End-user Perspective Research Articles

Artificial Intelligence for Optimizing Cancer Imaging: User Experience Study.

The need for increased clinical efficacy and efficiency has been the main force in developing artificial intelligence (AI) tools in medical imaging. The INCISIVE project is a European Union-funded initiative aiming to revolutionize cancer imaging methods using AI technology. It seeks to address limitations in imaging techniques by developing an AI-based toolbox that improves accuracy, specificity, sensitivity, interpretability, and cost-effectiveness. To ensure the successful implementation of the INCISIVE AI service, a study was conducted to understand the needs, challenges, and expectations of health care professionals (HCPs) regarding the proposed toolbox and any potential implementation barriers. A mixed methods study consisting of 2 phases was conducted. Phase 1 involved user experience (UX) design workshops with users of the INCISIVE AI toolbox. Phase 2 involved a Delphi study conducted through a series of sequential questionnaires. To recruit, a purposive sampling strategy based on the project's consortium network was used. In total, 16 HCPs from Serbia, Italy, Greece, Cyprus, Spain, and the United Kingdom participated in the UX design workshops and 12 completed the Delphi study. Descriptive statistics were performed using SPSS (IBM Corp), enabling the calculation of mean rank scores of the Delphi study's lists. The qualitative data collected via the UX design workshops was analyzed using NVivo (version 12; Lumivero) software. The workshops facilitated brainstorming and identification of the INCISIVE AI toolbox's desired features and implementation barriers. Subsequently, the Delphi study was instrumental in ranking these features, showing a strong consensus among HCPs (W=0.741, P<.001). Additionally, this study also identified implementation barriers, revealing a strong consensus among HCPs (W=0.705, P<.001). Key findings indicated that the INCISIVE AI toolbox could assist in areas such as misdiagnosis, overdiagnosis, delays in diagnosis, detection of minor lesions, decision-making in disagreement, treatment allocation, disease prognosis, prediction, treatment response prediction, and care integration throughout the patient journey. Limited resources, lack of organizational and managerial support, and data entry variability were some of the identified barriers. HCPs also had an explicit interest in AI explainability, desiring feature relevance explanations or a combination of feature relevance and visual explanations within the toolbox. The results provide a thorough examination of the INCISIVE AI toolbox's design elements as required by the end users and potential barriers to its implementation, thus guiding the design and implementation of the INCISIVE technology. The outcome offers information about the degree of AI explainability required of the INCISIVE AI toolbox across the three services: (1) initial diagnosis; (2) disease staging, differentiation, and characterization; and (3) treatment and follow-up indicated for the toolbox. By considering the perspective of end users, INCISIVE aims to develop a solution that effectively meets their needs and drives adoption.

Designing and testing clinical simulations of an early warning system for implementation in acute care settings.

Conducting simulation testing with end-users is essential for facilitating successful implementation of new health information technologies. This study designed a standardized simulation testing process with a system prototype prior to implementation to help study teams identify the system's interpretability and feasibility from the end-user perspective and to effectively integrate new innovations into real-world clinical settings and workflows. A clinical simulation model was developed to test a new Clinical Decision Support (CDS) system outside of the clinical environment while maintaining high fidelity. A web-based CDS prototype, the "CONCERN Smart Application," which leverages clinical data to measure and express a patient's risk of deterioration on a 3-level scale ("low," "moderate," or "high"), and audiovisual-integrated materials, were used to lead simulation sessions. A total of 6 simulation sessions with 17 nurses were held to investigate how nurses interact with the CONCERN Smart application and how it influences their critical thinking, and clinical responses. Four themes were extracted from the simulation debriefing sessions and used to inform implementation strategies. The strategies include how the CDS should be improved for practical real-world use. Standardized simulation testing procedures identified and informed the necessary CDS improvements, the enhancements needed for real-world use, and the training requirements to effectively prepare end-users for system go-live.

