Significant Error Rate Reduction Research Articles

Patient Safety and Medical Error Prevention in General Practice: A Comprehensive Systematic Review

Patient safety and medical error prevention remain critical issues in general practice, where medical errors can lead to adverse patient outcomes and increased healthcare costs. This systematic review synthesizes recent evidence on prevalent types of medical errors, effective prevention strategies, and outcomes of patient safety interventions within general practice settings. Following a comprehensive search across multiple databases, 52 studies published from 2014 to 2024 were included. Findings highlight medication errors, diagnostic inaccuracies, and communication lapses as the most common types of errors. Effective prevention strategies include the adoption of electronic health records (EHR), structured team communication protocols, checklist systems, and staff training programs on safety practices. These interventions have shown significant reductions in error rates and improvements in patient outcomes, though implementation is often hindered by barriers such as limited resources, resistance to change, and lack of training. This review underscores the importance of a multifaceted approach that combines technological, organizational, and educational interventions to enhance patient safety in general practice. Future research should explore long-term impacts of these interventions and address context-specific challenges to foster a culture of safety in healthcare settings.

Deep learning-driven channel coding: enhancing performance in LDS-CDMA and NOMA systems

This paper presents an innovative approach to channel coding for LDS-CDMA and NOMA systems through the integration of deep learning techniques. By leveraging neural networks within the channel coding process, the proposed model achieves substantial improvements in error correction, leading to a significant reduction in Bit Error Rate (BER) and computational complexity when compared to traditional methods such as Unpunctured Turbo Trellis Coded Modulation (UTTCM). The results demonstrate that the deep learning-based system is particularly effective in challenging communication environments, such as those with low Signal-to-Noise Ratios (SNR) and high levels of multi-user interference, where it outperforms conventional coding techniques. This research underscores the potential of deep learning to enhance the efficiency and adaptability of wireless communication systems, particularly as networks evolve towards 6G and beyond. The flexibility offered by this approach allows for improved handling of dynamic and complex environments, ensuring higher data throughput and reduced latency. Future work may focus on implementing the proposed model in real-time applications, as well as investigating its synergy with emerging technologies like massive MIMO and NOMA, which could lead to even greater performance enhancements in next-generation communication systems.

A-181 The Effectiveness Module to Reduce Laboratory Error Reporting Rates

Abstract Background In a hospital in central Taiwan, the number of tests conducted in 2022 was 1.88 million, there were 26 cases of laboratory error reports, with an error rate of 14 cases per million. Error reports may lead to diagnostic errors, posing a significant risk to patient safety. Therefore, our team aims to establish a simple and universal module that can be applicable to various laboratory operations to minimize errors. Methods Initially, we conducted an analysis of error causes, identifying 25 cases of human error and 1 case of computer system error. Human errors were categorized according to the laboratory process, including 4 specimen identification errors, 3 procedural errors, 9 typing errors, 7 validation of analytical data errors, and 2 instances of failure to communicate issues during handover. The proximate cause of human error was non-compliance with SOPs, and the root cause was inadequate SOPs leading to inconsistent practices. We reviewed the workflow, implemented corrective actions, revised SOPs, and provided training for personnel. Additionally, three important strategies were introduced: 1.High-Frequency Audits: Corrective actions were included in the internal audit checklist, initially increasing the frequency to at least once a month for the first 3 months, followed by at least once every 6 months within the next 6 months. If no further occurrences were noted, audits could be conducted annually or not. Each audit should be completed within 20 minutes to avoid excessive burden, with direct observation of operations and random questioning being the preferred audit methods. 2. Audit deficiencies should be documented: Personnel were requested to conduct cause analysis and improvements. This not only expressed the supervisor's commitment to improvement but also provided an opportunity to discover reasons why countermeasures cannot not be implemented, allowing for strategic adjustments until successful implementation. 3.Case-Based Training: Benchmark cases were developed into teaching materials, and compiled errors regularly for looking for patterns or trends to identity key steps. Make these key steps the focus of your educational training. Results In 2023, 1.94 million tests were conducted, there were 4 error reports, with an error rate of 3 cases per million, all were attributed to human error. In another hospital in southern Taiwan, laboratory error reports were primarily due to human error. From June 2022 to July 2023, a total of 3.46 million tests were conducted, there were 14 error reports and an error rate of 5 cases per million. After implementing the same improvement module from August to December 2023, a total of 1.56 million tests were conducted, there were 2 error reports and an error rate of 2 cases per million, all were attributed to human error. Conclusions We applied this improvement module to two hospitals in central and southern Taiwan, resulting in a significant reduction in error rate. The key factors of effectiveness were utilizing high-frequency audits to ensure sustained results, and demonstrating the positive attitude of supervisors during the process. This motivated personnel to demand higher standards from themselves, ultimately showcasing remarkable improvements.

