correctly and many clinicians cannot demonstrate correct inhaler use. Critical handling errors are associated with poor clinical outcomes in asthma with increased healthcare resource utilization (HRU). Identifying errors and optimizing inhaler technique in routine care is resource intensive and training effects are usually not sustained beyond three months. Digital technologies for self-management of regular device training are a promising approach to optimize inhalation technique.

Adaptive Event-Triggered Near-Optimal Tracking Control for Unknown Continuous-Time Nonlinear Systems

This paper studies the event-triggered optimal tracking control (ETOTC) problem of continuous-time (CT) unknown nonlinear systems. In order to solve the ETOTC problem, an augmented system composed of the error system dynamics and the reference dynamics is used to introduce a new discounted performance index function (DPIF). A novel event-triggered (ET) adaptive dynamic programming (ADP) method is developed to solve the ET Hamilton-Jacobi-Bellman equation (HJBE). The presented method is implemented via an identifier-critic architecture, which consists of two neural networks (NNs): an identifier NN is applied to estimate the unknown system dynamics, and a critic NN is constructed to obtain the approximate solution of the ET HJBE. The augmented closed-loop system and the critic estimation error are proved to be ultimately uniformly bounded (UUB) by the Lyapunov direct method. Finally, two simulations illustrate the effectiveness of the developed method.

A Discussion of Critical Errors in the Study 'Longitudinal Study on Deterrent Effect of Drug-Induced Homicide Law on Opioid-Related Mortality Across 92 Counties and the District of Columbia in the U.S.'

A Discussion of Critical Errors in the Study 'Longitudinal Study on Deterrent Effect of Drug-Induced Homicide Law on Opioid-Related Mortality Across 92 Counties and the District of Columbia in the U.S.'

The dynamics of changes in security systems during the development of a company from small to large

The article is devoted to the study of the peculiarities of the company's information security management in the process of its development from small to large. General definitions and theoretical provisions of information security were studied; the current state and trends in the development of ISMS of small, medium and large businesses in Ukraine were investigated; IT support models for small, medium and large companies were developed and sound recommendations for their implementation were given. The results of the research can be used at enterprises and organizations of small, medium and large businesses in the process of solving IS problems, preventing the occurrence of critical errors, incidents and unnecessary costs in the process of growth and development of business from small to large.

Testing a Fault Tolerant Mixed-Signal Design Under TID and Heavy Ions

This work presents results of three distinct radiation tests performed upon a fault-tolerant data acquisition system comprising a design diversity redundancy technique. The first and second experiments are Total Ionizing Dose (TID) essays, comprising gamma and X-ray irradiations. The last experiment considers single event effects, in which two heavy ion irradiation campaigns are carried out. The case study system comprises three analog-to-digital converters and two software-based voters, besides additional software and hardware resources used for controlling, monitoring and memory management. The applied Diversity Triple Modular Redundancy (DTMR) technique, comprises different levels of diversity (temporal and architectural). The circuit was designed in a programmable System-on-Chip (PSoC), fabricated in a 130nm CMOS technology process. Results show that the technique may increase the lifetime of the system under TID if comparing with a non-redundant implementation. Considering the heavy ions experiments the system was proved effective to tolerate 100% of the observed errors originated in the converters, while errors in the processing unit present a higher criticality. Critical errors occurring in one of the voters were also observed. A second heavy-ion campaign was then carried out to investigate the voters reliability, comparing the dynamic cross-section of three different software-based voter schemes implemented in the considered PSoC.

A Rare Cause of ST Segment Elevation: Avoiding Critical Errors in an Emergency

A 72-year-old female with a history of hypertension and chronic cough presented with worsening dys-pnoea, left-sided chest pain, and vomiting that had been present for two days [...]

Sensitivity Criterion and Law on a Navigation Receiver under Single Frequency Electromagnetic Radiation

To objectively assess the anti-electromagnetic interference ability of the navigation receiver, the sensitivity criterion of a certain type of navigation receiver is tested under single-frequency continuous wave electromagnetic radiation with an optimized testing method. The experimental results show that it is difficult to guarantee the accuracy of the measurement value of the sensitivity level by using the carrier-to-noise density ratio (C/N0) as the sensitivity criterion, but the initial C/N0 of each satellite can be used as the basis for identifying whether the navigation system has recovered from the interference. The experimental error is dramatically decreased when the sensitivity criterion of “the sensitive phenomenon appears within the first 4 s, and the loss of positioning lasts for 30 s” is employed, the variable interference power step size is adopted and all of the satellites C/N0 are required to recover to the initial value after an interference. The critical interference field intensity error can be controlled within 1 dB by using all these measures. The sensitivity law of the navigation receiver is the same under different working signal intensities. It is significantly sensitive in the working frequency bandwidth. It is also quite sensitive in −11.5 MHz~55.5 MHz of the frequency offset range. The positive sensitive bandwidth is about 5 times that of the negative sensitive bandwidth.

Proficiency-based progression intern training to reduce critical blood sampling errors including ‘wrong blood in tube’

Background: Blood sampling errors including ‘wrong blood in tube’ (WBIT) may have adverse effects on clinical outcomes. WBIT errors occur when the blood sample in the tube is not that of the patient identified on the label. This study aims to determine the effect of proficiency-based progression (PBP) training in phlebotomy on the rate of blood sampling errors (including WBIT). Methods: A non-randomised controlled trial compared the blood sampling error rate of 43 historical controls who had not undergone PBP training in 2016 to 44 PBP trained interventional groups in 2017. In 2018, the PBP training programme was implemented and the blood sampling error rate of 46 interns was compared to the 43 historical controls in 2016. Data analysis was performed using logistic regression analysis adjusting for sample timing. Results: In 2016, 43 interns had a total blood sample error rate of 2.4%, compared to 44 interns in 2017, who had error rate of 1.2% (adjusted OR=0.50, 95% CI 0.36-0.70; <0.01). In 2018, 46 interns had an error rate of 1.9% (adjusted OR=0.89, 95% CI 0.65-1.21; p=0.46) when compared to the 2016 historical controls. There were three WBITs in 2016, three WBITs in 2017 and five WBITs in 2018. Conclusions: The study demonstrates that PBP training in phlebotomy has the potential to reduce blood sampling errors. Trial registration number: NCT03577561