B-121 Optimizing Test Ordering: Leveraging Heuristics in Naming Conventions - A PSA Case Study

Abstract Background A common screening test for prostate cancer is the Prostate-Specific Antigen (PSA) test, which may also be referred to as total PSA or total PSA plus free PSA, among other variations. Looking at the test menus of two major reference laboratories, it's apparent that PSA can be ordered under thirteen different test names. From an end-user perspective, the question arises: which of these thirteen tests should be ordered, and why are there so many options? This plethora of choices can lead to confusion and uncertainty for clinicians and patients alike. According to various guidelines, ordering Free PSA is only warranted when PSA values exceed 4 ng/ml. In such cases, the ratio of free PSA to total PSA can aid in further clinical decision-making and patient workup. At Zuckerberg San Francisco General Hospital (ZSFGH), we offer two tests for PSA. The first is named “Prostate Specific Antigen” is total PSA done in house, while the second is labeled “Prostate Spec. Ag. Total and Free.” which is sent out to a reference lab. Upon reviewing our send-out orders, we observed a significant increase (376%) from 2022 to 2023 in the use of the send out order. Methods The ordering data for our providers was collected from February 2022 to January 2024 for both in-house and send-out orders. The data was analyzed using descriptive statistics in Microsoft Excel. Results In our population, we observed an abnormal concentration of send-outs originating from a select group of physicians. Additional we also identified a group of providers who only selected the send out test order. Analysis of in-house assay results for PSA revealed that only 15% (1292 out of 8136) exceeded the cutoff of 4 ng/ml, necessitating further testing to inform clinical decision-making. Yet our send out volume of 4157 test is in discordance with how many PSA would need further evaluation. Based on a cost analysis reducing the send out volume by 80% could result in $53,210 savings. Therefore, to reduce the send out amount and to keep the naming convention similar we decided to implement a new test name that would reflex the PSA to Free PSA if the PSA is above 4 ng/ml. We decided on the name “Prostate Specific Antigen total with reflex to Free Prostate Specific Antigen”. Conclusions Our investigation into the variability in PSA test ordering revealed a significant disparity in the number of send-out orders, particularly originating from a select group of physicians. However, the disproportionately high volume of send-out tests suggested an inefficiency in the current testing approach. To address this issue and align with clinical guidelines, we decided to process a request to our medical executive committee for approval for this reflex test in order to meet reimbursement compliance with the Office of the Inspector General. This change aims to streamline the process, reduce unnecessary send-out tests, and ultimately improve the efficiency and accuracy of prostate cancer screening at our institution.

Informing the Design of Intervention Solutions for Body-Focused Repetitive Behaviors

Body-Focused Repetitive Behaviors (BFRBs), such as nail biting, impact a wide demographic, and can negatively affect physical, psychological, and social well-being. Although pharmacological and behavioral therapies are common treatments, many avoid seeking help, and not everyone responds fully to treatment. Recent advances in wearable sensing enable new digital solutions that can detect BFRB episodes and intervene to mitigate them. While BFRBs have been extensively studied in medical research, translating this knowledge into effective digital intervention solutions may not be straightforward, and the end user's perspective may be overlooked. We report a user study with 12 frequent nail biters, who shared their experiences about nail biting and expectations of intervention solutions in semi-structured qualitative interviews. We describe the progression of a nail biting episode from a nail biter's perspective and present a taxonomy of intervention strategies to mitigate nail biting. Our results inform the design of future digital BFRB intervention solutions.