Integrating international Chinese visualization teaching and vocational skills training: leveraging attention-connectionist temporal classification models.

The teaching of Chinese as a second language has become increasingly crucial for promoting cross-cultural exchange and mutual learning worldwide. However, traditional approaches to international Chinese language teaching have limitations that hinder their effectiveness, such as outdated teaching materials, lack of qualified instructors, and limited access to learning facilities. To overcome these challenges, it is imperative to develop intelligent and visually engaging methods for teaching international Chinese language learners. In this article, we propose leveraging speech recognition technology within artificial intelligence to create an oral assistance platform that provides visualized pinyin-formatted feedback to learners. Additionally, this system can identify accent errors and provide vocational skills training to improve learners' communication abilities. To achieve this, we propose the Attention-Connectionist Temporal Classification (CTC) model, which utilizes a specific temporal convolutional neural network to capture the location information necessary for accurate speech recognition. Our experimental results demonstrate that this model outperforms similar approaches, with significant reductions in error rates for both validation and test sets, compared with the original Attention model, Claim, Evidence, Reasoning (CER) is reduced by 0.67%. Overall, our proposed approach has significant potential for enhancing the efficiency and effectiveness of vocational skills training for international Chinese language learners.

Demonstration of D-band 1×2 SIMO millimeter-wave wireless delivery over 1.2 km employing MRC technology

We experimentally demonstrate the transmission of 20-Gbaud QPSK millimeter-wave signals within a 1.2-km wireless range at 128-GHz using constant-modulus-algorithm (CMA) and the maximum ratio combining (MRC) technology with over 2.2-dB gain in a photonics-assisted millimeter wave (MMW) 1 × 2 Single Input Multi Output (SIMO) communication system. MRC is applied for SIMO signal merging which leads to Signal to Noise Ratio (SNR) enhancement and significant Bit Error Rate (BER) reduction. The comparison which is performed for Single Input Single Output (SISO) and SIMO system, demonstrates the BER improvement. To the best of our knowledge, this is the first experimental demonstration of high gain in a D-band MMW 1 × 2 SIMO system by using these technologies.

Dynamic Reserve Calculation Method of Fractured-Vuggy Reservoir Based on Modified Comprehensive Compression Coefficient

The low comprehensive compressibility coefficient characteristic of fracture-vuggy reservoirs often leads to imprecise dynamic reserve calculations. This study introduces a novel method for estimating dynamic reserves, which incorporates a modified comprehensive compressibility coefficient to enhance accuracy. This methodology has been applied to 23 wells in the Tahe Oilfield, resulting in error rates substantially lower than those associated with traditional techniques, thereby markedly enhancing the accuracy of dynamic reserve estimations. Specifically, for karst cave and fracture-vuggy reservoirs, the error rate in dynamic reserve calculations is reduced to under 10%, surpassing conventional methods by more than fivefold. In the case of fractured reservoirs, despite minor fluctuations in error rates due to stress sensitivity, diversion capacity, and channel variations, the proposed method still demonstrates a significant reduction in error rates compared to standard practices.

