Support Tool Research Articles

Individual and System Level Factors Contributing to Guideline Non-Adherent Surgical Antibiotic Prophylaxis at a Tertiary Health Care System: A Qualitative Analysis

Background Antibiotics play a crucial role in preventing surgical site infections, yet adherence to Infectious Disease Society of America (IDSA) guidelines varies widely. This qualitative study aimed to explore factors influencing perioperative antibiotic administration and assess the potential impact of a clinical decision support (CDS) tool on guideline adherence. Methods In this qualitative study, perioperative personnel with diverse roles (surgeons, anesthesiologists, certified nurse anesthetists, trainees, and pharmacists) were interviewed using a semi-structured interview format from September 2023 through April 2024. Interviews were then analyzed for codes which were assigned to concepts using the constant comparison method for assessment of factors that were described as barriers or facilitators of guideline adherence. Results After piloting with three interviews, we conducted nine sessions with a total of 17 participants: 7 attending anesthesiologists, 3 resident trainees, 2 perioperative pharmacists, 3 Certified Registered Nurse Anesthetists (CRNAs), and 2 attending surgeons. Key themes emerged: (1) Limited familiarity with Infectious Disease Society of America (IDSA) antibiotic guidelines, (2) Lack of standardization and optimization of antibiotic decision-making process, (3) Challenges with managing beta-lactam allergies, (4) Difficulty optimizing vancomycin timing, and (5) Perceived benefit of a Clinical Decision Support (CDS) tool in enhancing workflow and guideline adherence. Conclusions Non-adherence to antibiotic guidelines in the perioperative setting often results from a lack of structured workflow. Our interviews provide a foundation for developing a clinical decision support tool tailored to provider needs, aiming to improve user satisfaction and promote better adherence to perioperative antibiotic guidelines.

CTN-0138: adaptation, implementation, and cluster randomized trial of a Community Pharmacy-Based Prescription Drug Monitoring Program Opioid Risk Assessment Tool—a protocol paper

Abstract Background As the opioid epidemic continues to have a major negative impact across the US, community pharmacies have come under scrutiny from legal systems attempting to hold them accountable for their role in over dispensing and lack of patient intervention. While the most available tool for monitoring patients’ opioid use is Prescription Drug Monitoring Programs (PDMP), these do not provide pharmacists with actionable information and decision support. Our study addresses this gap through three objectives: [1] incorporate validated opioid risk metric thresholds into a PDMP platform to create the Opioid Risk Reduction Clinical Decision Support (ORRCDS) tool; [2] assess ORRCDS’ ability to reduce patient opioid risk; [3] assess ORRCDS’ sustainability and viability for broader dissemination in community pharmacy. Methods For objective 1, our team is partnering with leadership from the largest US PDMP organization and a top-five pharmacy chain to implement ORRCDS into the pharmacy chain’s workflow following the Guideline Implementation with Decision Support (GUIDES) framework. For objective 2, our team will conduct a type-1 implementation mixed methods study using a 2-arm parallel group clustered randomized design. We anticipate enrolling ~ 6,600 patients with moderate and high opioid use risk during the 6-month enrollment phase across 80 pharmacies. This sample size will provide 96.3% power to detect a 5% or greater difference in responder rate between the intervention and control arm. Responders are patients with moderate-risk at baseline who reduce to low-risk or those with high-risk at baseline who reduce to moderate or low-risk at 180 days post last intervention. To accomplish objective 3, we will use the Consolidated Framework for Implementation Research (CFIR) to develop and execute cross-sectional qualitative interviews with pharmacists (n = 15), pharmacy leaders (n = 15), and PDMP leaders (n = 15) regarding long term adoption and sustainability of the ORRCDS tool. Conclusions A PDMP tool that addresses moderate- and high-risk opioid use is not available in community pharmacy. This study will implement ORRCDS in a large retail pharmacy chain that will include additional screening and guidance to pharmacy staff to address risky opioid medication use. Our results will make critical advancements for protecting patient health and addressing the opioid epidemic.