Pharmacovigilance Consideration for Ayurvedic Medicines in Pediatric Practice: Developing Protocols for Documenting Clinical Safety

Background The rising global trend of traditional medicine and complementary and alternative medicine use necessitates a structured approach to ensure patient safety and efficacy. Due to its unique theoretical framework, directly applying modern pharmacovigilance principles to Ayurveda may be insufficient. Purpose This study aims to highlight the need for a collaborative approach that integrates traditional Ayurvedic knowledge with modern safety and efficacy evaluation methods. It critiques the limitations of current pharmacovigilance practices, especially in the context of Ayurveda, and proposes improvements to better capture the complexities of traditional medicine. Methods An analysis of the “AyushSuraksha” program, a key initiative for the pharmacovigilance of Ayurveda, Siddha, Unani, and homoeopathy (ASU&amp;H) medicines in India, was conducted. The program’s focus areas, including monitoring medications, specific materials, and procedure-based therapies, were reviewed in light of their efficacy and safety reporting mechanisms. Additionally, the applicability of current pharmacovigilance methodologies, particularly in the context of personalized Ayurveda treatments, was evaluated through case studies and literature reviews. Results The “AyushSuraksha” program, while focused on medication and therapy monitoring, overlooks the safety of food items used in Ayurveda. Current pharmacovigilance systems, designed for standardized medications, may be inadequate for Ayurveda, as demonstrated by positive outcomes in autism treatment despite the presence of microbiologically harmful products. Furthermore, existing pharmacovigilance proformas lack a distinction between physicians’ and end-users perspectives, limiting the reporting scope. Conclusion A transition to a holistic, evidence-based pharmacovigilance approach that incorporates Ayurvedic assessments and nonlinear herbal interactions is crucial. Separate proformas for physicians and end-users, along with disease-specific questions, are recommended to improve reporting accuracy. Expanding the scope of pharmacovigilance beyond standardized medication monitoring is essential for the evolution and refinement of Ayurvedic science.

Oncology Clinicians' Perspectives of a Remote Patient Monitoring Program: A Multi-Modal Case Study Approach.

Remote patient monitoring (RPM) aims to improve patient access to care and communication with clinical providers. Overall, understanding the usability of RPM applications and their influence on clinical care workflows is limited from the perspectives of clinician end users at a cancer center in the Northeast, United States. Explore the usability and functionality of RPM and elicit the perceptions and experiences of oncology clinicians using RPM for oncology patients after hospital discharge. The sample included 30 of 98 clinicians (31% response rate) managing at least five patients in the RPM program and responding to the m-Health Usability between March 2021- October 2021. Overall, clinicians responded positively to the survey. Item responses with the highest proportion of disagreement were explored further. A nested sample of five clinicians who responded to the study survey (30% response rate) participated in interview sessions conducted from November 2021 to February 2022, and averaged 60 minutes each. Survey responses highlighted that RPM was easy to use and learn and verified symptom alerts during follow-up phone calls. Areas to improve identified practice changes from reporting RPM alerts through digital portals and its influence on clinicians' workload burden. Interview sessions revealed three main themes: clinician understanding and usability constraints, patient constraints, and suggestions for improving the program. Subthemes for each theme were explored, characterizing technical and functional limitations that could be addressed to enhance efficiency, workflow, and user experience. Clinicians support the value of RPM for improving symptom management and engaging with providers. Functional changes to enhance the program's utility, such as input from patients about temporal changes in their symptoms and technical resources for home monitoring devices.

Performance Analysis of Light Detection and Ranging (LIDAR) in Augmented Reality (AR)

Augmented reality (AR) has existed for decades, dating back from roughly 50 years ago when the renowned ‘father of computer graphics’ Ivan Sutherland first invented an AR head-mounted display system. Ever since, various organizations strive to improve the technology but while the AR technology definitely evolved significantly since its advent, the technology progress is relatively stagnant these past few years whereby we see little to no usage at least from common end user perspective. This may be due to various limitations of technologies currently utilized in AR. However, things may change since in 2020, the tech giant, Apple Inc. has made an unprecedented breakthrough by including the light detection and ranging (LiDAR) scanner on consumers’ smartphones which could potentially revolutionize the AR industry. This research primarily revolves around analyzing the performance of utilization of LiDAR technology in AR mainly in mobile devices available for the mass. The methodology and concept for this research is fairly straight-forward, the AR experience boosted by the newly introduced LiDAR technology will be compared with the widely used technology which primarily consist of the implementation of Simultaneous Localization and Mapping (SLAM) and RGB camera with the exclusion of LiDAR and assessing the potential of LiDAR to further improvise AR experience and implementation in various fields. Testing results has shown significant improvement from various important aspects in AR whereby occlusion is possible in object rendering, the estimations based on live visual stimuli accuracy at roughly 99% in comparison to the true values as well as the ability to accurately perceive the geometry of objects in the real world environment through advanced depth and space perception.

