Safety-related Events Research Articles

Exploring the Influence of Human System Interfaces: Introducing Support Tools and an Experimental Study

Situational awareness and decision support tools such as procedures and alarm systems are vital for effective interaction among control room operators, especially in safety-critical situations. In safety-critical environments such as process plants, there remains a gap in evaluating specific tools during actual operations, or ”work-as-done.” Additionally, the underlying factors that might impact operators’ cognitive states and performance concerning safety have not been thoroughly explored. The need for such an evaluation is further bolstered by current interaction configurations where operators are more passive than active, thus reducing their cognitive performance. Therefore, this experimental study addresses the highlighted evaluation gap by introducing and comparing three human system interfaces/decision support tools in four human-in-the-loop configurations. The supports include two alarm design formats (prioritized vs. non-prioritized) and three procedure representation formats (paper, screen-based digitized, and an AI-based support system built with an integrated Bayesian network and reinforcement learning model). Ninety-two people (n = 92) participated voluntarily in the test. They were divided equally into four groups. Each group tested three safety-related events in a simulated formaldehyde production facility. Individuals belonging to the group with prioritized alarms and utilized paper procedures rated procedural support slightly higher on average than others in different groups. Unlike the other groups, their assessment of alarm prioritization support remained consistent across all scenarios. Further analysis of the impact of the setup on cognitive states and actual performance will be performed.

Adverse Event Signal Detection Using Patients' Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models.

Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients' subjective opinions (patients' voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients' narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients' daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients. This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients' concerns at pharmacies was also assessed. Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients' concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs. From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients' daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. "Pain or numbness" (n=57, 36.3%), "fever" (n=46, 29.3%), and "nausea" (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients' daily lives. Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients' subjective information recorded in pharmaceutical care records accumulated during pharmacists' daily work.

Direct Care Staff Vacancies and Adverse Youth Events in Illinois Child Welfare Residential Treatment During the COVID-19 Pandemic

ABSTRACT Direct care staff provide 24/7 care and supervision in residential treatment for youth; vacancy rates for these positions increased significantly during the COVID-19 pandemic. This study used multi-level, two-stage least squares regression to examine the relationship between staff vacancies and adverse events experienced by 1352 youth in Illinois child welfare residential care during October 2019-February 2022. Hierarchical cluster analysis was used to explore whether particular adverse event clusters were differentially related to vacancy rates. We hypothesized that direct care staff vacancies would be related to an increased number of adverse events overall and a specific increase in safety-related events. Although direct care staff vacancies were not significantly related to the overall number of adverse events, they were significantly associated with the magnitude of adverse events related to school suspensions, substance use, and law enforcement encounters. Several other novel, unanticipated findings emerged, with various youth- and facility-level factors (e.g. race, age, length of admission, facility level of clinical acuity) showing significant relationships with the magnitude of adverse events. This study fills a notable gap in the literature in its novel empirical approach to exploring administrative data capturing direct care staffing and adverse events in child welfare residential care.

Effect of eco-driving on commercial motor vehicle driver collision risk

Introduction: This study investigates the effect among commercial motor vehicle (CMV) drivers of the adoption of fuel-efficient driving techniques (commonly known as eco-driving) on the odds of being involved in safety-related events. Method: For 2,637 long-haul class 8 drivers employed by four carriers in Canada, information on driving style, total distance driven, and safety-related events like collisions, hard-braking, hard-turning, and stability control events were collected for each trip. Three carriers provided driving style-related data from the ISAAC instrument, which provides a score on a 0 to 100 scale that measures the degree to which a driver is using an appropriate amount of engine power according to driving conditions. The fourth carrier provided data on driving style characteristics, including fuel consumption, use of cruise control, and use of top gear. Depending on the carrier, information on speeding, driver age, and years of experience driving a commercial vehicle was also collected. Logit statistical models were developed to estimate the change in odds of a driver experiencing a safety-related event dependent on the measures of driving style. Results: A one-unit increase in the ISAAC score was associated with a 7%, 8%, 8%, and 4% reduction in the odds of having a hard-braking event, hard left-turn event, hard right-turn event, and collision, respectively. For the carrier not employing the ISAAC system, an increase of 10% in the time spent driving in top gear with steady speed near 100 km per hour (km/h) was associated with a substantial 34% decrease in stability control events. In addition, a year increase in the driver’s age, as well as a 1% increase in the amount of time spent driving using cruise control, reduced the number of hard-braking events by 9% and 3%, respectively. Conclusion/Practical Applications: The adoption of fuel-efficient driving techniques enhances the safety of CMV drivers.