Numerical Data Processing by The Implementation of Trees and Graphs

Trees and Graphs play a vital role in transport and logistics. In tree, decision tree is one of the important, not only implemented for data processing, but also considered for Numerical data analysis. The decision tree is a flow chart-like structure, in which each internal node represents a ‘test’ on an attribute, which has a node known as root being at the top, which further divides the given data into branches depending upon the conditions. Every branch consists of a rule, and each leaf node is its outcome. A support tool with a tree-like structure that models probable outcomes, cost of resources, utilities, and possible consequences. Decision trees are also used in operations research along with planning logistics. They can help in determining appropriate plans that will help a company achieve its target. In Graph, Dijkstra's algorithm is an admired algorithm used to find the shortest paths between nodes in a graph, which may represent road networks for example. Dijkstra's algorithm finds the shortest path from a given source node to every other node. It can also be used to find the shortest path to a specific destination node, by concluding the algorithm once the shortest path to the destination node is found. It is also commonly used on graphs where the edge weights are in real numbers. A common application of shortest path algorithms is network routing protocols and also support in route optimization for delivery services, ensuring timely and cost-effective deliveries by finding the best paths for transportation.

Adaptive Industrial Control Systems via IEC 61499 and Runtime Enforcement

This work envisions industrial control systems that can reliably adapt to requirements. We rely on the international standard IEC 61499 to achieve this goal. The standard allows downtimeless system evolution such that an application can be modified at runtime to satisfy the requirements. However, an IEC 61499 application consisting of multiple Function Blocks (FBs) can be modified in many different ways, such as inserting or deleting FBs, creating new FBs with their respective internal behaviours and adjusting the connections between FBs. These changes require considerable effort and cost, and there is no guarantee to satisfy the requirements. This article applies runtime enforcement techniques for supporting adaptive IEC 61499 applications. This set of techniques can modify the runtime behaviour of a system according to specific requirements. Our approach begins with specifying the requirements as a state machine-based notation called contract automaton. This automaton is then used to synthesise an enforcer as an FB. Finally, the new FB is integrated into the application to execute according to the requirements. A tool support is developed to automate the approach. Experiments were performed to evaluate the performance of enforcers by measuring the execution time of several applications before and after the integration of enforcers.

Visual Modeling Languages in Patient Pathways: Scoping Review.

Patient pathways (PPs) are presented as a panacea solution to enhance health system functions. It is a complex concept that needs to be described and communicated well. Modeling plays a crucial role in promoting communication, fostering a shared understanding, and streamlining processes. Only a few existing systematic reviews have focused on modeling methods and standardized modeling languages. There remains a gap in consolidated knowledge regarding the use of diverse visual modeling languages. This scoping review aimed to compile visual modeling languages used to represent PPs, including the justifications and the context in which a modeling language was adopted, adapted, combined, or developed. After initial experimentation with the keywords used to describe the concepts of PPs and visual modeling languages, we developed a search strategy that was further refined and customized to the major databases identified as topically relevant. In addition, we consulted gray literature and conducted hand searches of the referenced articles. Two reviewers independently screened the articles in 2 stages using preset inclusion criteria, and a third reviewer voted on the discordance. Data charting was done using an iteratively developed form in the Covidence software. Descriptive and thematic summaries were presented following rounds of discussion to produce the final report. Of 1838 articles retrieved after deduplication, 22 satisfied our inclusion criteria. Clinical pathway is the most used phrase to represent the PP concept, and most papers discussed the concept without providing their operational definition. We categorized the visual modeling languages into five categories: (1) general purpose-modeling language (GPML) adopted without major extension or modification, (2) GPML used with formal extension recommendations, (3) combination of 2 or more modeling languages, (4) a developed domain-specific modeling language (DSML), and (5) ontological modeling languages. The justifications for adopting, adapting, combining, and developing visual modeling languages varied accordingly and ranged from versatility, expressiveness, tool support, and extensibility of a language to domain needs, integration, and simplification. Various visual modeling languages were used in PP modeling, each with varying levels of abstraction and granularity. The categorization we made could aid in a better understanding of the complex combination of PP and modeling languages. Standardized GPMLs were used with or without any modifications. The rationale to propose any modification to GPMLs evolved as more evidence was presented following requirement analyses to support domain constructs. DSMLs are infrequently used due to their resource-intensive development, often initiated at a project level. The justifications provided and the context where DSMLs were created are paramount. Future studies should assess the merits and demerits of using a visual modeling language to facilitate PP communications among stakeholders and use evaluation frameworks to identify, modify, or develop them, depending on the scope and goal of the modeling need.