Exploring the individual adoption of human resource analytics: Behavioural beliefs and the role of machine learning characteristics

The technological capabilities of Human Resource Analytics (HRA), enhanced by recent innovations in Machine Learning (ML), offer exciting opportunities. However, organisations often fail to realise these potentials because of a limited understanding of why individuals choose to adopt or disregard respective tools. Prior research on innovation adoption offers preliminary insights but fails to aggregate the determinants of individual adoption into actionable suggestions for decisions in the ML adoption process. Our study applies focused interviews to examine non-ML experts' reasoning for using a specific tool tailored to a public sector organisation, which corresponds to the usual end-user perspective of ML-based HRA adoption. By drawing from the HRA adoption framework, provided by Vargas et al. (2018), we contribute to the literature by identifying relevant beliefs and experiences influencing one's intention to adopt ML-based HRA and by qualitatively linking these beliefs to ML characteristics such as transparency, automation and fairness. For practitioners, we provide actionable guidance emphasising the need to ensure fairness proactively, as interviewees do not consider this aspect when deciding to adopt ML-based HRA.

Key performance indicators and benchmarks in MCI prehospital response using technological tools: a qualitative study assessing the perception of practitioners and tool developers.

The aim of this study is to investigate the opinions and perspectives of The Novel Integrated Toolkit for Enhanced Prehospital Life Support and Triage in Challenging and Large Emergencies (NIGHTINGALE) end-users and tool developers regarding Key Performance Indicators (KPIs) and benchmarks that assess the prehospital response to Mass Casualty Incidents (MCIs) enhanced by the NIT-MR. A qualitative study employing focus group discussions was conducted to collect opinions and perspectives of end-users and tool developers regarding KPIs and benchmarks in MCI response using the NIT-MR. The criteria considered for the selection and distribution of participants within the groups was the nature of their involvement within the NIGHTINGALE project and their familiarity with the tools to be discussed. Thirty-one participants from different countries were included. Four themes emerged during data analysis which are: definition/explanation is the personal understanding of participants of the term KPI, process of KPI development and relationship with User Requirements is the decision process for assigning KPIs to user requirements, benchmarking is the mental process of associating a benchmark to a KPI or for developing a benchmark, and technical/medical gap is the gap of understanding between each sides' fields. This study emphasized the need for a structured approach to using KPIs and bridging the gap between technological and medical worlds, taking the NIGHTINGALE project, funded by the European Union, which aims to develop a technological toolkit for first responders in mass casualty incidents as an example. These insights are crucial for enhancing disaster response.

Automatic Classification of Nodules from 2D Ultrasound Images Using Deep Learning Networks.

