Text-document clustering-based cause and effect analysis methodology for steel plant incident data

Establishment-level safety analytics: a scoping review

The use of data analytics has seen widespread application in fields such as medicine and supply chain management, but their application in occupational safety has only recently become more common. The purpose of this scoping review was to summarize studies that employed analytics within establishments to reveal insights about work-related injuries or fatalities. Over 300 articles were reviewed to survey the objectives, scope and methods used in this emerging field. We conclude that the promise of analytics for providing actionable insights to address occupational safety concerns is still in its infancy. Our review shows that most articles were focused on method development and validation, including studies that tested novel methods or compared the utility of multiple methods. Many of the studies cited various challenges in overcoming barriers caused by inadequate or inefficient technical infrastructures and unsupportive data cultures that threaten the accuracy and quality of insights revealed by the analytics.

A chemical accident cause text mining method based on improved accident triangle

BACKGROUND

With the rapid development of China's chemical industry, although researchers have developed many methods in the field of chemical safety, the situation of chemical safety in China is still not optimistic. How to prevent accidents has always been the focus of scholars' attention.

METHODS

Based on the characteristics of chemical enterprises and the Heinrich accident triangle, this paper developed the organizational-level accident triangle, which divides accidents into group-level, unit-level, and workshop-level accidents. Based on 484 accident records of a large chemical enterprise in China, the Spearman correlation coefficient was used to analyze the rationality of accident classification and the occurrence rules of accidents at different levels. In addition, this paper used TF-IDF and K-means algorithms to extract keywords and perform text clustering analysis for accidents at different levels based on accident classification. The risk factors of each accident cluster were further analyzed, and improvement measures were proposed for the sample enterprises.

RESULTS

The results show that reducing unit-level accidents can prevent group-level accidents. The accidents of the sample enterprises are mainly personal injury accidents, production accidents, environmental pollution accidents, and quality accidents. The leading causes of personal injury accidents are employees' unsafe behaviors, such as poor safety awareness, non-standard operation, illegal operation, untimely communication, etc. The leading causes of production accidents, environmental pollution accidents, and quality accidents include the unsafe state of materials, such as equipment damage, pipeline leakage, short-circuiting, excessive fluctuation of process parameters, etc. CONCLUSION: Compared with the traditional accident classification method, the accident triangle proposed in this paper based on the organizational level dramatically reduces the differences between accidents, helps enterprises quickly identify risk factors, and prevents accidents. This method can effectively prevent accidents and provide helpful guidance for the safety management of chemical enterprises.

Artificial Intelligence for Identifying the Prevention of Medication Incidents Causing Serious or Moderate Harm: An Analysis Using Incident Reporters' Views

The purpose of this study was to describe incident reporters’ views identified by artificial intelligence concerning the prevention of medication incidents that were assessed, causing serious or moderate harm to patients. The information identified the most important risk management areas in these medication incidents. This was a retrospective record review using medication-related incident reports from one university hospital in Finland between January 2017 and December 2019 (n = 3496). Of these, incidents that caused serious or moderate harm to patients (n = 137) were analysed using artificial intelligence. Artificial intelligence classified reporters’ views on preventing incidents under the following main categories: (1) treatment, (2) working, (3) practices, and (4) setting and multiple sub-categories. The following risk management areas were identified: (1) verification, documentation and up-to-date drug doses, drug lists and other medication information, (2) carefulness and accuracy in managing medications, (3) ensuring the flow of information and communication regarding medication information and safeguarding continuity of patient care, (4) availability, update and compliance with instructions and guidelines, (5) multi-professional cooperation, and (6) adequate human resources, competence and suitable workload. Artificial intelligence was found to be useful and effective to classifying text-based data, such as the free text of incident reports.

Using text mining to glean insights from COVID-19 literature

The purpose of this study is to develop a text clustering–based analysis of COVID-19 research articles. Owing to the proliferation of published COVID-19 research articles, researchers need a method for reducing the number of articles they have to search through to find material relevant to their expertise. The study analyzes 83,264 abstracts from research articles related to COVID-19. The textual data are analysed using singular value decomposition (SVD) and the expectation–maximisation (EM) algorithm. Results suggest that text clustering can both reveal hidden research themes in the published literature related to COVID-19, and reduce the number of articles that researchers need to search through to find material relevant to their field of interest.

Factors contributing to reported medication administration incidents in patients' homes - A text mining analysis

AIMS

To describe the characteristics of medication administration (MA) incidents reported to have occurred in patients' own homes (reporters' profession, incident types, contributing factors, patient consequence, and most common medications involved) and to identify the connection terms related to the most common contributing factors based on free text descriptions.

DESIGN

A retrospective study using descriptive statistical analysis and text mining.

METHODS

Medication administration incidents (N = 19,725) reported to have occurred in patients' homes between 2013-2018 in one district in Finland were analysed, describing the data by the reporters' occupation, incident type, contributing factors, and patient consequence. SAS® Text Miner was used to analyse free text descriptions of the MA incidents to understand contributing factors, using concept linking.

RESULTS

Most MA incidents were reported by practical (lower level) nurses (77.8%, N = 15,349). The most common category of harm was 'mild harm' (40.1%, N = 7,915) and the most common error type was omissions of drug doses (47.4%, N = 9,343). The medications most commonly described were Marevan [warfarin] (N = 2,668), insulin (N = 811), Furesis [furosemide] (N = 590), antibiotic (N = 446), and Panadol [paracetamol] (N = 416). The contributing factors most commonly reported were 'communication and flow of information' (25.5%, N = 5,038), 'patient and relatives' (22.6%, N = 4,451), 'practices' (9.9%, N = 1,959), 'education and training' (4.8%, N = 949), and 'work environment and resources' (3.0%, N = 598).

CONCLUSION

There is need for effective communication and clear responsibilities between home care patients and their relatives and health providers, about MA and its challenges in home environments. Knowledge and skills relating to safe MA are also essential.

IMPACT

These findings about MA incidents that have occurred in patients' homes and have been reported by home care professionals demonstrate the need for medication safety improvement in home care.

Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports

BACKGROUND

Some medications carry increased risk of patient harm when they are given in error. In incident reports, names of the medications that are involved in errors could be found written both in a specific medication field and/or within the free text description of the incident. Analysing only the names of the medications implicated in a specific unstructured medication field does not give information of the associated factors and risk areas, but when analysing unstructured free text descriptions, the information about the medication involved and associated risk factors may be buried within other non-relevant text. Thus, the aim of this study was to extract medication names most commonly used in free text descriptions of medication administration incident reports to identify terms most frequently associated with risk for each of these medications using text mining.

METHOD

Free text descriptions of medication administration incidents (n = 72,390) reported in 2016 to the National Reporting and Learning System for England and Wales were analysed using SAS® Text miner. Analysis included text parsing and filtering free text to identify most commonly mentioned medications, followed by concept linking, and clustering to identify terms associated with commonly mentioned medications and the associated risk areas.

RESULTS

The following risk areas related to medications were identified: 1. Allergic reactions to antibacterial drugs, 2. Intravenous administration of antibacterial drugs, 3. Fentanyl patches, 4. Checking and documenting of analgesic doses, 5. Checking doses of anticoagulants, 6. Insulin doses and blood glucose, 7. Administration of intravenous infusions.

CONCLUSIONS

Interventions to increase medication administration safety should focus on checking patient allergies and medication doses, especially for intravenous and transdermal medications. High-risk medications include insulin, analgesics, antibacterial drugs, anticoagulants, and potassium chloride. Text mining may be useful for analysing large free text datasets and should be developed further.