A qualitative study to inform the development of a decision support tool for the diagnosis of pulmonary tuberculosis in Tigray, Ethiopia.

Tuberculosis (TB) is Ethiopia's leading infectious killer disease. The war in the Tigray region of Ethiopia has resulted in the disruption of TB care services. Prediction models are recommended to aid the diagnosis of TB in resource-limited settings. However, the development of such decision-support tools without the participation of end users may not be successful. To inform the tool development, we described barriers to diagnosing TB and identified applicable and desirable parameters for the proposed tool. We conducted a qualitative study between February and June 2023 in two cities in Tigray, Northern Ethiopia. We conducted 12 in-depth interviews and four focus group discussions with healthcare workers (HCWs). Interviews were translated, coded, and analyzed to identify predefined and emergent themes during the thematic analysis. Healthcare workers used symptoms, risk factors, signs, and investigations to diagnose TB. However, failure to ask about antibiotic use, the absence and non-affordability of investigations, and patient load were barriers affecting the diagnosis of TB. Most of the classic TB symptoms and their duration were sorted as very important, simple, reliable, generalizable, and desirable indices. In addition, a trial of antibiotics, being chronically sick-looking, having HIV, having a contact history with a TB patient, and an erythrocyte sedimentation rate fulfilled the above criteria. In the TB diagnostic process, HCWs account for a variety of data, but they prefer the classic symptoms of TB to heighten their clinical suspicion. Antibiotic trials and some risk factors were also considered reasonable. However, when HCWs have a heavy workload and a shortage of investigations, they experience a suboptimal TB diagnostic process. Hence, appropriate context consideration and care providers' preferences for parameters will inform tool development.

Evaluating small-scale eucalyptus growers’ experience of the EVO mobile application: a comparative analysis of quantitative and qualitative assessment techniques

ABSTRACT Mobile applications (apps) have become widely available as decision support tools in agriculture and forestry worldwide. Owing to the above-mentioned development trend, identifying testing methods that can be easily implemented for evaluating mobile app development targeting rural livelihood-related topics is urgently required. This study examined user experiences with a previously launched and published mobile app (EVO) that small-scale eucalyptus growers can utilize to estimate their gross income and net profit from timber sales. The survey incorporated three user experience assessment methods. The study involved two rounds of data collection regarding the mobile app: the first round concerned version 1.0, whereas the second round referred to version 2.0. The aim was to identify the user experience of the EVO app for guiding further software development to improve its suitability among potential user groups, as well as to estimate the feasibility and usability of survey methods for evaluating mobile apps among smallholders and related stakeholders. The results indicated a remarkable improvement in user satisfaction between the two rounds. Using a combination of system usability scale, user acceptance testing, and quality questions is recommended for collecting valuable and useful feedback for the development of mobile apps.

Enhancing Women's Leadership and Agency in the Context of University Spinouts and Businesses

This paper is reporting the preliminary results of a case study at Coventry University. Using innovative methodologies, we map needs of women in academia who intend to start a university spinout/business and develop a supportive tool in line with the co-creational approach of Living Labs. Data collected at our creative workshop reveals how women struggle in a toxic masculine environment, in which they both identify external and internal barriers and difficulties in defining ‘business success’. Our suggestion to support them is to invest in Living Lab based networking opportunities, role models, and to develop a tool that models the business journey.

