scholarly journals What do we Know about Contributing Factors for “Never Events” in Operating Rooms? A Machine Learning Analysis

2021 ◽  
Author(s):  
Dana Arad ◽  
Ariel Rosenfeld ◽  
Racheli Magnezi
Keyword(s):  
Machine Learning ◽  
General Surgery ◽  
Improve Patient Safety ◽  
Supervised Machine Learning ◽  
Contributing Factors ◽  
Safety Standards ◽  
Wrong Site Surgery ◽  
Retained Foreign Body ◽  
The Impact ◽  
Wrong Site

Abstract BackgroundA Surgical “Never Event” (NE) is a preventable error. Various factors contribute to the occurrence of wrong site surgery and retained foreign item, but little is known about their quantified risk in relation to surgery's characteristics. Our study uses machine learning to reveal factors and quantify their risk to improve patient safety and quality of care.MethodsWe used data from 9,234 observations on safety standards and 101 Root-Cause Analysis from actual NEs, and utilized three Random Forest supervised machine learning models. Using a standard 10-cross validation technique, we evaluated the model's metrics, and, through Gini impurity we measured the impact of factors thereof to occurrence of the two types of NEs. ResultsWe identified 24 contributing factors in six surgical departments. Two had an impact of >900% in Urology, Orthopedics and General Surgery, six had an impact of 0–900% in Gynecology, Urology and Cardiology, and 17 had an impact of <0%. Factors' combination revealed 15-20 pairs with an increased probability in five departments: Gynecology:875–1900%; Urology: 1,900:2,600%; Cardiology:833–1,500%; Orthopedics:1,825–4,225%; and General Surgery:2,720–13,600%. Five factors affected the occurrence of wrong site surgery (-60.96–503.92%) and five of retained foreign body (-74.65–151.43%), three of them overlapping: two nurses (66.26–87.92%), Surgery length<1 hour (85.56–122.91%), Surgery length 1-2 hours (-60.96–85.56%).ConclusionsThe use of machine learning has enabled us to quantify the potential impact of risk factors for wrong site surgeries and retained foreign items, in relation to surgery's characteristics, which in turn suggests tailoring the safety standards accordingly. Trial registration number: MOH 032-2019

Common general surgical never events: analysis of NHS England never event data

2021 ◽  
Vol 33 (1) ◽  
Author(s):  
Islam Omar ◽  
Rishi Singhal ◽  
Michael Wilson ◽  
Chetan Parmar ◽  
Omar Khan ◽  
...  
Keyword(s):  
Breast Implants ◽  
Skin Condition ◽  
Chest Drain ◽  
Event Data ◽  
Wrong Site Surgery ◽  
Wrong Side ◽  
Retained Foreign Body ◽  
Never Events ◽  
Never Event ◽  
Wrong Site

Abstract Background There is little available data on common general surgical never events (NEs). Lack of this information may have affected our attempts to reduce the incidence of these potentially serious clinical incidents. Objectives The purpose of this study was to identify common general surgical NEs from the data held by the National Health Service (NHS) England. Methods We analysed the NHS England NE data from April 2012 to February 2020 to identify common general surgical NEs. Results There was a total of 797 general surgical NEs identified under three main categories such as wrong-site surgery (n = 427; 53.58%), retained items post-procedure (n = 355; 44.54%) and wrong implant/prosthesis (n = 15; 1.88%). We identified a total of 56 common general surgical themes—25 each in the wrong-site surgery and retained foreign body categories and six in wrong implants category. Wrong skin condition surgery was the commonest wrong-site surgery (n = 117; 27.4%). There were 18 wrong-side chest drains (4.2%) and 18 (4.2%) wrong-side angioplasty/angiograms. There were seven (1.6%) instances of confusion in pilonidal/perianal/perineal surgeries and six (1.4%) instances of biopsy of the cervix rather than the colon or rectum. Retained surgical swabs were the most common retained items (n = 165; 46.5%). There were 28 (7.9%) laparoscopic retrieval bags with or without the specimen, 26 (7.3%) chest drain guide wires, 26 (7.3%) surgical needles and 9 (2.5%) surgical drains. Wrong stents were the most common (n = 9; 60%) wrong implants followed by wrong breast implants (n = 2; 13.3%). Conclusion This study found 56 common general surgical NEs. This information is not available to surgeons around the world. Increased awareness of these common themes of NEs may allow for the adoption of more effective and specific safeguards and ultimately help reduce their incidence.