Extraction of single diode PV cell/module model parameters using a hybrid BMO approach with Lambert's W function

The accurate estimation of photovoltaic (PV) system parameters is crucial for effective simulation. Evaluation, modelling, and control of solar energy systems. The performance of photovoltaic installations is significantly influenced by the variability of model parameters and the difficulties in accessing them. As a result, the continuous objective is to consistently strive to search for and identify these parameters. This study proposes a hybrid algorithm that combines Bird Mating Optimiser algorithm with Lambert W-function (LBMO) with Wang's analytical method called (WLBMO) to optimise the parameters of the single diode model (SDM). The algorithm utilises a fast and precise iterative method for extracting I0 and Iph , and a Bird Mating Optimiser with Lambert W-function for extracting α, Rsh and Rs . The effectiveness of the proposed method is evaluated on the RTC solar cell and three commercial photovoltaic models. The specific results demonstrate the superior performance of our proposed WLBMO algorithm compared to known alternatives. For the R.T.C. France solar cell's single diode model, WLBMO achieves the smallest RMSE of 9.8630E-04, and similarly for the PWP201 model, yielding a low RMSE of 2.3845E-03. Comparative analysis, including Wang and LBMO, further validates our approach. Additionally, WLBMO excels among other methods for the STM6-120/36 commercial PV module, with a lowest RMSE of 1.7599E-03 and an exceptional minimum power error of 3.2270E-05. Similarly, for the KC200GT model, our method produces the smallest RMSE of 4.7569E-03. These consistent findings affirm the accuracy and efficacy of our WLBMO algorithm for PV model parameter estimation, emphasising its capacity to achieve significant error rate reductions of 92.856%, 1.147%, 49.732%, and 89.221% for the R.T.C France solar cell, Photowatt-PWP201, STM6-40/36, and KC200GT modules, respectively, and highlighting its pivotal role in optimising solutions.

Speech Recognition Enhancement and Compression Perception in Russian Translation Teaching Cooperative System Application

Abstract In the digital age, characterized by the ubiquity of big data and artificial intelligence, Russian translation education is poised for transformative change. This study explores the integration of advanced speech recognition and compressed perception technologies into Russian translation instruction, highlighting their potential to elevate educational quality and translator training. Utilizing the Spring Boot framework, we developed a comprehensive Russian translation teaching system, incorporating a Russian-Chinese speech translation model that leverages the RNN Transducer model and an N-gram language model, alongside compressed perception theory for efficient speech signal processing. Our findings reveal a notable enhancement in Russian speech recognition accuracy and processing speed, with a 4.73% improvement in endto-end speech detection accuracy and a significant reduction in word error rate, outperforming CTC and CTC-LM models by 14.79% to 24.85%. Furthermore, speech quality and intelligibility scores experienced marked improvements. The application of speech recognition enhancement and compressed perception technologies emerges as a vital strategy for enriching Russian translation pedagogy and fostering the development of proficient translators.

K-Nearest Neighbor for Gorontalo City Chili Price Prediction Using Feature Selection, Backward Elimination, and Forward Selection

This study addresses chili price volatility, an important concern that impacts the national economy and societal welfare. Fluctuations in chili prices in the retail market greatly influence market demand, thereby influencing farming decisions, especially chili cultivation. To help make better decisions, Researchers use forecasting, which is defined as the projection of future trends based on the analysis of historical data, using statistical methods. The K-Nearest Neighbor (K-NN) algorithm is used because of its resistance to high noise on large training datasets. However, challenges arise in determining the optimal value of 'k' and selecting related attributes. To overcome this, Feature Selection is applied to refine the model by removing irrelevant features, resulting in a significant reduction in the model error rate. This improvement indicates an increase in the efficiency of the K-NN algorithm with the incorporation of Feature Selection. Our findings show that the model, with backward elimination in Feature Selection, achieves a Root Mean Square Error (RMSE) of 0.202, outperforming the model using forward selection. The prediction accuracy of this model reaches an average of 78.86%, which is much higher than the baseline data of 50%. This shows the success of the proposed method in predicting chili prices.