Protocol for a mixed methods process evaluation for a randomised controlled trial to improve shared decision-making about, and uptake of, osteoporosis medicines: the iFraP study

Background High quality shared decision-making (SDM) conversations involve people with or at risk of osteoporosis and clinicians working together to decide, where appropriate, which evidence-based medicines best fit the person’s life, beliefs, and values. The improving uptake of Fracture Prevention drug treatments (iFraP) intervention comprises a computerised Decision Support Tool (DST), clinician training package and information resources, designed for use in UK Fracture Liaison Service (FLS) consultations. The iFraP intervention will be tested in a pragmatic, parallel-group, individual randomised controlled trial in patients referred to four FLSs in England. This mixed methods process evaluation aims to assess which components of iFraP were delivered and how (fidelity), whether iFraP results in a change in osteoporosis drug treatment initiation rates and how, and how context affects implementation of iFraP and outcomes. Methods We will collect quantitative data using (1) Case Report Forms completed by FLS clinicians; (2) self-reported questionnaires completed by patient participants; and (3) DST analytic data. We will collect qualitative data using (1) semi-structured interviews with patients who receive the iFraP intervention in their FLS appointment, FLS clinicians delivering iFraP appointments, and primary care clinicians that have consulted with a patient following their iFraP FLS appointment; and (2) FLS consultation recordings. A triangulation protocol will be used to integrate the quantitative and qualitative findings to generate novel insights about the intervention under evaluation. Discussion The process evaluation, alongside the trial, will help to understand what elements of the iFraP intervention were delivered and how, the mechanisms of impact and how context affected implementation and outcomes, and intervention acceptability. Mixed methods interpretation will lead to further insights about the implementation of SDM and DSTs in-practice. Trial registration ISRCTN: 10606407, 21/11/2022 https://doi.org/10.1186/ISRCTN10606407

STUDI PENGAPLIKASIAN APPSHEET SEBAGAI MEDIA PENDUKUNG MONITORING PRAKTIK KERJA LAPANGAN PRODI TEKNIK OTOMOTIF SMK N 5 SURAKARTA

<p class="abstrak"><em><span>The duration of the internship program in Indonesia has changed, from 3 months to 6 months. Increasing the duration of the internship program requires a supportive monitoring model, considering the problem of student attendance and the limited costs of monitoring the internship program. The AppSheet platform is a solution that needs to be developed and implemented as a support tool for student monitoring. The research method uses a qualitative phenomenological, to find out the results of implementing the AppSheet application as an online attendance and recap of the attendance of automotive engineering students at SMK N 5 Surakarta during the internship program from April 1 to September 30, 2023. Observation Interview data and documentation are presented in text form descriptive narrative, through interactive analysis techniques. As a result, the AppSheet application helps teachers monitor student attendance during industrial work practices, makes it easier to detect student absences, and becomes the basis for providing personal and group guidance for students who experience problems during the internship program.</span></em></p><p class="abstrak"> </p>

A Workshop and Toolkit to Support Late-Career Transitions for Faculty.

Twenty percent of medical school faculty are 60 years or older. These senior-career academic faculty often find a paucity of support for decision-making about late-career transitions. To help fill this professional development gap, we developed and tested an interactive workshop to facilitate deliberation and discussion among mid- and late-career faculty in various stages of career transition planning. The workshop included individual and small-group activities and a takeaway packet/toolkit for career transition planning. We conducted mixed methods analyses of postworkshop survey data to evaluate the workshop content, methods, and toolkit. The workshop was implemented six times, at four national conferences, one state-level conference, and one single institution, for a total of 207 participants. Evaluations were completed by 60% of participants. Thirty-one percent were thinking generally about transition, 40% were making specific plans for transition within 1-5 years, 15% were currently in transition, and 10% had fully transitioned. Perception of workshop objectives met received a median ranking of 4 (rated on a 5-point Likert scale). Participants reported positive outcomes, including taking time to think/reflect, finding starting points for transition planning, and sharing conversations. This highly rated, interactive workshop provides support and practical tools for faculty considering late-career transitions. In addition to providing resources for individual faculty, this workshop has value for an institution to support their senior-level faculty. We encourage the incorporation of this workshop into institutional faculty development programming to ensure a career lifespan approach to faculty development.