In clinical practice, thyroid nodules are typically visually evaluated by expert physicians using 2D ultrasound images. Based on their assessment, a fine needle aspiration (FNA) may be recommended. However, visually classifying thyroid nodules from ultrasound images may lead to unnecessary fine needle aspirations for patients. The aim of this study is to develop an automatic thyroid ultrasound image classification system to prevent unnecessary FNAs. An automatic computer-aided artificial intelligence system is proposed for classifying thyroid nodules using a fine-tuned deep learning model based on the DenseNet architecture, which incorporates an attention module. The dataset comprises 591 thyroid nodule images categorized based on the Bethesda score. Thyroid nodules are classified as either requiring FNA or not. The challenges encountered in this task include managing variability in image quality, addressing the presence of artifacts in ultrasound image datasets, tackling class imbalance, and ensuring model interpretability. We employed techniques such as data augmentation, class weighting, and gradient-weighted class activation maps (Grad-CAM) to enhance model performance and provide insights into decision making. Our approach achieved excellent results with an average accuracy of 0.94, F1-score of 0.93, and sensitivity of 0.96. The use of Grad-CAM gives insights on the decision making and then reinforce the reliability of the binary classification for the end-user perspective. We propose a deep learning architecture that effectively classifies thyroid nodules as requiring FNA or not from ultrasound images. Despite challenges related to image variability, class imbalance, and interpretability, our method demonstrated a high classification accuracy with minimal false negatives, showing its potential to reduce unnecessary FNAs in clinical settings.

Implementation contexts and strategies for alternative peripherally inserted central catheter material and design selection: A qualitative exploration using CFIR/ERIC approach.

To explore the implementation contexts and strategies that influence the uptake and selection of alternative peripherally inserted central catheter (PICC) materials and design. Qualitative evaluation of end user perspectives within a randomized control trial of different PICC materials and design. Semi-structured interviews with key stakeholders were undertaken via an adapted, rapid-analytic approach using the Consolidated Framework for Implementation Research. Outcomes were mapped against the Expert Recommendations for Implementing Change (ERIC) tool for strategies to guide innovation in PICC practice. Participants (n = 23) represented a combination of users and inserters/purchasers, from adult and paediatric settings. Dominant themes included intervention characteristics (intervention source), inner setting (structural characteristics) and individuals involved (self-efficacy). Strategies emerging to support a change from ERIC mapping (n = 16) included promotion of intervention adaptability, inclusion of staff and consumer perspectives and sufficient funding. Implementation contexts such as inner setting and individuals involved equally impacted PICC success and implementation effectiveness and enabled a greater understanding of barriers and facilitators to intervention implementation in this trial. Trial evidence is important, but healthcare decision-making requires consideration of local contexts especially resourcing. Implementation contexts for Australian healthcare settings include a practical, strategic toolkit for the implementation of alternative PICC materials and designs. This study adhered to COREQ guidelines. No patient or public contribution.

A qualitative, multi-centre approach to the current state of digitalisation and automation of surveillance in infection prevention and control in German hospitals

BackgroundHealthcare associated infections (HAI) pose a major threat to healthcare systems resulting in an increased burden of disease. Surveillance plays a key role in rapidly identifying these infections and preventing further transmissions. Alas, in German hospitals, the majority of surveillance efforts have been heavily relying on labour intensive processes like manual chart review. In order to be able to identify further starting points for future digital tools and interventions to aid the surveillance of HAI we aimed to gain an understanding of the current state of digitalisation in the context of the general surveillance organisation in German clinics across all care-levels. The end user perspective of infection prevention and control (IPC) professionals was chosen to identify digital interventions that have the biggest impact on the daily surveillance work routines of IPC professionals. Perceived impediments in the advancement of surveillance digitalisation should be explored.MethodsFollowing the development of an interview guideline, eight IPC professionals from seven German hospitals of different care levels were questioned in semi- structured interviews between December 2022 and January 2023. These included questions about general surveillance organisation, access to digital data sources, software to aid the surveillance process as well as current issues in the surveillance process and implementation of software systems. Subsequently, after full transcription, the interview sections were categorized in code categories (first deductive then inductive coding) and analysed qualitatively.ResultsResults were characterised by high heterogeneity in terms of general surveillance organisation and access to digital data sources. Software configuration of hospital and laboratory information systems (HIS/LIS) as well as patient data management systems (PDMS) varied not only between hospitals of different care levels but also between hospitals of the same care level. Outside research projects, neither fully automatic software nor solutions utilising artificial intelligence have currently been implemented in clinical routine in any of the hospitals.ConclusionsAccess to digital data sources and software is increasingly available to aid surveillance of HAI. Nevertheless, surveillance processes in hospitals analysed in this study still heavily rely on manual processes. In the analysed hospitals, there is an implementation and funding gap of (semi-) automatic surveillance solutions in clinical practice, especially in healthcare facilities of lower care levels.