Effect of single or combined caffeine and L-Theanine supplementation on shooting and cognitive performance in elite curling athletes: a double-blind, placebo-controlled study.

Previous studies have investigated the effect of single or combined caffeine (CAF) and L-theanine (THE) intake on attention performance. However, its effect on shooting performance and cognitive performance in a sport is unknown. The aim of this study was to investigate the hypothesis "Does single or combined CAF and THE supplementation have an effect on shooting and cognitive performance in elite curling athletes?." It is predicted that over the next decade, studies based on nutritional ergogenic supplements in the developing sport of curling will continue to increase, leading to a significant increase in studies examining the effects of CAF and THE supplementation, alone or in combination, on throwing and cognitive performance in elite curling athletes. In this double-blind, randomized controlled crossover study, twenty-two elite national curling athletes (age 20.20 ± 1.61 and sports age 6.20 ± 0.51 years, height 174.10 ± 7.21 cm, BMI 21.80 ± 3.47 kg/m2) were randomly assigned to CAF (6 mg/kg single dose CAF), THE (6 mg/kg single dose THE), CAF*THE (6 mg/kg CAF and 6 mg/kg THE combined) and PLA (400 mg maltodextrin) groups at each of four sessions. 60 minutes after taking the supplement, the athletes were first given the Stroop test and then asked to shoot. Our main findings have shown that the performance of athletes in guard (F=3.452, P < .001, ηp2 = .842), draw (F=1.647, P < .001, ηp2 = .485), and take-out (F=3.121, P < .001, ηp2 = .743) shot styles significantly improved when comparing the combined intake of CAF and THE to the PLA. Regarding cognitive performance evaluation through the Stroop test, during the NR task (F=4.743, P = .001, ηp2 = .653), the combined intake of CAF and THE significantly improved reaction times compared to the intake of single CAF, THE, or PLA. The best reaction times during the CR and ICR (respectively; F=2.742, P = .004, ηp2 = .328; F = 1.632, P < .001, ηp2 = .625) tasks were achieved with the combined CAF and THE intake, showing a significant improvement compared to PLA. During the NER (F=2.961, P < .001, ηp2 = .741), task, the combined intake of CAF and THE significantly improved error rates compared to the intake of CAF, THE, or PLA single. The best accuracy rates during the CER and ICER (respectively; F=4.127, P < .001, ηp2 = .396; F=3.899, P < .001, ηp2 = .710) tasks were achieved with the combined CAF and THE intake, leading to a significant reduction in error rates compared to PLA. Based on these findings, it has been demonstrated in this study that the best shooting scores and cognitive performance were achieved, particularly with the combined intake of CAF and THE. Based on these findings, it has been demonstrated in this study that the best shooting scores and cognitive performance were achieved, particularly with the combined intake of CAF and THE. The combined use of these supplements has been found to be more effective on shooting and cognitive performance than their single use.