Redesign of work facilities at the tofu pressing station with a participatory ergonomic approach

Tahu Palda is a small and medium-sized micro business in the food industry that produces tofu. This factory has been producing tofu since 2005, located in North Aceh. The workers in this tofu usually five workers, with an average of 7 working hours per day. Based on the observations at several workstations, initial data indicate that the dominant work posture is standing, bending, and lifting heavy loads such as a 20 kg weight for pressing and lifting a mold holder with a 15 kg load. This is done continuously for 7 working hours each day. Based on the results of the distribution of the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ), it was found that workers at the station experience complaints such as back pain, lower back pain, and upper arm pain due to the high frequency of lifting heavy loads. If this continues to be neglected, it can lead to work-related diseases such as musculoskeletal disorders. The assessment of workers' posture using the Rapid Entire Body Assessment (REBA) method shows that the score obtained for the job of pouring soybean paste is 7, which means a moderate risk, the job of lifting the press weight is 5, which means a moderate risk, and the job of flipping molds has a score of 10, which falls into the high-risk category and needs improvement. Improvements were made using a participatory ergonomics approach, resulting in the decision to redesign the tofu press tool to fit the workers' body anthropometry. The anthropometric calculations for the height range of the press support tool and cutting tool are 141.1 cm, the press support range is 68.78 cm, the width of the press tool is 49.4 cm, the height of the tray is 4.54 cm, and the height of the tray holder is 103.85 cm.

Explainable early detection of Alzheimer's disease using ROIs and an ensemble of 138 3D vision transformers.

Early detection and accurate diagnosis of brain morphological abnormalities are essential for the effective management and treatment of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Structural magnetic resonance imaging (MRI) is a powerful support tool to aid in disease diagnosis and prediction. In this research study, we present an innovative approach to predict Alzheimer's disease (AD) and mild cognitive impairment (MCI) using MRI data, which integrates regional interest (ROI)-based methodology and deep learning within a comprehensible framework. The proposed method involves dividing the brain into 138 predetermined sections based on anatomical information. Next, we apply three-dimensional vision transformers (3D-ViTs) to each ROI individually, harnessing the power of deep learning. To improve prediction accuracy, we employ a deep belief network (DBN) as an ensemble learning model. Evaluating our approach on the baseline structural MRI dataset obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, and comparing it against five other competing models, we demonstrate its performance across four binary classification tasks and a three-class classification test (AD vs MCI vs CN (Cognitively Normal)). The proposed system outperforms existing models and provides interpretable insights into the brain regions that significantly contribute to solving each classification problem. Our findings align with the existing body of literature and hold promise for guiding future research directions in this domain.

Evaluation of a clinical decision support alert to identify hepatic dysfunction and need for medication therapy adjustment in hospitalized patients.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. To optimize the hepatic dysfunction alert tool at our institution to identify appropriate patients and minimize irrelevant alerts. This single-center, retrospective review included adults hospitalized over a 1-month period for whom a hepatic dysfunction alert fired for a medication order placed in the electronic health record. The existing alert determines hepatic dysfunction based on laboratory tests. The primary objective was to determine the proportion of patients with an alert that was deemed to be clinically relevant. Alerts were considered relevant if the patient had a Child-Pugh score in class B or C and were ordered a medication with a hepatic warning from FDA or LiverTox. The performance of 14 alternative models was evaluated. A total of 1,541 alerts fired for 309 patients. Of these patients, 155 were randomly selected for the analysis, and the alert was deemed relevant in 86 patients (55%). Patients with relevant alerts were more likely to have documented liver disease and worsening measures on liver function tests. Of the alternative models evaluated, a model that excluded INR and albumin resulted in a 27% decrease in the number of alerts fired, of which 73% were relevant; however, it failed to identify 30% of patients with relevant hepatic dysfunction. None of the other models performed better. The existing hepatic dysfunction clinical decision support tool correctly identifies patients with relevant hepatic dysfunction only 55% of the time. Alternative models were able to improve the rate of relevant results, but not without missing patients with relevant hepatic dysfunction.

