Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review.

BACKGROUND: Artificial intelligence (AI) technology has the potential to transform medical practice within the medical imaging industry and materially improve productivity and patient outcomes. However, low acceptability of AI as a digital healthcare intervention among medical professionals threatens to undermine user uptake levels, hinder meaningful and optimal value-added engagement, and ultimately prevent these promising benefits from being realised. Understanding the factors underpinning AI acceptability will be vital for medical institutions to pinpoint areas of deficiency and improvement within their AI implementation strategies. This scoping review aims to survey the literature to provide a comprehensive summary of the key factors influencing AI acceptability among healthcare professionals in medical imaging domains and the different approaches which have been taken to investigate them. METHODS: A systematic literature search was performed across five academic databases including Medline, Cochrane Library, Web of Science, Compendex, and Scopus from January 2013 to September 2023. This was done in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Overall, 31 articles were deemed appropriate for inclusion in the scoping review. RESULTS: The literature has converged towards three overarching categories of factors underpinning AI acceptability including: user factors involving trust, system understanding, AI literacy, and technology receptiveness; system usage factors entailing value proposition, self-efficacy, burden, and workflow integration; and socio-organisational-cultural factors encompassing social influence, organisational readiness, ethicality, and perceived threat to professional identity. Yet, numerous studies have overlooked a meaningful subset of these factors that are integral to the use of medical AI systems such as the impact on clinical workflow practices, trust based on perceived risk and safety, and compatibility with the norms of medical professions. This is attributable to reliance on theoretical frameworks or ad-hoc approaches which do not explicitly account for healthcare-specific factors, the novelties of AI as software as a medical device (SaMD), and the nuances of human-AI interaction from the perspective of medical professionals rather than lay consumer or business end users. CONCLUSION: This is the first scoping review to survey the health informatics literature around the key factors influencing the acceptability of AI as a digital healthcare intervention in medical imaging contexts. The factors identified in this review suggest that existing theoretical frameworks used to study AI acceptability need to be modified to better capture the nuances of AI deployment in healthcare contexts where the user is a healthcare professional influenced by expert knowledge and disciplinary norms. Increasing AI acceptability among medical professionals will critically require designing human-centred AI systems which go beyond high algorithmic performance to consider accessibility to users with varying degrees of AI literacy, clinical workflow practices, the institutional and deployment context, and the cultural, ethical, and safety norms of healthcare professions. As investment into AI for healthcare increases, it would be valuable to conduct a systematic review and meta-analysis of the causal contribution of these factors to achieving high levels of AI acceptability among medical professionals.

Frequency Domain Channel-Wise Attack to CNN Classifiers in Motor Imagery Brain-Computer Interfaces.

OBJECTIVE: Convolutional neural network (CNN), a classical structure in deep learning, has been commonly deployed in the motor imagery brain-computer interface (MIBCI). Many methods have been proposed to evaluate the vulnerability of such CNN models, primarily by attacking them using direct temporal perturbations. In this work, we propose a novel attacking approach based on perturbations in the frequency domain instead. METHODS: For a given natural MI trial in the frequency domain, the proposed approach, called frequency domain channel-wise attack (FDCA), generates perturbations at each channel one after another to fool the CNN classifiers. The advances of this strategy are two-fold. First, instead of focusing on the temporal domain, perturbations are generated in the frequency domain where discriminative patterns can be extracted for motor imagery (MI) classification tasks. Second, the perturbing optimization is performed based on differential evolution algorithm in a black-box scenario where detailed model knowledge is not required. RESULTS: Experimental results demonstrate the effectiveness of the proposed FDCA which achieves a significantly higher success rate than the baselines and existing methods in attacking three major CNN classifiers on four public MI benchmarks. CONCLUSION: Perturbations generated in the frequency domain yield highly competitive results in attacking MIBCI deployed by CNN models even in a black-box setting, where the model information is well-protected. SIGNIFICANCE: To our best knowledge, existing MIBCI attack approaches are all gradient-based methods and require details about the victim model, e.g., the parameters and objective function. We provide a more flexible strategy that does not require model details but still produces an effective attack outcome.