An automated and portable method for selecting an optimal GPU frequency

Power consumption poses a significant challenge in current and emerging graphics processing unit (GPU) enabled high-performance computing systems. In modern GPUs, dynamic voltage frequency scaling (DVFS) appears to be a reliable control to regulate power consumption and performance. However, the DVFS design space is large - hence, brute-force approaches are infeasible to select the optimal frequency. Furthermore, no single frequency can be universally optimal for applications with varying computational intensities. Thus, the application’s complexity and the availability of a wide range of frequency settings are a challenge in selecting the optimal frequency configuration for a given GPU workload. To that end, this paper proposes a systematic approach that consists of three steps. The feature characterization study identifies the fine-grain GPU utilization metrics that influence the power consumption and execution time of a given workload. To understand the performance, power, and energy consumption behaviors of a workload across GPU’s DVFS design space, we derived analytical power and performance models using the identified fine-grain features. It is shown that the same set of GPU utilization metrics can estimate both the power consumption and execution time while being agnostic of changes to frequency and input sizes. Applying a power control with the single objective of reducing power may cause performance degradation, leading to more energy consumption. A multi-objective approach is proposed to select the optimal GPU DVFS configuration for a workload that reduces power consumption with negligible degradation in performance. The evaluation was conducted using SPEC ACCEL benchmarks and three real applications - NAMD LAMMPS, and LSTM on NVIDIA GV100, GA100, and AMD MI210 GPUs. On average, real applications showed 29.6% energy savings with a performance loss of 5.2% on GA100 and 22.6% energy savings with a performance loss of 4.7% on GV100. Moreover, the proposed models are portable to real applications, GPU architectures, and vendors, and require metric collection at only the default frequency rather than all supported DVFS configurations. Additionally, we conducted a comparison between our models and the GPU assembly instructions (PTX)-based static models. The results revealed a significant reduction in the average error rates, with a decrease from 19.7% to 3.1% for power models and from 29.4% to 5.2% for performance models.

Objective Assessment of Adherence and Inhaler Technique among Asthma and COPD Patients in London: A Study in Community Pharmacies Using an Electronic Monitoring Device.

The INhaler Compliance Assessment (INCATM) device is an electronic monitoring device (EMD) that assesses both patient's adherence and inhaler technique (IT). This study aimed, first, to assess the value of using the INCATM device as an objective measure during medicine use review (MUR) consultations provided by community pharmacists (CPs) on patients' adherence and IT. Second, we aimed to explore patients' perceptions about the INCATM device. A mixed methods approach was used, involving two phases. Phase one was a service evaluation in independent community pharmacies in London with a before-and-after study design. The service included provision of an MUR consultation to asthma and COPD patients using objective feedback about adherence and IT generated with the INCATM device. Descriptive and inferential statistics were performed using SPSS. Phase two involved semi-structured interviews with respiratory patients. Thematic analysis was performed to generate key findings. Eighteen patients participated in the study (12 COPD and 6 asthma). The results showed significant improvement in the INCATM actual adherence from 30% to 68% (p = 0.001) and significant reduction in IT error rate from 51% to 12% (p = 0.002) after conducting the service. Analysis of the interviews revealed patients' positive attitudes in terms of the perceived benefits of the technology and a desire for future use and recommendation for others. Patients had also positive attitudes towards the consultations provided. Embedding an objective measure about adherence and IT during CPs' consultations showed a significant improvement in patients' adherence and IT and was accepted by patients as well.

Simultaneous hand/wrist motion recognition and continuous grasp force estimation based on nonlinear spectral sEMG features for transradial amputees

Simultaneously and accurately estimating both grasp motion classes and force based on surface electromyogram (EMG) of residual arms is essential for precise grasping with a dexterous prosthetic hand. However, accurate grasping-mode prediction and grasping-force estimation often contradict each other. When performing hand grasping motion, different grasping forces will generate different EMG patterns, which would decay the grasp motion prediction accuracy. In this study, we proposed a framework of fusing motion classification and continuous grasp force estimation based on a new set of nonlinear Mel-Frequency Spectrum features of EMG signals. The performance of the proposed method was tested on eight able bodied (AB) subjects and four transradial amputees (TR) who are the end-users of dexterous prostheses. The experimental results showed that the newly proposed features obtained a significant reduction in the average classification error rate compared to other well-known feature sets, achieving improvements of about 5 % to 28 % in the average classification accuracy across all subjects and force levels. Simultaneously, the proposed features obtained the best continuous grasp force estimation accuracies with R2 (coefficient of determination) of 0.96 and 0.83, and root mean square of maximum voluntary contractions of grasp force (std%GFMVC) of 6.2 % and 10.5 % for the AB and TR subjects, respectively, compared to the previous EMG features. This study suggested that the proposed method would be helpful for the stable and fine manipulation of multifunctional prosthetic hand.