scholarly journals Leveraging Road Characteristics and Contributor Behaviour for Assessing Road Type Quality in OSM

2021 ◽  
Vol 10 (7) ◽  
pp. 436
Author(s):  
Amerah Alghanim ◽  
Musfira Jilani ◽  
Michela Bertolotto ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Classification Accuracy ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Semantic Inference ◽  
Road Type ◽  
The Impact

Volunteered Geographic Information (VGI) is often collected by non-expert users. This raises concerns about the quality and veracity of such data. There has been much effort to understand and quantify the quality of VGI. Extrinsic measures which compare VGI to authoritative data sources such as National Mapping Agencies are common but the cost and slow update frequency of such data hinder the task. On the other hand, intrinsic measures which compare the data to heuristics or models built from the VGI data are becoming increasingly popular. Supervised machine learning techniques are particularly suitable for intrinsic measures of quality where they can infer and predict the properties of spatial data. In this article we are interested in assessing the quality of semantic information, such as the road type, associated with data in OpenStreetMap (OSM). We have developed a machine learning approach which utilises new intrinsic input features collected from the VGI dataset. Specifically, using our proposed novel approach we obtained an average classification accuracy of 84.12%. This result outperforms existing techniques on the same semantic inference task. The trustworthiness of the data used for developing and training machine learning models is important. To address this issue we have also developed a new measure for this using direct and indirect characteristics of OSM data such as its edit history along with an assessment of the users who contributed the data. An evaluation of the impact of data determined to be trustworthy within the machine learning model shows that the trusted data collected with the new approach improves the prediction accuracy of our machine learning technique. Specifically, our results demonstrate that the classification accuracy of our developed model is 87.75% when applied to a trusted dataset and 57.98% when applied to an untrusted dataset. Consequently, such results can be used to assess the quality of OSM and suggest improvements to the data set.

scholarly journals Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 647
Author(s):  
Meijun Shang ◽  
Hejun Li ◽  
Ayaz Ahmad ◽  
Waqas Ahmad ◽  
Krzysztof Adam Ostrowski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Mean Square Error ◽  
Coarse Aggregate ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Environmental Damage ◽  
Fine Aggregate ◽  
Mean Square ◽  
The Impact

Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.

Management of the deteriorating adult patient: does simulation-based education improve patient safety?

2021 ◽  
pp. 1-8
Author(s):  
Jacqueline Bennion ◽  
Stephanie K Mansell
Keyword(s):  
Human Factors ◽  
Adult Patient ◽  
Patient Outcomes ◽  
Improve Patient Safety ◽  
Low Grade ◽  
Contributing Factors ◽  
Educational Method ◽  
Simulation Based ◽  
The Impact

Failure to recognise the deteriorating patient can cause severe harm and is related to preventable death. Human factors are often identified as contributing factors. Simulation-based education is used to develop clinicians' human factors skills. This article discusses the evidence concerning the efficacy of simulation-based education for improving the recognition and management of the acutely deteriorating adult patient, and the limitations of simulation-based education. Findings demonstrated simulation-based education was the most effective educational method identified for training staff in recognising unwell patients. The evidence demonstrating the impact of simulation-based education on patient outcomes was equivocal. The quality of the evidence was low grade regarding the efficacy of simulation-based education on human factors. Further research is required to confirm the efficacy of simulation-based education for human factors and patient outcomes.

