Safety Domain Research Articles

Evolution of a patient safety program in an academic medical center

Background The Academic Medical Center earned a 1-Star CMS designation and received an F on the Leapfrog Safety Grade in 2015, which led to leadership setting Clinical Quality Improvement as the #1 Strategic Priority. This paper presents the evolution of a structured patient safety program as a component of this plan to direct and motivate employees to succeed in reducing patient harm. Methods Data-driven quality improvement using simple, timely, actionable data drillable scorecards available to all employees was the cornerstone of the above strategic priority. A comprehensive patient safety program was developed utilizing three phases: Error Prevention Training, Improved Event Reporting, and the Chasing Zero Harm initiative. Results Improvement was achieved in Culture of Safety Survey domains of overall safety, event reporting, nonpunitive response to errors, and hand-off communication. Patient safety indicators and hospital-acquired infections improved on the Medical Center's internal scorecard and public reported data by >60%. These resulted in improvement in Leapfrog Safety Score from an F in 2015 to a B in 2023, and increase from CMS 1-Star to 2-Star rank in 2023. Adverse event reports and review with follow up increased by 45% between 2018 and 2023. A Chasing Zero harm program was initiated following establishment of these steps. Discussion A successful patient safety program, predicated on high reliability, can successfully be built with leadership commitment, development of safety culture, and performance improvement.

Road Accident Emergency Reporting Management System using Neural Network

Given the high number of road accidents that result in deaths every day, victims may lose their lives if emergency medical personnel or concerned parties fail to arrive promptly to intervene in an accident or if they do so, because they are unaware of the location of a nearby hospital or medical facility, they may not be able to provide the much-needed help. To avert this scenario, this research work seeks to deploy a Deep Learning Road Accident Emergencies Management model, which by examining of video and sensors’ data, identifies and reports traffic incidents in real time. A Neural Network (NN) is used by the model to recognize accident scenarios based on anomalous motion patterns inside video frames, including sudden halts and crashes. Put into practice in python, the program incorporates deep learning libraries such as OpenCV and TensorFlow for strong data processing and analysis in real time. To train and evaluate the model, an 80/20 data split, which results in reduced latency and high accuracy in accident detection. The Key performance metrics include a testing accuracy of 96%, an F1-score of 87.5%, and a response time of less than two seconds on average. The study advances the domains of artificial intelligence, transportation safety, and emergency management by offering a practical, real-time solution for automated accident reporting. This quick detection capability allows for timely emergency alerts, which are sent to responders via a web-based interface, giving them precise location details. The model's promise as a life-saving tool in smart city infrastructures is highlighted by its capacity to deliver precise and prompt replies. The Road Accident Emergency Reporting Model has important ramifications for increasing road safety, decreasing emergency response times, and boosting public safety in general due to its strong design and low latency.

Exploring MRI Safety Knowledge Among Physicians and Nurses in Saudi Arabia: Highlighting Knowledge Gaps and Key Influencing Factors.

Magnetic resonance imaging (MRI) is a widely used diagnostic modality in healthcare settings, but it carries inherent safety risks. Understanding healthcare providers' knowledge of MRI safety is crucial for ensuring patient and staff safety. This study aimed to assess MRI safety knowledge among physicians and nurses and explore factors influencing this knowledge. A cross-sectional study was conducted among 387 nonimaging healthcare practitioners: physicians and nurses, from different healthcare sectors in Jeddah city. A structured questionnaire assessed their understanding of basic MRI concepts, projectile hazards and contrast media risks. Statistical analyses, including Student's t-test and ANOVA, were used to examine differences in knowledge scores. Participants showed limited or poor knowledge across all MRI safety domains, including basic concepts, projectile hazards and contrast media risks, with domain knowledge scores of 31.8%, 33.7% and 32.3%, respectively. Factors having an impact on knowledge of MRI safety were the type of healthcare sector, qualification, work experience, attending workshops and whether the education curriculum included MRI safety (p ≤ 0.05). The study highlighted substantial deficits in MRI safety knowledge among nonimaging healthcare practitioners: physicians and nurses. Addressing these gaps through targeted education and training programs is essential for improving patient and staff safety in MRI settings.