P-475 Introducing the future of personalized fertility care - AndreiaAI

Abstract Study question Is it possible to build a robust and accurate large language model, from anonymized patient records, to predict live birth occurrence? Summary answer A prototype provides answers in different languages, creates charts/tables based on natural language inputs, and runs statistical analysis, predicting live birth occurrence with 78% accuracy. What is known already The capability for a non-technical person to query clinically valid information and safely managed data represents the single largest step towards the democratization of clinical knowledge to the layperson in the history of ART. Whilst patients are used to Googling their symptoms, Chat GPT was trained on the open internet and will provide equally poor advice. However, when the information is locked down and limited to a specific data set, the ability of a LLM to parse questions as code and return answers specific to the underlying data set presents great opportunities to improve patient and clinician awareness. Study design, size, duration Design: proof of concept Size: 1 million anonymized patient outcome records were gathered from the regulator’s anonymized register (i.e., HFEA, from 1991 to 2018) Duration: between August and December 2023 Participants/materials, setting, methods Participants/materials: 1 million anonymized patient outcome records were gathered from the regulator’s anonymized register (i.e., HFEA, from 1991 to 2018) Method: Data was merged and pre-processed for analysis. As an approach to machine-learning modeling, Ensembling with Random Forest and GB was chosen. Different models were tested to reach maximum accuracy and performance. Patient scenarios based on evidence-based cases were used. A ChatGPT-like interface was preferred as it is simple for doctors and patients to interact with. Main results and the role of chance A prototype model was created, using anonymized patient outcome records, and formulated various patient scenarios based on evidence-based cases, to demonstrate new capabilities. The capabilities include the ability to provide answers in different languages for non-native English speakers, to dynamically create charts and tables based on natural language inputs, and to run sophisticated statistical analysis linked to the underlying dataset for example predict live birth occurrence based on demographic inputs,s and presentation of symptoms. The prototype went as far as creating an ML model to predict live birth occurrence with a 78% accuracy. This was achieved by using an Emsebling method for a binary classification problem (random forest) with live birth occurrence as the target and 14 features, such as age, treatment, and diagnosis. Whilst the technical nature of how this was delivered is extremely important for explainable AI and to engender trust as opposed to replacing the doctor with just another black box, the fact that a predictive model based on 1 million patients and linked back to a specific patient scenario in under 30 seconds for the end-user perspective is proof of capability that personalized insights can be quickly and accurately generated in clinical consultation. Limitations, reasons for caution The study was a proof of concept which needs further research and validation in a clinical setting. Wider implications of the findings The proof of concept evidences the capabilities of LLMs to increase awareness of fertility information. It is accessible, in 40 different languages, on-demand, free to the end-user, and clearly visualized. Access to accurate information that provides personalized insights based on specific criteria, speeds up the time to diagnosis and treatment. Trial registration number n/a

User-centric approaches to remote inspection techniques for seagoing vessels: The impact of end-User feedback on technological accuracy and policy development

ABSTRACT This paper delves into end-user perspectives on the adoption of Remote Inspection Techniques (RIT) that can serve the needs of the maritime transport industry (as examined under the European Union’s (EU) BUGWRIGHT2 project), highlighting their potential to meet classification and statutory survey requirements while adhering to regulatory standards and maintaining stringent safety norms. The successful integration and effectiveness of these technologies are largely contingent upon end users’ acceptance and feedback, which are crucial in shaping the design, development, and deployment phases. Drawing on sixteen (16) in-depth interviews with end users, this effort provides valuable insights into the benefits, challenges, and necessary future directions for RIT development. By exploring users’ experiences and perspectives, key factors that influence the successful adoption of RIT are pin-pointed down, followed by strategic recommendations for policymakers and stakeholders in the maritime industry.