FDPBoost: Federated differential privacy gradient boosting decision trees

The big data era has led to an exponential increase in data usage, resulting in significantly advancements in data-driven domains and data mining. However, due to privacy and regulatory requirements, sharing data among various institutions is not always possible. Federated learning can help address this problem, but existing studies that combine differential privacy with tree models have shown significant accuracy loss. In this study, we propose a Federated Differential Privacy Gradient Boosting Decision Tree (FDPBoost) that protects the private datasets of different owners while improving model accuracy. We select sensitive features according to the secure feature set indicator, and use an exponential mechanism to protect sensitive features and assign significant weight to the Laplace mechanism to protect leaf node values. Additionally, a distributed two-level boosting framework is designed to allocate the privacy budget between intra-iteration and inter-iteration decision trees while protecting model communication. The FDPBoost is tested on five datasets sourced from the materials and medical domains. Our experiments reveal that FDPBoost achieves competitive accuracy with traditional federated gradient boosting decision trees while also exhibiting a significant reduction in error rate as compared to PPGBDT (Zhao et al.) and FV-tree (Gao et al.). Notably, FDPBoost’s error rate on the tumor-diagnosis dataset is 30% lower than that of FV-tree.

EVALUATING THE IMPACT OF ELECTRONIC HEALTH RECORD SYSTEMS ON REDUCING MEDICATION ERRORS: A STUDY AT LADY READING HOSPITAL, PESHAWAR

Medication mistakes significantly threaten hospitals, leading to adverse events and increased healthcare costs. Electronic health records (EHRs) have been introduced to reduce the frequency of errors and mitigate these issues. This study aimed to determine if the EHR system reduced medical errors at Lady Reading Hospital (LRH) in Peshawar, Pakistan, thereby enhancing patient safety. A retrospective study was conducted from January 3, 2021, to July 3, 2021. The assessment involved a detailed analysis of medication error incidents at LRH, recorded during this period, involving 200 patients. Data from the Department of Pharmacy were collected and analyzed concerning the occurrence, types, and possible contributing causes of medication errors. The results showed a significant reduction in medication error rates following the implementation of the EHR system. The number of medication errors decreased from 80 incidents to 30 occurrences. Specifically, overall mistakes dropped from 25 to 5, omissions from 15 to 10, and wrong medication reports from 20 to 8. The most common types of errors included dosage errors (accounting for 50 incidents), followed by wrong medication (30 incidents), and omissions (25 incidents). Factors contributing to these errors included communication gaps (20 incidents), transcribing errors (15 incidents), and inadequate training (10 incidents). An evaluation of the severity of these errors revealed 15 minor, 25 moderate, and 10 severe problems. Implementing the EHR system significantly enhanced patient safety by improving communication and providing continuous access to accurate patient information. The findings indicate that EHRs are a crucial component of hospital drug safety, significantly reducing medication errors. Integrating electronic record-keeping facilitates medication management processes, provides real-time information access, and improves communication among healthcare professionals. However, addressing other aspects of system malfunctions and ensuring effective system distribution is essential for successful EHR implementation in healthcare facilities. Future research should focus on designing longitudinal studies to evaluate the long-term safety of medications, the use of EHR systems, and strategies to further improve patient care quality.

Must Clinics Replace 2D by 3D Environments for an Efficient Training of Laparoscopic Novices? A Critical Analysis of the Learning Curve for Basic Skills.