Water Supply Pipeline Failure Evaluation Model Based on Particle Swarm Optimization Neural Network

The degradation and failure of the urban water supply network may lead to serious safety hazards, including pipe breaks, water supply interruptions, water resource losses, and contaminant intrusions. The risk evaluation of water supply pipeline failure in a distribution network is a challenging task, because most of the available data cannot fully reflect pipeline failure events and many of the mechanisms still cannot be fully understood. Therefore, a predictive model is urgently needed to assess pipeline failure risk based on available data. In this paper, based on the traditional risk assessment theory, seven main factors affecting pipeline failure are selected and scored, and then a pipeline failure model is established by using the particle swarm optimization (PSO) neural network. The model uses the neural network training of historical data to evaluate the failure of the water supply pipeline, and the PSO is used to optimize the neural network to effectively improve the training time and accuracy. The model error and correlation coefficient are 0.003 and 0.987, respectively. The proposed model can be used as a powerful support tool to assist infrastructure managers and pipeline maintainers in their plans and decision-making.

Parental stress and nurse-parent support in the neonatal intensive care unit: a cross-sectional study.

Admission of a preterm infant to the Neonatal Intensive Care Unit (NICU) is highly stressful for parents. NICU nurses play a crucial role in providing support, positively impacting health and aiding effective stress management during this challenging period. This study assesses stress and nurse-parent support for parents with preterm infants in the NICU, exploring connections between stress, support, and various parental and infant variables. This descriptive cross-sectional study was conducted between January and July 2019 on a total of 150 parents (75 mothers and 75 fathers) with preterm infants in a private hospital's NICU in Ankara, Turkey. Data were collected using the Parent and Infant Information Form, Parental Stressor Scale: NICU (PSS: NICU), and Nurse-Parent Support Tool (NPST). There was no significant correlation between PSS: NICU and NPST scores (p > 0.05). However, a significant difference was observed in the overall NPST score (p = 0.036) and its emotional support subscale (p = 0.013). Mothers' PSS: NICU was influenced by infant information (p = 0.027) and marital duration ((p = 0.002), while fathers' was influenced by number of children (p = 0.040) and infant care participation (p = 0.001). NPST was affected by number of children (p = 0.004) and infant respiratory status (p = 0.011) for mothers and number of children for fathers (p = 0.038). Since parents require informational, emotional, and care-related support, nurses should continuously enhance their professional and communication skills to establish effective communication with parents and provide targeted support. This study found mothers received greater nurse support than fathers. Mothers with one child had higher nurse support, while mechanical ventilation for mothers and having more than one child for fathers were linked to reduced support. Lower stress levels were noted in mothers married for 6-10 years, those informed about the infant, and fathers actively participating in infant care with more than one child.

INNV-16. ONCOPATH: OPTIMIZED TREATMENT GUIDELINE CONCORDANT PATHWAY PRESENTED ON A VISUAL-INTERACTIVE DASHBOARD FOR CLINICAL DECISION SUPPORT COMPUTED BY OVERLAYING PATIENT LONGITUDINAL DATA ON A KNOWLEDGE GRAPH

Abstract BACKGROUND OncoPath provides a visual analysis of a brain tumor patient’s longitudinal clinical data overlayed on disease specific pathways with the goal of reducing knowledge discordant care and insurance authorization burden. By ingesting, curating and visually presenting the patient experience on guidelines, OncoPath aims to streamline clinical decision making and related processes. Understanding the patient’s journey compared to treatment guidelines is of value in addressing health equity and guideline adoption in real world settings. METHODS Data from 44 glioma patients diagnosed and treated between 2016-2021 were uploaded to OncoPath using natural language processing and other tools to capture abstractable data elements. The data was overlayed on guidelines using recursive graph modeling. Using the knowledge graph of a patient’s history, the model also recommends treatment options in an interactive visual dashboard representing NCCN guidelines. The dashboard includes the guidelines in graphical format with associated references and notation. RESULTS 28 males and 16 females age 21-38 years at diagnosis were abstracted. Cases represented 4 oligodendrogliomas, 13 astrocytomas, and 27 glioblastomas. Data was available through second line therapy, discharge to hospice or death. Cases were matched to the NCCN 2021 guidelines which was used for treatment decisions until November 2022. The patient data matched OncoPath except in 3 cases where KPS was not available resulting in premature pathway truncation. For these cases we inferred KPS based on subsequent treatment received to optimize the historic data. CONCLUSION To our knowledge, this is a first-of-a-kind technology in neuro-oncology that may improve time to treatment, reduce health utilization resources and can serve as a benchmarking tool for care delivery. The feasibility of clinically implementing such tools for decision support was demonstrated. This type of tool could be particularly useful in low-resource areas where disease specific expertise may not be available or to illuminate care discrepancies.