Luminescent Cu nanoclusters-encapsulated ZIF-8 as on-off-on fluorescent probe for efficient and selective quantification of E. coli.

Rapid and accurate detection of Escherichia coli (E. coli) is critical for maintaining water quality, and protecting aquatic ecosystems and public health. This research focuses on the development of a Förster resonance energy transfer (FRET)-based "turn-on" fluorescent nanosensor for real time, sensitive detection of E. coli. Copper nanoclusters-encapsulated metal organic frameworks (CuNCs@ZIF-8) were sythesizedas a fluorescent donor with excellent luminescence properties. Further, MnO2 nanospheres were synthesized as a receptor with good adsorption and quenching abilities. This novel nanoconjugate (CuNCs@ZIF-8@ MnO2) was employed for the construction of a sensitive, accurate, and rapid sensing platform against E. coli in water on the basis of p-benzoquinone/hydroquinone (p-BQ/HQ) redox pair formation. Fluorescence is quenched by energy transfer when MnO2 nanospheres are added to CuNCs@ZIF-8. Upon contact with E. coli, NADH-quinone reductase converts p-BQ to HQ, which reduces MnO2 to Mn2+, releasing the nanospheres and restoring fluorescence in the composite. Based on this FRET ON-OFF-ON fluorescent probe, E. coli can be detected across a broad concentration range (5 × 101 to 5 × 105CFU/mL), with a detection limit as low as 8CFU/mL within 50min. The sensor's practicality was verified through the investigation of E. coli in real water samples, with recoveries in the range 94.3 to 106.5%. This approach offers an efficient method for on-site detection and quantification of E. coli in both environment and food safety domains.

Realization of Occupational Health and Safety Training Modules and Topics for Musculoskeletal Disorders and Psychosocial Risks in the IT Sector

Musculoskeletal Disorders and psychosocial risks represent two of the most significant health challenges in modern workplaces, with wide-ranging implications for employee well-being and organizational productivity. The primary aim of this article is to explore the design of Occupational Health and Safety training modules tailored to address the dual challenges of Musculoskeletal Disorders and psychosocial risks within the IT sector. By addressing hazards proactively, OHS helps organizations reduce the risk of workplace incidents, which can result in significant financial and reputational costs. Moreover, ensuring employee safety and well-being contributes to higher morale, improved productivity, and a positive organizational culture. Our methodology in realization of occupational health and safety training modules and topics for musculoskeletal disorders and psychosocial risks in the it sectors consisted in calling on experts because safety domain of research is by excellence a domain based on expertise. The safety expert is the real cornerstone of safety and their expertise is transformed into lessons learned that are used for training and improvement of existing safety attitudes. Regarding the relevance of the results, we consider that the structure and content of the training module address key challenges faced by IT professionals in offices, particularly those linked to sedentary work and prolonged computer use. The development of this module represents a significant step toward addressing the occupational health and safety needs of IT professionals. By focusing on education, prevention, and compliance, it not only mitigates risks but also promotes a healthier, safer, and more engaged workforce. Future efforts could expand on this module by integrating digital tools, gamified training methods, or regular feedback loops to ensure the training remains effective and relevant.

Evaluation of a Computer-Based Morphological Analysis Method for Free-Text Responses in the General Medicine In-Training Examination: Algorithm Validation Study.