National Institute of Mental Health Support for Cognitive Treatment Development in Schizophrenia: A Narrative Review.

For several decades the National Institute of Mental Health (NIMH) has supported basic and translational research into cognitive impairment in schizophrenia. This article describes the Institute's ongoing commitment to cognitive assessment and intervention research, as reflected by three signature initiatives-Measurement and Treatment Research to Improve Cognition in Schizophrenia; Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia; and Research Domain Criteria-and related funding announcements that span basic experimental studies, efficacy and comparative effectiveness trials, and implementation research designed to promote cognitive healthcare in real-world treatment settings. We discuss how trends in science and public health policy since the early 2000s have influenced NIMH treatment development activities, resulting in greater attention to (1) inclusive teams that reflect end-user perspectives on the utility of proposed studies; (2) measurement of discrete neurocognitive processes to inform targeted interventions; (3) clinical trials that produce useful information about putative illness mechanisms, promising treatment targets, and downstream clinical effects; and (4) "productive urgency" in pursuing feasible and effective cognitive interventions for psychosis. Programs employing these principles have catalyzed cognitive measurement, drug development, and behavioral intervention approaches that aim to improve neurocognition and community functioning among persons with schizophrenia. NIMH will maintain support for innovative and impactful investigator-initiated research that advances patient-centered, clinically effective, and continuously improving cognitive health care for persons with psychotic disorders.

Insights into optimizing phantom pain management: A human-centered approach to end-user perspectives.

This report explores and summarizes perspectives from end users on unmet needs in achieving optimal and effective phantom pain management through a human-centered design approach. To examine current strategies, pharmacological, nonpharmacological, surgical procedures, virtual reality, and mirror therapy, and the evidence supporting them, in the management of phantom limb pain. This study reviewed and analyzed transcripts acquired in nonresearch contexts from the Veterans Affairs Translational Education and Mentoring Center's commercialization training program and from a Veteran Engagement Panel. Key themes were extracted using quasi-qualitative analysis of one-on-one interviews. Clinicians and patients report that early patient intervention and education will yield improved management of phantom limb pain, which aligns with the growing recognition of the impact of patient-centered care on overall treatment outcomes. Mirror therapy is viewed as an effective and low-risk therapy, though compliance and buy-in may be barriers to clinical practice. Patient engagement can contribute to better treatment adherence and outcomes. The study highlights implementation barriers, importance of end-user input, and the role of the Veteran Engagement Panel in providing feedback to pain researchers. The findings help explain unaddressed challenges and areas requiring further research to direct phantom pain management.

Challenges and advantages of electronic prescribing system: a survey study and thematic analysis

IntroductionElectronic prescribing (e-prescribing) systems can bring many advantages and challenges. This system has been launched in Iran for more than two years. This study aimed to investigate the challenges and advantages of the e-prescribing system from the point of view of physicians.MethodsIn this survey study and thematic analysis, which was conducted in 2023, a researcher-made questionnaire was created based on the literature review and opinions of the research team members and provided to the physician. Quantitative data were analyzed using SPSS software, and qualitative data were analyzed using ATLAS.ti software. Rank and point biserial, Kendall’s tau b, and Phi were used to investigate the correlation between variables.ResultsEighty-four physicians participated in this study, and 71.4% preferred to use paper-based prescribing. According to the results, 53.6%, 38.1%, and 8.3% of physicians had low, medium, and high overall satisfaction with this system, respectively. There was a statistically significant correlation between the sex and overall satisfaction with the e-prescribing system (p-value = 0.009) and the computer skill level and the prescribing methods (P-value = 0.042). Physicians face many challenges with this system, which can be divided into five main categories: technical, patient-related, healthcare providers-related, human resources, and architectural and design issues. Also, the main advantages of the e-prescribing system were process improvement, economic efficiency, and enhanced prescribing accuracy.ConclusionThe custodian and service provider organizations should upgrade the necessary information technology infrastructures, including hardware, software, and network infrastructures. Furthermore, it would be beneficial to incorporate the perspectives of end users in the system design process.