The Plant Defensin Ppdef1 Is a Novel Topical Treatment for Onychomycosis.

Onychomycosis, or fungal nail infection, causes not only pain and discomfort but can also have psychological and social consequences for the patient. Treatment of onychomycosis is complicated by the location of the infection under the nail plate, meaning that antifungal molecules must either penetrate the nail or be applied systemically. Currently, available treatments are limited by their poor nail penetration for topical products or their potential toxicity for systemic products. Plant defensins with potent antifungal activity have the potential to be safe and effective treatments for fungal infections in humans. The cystine-stabilized structure of plant defensins makes them stable to the extremes of pH and temperature as well as digestion by proteases. Here, we describe a novel plant defensin, Ppdef1, as a peptide for the treatment of fungal nail infections. Ppdef1 has potent, fungicidal activity against a range of human fungal pathogens, including Candida spp., Cryptococcus spp., dermatophytes, and non-dermatophytic moulds. In particular, Ppdef1 has excellent activity against dermatophytes that infect skin and nails, including the major etiological agent of onychomycosis Trichophyton rubrum. Ppdef1 also penetrates human nails rapidly and efficiently, making it an excellent candidate for a novel topical treatment of onychomycosis.

Attaining consensus on a core dataset for upper limb lymphoedema using the Delphi method: A foundational step in creating a clinical support system.

Background: Lymphoedema is a condition of localised swelling caused by a compromised lymphatic system. The protein-rich fluid accumulating in the interstitial tissue can create inflammation and irreversible changes to the skin and underlying tissue. An array of methods has been used to assess and report these changes. Heterogeneity is evident in the clinic and in the literature for the domains assessed, outcomes and outcome measures selected, measurement protocols followed, methods of analysis, and descriptors used to report change. Objective: This study seeks consensus on the required items for inclusion in a core data set for upper limb lymphoedema to digitise the monitoring and reporting of upper limb lymphoedema. Methods: The breadth of outcomes and descriptors in common use were captured in prior studies by this research group. This list was refined by frequency and proposed to experts in the field (n = 70) through a two-round online modified Delphi study. These participants rated the importance of each item for inclusion in the dataset and identified outcomes or descriptors they felt were missing in Round 1. In Round 2, participants rated any new outcomes or descriptors proposed and preference for how numeric data is displayed. Results: The core dataset was confirmed on completion of Round 2. Interlimb difference as a percentage, and limb volume were preferred for graphed display over time; and descriptors for observed and palpated change narrowed from 42 to 20. Conclusion: This dataset provides the foundation to create a clinical support system for upper limb lymphoedema.

Benchmarking the diagnostic test accuracy of certified AI products for screening pulmonary tuberculosis in digital chest radiographs: Preliminary evidence from a rapid review and meta-analysis.

BACKGROUND AND OBJECTIVE: The global market for AI systems used in lung tuberculosis (TB) detection has expanded significantly in recent years. Verifying their performance across diverse settings is crucial before medical organisations can invest in them and pursue safe, wide-scale deployment. The goal of this research was to synthesise the clinical evidence for the diagnostic accuracy of certified AI products designed for screening TB in chest X-rays (CXRs) compared to a microbiological reference standard. METHODS: Four databases were searched between June to September 2022. Data concerning study methodology, system characteristics, and diagnostic accuracy metrics was extracted and summarised. Study bias was evaluated using QUADAS-2 and by examining sources of funding. Forest plots for diagnostic odds ratio (DOR) and summary receiver operating characteristic (SROC) curves were constructed for the AI products individually and collectively. RESULTS: 10 out of 3642 studies satisfied the review criteria however only 8 were subject to meta-analysis following bias assessment. Three AI products were evaluated with a 95 % confidence interval producing the following pooled estimates for accuracy rankings: qXR v2 (sensitivity of 0.944 [0.887-0.973], specificity of 0.692 [0.549-0.805], DOR of 3.63 [3.17-4.09], Lunit INSIGHT CXR v3.1 (sensitivity of 0.853 [0.787-0.901], specificity of 0.646 [0.627-0.665], DOR of 2.37 [1.96-2.78]), and CAD4TB v3.07 (sensitivity of 0.917 [0.848-0.956], specificity of 0.371 [0.336-0.408], DOR of 1.91 [1.4-2.47]). Overall, the products had a sensitivity of 0.903 (0.859-0.934), specificity of 0.526 (0.409-0.641), and DOR of 2.31 (1.78-2.84). CONCLUSION: Current publicly available evidence indicates considerable variability in the diagnostic accuracy of available AI products although overall they have high sensitivity and modest specificity which is improving with time. These preliminary results are limited by the small number of studies and poor coverage for low TB burden settings. More research is needed to expand the clinical evidence base for the performance of AI products.