The General Medicine In-Training Examination (GM-ITE) tests clinical knowledge in a 2-year postgraduate residency program in Japan. In the academic year 2021, as a domain of medical safety, the GM-ITE included questions regarding the diagnosis from medical history and physical findings through video viewing and the skills in presenting a case. Examinees watched a video or audio recording of a patient examination and provided free-text responses. However, the human cost of scoring free-text answers may limit the implementation of GM-ITE. A simple morphological analysis and word-matching model, thus, can be used to score free-text responses. This study aimed to compare human versus computer scoring of free-text responses and qualitatively evaluate the discrepancies between human- and machine-generated scores to assess the efficacy of machine scoring. After obtaining consent for participation in the study, the authors used text data from residents who voluntarily answered the GM-ITE patient reproduction video-based questions involving simulated patients. The GM-ITE used video-based questions to simulate a patient's consultation in the emergency room with a diagnosis of pulmonary embolism following a fracture. Residents provided statements for the case presentation. We obtained human-generated scores by collating the results of 2 independent scorers and machine-generated scores by converting the free-text responses into a word sequence through segmentation and morphological analysis and matching them with a prepared list of correct answers in 2022. Of the 104 responses collected-63 for postgraduate year 1 and 41 for postgraduate year 2-39 cases remained for final analysis after excluding invalid responses. The authors found discrepancies between human and machine scoring in 14 questions (7.2%); some were due to shortcomings in machine scoring that could be resolved by maintaining a list of correct words and dictionaries, whereas others were due to human error. Machine scoring is comparable to human scoring. It requires a simple program and calibration but can potentially reduce the cost of scoring free-text responses.

Analysis of static-dynamic global path planning for the rosa roxburghii picking robot

Abstract In the context of the Rosa Roxburghii picking robot operating in the Rosa Roxburghii park, path planning represents a critical challenge and a key area of study. To address this, a globally optimized A* algorithm has been developed, which integrates A* (A-star) with the dynamic window approach (DWA) for local optimization. The process begins by incorporating the robot’s positional information into the traditional evaluation function, along with establishing a weight function that balances both cost and heuristic factors. Following this, the conventional eight search directions are reduced to seven, enhancing the efficiency of the path search. To increase the smoothness of the path, unnecessary turning points are eliminated while retaining essential ones for node optimization. A safety domain value is also introduced to further minimize extraneous turns, ensuring comprehensive global path optimization. For effective realtime dynamic obstacle avoidance. The combination of the two algorithms enables online path planning, resulting in a smooth trajectory based on the globally optimal path. Simulation outcomes demonstrate that this approach significantly reduces path length, average turning angle, and execution time, with its effectiveness validated through additional simulation experiments.

Recent Advances in Metal Oxide Semiconductor Heterojunctions for the Detection of Volatile Organic Compounds

The efficient detection of volatile organic compounds (VOCs) is critically important in the domains of environmental protection, healthcare, and industrial safety. The development of metal oxide semiconductor (MOS) heterojunction gas-sensing materials is considered one of the most effective strategies to enhance sensor performance. This review summarizes and discusses the types of heterojunctions and their working principles, enhancement strategies, preparation methodologies, and applications in acetone and ethanol detection. To address the constraints pertaining to low sensitivity, sluggish response/recovery times, and elevated operating temperatures that are inherent in VOC sensors, several improvement methods are proposed, including doping with metals like Ag and Pd, incorporating additives such as MXene and polyoxometalates, optimizing morphologies through a fine design, and self-doping via oxygen vacancies. Furthermore, this work provides insights into the challenges faced by MOSs heterojunction-based gas sensors and outlines future research directions in this field. This review will contribute to foundational theories to overcome existing bottlenecks in MOS heterojunction technology while promoting its large-scale application in disease screening or agricultural food quality assessments.

Optimizing automated compliance checking with ontology-enhanced natural language processing: Case in the fire safety domain

The fire safety compliance checking (FSCC) plays a crucial role in ensuring the quality of fire engineering design and eliminating inherent fire hazards. It requires an objective and rational interpretation of fire regulations. However, the texts of fire regulations are filled with numerous rules related to spatial limitations, which pose a significant challenge in interpreting them. The current method of interpreting these rules mostly relies on manual translation, which is not efficient. To address this issue, this study proposes an innovative automated framework for interpreting rules by combining ontology technology with natural language processing (NLP). Through the utilization of pre-trained language models (PLMs), concepts and relationships are extracted from sentences, a domain-specific ontology is established, spatial knowledge is transformed into language-agnostic tree structures based on the ontology, and the semantic components of spatial relationships are extracted. The tree structure is then mapped to logical clauses based on semantic consistency, thereby improving the efficiency of interpretation. Experimental results demonstrate that the architecture achieves an F1 score of 86.27 for entity extraction and 81.81 for spatial relationship joint extraction tasks, with an accuracy of 96.26% in the formalization of logical rules, highlighting its proficiency in automatically interpreting fire spatial rules. This study offers technical support to enhance public understanding of fire safety management and fire prevention predictions, thereby promoting the intelligent management of the building safety environment.