NNI nanoinformatics conference 2023: Movement toward a common infrastructure for federal nanoEHS data computational toxicology: Short communication

The National Nanotechnology Initiative organized a Nanoinformatics Conference in the 2023 Biden-Harris Administration’s Year of Open Science, which included interested U.S. and EU stakeholders, and preceded the U.S.-EU COR meeting on November 15th, 2023 in Washington, D.C. Progress in the development of a common nanoinformatics infrastructure in the European Union and United States were discussed. Development of contributing, individual database projects, and their strengths and weaknesses, were highlighted. Recommendations and next steps for a U.S. nanoEHS common infrastructure were discussed in light of the pending update of the National Nanotechnology Initiative (NNI)’s Environmental, Health and Safety Research Strategy, and U.S. efforts to curate and house nano Environmental Health and Safety (nanoEHS) data from U.S. federal stakeholder groups. Improved data standards, for reporting and storage have been identified as areas where concerted efforts could most benefit initially. Areas that were not addressed at the conference, but that are critical to progress of the U.S. federal consortium effort are the evaluation of data formats according to use and sustainability measures; modeler and end user, including risk-assessor and regulator perspectives; a need for a community forum or shared data location that is not hosted by any individual U.S. federal agency, and is accessible to the public; as well as emerging needs for integration with new data types such as micro and nano plastics, and interoperability with other data and meta-data, such as adverse outcome pathway information. Future progress will depend on continued interaction of the U.S. and EU CORs, stakeholders and partners in the continued development goals for shared or interoperable infrastructure for nanoEHS.

Examining the Relation of Transformational Leadership in Clinical Engineering on the Performance of Medical Equipment: A Neural Network Approach

In the realm of healthcare administration, heightened expectations can lead to stress, therefore impacting working conditions. Specifically, studies on leadership styles have provided a valuable understanding of the factors that hinder performance, particularly in relation to the implementation of transactional leadership. Despite its crucial significance, there is a dearth of research on the management styles employed in clinical engineering. This study examines the impact of leadership styles on the functioning of medical equipment. We assess leadership styles and the performance of medical equipment from the perspective of end-users using a cross-sectional survey and questionnaires that consider many significant criteria. A neural network model is employed to classify the leadership styles exhibited by the Clinical Engineering Department (CED) and to analyze the correlation between these styles and the equipment’s performance. The results suggest a significant correlation between the leadership styles of those in charge of CED and the functioning of medical equipment. A strong and favorable correlation exists between transformative leadership and equipment performance (r = 0.856**, P = 0.000). The data suggests that transformative leadership is highly significant, with a mean score of 3.07 ± 0.817.

Human-centered design in the development of innovative technology for the food processing medium, small and micro enterprises (MSMEs) in Uganda

Taking account of end-user perspectives is crucial in the design and development process to ensure successful technology adoption considering the evolving customer demands. This study utilized the user-centric Human Center Design (HCD) framework to re-develop food processing equipment, specifically pulpers and pasteurizers, previously developed by the Department of Agricultural and Biosystems Engineering (DABE) of Makerere University. The study implemented several interventions to augment the adoption of processing equipment among MSME agro-processors in Uganda. This included development of modified pasteurizers (biomass and electrical) and pulpers, development of user manuals, and training of processors on the use of equipment. All interventions were designed with a focus on the needs and preferences of the end users. This study demonstrates how HCD can be used to develop food processing equipment that addresses the dynamic needs of micro, small, and medium-scale agro-processors in form of production capacity and cost of ownership. The study findings can influence large-scale research and policy toward the production and adoption of user-centric food processing equipment, which in turn can provide agro-processors with increased livelihoods and reduce the major challenge of post-harvest losses.