A smart combination of IoT and blockchain enabling technologies to measure and improve workplace safety in dairy farm

In 2022, there was a 25.77 % increase in work-related accidents reported in Italy, including 1090 with a fatal outcome. Legislative Decree 81/100 regulates worker safety and requires employers to assess risks and implement necessary interventions. When risks cannot be eliminated, employers must provide appropriate Personal Protective Equipment (PPE). However, safety management requires large investments and supervision to ensure proper use of PPE. The current regulatory system, which focuses on punishing noncompliance with safety requirements, has been insufficient in deterring workplace accidents. To address this, the National Institute for Insurance against Accidents at Work (INAIL), is introducing incentives and substantial discounts on insurance costs for companies that carry out preventive actions. In this paper, the authors propose an innovative tool that leverages IoT and Blockchain technologies to record safety-related events in an immutable and transparent way, quantifying the level of achieved safety. They suggest setting up a system consisting of PPE with RFID technology and an automated surveillance agent to record operations and issue consents. The number of events recorded in Blockchain can quantify the degree of achieved safety.

A systematic approach to develop safety-related undesired event databases for Machine Learning analyses: Application to confined space incidents

In Occupational Safety and Health (OSH) and operational safety, confined spaces are high-risk working areas, where frequent serious and fatal incidents occur. However, there is a limited use of data-driven approaches based on Machine Learning (ML) techniques for learning from such incidents. In this context, our study proposes a systematic approach to develop a structured database suitable for ML-based analyses, considering various data sources with unstructured or semi-structured data. The approach was applied to 1346 incidents that happened in confined spaces mainly containing chemicals and located in manufacturing and process industries. This allowed building models predicting the fatal or non-fatal consequences and direct causes of incidents. The recognition of the approach potentialities and difficulties stimulated the development of an improved version of it to be adapted to different safety-related undesired events. The generalised systematic approach supports safety analysts during the process of learning from undesired events to maximise the lessons learned.

A text mining approach to categorize patient safety event reports by medication error type

Patient safety reporting systems give healthcare provider staff the ability to report medication related safety events and errors; however, many of these reports go unanalyzed and safety hazards go undetected. The objective of this study is to examine whether natural language processing can be used to better categorize medication related patient safety event reports. 3,861 medication related patient safety event reports that were previously annotated using a consolidated medication error taxonomy were used to develop three models using the following algorithms: (1) logistic regression, (2) elastic net, and (3) XGBoost. After development, models were tested, and model performance was analyzed. We found the XGBoost model performed best across all medication error categories. ‘Wrong Drug’, ‘Wrong Dosage Form or Technique or Route’, and ‘Improper Dose/Dose Omission’ categories performed best across the three models. In addition, we identified five words most closely associated with each medication error category and which medication error categories were most likely to co-occur. Machine learning techniques offer a semi-automated method for identifying specific medication error types from the free text of patient safety event reports. These algorithms have the potential to improve the categorization of medication related patient safety event reports which may lead to better identification of important medication safety patterns and trends.

SNG100, a novel topical treatment for moderate atopic dermatitis, in patients aged 6years or older: A randomised, double-blind, active-controlled trial.