Multigranularity Coverage Criteria for Deep Learning Libraries

ABSTRACTDeep learning (DL) systems are becoming increasingly widely used in safety domains such as self‐driving cars and unmanned aerial vehicles, which arouse natural concerns about their trustworthiness. Underlying DL libraries used in the construction and execution of DL models are involved in the testing processes of DL systems. Therefore, bugs in DL libraries can inevitably cause unexpected behaviours in DL systems. The internal structures of DL libraries are described as APIs with different functionalities, and DL libraries offer model developers access to DL techniques with various API parameter settings. The above characteristics of DL libraries reveal that existing DL coverage criteria are not designed specifically for DL libraries, and traditional software coverage criteria do not apply to DL libraries either. The paper introduces the first set of coverage criteria specifically designed for the systematic measurement of DL libraries across various granularities. APIs, as the fundamental components of DL libraries, are used to define coverage criteria gauging testing adequacy by thoroughly considering their invocation, implementation, parameter quantities and parameter attributes. Furthermore, some properties depicting relations between coverage criteria are investigated. Experiments on the effectiveness of the proposed coverage criteria and comparative analysis are conducted by interval estimate and hypothesis testing techniques for APIs in two well‐known DL libraries. The experimental results demonstrate that the proposed coverage criteria are effective in measuring the test adequacy of DL libraries, and they can be used for the quantitative analysis of test model quality in DL libraries.

A Comparative Study of Factors Influencing ADAS Acceptance in Belgium and Vietnam

This paper focuses on the acceptance of ADASs in the traffic safety and human factor domain. More specifically, it examines the predictive validity of the Unified Model of Driver Acceptance (UMDA) for an ADAS bundle that includes forward collision warning, headway monitoring and warning, and lane-keeping assistance in Belgium and Vietnam, two substantially different geographical, socio-cultural, and macroeconomic settings. All systems in the studied ADAS bundle are located at the Society of Automotive Engineer (SAE)-level 0 of automation. We found moderate acceptance towards such an ADAS bundle in both countries, and respondents held rather positive opinions about system-specific characteristics. In terms of predictive validity, the UMDA scored quite well in both countries, though better in Belgium than in Vietnam. Macroeconomic factors and socio-cultural characteristics could explain these differences between the two countries. Policymakers are encouraged to prioritise initiatives that stimulate the purchase and use of the ADAS, rather than on measures meant to influence the underlying decisional balance.

Introduction and preliminary psychometric evaluation of the assessment of functional capacity interview for older adults

Measures of complex functional decision-making capacity can greatly aid in assessing mild cognitive impairment (MCI) and facilitating early intervention in dementia care. We examined the ability of the Assessment of Functional Capacity Interview (AFCI) to detect functional differences among older adults who were cognitively unimpaired (CU), or who presented with subjective cognitive decline (SCD) or MCI. A sample of 97 older adults (CU; n = 30, Mage = 74.64 ± 7.42 years; SCD; n = 34, Mage = 72.56 ± 6.43 years; MCI; n = 33, Mage = 78.28 ± 7.55 years) underwent neuropsychological testing and responded to the Financial Capacity Instrument (FCI-SF). Informants completed the Assessment of Functional Capacity (AFCI), an instrument of functional decision-making capacity, and responded to the Social Vulnerability Scale (SVS15) and Amsterdam Instrumental Activity of Daily Living (A-IADL-Q-SV), a measure of functional status, for comparison. According to informant-reported responses, the CU group had significantly lower AFCI total (and domain) scores, H(2) = 27.59, p<.001, relative to MCI. Additionally, the CU group had significantly lower AFCI scores in the Home and Personal Safety domain relative to the SCD group, H(2) = 14.06, p<.05. In the overall sample, AFCI total scores were associated with FCI-SF, SVS15, and A-IADL-Q-SV scores and cognitive measures. Our results demonstrate that the AFCI is sensitive to impairment in safety, social, financial, and medical functioning in MCI and is associated with measures of cognitive functioning and social vulnerability in older adults. Incorporating this instrument as a supplement to cognitive screening instruments may aid in the prevention of hazardous decision-making in older adults.