Adoption and Impact of ChatGPT in Computer Science Education: A Case Study on a Database Administration Course

The irruption of GenAI such as ChatGPT has changed the educational landscape. Therefore, methodological guidelines and more empirical experiences are needed to better understand these tools and know how to use them to their fullest potential. This contribution presents an exploratory and correlational study conducted with 37 computer science students who used ChatGPT as a support tool to learn database administration. The article addresses three questions: The first one explores the degree of use of ChatGPT among computer science students to learn database administration, the second one explores the profile of students who get the most out of tools like ChatGPT to deal with database administration activities, and the third one explores how the utilization of ChatGPT can impact in academic performance. To empirically shed light on these questions the student’s grades and a comprehensive questionnaire were employed as research instruments. The obtained results indicate that traditional learning resources, such as teacher’s explanations and student’s reports, were widely used and correlated positively with student’s grades. The usage and perceived utility of ChatGPT were moderate, but positive correlations between students’ grades and ChatGPT usage were found. Indeed, a significantly higher use of this tool was identified among the group of outstanding students. This indicate that high-performing students are the ones who are using ChatGPT the most. So, a new digital trench could be rising between these students and those with a lower degree of fundamentals and worse prompting skills, who may not take advantage of all the ChatGPT possibilities.

Demystifying the Power of Generative Artificial Intelligence Tools in Higher Education

Artificial intelligence represents an emerging frontier for higher education institutions, potentially personalising learning, automating tasks, and supporting student outcomes. This study examines international students’ perceptions of the recent proliferation of generative artificial intelligence tools in the context of academic learning and assessment. The study involved N = 223 students from three different higher education institutions located in Australia, Germany and Italy. The focus was on the student’s competence in artificial intelligence and their perception of six different generative artificial intelligence tools concerning learning and assessment. The findings suggest that the dimensions of competence in artificial intelligence vary considerably and that students from different countries have a comparable level of competence in artificial intelligence. Further findings indicate that the expected support of generative artificial intelligence tools for learning and assessment is perceived differently. This study highlights the need for increased pedagogical attention to artificial intelligence, bridging the gap between students' enthusiasm and technical knowledge. It suggests that effective integration of generative artificial intelligence tools should also prioritise the development of critical thinking and comprehension skills over content generation.

Artificial intelligence assisted operative anatomy recognition in endoscopic pituitary surgery

Pituitary tumours are surrounded by critical neurovascular structures and identification of these intra-operatively can be challenging. We have previously developed an AI model capable of sellar anatomy segmentation. This study aims to apply this model, and explore the impact of AI-assistance on clinician anatomy recognition. Participants were tasked with labelling the sella on six images, initially without assistance, then augmented by AI. Mean DICE scores and the proportion of annotations encompassing the centroid of the sella were calculated. Six medical students, six junior trainees, six intermediate trainees and six experts were recruited. There was an overall improvement in sella recognition from a DICE of score 70.7% without AI assistance to 77.5% with AI assistance (+6.7; p < 0.001). Medical students used and benefitted from AI assistance the most, improving from a DICE score of 66.2% to 78.9% (+12.8; p = 0.02). This technology has the potential to augment surgical education and eventually be used as an intra-operative decision support tool.