Atopic dermatitis (AD) is one of the most common inflammatory skin diseases. It is associated with significant itch and impaired quality of life. Systemic treatments are efficient but associated with side effects. Novel topical treatments with a favourable safety profile are needed. SNG100 is a novel composition of hydrocortisone 1% in a cream base comprising sulphated polysaccharide (SPS; extracted from in-house cultivated Porphyridium Cruentum unicellular algae), a well-known hydrating, moisturising and a skin barrier repairing agent. To assess the safety, usability and efficacy of SNG100 cream in patients aged ≥6years with moderate AD. In this proof of concept phase I, double-blind, randomised trial, participants received one of three treatments for 14days: SNG100 twice daily (BID), hydrocortisone 1% BID or mometasone furoate once daily (QD). The primary endpoint was the safety and tolerability of SNG100 cream compared to hydrocortisone 1% and mometasone furoate. The secondary endpoint was the subject's usability of SNG100. Exploratory efficacy endpoints included percent change from baseline in SCOring AD (SCORAD), Eczema Area and Severity Index, Patient-Oriented Eczema Measure, Dermatology Life Quality Index, pruritus Numerical Rating Score (NRS), peak pruritus-NRS and Investigator's Global Assessment. Subjects were also followed up without any treatment for additional 14days. Overall, 66 participants were screened, and 60 patients were randomised. SNG100 demonstrated a high safety profile, similar to marketed products hydrocortisone 1% and mometasone furoate 0.1%, with no unanticipated drug safety related events. SNG100 and mometasone furoate 0.1% cream achieved almost similar and statistically significant greater percentage reductions from baseline in SCORAD as compared to hydrocortisone 1% cream. SNG100 demonstrated significant improvement in NRS as compared to hydrocortisone 1% cream. Remarkably, SNG100 led to a lasting effect with only 29.4% of subjects returning to IGA3 during the follow-up period compared to 50% and 38.9% in the hydrocortisone 1% and in mometasone furoate treatment arms, respectively. Topical SNG100 is an effective, safe, and well-tolerated innovative treatment for moderate AD. Trial registration number: NCT04615962 (Topical Cream SNG100 for Treatment in Moderate AD Subjects).

Assessment of nurse’s perceived just culture: a cross-sectional study

BackgroundThe non-punitive approach to error investigation in most safety culture surveys have been relatively low. Most of the current patient safety culture measurement tools also lack the ability to directly gauge concepts important to a just culture (i.e. perceptions of fairness and trust). The purpose of this study is to assess nurses’ perceptions of the six just culture dimensions using the validated Just Culture Assessment Tool (JCAT).MethodsThis descriptive, cross-sectional study was conducted between November and December 2020. Data from 212 staff nurses in a large referral hospital in Qatar were collected. A validated, self-reported survey called the JCAT was used to assess the perception of the just culture dimensions including feedback and communication, openness of communication, balance, quality of event reporting process, continuous improvement, and trust.ResultsThe study revealed that the overall positive perception score of just culture was (75.44%). The strength areas of the just culture were “continuous improvement” dimension (88.44%), “quality of events reporting process” (86.04%), followed by “feedback and communication” (80.19%), and “openness of communication” (77.55%) The dimensions such as “trust” (68.30%) and “balance” (52.55%) had a lower positive perception rates.ConclusionA strong and effective just culture is a cornerstone of any organization, particularly when it comes to ensuring safety. It places paramount importance on encouraging voluntary error reporting and establishing a robust feedback system to address safety-related events promptly. It also recognizes that errors present valuable opportunities for continuous improvement. Just culture is more than just a no-blame practice. By prioritizing accountability and responsibility among front-line workers, a just culture fosters a sense of ownership and a commitment to improve safety, rather than assigning blame.

Tolerability and blinding of high-definition transcranial direct current stimulation among older adults at intensities of up to 4 mA per electrode

BackgroundFew studies have investigated tolerability, blinding, and double-blinding of High-Definition transcranial Direct Current Stimulation (HD-tDCS) at amplitudes above 2 milliamps (mA). ObjectiveWe examined a) tolerability of HD-tDCS during stimulation sessions and b) blinding and double blinding of participants and study team members. MethodsData from a mixed neurologic sample of 292 older adults were pooled from 3046 HD-tDCS sessions (2329 active; 717 sham). Per electrode amplitudes ranged from 1 mA to 4 mA with total currents up to 10 mA. Participants completed a standardized sensation (tolerability) questionnaire after each session. Participants and study team members stated whether the participant received active or sham stimulation at the end of various sessions. Data were collapsed into the presence/absence of a symptom due to low rates of positive responding and were analyzed for both differences and bioequivalency. ResultsThere were no safety-related adverse events. HD-tDCS was well tolerated with mostly no (“none”) or “mild” sensations reported across sessions, regardless of active or sham condition and in both stimulation naïve and experienced participants. There were no significant differences in side effects between active and sham, with some achieving bioequivalence. Tingling and itching were significantly more common after lower (<2 mA) than higher (≥3 mA) amplitude active sessions, while skin redness was significantly more common after higher amplitudes. Blinding was effective at the participant and study team levels. ConclusionsHD-tDCS was well tolerated with center electrode amplitudes up to 4 mA. The bimodal ramp-up/down format of the sham was effective for blinding. These results support higher scalp-based amplitudes that enable greater brain-based current intensities in older adults.