Insider Threat Detection Using Supervised Machine Learning Algorithms on an Extremely Imbalanced Dataset

2020 ◽  
Vol 10 (2) ◽  
pp. 1-26
Author(s):  
Naghmeh Moradpoor Sheykhkanloo ◽  
Adam Hall
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Machine Learning Algorithms ◽  
Third Party ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Insider Threat ◽  
Threat Detection ◽  
Imbalanced Dataset ◽  
The Impact

An insider threat can take on many forms and fall under different categories. This includes malicious insider, careless/unaware/uneducated/naïve employee, and the third-party contractor. Machine learning techniques have been studied in published literature as a promising solution for such threats. However, they can be biased and/or inaccurate when the associated dataset is hugely imbalanced. Therefore, this article addresses the insider threat detection on an extremely imbalanced dataset which includes employing a popular balancing technique known as spread subsample. The results show that although balancing the dataset using this technique did not improve performance metrics, it did improve the time taken to build the model and the time taken to test the model. Additionally, the authors realised that running the chosen classifiers with parameters other than the default ones has an impact on both balanced and imbalanced scenarios, but the impact is significantly stronger when using the imbalanced dataset.

scholarly journals Evolution of corporate reputation during an evolving controversy

2019 ◽  
Vol 23 (1) ◽  
pp. 52-71 ◽  
Author(s):  
Siyoung Chung ◽  
Mark Chong ◽  
Jie Sheng Chua ◽  
Jin Cheon Na
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Corporate Reputation ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Content Type ◽  
Twitter Users ◽  
Corporate Crisis ◽  
The Impact

PurposeThe purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments.Design/methodology/approachUsing a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and relevance classification consisted of five steps: sampling, labeling, tokenization, augmentation of semantic representation, and the training of supervised classifiers for relevance and sentiment prediction.FindingsThe findings show that: the overall sentiment of tweets specific to the crisis was neutral; promotions and marketing communication may not be effective in converting negative sentiments to positive sentiments; a corporate crisis drew public attention and sparked public discussion on social media; while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety; and some Twitter users exerted a significant influence on online sentiments through their popular tweets, which were heavily retweeted among Twitter users.Research limitations/implicationsEven with multiple training sessions and the use of a voting procedure (i.e. when there was a discrepancy in the coding of a tweet), there were some tweets that could not be accurately coded for sentiment. Aspect-based sentiment analysis and deep learning algorithms can be used to address this limitation in future research. This analysis of the impact of Chipotle’s apologies on sentiment did not test for a direct relationship. Future research could use manual coding to include only specific responses to the corporate apology. There was a delay between the time social media users received the news and the time they responded to it. Time delay poses a challenge to the sentiment analysis of Twitter data, as it is difficult to interpret which peak corresponds with which incident/s. This study focused solely on Twitter, which is just one of several social media sites that had content about the crisis.Practical implicationsFirst, companies should use social media as official corporate news channels and frequently update them with any developments about the crisis, and use them proactively. Second, companies in crisis should refrain from marketing efforts. Instead, they should focus on resolving the issue at hand and not attempt to regain a favorable relationship with stakeholders right away. Third, companies can leverage video, images and humor, as well as individuals with large online social networks to increase the reach and diffusion of their messages.Originality/valueThis study is among the first to empirically investigate the dynamics of corporate reputation as it evolves during a crisis as well as the effects of corporate apology on online sentiments. It is also one of the few studies that employs sentiment analysis using a supervised machine learning method in the area of corporate reputation and communication management. In addition, it offers valuable insights to both researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis.

scholarly journals Systematic evaluation of supervised machine learning for sample origin prediction using metagenomic sequencing data