Background and AimsPublished studies repeatedly demonstrate an advantage of three-dimensional (3D) laparoscopic surgery over two-dimensional (2D) systems but with quite heterogeneous results. This raises the question whether clinics must replace 2D technologies to ensure effective training of future surgeons.MethodsWe recruited 45 students with no experience in laparoscopic surgery and comparable characteristics in terms of vision and frequency of video game usage. The students were randomly allocated to 3D (n = 23) or 2D (n = 22) groups and performed 10 runs of a laparoscopic “peg transfer” task in the Luebeck Toolbox. A repeated-measures ANOVA for operation times and a generalized linear mixed model for error rates were calculated. The main effects of laparoscopic condition and run, as well as the interaction term between the two, were examined.ResultsNo statistically significant differences in operation times and error rates were observed between 2D and 3D groups (p = 0.10 and p = 0.72, respectively). The learning curve showed a significant reduction in operation time and error rates (both p's < 0.001). No significant interactions between group and run were detected (operation time: p = 0.342, error rates: p = 0.83). With respect to both endpoints studied, the learning curves reached their plateau at the 7th run.ConclusionThe result of our study with laparoscopic novices revealed no significant difference between 2D and 3D technology with respect to performance time and the error rate in a simple standardized test. In the future, surgeons may thus still be trained in both techniques.

Efficient Channel Prediction Technique Using AMC and Deep Learning Algorithm for 5G (NR) mMTC Devices

Efficient utilisation of adaptive modulation and coding ensures the quality transmission of information bits through the significant reduction in bit error rate (BER). Channel prediction using parametric estimation is not efficient for massive machine-type communication (mMTC) devices under the 5G New Radio (NR). In this paper, we have proposed a channel prediction scheme based on a deep learning (DL) algorithm possessed by parametric analysis. In deep learning, the pipeline methodology is used along with the image processing technique to predict the channel condition for optimal selection of the adaptive modulation and coding (AMC) profile. The deep learning-based pipelining approach utilises image restoration (IR) and image super-resolution (SR). The super-resolution method is used to de-noise the low-pixel 2-D image that is obtained from the parametric value of the beacon to predict the channel condition. The estimation results are compared with the conventional minimum mean square error (MMSE) and an approximation to the linear MMSE (ALMMSE) method, which is obtained through channel state information (CSI). The comparison results show that the parametric-enabled deep learning approach is superior, especially in poorer channel conditions. The performance of BER through parametric estimation along with the DL approach is ~66% more efficient as compared to the conventional MMSE method for BPSK mapping.

High-throughput hardware deployment of pruned neural network based nonlinear equalization for 100-Gbps short-reach optical interconnect.

Hardware implementation of neural network based nonlinear equalizers will encounter tremendous challenges due to a high-throughput data stream and high computational complexity for 100-Gbps short-reach optical interconnects. In this Letter, we propose a parallel pruned neural network equalizer for high-throughput signal processing and minimized hardware resources. The structure of a time-interleaved neural network equalizer with a delay module is deployed in a field programmable gate array with advanced pruned algorithms, demonstrating significant bit error rate reduction for 100-Gbps real-time throughput with 200 parallel channels. Moreover, the dependence of processing throughput, hardware resources, and equalization performance is investigated, showing that over 50% resource reduction without performance degradation can be achieved with the pruning strategy.

Application of the inverted classroom model in the teaching module "new classification of periodontal and peri-implant diseases and conditions" during the COVID-19 pandemic.