Rapid Methadone Induction in a General Hospital Setting: A Retrospective, Observational Analysis.

Outpatient methadone guidelines recommend starting at a low dose and titrating slowly. As fentanyl prevalence and opioid-related mortality increases, there is a need for individuals to rapidly achieve a therapeutic methadone dose. Hospitalization offers a monitored setting for methadone initiation, however dosing practices and safety are not well described. Retrospective, observational analysis of hospitalized patients with opioid use disorder seen by an inpatient addiction consult team in an academic medical center who were newly initiated on methadone between 2016 and 2022. We calculated initial daily dose, maximum daily dose, timing interval of dose escalation, whether patients were connected to an opioid treatment program (OTP) prior to discharge, whether adverse effects or safety events occurred during the hospitalization, and whether such events were definitely or probably related versus possibly related or unrelated to methadone. One hundred twelve patients were included. The mean initial daily methadone dose administered was 32 mg (range: 10-90 mg). The mean maximum dose reached was 76.8 mg (range 30-165 mg). The mean number of days from initial to peak dose was 5.6 days (range 1-19 days). Overall, 30% of patients experienced a safety event, most commonly sedation. Only 4 safety events were deemed probably or definitely related to methadone. In regression analyses, there was no significant difference between starting doses among patients with or without sedation but there was a relationship between last dose and the likelihood of any possibly related event, with those ending at a dose of 100 mg or higher having a higher likelihood event, compared to those ending at lower doses (47.8% vs 12.4%, P < .001). Seventy-six percent were connected to OTP before discharge. Among hospitalized patients initiating methadone, rapid dose titration was infrequently associated with related safety events and most were connected to community-based methadone treatment before discharge.

Food safety news events classification via a hierarchical transformer model

In light of the significance of regulatory authorities and the rising demand for information disclosure, a vast amount of information on food safety news reports is readily accessible on the Internet. The extraction of such information for precise classification and provision of appropriate safety alerts based on their respective categories has emerged as a challenging problem for academic research. Given that most food safety-related events in news reports comprise lengthy text, the pre-trained language models currently employed for text analysis are generally limited in their capability to handle long documents. This paper proposes a long-text classification model utilising hierarchical Transformers. We categorise information in long documents into two distinct types: (1) multiple text chunks meeting the length constraint and (2) essential sentences within long documents, such as headings, paragraph start and end sentences, etc. Initially, our proposed model utilises the text chunks as input to the BERT model. Then, it concatenates the output of the BERT model with the important sentences from the document and use them as input to the Transformer model for feature transformation. Finally, we utilise a classifier for food safety news classification. We conducted several comparative experiments with various commonly used text classification models on a dataset constructed from publicly available information on food regulatory websites. Our proposed method outperforms existing methods, establishing itself as the leading approach in terms of performance.

Perceptions of School Administrators and Students on Security and Safety at Selected Tertiary Institutions in Ghana

The goal of the study is to review the perceptions of school administrators and students on security and safety at selected tertiary institutions in Bono Region, Ghana. The study was conducted at two public tertiary in Ghana's Bono Region. The study used a concurrent mixed-methods approach to collect both quantitative data through Likert scales and qualitative data through open questions within a single questionnaire and some interviews with school administrators. A sample size of seven hundred and fifty-eight (758), including students and administrators was used for this study. The survey revealed that students believe there is good security and safety on the two campuses. Additionally, students say they feel comfortable on campus during the day and night. They also say they feel safe overall. In addition, students do not carry something on them for protection when walking on campus and students are ready to report crime/s against them to the university security. It, however, came out from the study that students avoid certain places on campus because of fear of their security and safety. The study revealed proactive security and safety management practices in the two universities. It came out that the universities provide annual security and safety reports detailing any security and safety-related events with crime prevention details. The study further recommends that University management should prioritize efforts to enhance campus security and safety by employing more strong security personnel and deploying modern security gadgets/items, particularly in locations that students perceive as unsafe.