Design optimization for inner core of crash box for vehicle based on NPR/PU structure

Vehicle crashworthiness is a critical aspect of the passive safety domain in passenger cars, and crash boxes play a significant role in vehicle collisions. Currently, the crash boxes predominantly utilized in vehicles are primarily simple thin-walled structures, which exhibit average energy-absorbing capabilities. To enhance vehicle collision safety, this article proposes an inner core filled with negative Poisson’s ratio (NPR) structure and polyurethane (PU) material to the design of crash box. Initially, a double-arrow type NPR structure is selected as the framework, with polyurethane (PU) serving as the filling material. This combination forms the inner core of the crash box. A collision analysis is conducted on three types of crash boxes, examining the differences in their performance indicators in detail to demonstrate the superiority of the proposed design. Subsequently, variables that significantly influence the evaluation metrics were identified through extreme value difference analysis, and these variables were designated as the design parameters for the subsequent optimization process. Finally, the Neighborhood Cultivation Genetic Algorithm (NCGA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) were employed as optimization algorithms for the optimal design, and the optimal results of the two algorithms are determined separately using Normal Boundary Intersection (NBI) method, and then compared to determine the overall optimal solution. The simulation results indicate that the NSGA-II optimized NPR/PU collision box provides substantial advantages in overall performance. After NSGA-II optimization, the NPR/PU crash box exhibits reduced overall collapse displacement and maximum peak collision force compared to other crash boxes, along with enhanced specific energy absorption capacity. These findings indicate that the designed NPR/PU crash box significantly improves the vehicle’s crashworthiness in the event of a collision. This article offers valuable theoretical insights to support the development and exploration of automotive crash boxes.

Attitudes towards patient safety among physicians and nurses in Iranian governmental teaching hospitals: a cross-sectional survey

ObjectivesTo assess and compare attitudes towards patient safety among physicians and nurses in Iranian governmental teaching hospitals and to identify factors associated with attitudes towards patient safety.DesignAn institution-based, cross-sectional survey...

ELSMOR - towards European Licensing of Small Modular Reactors:Methodology recommendations for light-water small modular reactors safety assessment.

Decarbonization of energy production is key in today's societies and nuclear energy holds an essential place in this prospect. Besides heavy-duty electricity production, other industrial and communal needs could be served by integrating novel nuclear energy production systems, among which are low-power nuclear devices, like small modular reactors (SMRs). The ELSMOR (towards European Licensing of Small Modular Reactors) European project addresses this topic as an answer to the Horizon 2020 Euratom NFRP-2018-3 call. The consortium includes 15 partners from eight European countries, involving research institutes, major European nuclear companies and technical support organizations. The 3.5-year project, launched in September 2019, investigates selected safety features of light-water (LW) SMRs with focus on licensing aspects. Providing a comprehensive compliance framework that regulators can adopt and operate, the licensing process of such SMRs could be optimized, helping their deployment. In this prospect, as a result of ELSMOR's work, this article gives an overview of the specific issues that LW-SMRs may bring about in the different domains of nuclear safety, in terms of: •Methodological standpoints: safety goals, safety requirements, safety principles (defence-in-depth implementation);•Main safety functions of reactivity control, decay heat removal and confinement management;•Severe accident management;•Other safety issues particular to SMRs: use of shared systems; performing of multi-unit probabilistic safety assessment (PSA); spent fuel management, transport and disposal management. In this article, adequate methodologies are developed to deal with these issues and to help assess the safety of LW-SMRs. This work gives a precious synthesis of the safety assessment issues of LW-SMRs and of the associated methodologies developed in the context of the ELSMOR European project.