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Julie Chih-yu Chen ◽  
Andrea D. Tyler
Keyword(s):  
Machine Learning ◽  
Prediction Error ◽  
Supervised Machine Learning ◽  
Detection Sensitivity ◽  
Metagenomic Sequencing ◽  
Learning Approaches ◽  
Microbial Abundance ◽  
Classification Models ◽  
The Impact ◽  
Analytical Approaches

Abstract Background The advent of metagenomic sequencing provides microbial abundance patterns that can be leveraged for sample origin prediction. Supervised machine learning classification approaches have been reported to predict sample origin accurately when the origin has been previously sampled. Using metagenomic datasets provided by the 2019 CAMDA challenge, we evaluated the influence of variable technical, analytical and machine learning approaches for result interpretation and novel source prediction. Results Comparison between 16S rRNA amplicon and shotgun sequencing approaches as well as metagenomic analytical tools showed differences in normalized microbial abundance, especially for organisms present at low abundance. Shotgun sequence data analyzed using Kraken2 and Bracken, for taxonomic annotation, had higher detection sensitivity. As classification models are limited to labeling pre-trained origins, we took an alternative approach using Lasso-regularized multivariate regression to predict geographic coordinates for comparison. In both models, the prediction errors were much higher in Leave-1-city-out than in 10-fold cross validation, of which the former realistically forecasted the increased difficulty in accurately predicting samples from new origins. This challenge was further confirmed when applying the model to a set of samples obtained from new origins. Overall, the prediction performance of the regression and classification models, as measured by mean squared error, were comparable on mystery samples. Due to higher prediction error rates for samples from new origins, we provided an additional strategy based on prediction ambiguity to infer whether a sample is from a new origin. Lastly, we report increased prediction error when data from different sequencing protocols were included as training data. Conclusions Herein, we highlight the capacity of predicting sample origin accurately with pre-trained origins and the challenge of predicting new origins through both regression and classification models. Overall, this work provides a summary of the impact of sequencing technique, protocol, taxonomic analytical approaches, and machine learning approaches on the use of metagenomics for prediction of sample origin.

scholarly journals Litigation costs of wrong-site surgery and other non-technical errors in orthopaedic operating theatres

2015 ◽  
Vol 97 (8) ◽  
pp. 592-597 ◽  
Author(s):  
WD Harrison ◽  
B Narayan ◽  
AW Newton ◽  
JV Banks ◽  
G Cheung
Keyword(s):  
Patient Safety ◽  
National Health Service ◽  
Health Service ◽  
National Health ◽  
Improve Patient Safety ◽  
World Health ◽  
Wrong Site Surgery ◽  
Technical Errors ◽  
Operating Theatres ◽  
Wrong Site

Introduction This study reviews the litigation costs of avoidable errors in orthopaedic operating theatres (OOTs) in England and Wales from 1995 to 2010 using the National Health Service Litigation Authority Database. Materials and methods Litigation specifically against non-technical errors (NTEs) in OOTs and issues regarding obtaining adequate consent was identified and analysed for the year of incident, compensation fee, cost of legal defence, and likelihood of compensation. Results There were 550 claims relating to consent and NTEs in OOTs. Negligence was related to consent (n=126), wrong-site surgery (104), injuries in the OOT (54), foreign body left in situ (54), diathermy and skin-preparation burns (54), operator error (40), incorrect equipment (25), medication errors (15) and tourniquet injuries (10). Mean cost per claim was £40,322. Cumulative cost for all cases was £20 million. Wrong-site surgery was error that elicited the most successful litigation (89% of cases). Litigation relating to implantation of an incorrect prosthesis (eg right-sided prosthesis in a left knee) cost £2.9 million. Prevalence of litigation against NTEs has declined since 2007. Conclusions Improved patient-safety strategies such as the World Health Organization Surgical Checklist may be responsible for the recent reduction in prevalence of litigation for NTEs. However, addition of a specific feature in orthopaedic surgery, an ‘implant time-out’ could translate into a cost benefit for National Health Service hospital trusts and improve patient safety.

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