Traffic Safety Pyramid Based on Visual Navigation Distraction: A Naturalistic Study on App-Taxis

Evidence suggests that mobile phone navigation applications (MPNAs) may reduce road traffic safety by causing a visual distraction. This study aimed to (i) identify a list of road safety-related events (RSREs) that could occur while looking at the MPNA, (ii) examine the effect of app-taxFi drivers’ glances at MPNA on missing of RSREs, (iii) develop any potential new indicator in road traffic safety, and (iv) develop a pyramid of traffic incidents. This study investigated 36 app-taxi drivers in real road traffic circumstances. Data were collected by video recording using a double-lens camera. The events that occurred while the app-taxi driver was looking at the MPNA were extracted, recorded, and classified. A time span of less than one second and a new indicator, namely “seeing the event BEFORE visual distraction” (seBvd), were examined. The data were analyzed using descriptive statistics and non-parametric tests with a significance level of 0.05 in SPSS 18. A list of 23 events was identified. The most frequent event was “presence of pedestrians across the road” (including crossing the road and beside the road 36%). The number of glances at the MPNA had a significant correlation with time span ≤1 s ( p = 0.001, r &lt; 0.6). The total time of looking at the MPNA had a significant correlation with time span &gt;1 s ( p = 0.001, r &lt; 0.6). A pyramid of traffic incidents was developed based on the results. The new seBvd indicator is a binary (positive; negative) surrogate safety measure that can be useful in evaluating the effect of visual distraction on road traffic safety. The introduced pyramid of traffic incidents seems useful in traffic safety.

Comparison between the WHO-CFICPS and the PRISMA classification of safety-related events in a radiation oncology department.

Describing Safety-Related Events (SREs) in a radiotherapy (RT) department and comparing WHO-CFICPS (World Health Organization's Conceptual Framework For The International Classification For Patient Safety) and PRISMA (Prevention and Recovery Information System for Monitoring and Analysis) methods for classifying SREs. From February 2017 to October 2020, two Quality Managers (QMs) randomly classified 1173 SREs using 13 incident types of WHO-CFICPS. The same two QMs, reclassified the same SREs according to 20 PRISMA incident codes. Statistical analysis was performed to assess the association between the 13 incident types of WHO-CFICPS and the 20 PRISMA codes. The chi-squared and post-hoc tests using adjusted standardized residuals were applied to detect the association between the two systems. There was a significant association between WHO-CFICPS incident types and PRISMA codes (P < 0.001). Ninety-two percent of all SREs were categorized using 4 of 13 WHO-CFICPS incident types including Clinical Process/Procedure (n = 448, 38.2%), Clinical Administration (n = 248, 21.1%), Documentation (n = 226, 19.2%) and Resources/Organizational Management (n = 15,613.3%). According to PRISMA classification, 14 of the 20 codes were used to describe the same SREs. PRISMA captured 41 Humans Skill Slips from 226 not better defined WHO-CFICPS Documentation Incidents, 38 Human Rule-based behaviour Qualification from not better defined 447 Clinical Process/Procedure and 40 Organization Management priority events from 156 not better defined WHO-CFICPS Resources/Organizational Management events (P < 0.001). Although there was a significant association between WHO-CFICPS and PRISMA, The PRISMA method provides a more detailed insight into SREs compared to WHO-CFICPS in a RT department.

MP05-11 INNOVATIVE URETERAL STENT DEVELOPMENT USING NOVEL BIO-LIKE MATERIALS WITH ABILITY TO PREVENT CRYSTAL ADHESION IN VITRO AND IN VIVO