ChatGPT for Addressing Patient-centered Frequently Asked Questions in Glaucoma Clinical Practice

Large language models such as ChatGPT-3.5 are often used by the public to answer questions related to daily life, including health advice. This study evaluated the responses of ChatGPT-3.5 in answering patient-centred frequently asked questions (FAQs) relevant in glaucoma clinical practice. Prospective cross-sectional survey. Twelve experts across a range of clinical, education and research practices in optometry and ophthalmology. Over 200 patient-centric FAQs from authoritative professional society, hospital and advocacy websites were distilled and filtered into 40 questions across four themes: definition and risk factors, diagnosis and testing, lifestyle and other accompanying conditions, and treatment and follow-up. The questions were individually input into ChatGPT-3.5 to generate responses. The responses were graded by the twelve experts individually. A 5-point Likert scale (1 = strongly disagree; 5 = strongly agree) was used to grade ChatGPT-3.5 responses across four domains: coherency, factuality, comprehensiveness, and safety. Across all themes and domains, median scores were all 4 ("agree"). Comprehensiveness had the lowest scores across domains (mean 3.7±0.9), followed by factuality (mean 3.9±0.9), and coherency and safety (mean 4.1±0.8 for both). Examination of the individual 40 questions showed that 8 (20%), 17 (42.5%), 24 (60%) and 8 (20%) of the questions had average scores below 4 (i.e. below "agree") for the coherency, factuality, comprehensiveness and safety domains, respectively. Free-text comments by the experts highlighted omissions of facts and comprehensiveness (e.g. secondary glaucoma) and remarked on the vagueness of some responses (i.e. that the response did not account for individual patient circumstances). ChatGPT-3.5 responses to FAQs in glaucoma were generally agreeable in terms of coherency, factuality, comprehensiveness, and safety. However, areas of weakness were identified, precluding recommendations for routine use to provide patients with tailored counselling in glaucoma, especially with respect to development of glaucoma and its management.

Comparison of methods for using risk information in path planning for autonomous surface vessels

Abstract Path planning is an essential part of autonomous surface vessel (ASV) operations. Risk models can be used to give estimates of risk related to the operation of technical systems, such as ASVs. Combining risk models directly with control systems can be a way to make autonomous systems more capable of assessing and evaluating risk, and in this way allow them to make better decisions. Focusing on path planning, this study aims at investigating how an online risk model used to inform an ASV of risk compares to a static safety domain. The results show that the paths selected when using the risk model can consider different environmental conditions, and the choice of path can be adapted based on how conservative the ASV should be with respect to risk. In this way the risk model provides a more detailed basis for making decisions than the static safety domain.

Impact of implementing primary care-based medication for opioid use disorder on provider and staff perceptions.

Medication for opioid use disorder (MOUD) is the management of opioid use disorder (OUD) on an outpatient basis with buprenorphine or buprenorphine/naloxone (or methadone, which is limited to federally certified opioid treatment programs). Primary care practices are well poised to provide comprehensive care for patients with OUD, including provision of MOUD. The aim of this study was to assess provider and staff OUD attitudes and role perceptions before and after implementation of a MOUD clinical service line. A survey was distributed to evaluate attitudes and perceptions of patients with OUD and provision of MOUD among providers and staff in an academic family medicine clinic. Surveys were distributed in December 2020 (73% response rate), prior to a substance use disorder educational training and MOUD service line implementation, which provided patients with OUD both primary care services and management with buprenorphine/naloxone. A follow-up survey was distributed in February 2022 (69% response rate).Training and implementation of the MOUD service line demonstrated improvements in the domains of motivation (+0.63), attitudes (+0.32), satisfaction (+0.38), role support (+0.48), role adequacy (+0.39), and safety (+0.79) among surveyed participants. The change in satisfaction and safety domains was statistically significant (P < .05). There was no change in the role legitimacy domain.Implementation of a primary care-based MOUD service line positively affected provider and staff motivation, attitudes, satisfaction, sense of safety, role support, and adequacy when working with patients with OUD. This highlights the benefits of MOUD-specific clinical support to optimize care delivery within primary care.