Background: Due to the need for patient-free dental education during the COVID-19 pandemic, Hannover Medical School (MHH) implemented a new periodontology module. Its didactic structure was based on the “inverted classroom model” (ICM) in combination with elements of case-based learning. The educational objective was to increase the diagnostic confidence of dental students in the classification of periodontal patients (staging & grading), based on 33 digitized patient cases. To assess the suitability of the module for future dental curricula, this study aimed to evaluate student satisfaction and skills acquisition. Methods: The periodontology module, which was attended by final year dental students of MHH (n=55, mean age: 26.5±3.9 years, male/female ratio: 24.1%/75.9%) was evaluated in a two-tiered way. Student satisfaction was recorded using a questionnaire. Learning success was assessed by comparing error rates in patient case classifications before (T0) and after (T1) participation in the periodontology module.Results: The study found a high level of student satisfaction with the ICM format and a significant reduction in error rates (T0 error rate=28.3%; MV±SD=3.12±1.67 vs. T1 error rate=18.7%; MV±SD=2.06±1.81; Δ=9.6%). However, of the 11 diagnostic decisions required, only four parameters (extent, grading, percentage of bone loss per age, phenotype) showed significant improvements, with effect sizes ranging from small to medium.Conclusions: The ICM-based teaching concept is definitely not an alternative to patient-based learning. However, in regard to student satisfaction and learning success, it might be superior to conventional classroom-based lectures, especially when complex topics are covered. In summary, the newly developed periodontology module may be a useful addition to traditional dental education in future curricula, even for the time after the COVID-19 pandemic.

The Impact of a Bedside Medication Scanning Device on Administration Errors in the Hospital Setting: A Prospective Observational Study

IntroductionThe medication administration process is complex and influenced by interruptions, multi-tasking and responding to patient’s needs and is consequently prone to errors.1 Over half (54.4%) of the 237 million medication errors estimated to have occurred in England each year were found to have taken place at the administration stage and 7.6% were associated with moderate or severe harm. The implementation of a Closed Loop Medication Administration solution aims to reduce medication administration errors and prevent patient harm.AimWe conducted the first evaluation to assess the impact of a novel optical medication scanning device, MedEye, on the rate of medication administration errors in solid oral dosage forms.MethodsWe performed a before and after study on one ward at a tertiary-care teaching hospital that used a commercial electronic prescribing and medication administration system and was implementing MedEye (a bedside tool for stopping and preventing medication administration errors). Pre-MedEye data collection occurred between Aug-Nov 2019 and post-MedEye data collection occurred between Feb-Mar 2020. We conducted direct observations of nursing drug administration rounds before and after the MedEye implementation. Observers recorded what they observed being administered (e.g., drug name, form, strength and quantity) and compared this to what was prescribed. Errors were classified as either a ‘timing’ error, ‘omission’ error or ‘other’ error. We calculated the rate and type of medication administration errors (MAEs) before and after the MedEye implementation. A sample size calculation suggested that approximately 10,000 medication administrations were needed. Data collection was reduced due to the COVID 19 pandemic and implementation delays.ResultsTrained pharmacists or nurses observed a total of 1,069 administrations of solid oral dosage forms before and 432 after the MedEye intervention was implemented. The percentage of MAEs pre-MedEye (69.1%) and post-MedEye (69.9%) remained almost the same. Non-timing errors (combination of ‘omission’ + ‘other’ errors) reduced from 51 (4.77%) to 11 (2.55%), which had borderline significance (p=0.05) however after adjusting for confounders, significance was lost. We also saw a non-significant reduction in ‘other’ error types (e.g., dose and documentation errors) following the implementation of MedEye from 34 (3.2%) to 7 (1.62%). An observer witnessed a nurse dispense the wrong medication (prednisolone) instead of the intended medication (furosemide) in the post-MedEye period. After receiving a notification from MedEye that an unexpected medication had been dispensed, the nurse corrected the dose thus preventing an error. We also identified one instance where the nurse correctly dispensed a prescribed medication (amlodipine) but this was mistakenly identified by the MedEye scanner as another prescribed medication (metoclopramide).ConclusionsThis is the first evaluation of a novel optical medication scanning device, MedEye on the rate of MAEs in one of the largest NHS trusts in England. We found a non-statistically significant reduction in non-timing error rates. This was notable because incidents within this category e.g., dose errors, are more likely to be associated with harm compared to timing errors.2 However, further research is needed to investigate the impact of MedEye on a larger sample size and range of medications.