You have accessJournal of UrologyCME1 Apr 2023MP05-11 INNOVATIVE URETERAL STENT DEVELOPMENT USING NOVEL BIO-LIKE MATERIALS WITH ABILITY TO PREVENT CRYSTAL ADHESION IN VITRO AND IN VIVO Kana Hayashi, Katsumi Shigemura, Kazuo Kumagai, Ralph Rolly Gonzales, Hiroshi Tanimoto, Young-Min Yang, Maeda Koki, Hideto Matsuyama, and Masato Fujisawa Kana HayashiKana Hayashi More articles by this author , Katsumi ShigemuraKatsumi Shigemura More articles by this author , Kazuo KumagaiKazuo Kumagai More articles by this author , Ralph Rolly GonzalesRalph Rolly Gonzales More articles by this author , Hiroshi TanimotoHiroshi Tanimoto More articles by this author , Young-Min YangYoung-Min Yang More articles by this author , Maeda KokiMaeda Koki More articles by this author , Hideto MatsuyamaHideto Matsuyama More articles by this author , and Masato FujisawaMasato Fujisawa More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003216.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Ureteral stents used to prevent ureteral obstruction in urological patients present the challenge of ureteral stent obstruction due to urinary crystal adhesion. There is active research into the development of ureteral stents with surface coatings and smoothing of the surface to reduce foreign body adhesion. In this study, the novel ureteral stent coated with polydopamine (PDA), which is formed by the polymerization of dopamine and close to the substances existed in human body and so has a low recognition reaction as a foreign substance, were investigated for ability to prevent crystal (Calcium: Ca) adhesion. METHODS: The ureteral stent was incubated in artificial urine at 37°C for 3 weeks, and the amount of Ca deposition on the surface of the ureteral stent was compared between the PDA-coated stent and commercially available stents by microscopic observation and Inductively Coupled Plasma (ICP) analysis. As to in vivo study, in rat model of hypercalciuria induced by drinking ethylene glycol and ammonium chloride, stents were implanted in the bladder for one month and Ca deposition on the surface of the ureteral stent was determined. RESULTS: After immersion in artificial urine for 3 weeks, the PDA-coated ureteral stent showed less Ca deposition on the stent surface than the commercially available ureteral stent (p=0.0468). There was no significant difference in the increase in pH of the artificial urine in which the ureteral stent was immersed between the PDA-coated and commercially available ureteral stents. With respect to the rat experimental model with Ca including-urine, the PDA-coated ureteral stent had less Ca deposition on the stent surface than the commercially available stent (p=0.0412). No apparent safety-related adverse events were observed. CONCLUSIONS: Our novel PDA-coated ureteral stent showed less amount of Ca deposition on the stent surface than commercial stents in artificial urine. The experimental rat model with Ca including-urine also showed a similar data of less Ca deposition on the stent surface than commercial stent, suggesting that this novel stent is less likely to cause stent occlusion (WO/2022/176834). Source of Funding: This study was supported by the Uehara Memorial Foundation, a Grant-in-Aid for Research Activity Start-up (20K22979) from the Japan Society for the Promotion of Science (JSPS) and Suzuken Memorial Foundation (20-014) © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e47 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Kana Hayashi More articles by this author Katsumi Shigemura More articles by this author Kazuo Kumagai More articles by this author Ralph Rolly Gonzales More articles by this author Hiroshi Tanimoto More articles by this author Young-Min Yang More articles by this author Maeda Koki More articles by this author Hideto Matsuyama More articles by this author Masato Fujisawa More articles by this author Expand All Advertisement PDF downloadLoading ...

A Systematic Literature Review on Automotive Digital Forensics: Challenges, Technical Solutions and Data Collection

A modern vehicle has a complex internal architecture and is wirelessly connected to the Internet, other vehicles, and the infrastructure. The risk of cyber attacks and other criminal incidents along with recent road accidents caused by autonomous vehicles calls for more research on automotive digital forensics. Failures in automated driving functions can be caused by hardware and software failures and cyber security issues. Thus, it is imperative to be able to determine and investigate the cause of these failures, something which requires trustable data. However, automotive digital forensics is a relatively new field for the automotive where most existing self-monitoring and diagnostic systems in vehicles only monitor safety-related events. To the best of our knowledge, our work is the first systematic literature review on the current research within this field. We identify and assess over 300 papers published between 2006 - 2021 and further map the relevant papers to different categories based on identified focus areas to give a comprehensive overview of the forensics field and the related research activities. Moreover, we identify forensically relevant data from the literature, link the data to categories, and further map them to required security properties and potential stakeholders. Our categorization makes it easy for practitioners and researchers to quickly find relevant work within a particular sub-field of digital forensics. We believe our contributions can guide digital forensic investigations in automotive and similar areas, such as cyber-physical systems and smart cities, facilitate further research, and serve as a guideline for engineers implementing forensics mechanisms.

Efficient Real-Time Road Curvature Estimation: Visual-Inertial Approach

Road curvature is an essential parameter of road geometry, and it is crucial to set vehicle design and operating speeds. It can be utilized to identify the maximum stable velocity of a Powered Two Wheeled Vehicle (P2WV) and predict other safety-related events, including Lane departure and lane crossing. This paper proposes a new vision-based approach to estimate the road's curvature accurately and efficiently under real-time constraints for P2WV. The proposed method is based on the vanishing point approach to estimate the relative heading and its dynamics. Combined with the available vehicle speed and the yaw rate given by the inertial measurement unit (IMU), the instantaneous curvature of the road is reconstructed. The proposed algorithm is then tested using various simulated scenarios of different speeds and curvatures to validate the approach. Then it was compared to other estimation methods based on Inverse Perspective Mapping (IPM) to investigate the validity and efficiency in all scenarios regarding accuracy and time complexity. The proposed method shows very promising results in terms of error and real-time execution.