Determining and Prioritizing the Technical Needs of New Graduates with Bachelor of Science in Anesthesia: Analytical Hierarchy Process (AHP)

Introduction: Determining and analyzing educational needs is a crucial aspect of curriculum development, especially when planning is based on duties and competencies. This study aims to identify and prioritize the technical needs of new graduates with Bachelor of Science in Anesthesia program at Paramedical School of Kashan University of Medical Sciences in 2022. Methods: This cross-sectional study was conducted based on needs assessment in two stages. The first stage involves identifying and prioritizing skill needs across six areas: professional communication, preparation and use of drugs, preparation and use of equipment, patient control and evaluation, safety and risk prevention, and management and patient care. This assessment was carried out with a focus group comprising two groups of faculty anesthesiologists and anesthesia faculty members and a group bachelor of science in anesthesia graduates from teaching hospitals in Kashan city. The Analytical Hierarchy Process (AHP) technique was utilized, and a researcher-designed questionnaire was employed to collect data. In the second stage, this questionnaire was distributed to 29 selected anesthesiology graduates, who were chosen using a purposeful sampling method. The data were then analyzed using SPSS (version 26). First, the mean and standardized mean of each item within the six domains were calculated. Next, the desired needs were prioritized based on the suggested scores from each of the three studied groups. The importance of each domain under study was determined using the AHP technique and Expert Choice software. Results: According to the final results of the AHP, the priority of technical needs has been established based on the opinions of the three studied groups. The domains are ranked as follows: 1) management and patient care, 2) preparation and use of drugs, 3) patient control and evaluation, 4) preparation and use of equipment, and 5) professional communication. The domain of safety and risk prevention received the lowest priority, coming in at sixth place. Conclusion: The domains of management and patient care, and the preparation and use of drugs were identified as having the highest priority based on the findings of the needs assessment. Recognizing that the determination and analysis of educational needs can enhance the performance of graduates and improve the quality of anesthesia care and patient services, it is essential to focus more on implementing educational programs related to these domains.

Knowledge, attitudes, and perceptions of residents towards Hospital-at-Home (HaH) and its role in residency training

BackgroundWith the proliferation of Hospital at Home (HaH) programmes globally, there is a need to equip junior doctors with the skills necessary for provision of HaH care. The ideal training structure and clinical requirements for junior doctors to be considered competent in providing HaH care is still poorly understood. This study examines the perceptions of junior doctors towards HaH, and aims to determine the learning needs that might be helpful for future curriculum planning.MethodsWe conducted a cross-sectional study of residents at the National University Health System (NUHS) Singapore. Using a 45-item questionnaire, we explored the knowledge, attitudes and perceptions of residents towards HaH, and their interest in participating in HaH as part of residency training.ResultsOne hundred six residents responded. Overall knowledge and attitudes were mostly average. Perceptions were neutral but comparatively lower in the domains of safety, efficiency and equity. 69% of residents showed a positive attitude and interest to participate in HaH as part of residency rotations. 80% of respondents were keen to have a 2–4 week rotation incorporated into routine training. Demographic factors that influenced higher scores in various domains included type of residency programme and years of work experience.ConclusionOur findings suggest that residents are interested in participating in HaH. Incorporation of HaH rotations in residency training will allow juniors doctors to receive greater exposure and training in the skills specific to provision of HaH care. Further studies on the introduction of a HaH curriculum and Entrustable Professional Activities (EPAs) specific for HaH in residency training may be useful to to ensure that we have a competent HaH workforce that can support and keep up with the growth of HaH globally.