Patient and Clinician Perceptions of the Pulse Oximeter in a Remote Monitoring Setting for COVID-19: Qualitative Study.

BACKGROUND: As a response to the COVID-19 pandemic, the Sydney Local Health District in New South Wales, Australia, launched the rpavirtual program, the first full-scale virtual hospital in Australia, to remotely monitor and follow up stable patients with COVID-19. As part of the intervention, a pulse oximeter wearable device was delivered to patients to monitor their oxygen saturation levels, a critical indicator of COVID-19 patient deterioration. Understanding users' perceptions toward the device is fundamental to assessing its usability and acceptability and contributing to the effectiveness of the intervention, but no research to date has explored the user experience of the pulse oximeter for remote monitoring in this setting. OBJECTIVE: This study aimed to explore the use, performance, and acceptability of the pulse oximeter by clinicians and patients in rpavirtual during COVID-19. METHODS: Semistructured interviews and usability testing were conducted. Stable adult patients with COVID-19 (aged ≥18 years) who used the pulse oximeter and were monitored by rpavirtual, and rpavirtual clinicians monitoring these patients were interviewed. Clinicians could be nurses, doctors, or staff who were part of the team that assisted patients with the use of the pulse oximeter. Usability testing was conducted with patients who had the pulse oximeter when they were contacted. Interviews were coded using the Theoretical Framework of Acceptability. Usability testing was conducted using a think-aloud protocol. Data were collected until saturation was reached. RESULTS: Twenty-one patients (average age 51, SD 13 years) and 15 clinicians (average age 41, SD 11 years) completed the interview. Eight patients (average age 51, SD 13 years) completed the usability testing. All participants liked the device and thought it was easy to use. They also had a good understanding of how to use the device and the device's purpose. Patients' age and device use-related characteristics (eg, the warmth of hands and hand steadiness) were identified by users as factors negatively impacting the accurate use of the pulse oximeter. CONCLUSIONS: Patients and clinicians had very positive perceptions of the pulse oximeter for COVID-19 remote monitoring, indicating high acceptability and usability of the device. However, factors that may impact the accuracy of the device should be considered when delivering interventions using the pulse oximeter for remote monitoring. Targeted instructions about the use of the device may be necessary for specific populations (eg, older people and patients unfamiliar with technology). Further research should focus on the integration of the pulse oximeter data into electronic medical records for real-time and secure patient monitoring.

The Impact of an Electronic Medication Management System on Medication Deviations on Admission and Discharge from Hospital.

Medication errors at transition of care remain a concerning issue. In recent times, the use of integrated electronic medication management systems (EMMS) has caused a reduction in medication errors, but its effectiveness in reducing medication deviations at transition of care has not been studied in hospital-wide settings in Australia. The aim of this study is to assess medication deviations, such as omissions and mismatches, pre-EMMS and post-EMMS implementation at transition of care across a hospital. In this study, patient records were reviewed retrospectively to identify medication deviations (medication omissions and medication mismatches) at admission and discharge from hospital. A total of 400 patient records were reviewed (200 patients in the pre-EMMS and 200 patients in the post-EMMS group). Out of 400 patients, 112 in the pre-EMMS group and 134 patients in post-EMMS group met the inclusion criteria and were included in the analysis. A total of 105 out of 246 patients (42.7%) had any medication deviations on their medications. In the pre-EMMS group, 59 out of 112 (52.7%) patients had any deviations on their medications compared to 46 out of 134 patients (34.3%) from the post-EMMS group (p = 0.004). The proportion of patients with medication omitted from inpatient orders was 36.6% in the pre-EMMS cohort vs. 22.4% in the post-EMMS cohort (p = 0.014). Additionally, the proportion of patients with mismatches in medications on the inpatient charts compared to their medication history was 4.5% in the pre-EMMS group compared to 0% in the post-EMMS group (p = 0.019). Similarly, the proportion of patients with medications omitted from their discharge summary was 23.2% in the pre-EMMS group vs. 12.7% in the post-EMMS group (p = 0.03). Our study demonstrates a reduction in medication deviations after the implementation of the EMMS in hospital settings.