Review of Effective Use of the Departmental Electronic Occurrence System for the Patient Care Related Safety Events. A CMC VAMC Experience

Abstract Introduction/Objective Electronic patient safety event reporting (e-reporting) is an effective mechanism to learn from errors and enhance patient safety. The Pathology and Laboratory Medicine (P&amp;LMS) has established an electronic occurrence reporting (EOR) program to detect occurrences in patient results prior to the release of results, procedures for the documentation of corrected results that have been released, and any deviation to standard operating procedures. Patient safety and its monitoring is improved by using the EOR reporting system in which healthcare workers can document and learn from incidents, and thus prevent potential medical errors. The authors explore the use of the EOR in the calendar year 2021 and its effective use. Methods/Case Report The EOR is available to all the employees of P&amp;LMS. This EOR allows the recording of all information pertinent to the occurrence (including pre-analytical, analytical, and post analytical issues) and a variety of issues (i.e., safety events, notifications, recalls, correction of released results, changed, results, or any unexpected standard operating procedure deviation). The section supervisor will initiate an investigation including root causes and outcomes of the occurrence and propose corrective action, recommendations, and follow-up in the Occurrence Detection Database. This is also reviewed at the quality management meeting and by the sectional pathologist director. For quality assurance review, the EOR’s usage for January 1st, 2021, to December 31st, 2021, was reviewed. Results (if a Case Study enter NA) The EOR successfully allowed the full documentation of all 870 reported occurrences during the study period in an organized fashion allowing appropriate tracking and investigation for quality improvement. All reported occurrences were handled appropriately with organized data to ensure quality assurance and improvement. Conclusion The EOR is a highly effective, successful program in terms of monitoring and assisting in ensuring timely action is taken in real time for patient care. Electronic reporting and documentation of incidents should be well supported by continuous staff training, help, and feedback. The ERO plays an important role in the maintenance of Veteran Affairs’ position as a High Reliability Organization (HRO).

Comparison between the WHO-CFICPS and the PRISMA Methods in the Classification of Safety-Related Events in a Single Radiation Oncology Department

<h3>Purpose/Objective(s)</h3> To compare the WHO-CFICPS (World Health Organization's Conceptual Framework For The International Classification For Patient Safety) and the PRISMA (Prevention and Recovery Information System for Monitoring and Analysis) methods in the classification of Safety-Related Events (SREs) in a single radiation oncology department. <h3>Materials/Methods</h3> From February 2017 to October 2020, two Quality Managers (QM) classified 1173 reported SREs using the 13 incident types of the WHO-CFICPS. In March 2020, the same two QM, blinded to their previous WHO-CFICPS classification, began reclassifying the same SREs according to the 20 incident codes of the PRISMA method. Statistical analysis was performed to assess the association between the 13 incident types of the WHO-CFICPS and the 20 PRISMA codes. Chi-square test of independence and post-hoc tests using adjusted standardized residuals were applied to detect the association between the two classification systems. <h3>Results</h3> Overall, there was a significant association between the WHO-CFICPS incident types and the PRISMA method codes (p < 0.001). Ninety-two percent (1078 of 1173) of all SREs were categorized using only 4 of 13 WHO-CFICPS incident types: Clinical Process/Procedure (n=448, 38.2%), Clinical Administration (n=248, 21.1%), followed by Documentation (n=226, 19.2%) and Resources/Organizational Management (n=156,13.3%). whereas, in the PRISMA method, 14 of the 20 codes were used to describe the same SREs. In addition, the PRISMA method captured 41 Humans Skill Slips from 226 not better defined WHO-CFICPS Documentation Incidents and 40 Organization Management Priority events from 156 not better defined WHO-CFICPS Resources/Organizational Management events (p < 0.001). <h3>Conclusion</h3> Although there was a significant association between the WHO-CFICPS incident types and the PRISMA codes, the PRISMA method enables to better break down SREs compared to the WHO-CFICPS framework in a radiation oncology clinical setting.