Phenonizer: A Fine-Grained Phenotypic Named Entity Recognizer for Chinese Clinical Texts.

Biomedical named entity recognition (BioNER) from clinical texts is a fundamental task for clinical data analysis due to the availability of large volume of electronic medical record data, which are mostly in free text format, in real-world clinical settings. Clinical text data incorporates significant phenotypic medical entities (e.g., symptoms, diseases, and laboratory indexes), which could be used for profiling the clinical characteristics of patients in specific disease conditions (e.g., Coronavirus Disease 2019 (COVID-19)). However, general BioNER approaches mostly rely on coarse-grained annotations of phenotypic entities in benchmark text dataset. Owing to the numerous negation expressions of phenotypic entities (e.g., "no fever," "no cough," and "no hypertension") in clinical texts, this could not feed the subsequent data analysis process with well-prepared structured clinical data. In this paper, we developed Human-machine Cooperative Phenotypic Spectrum Annotation System (http://www.tcmai.org/login, HCPSAS) and constructed a fine-grained Chinese clinical corpus. Thereafter, we proposed a phenotypic named entity recognizer: Phenonizer, which utilized BERT to capture character-level global contextual representation, extracted local contextual features combined with bidirectional long short-term memory, and finally obtained the optimal label sequences through conditional random field. The results on COVID-19 dataset show that Phenonizer outperforms those methods based on Word2Vec with an F1-score of 0.896. By comparing character embeddings from different data, it is found that character embeddings trained by clinical corpora can improve F-score by 0.0103. In addition, we evaluated Phenonizer on two kinds of granular datasets and proved that fine-grained dataset can boost methods' F1-score slightly by about 0.005. Furthermore, the fine-grained dataset enables methods to distinguish between negated symptoms and presented symptoms. Finally, we tested the generalization performance of Phenonizer, achieving a superior F1-score of 0.8389. In summary, together with fine-grained annotated benchmark dataset, Phenonizer proposes a feasible approach to effectively extract symptom information from Chinese clinical texts with acceptable performance.

Patient and clinician use characteristics and perceptions of pulse oximeter use: A scoping review.

BACKGROUND AND OBJECTIVES: The need to monitor patients outside of a formal clinical setting, such as a hospital or ambulatory care facility, has become increasingly important since COVID-19. It introduces significant challenges to ensure accurate and timely measurements, maintain strong patient engagement, and operationalise data for clinical decision-making. Remote Patient Monitoring (RPM) devices like the pulse oximeter help mitigate these difficulties, however, practical approaches to successfully integrate this technology into existing patient-clinician interactions that ensure the delivery of safe and effective care are vital. The objective of this scoping review was to synthesise existing literature to provide an overview of the variety of user perceptions associated with pulse oximeter devices, which may impact patients' and clinicians' acceptance of the devices in a RPM context. METHODS: A search over three databases was conducted between April 2021 - June 2021 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines. A total of 16 articles were included in this scoping review. RESULTS: Results indicate there has been an increase in use of pulse oximeters across hospital and community settings for continuous vital signs monitoring and remote monitoring of patients over time. Research in this area is shifting towards increasing accessibility of care through the development and implementation of telehealth systems and phone oximeters. Aspects of pulse oximeter UX most frequently investigated are usability and acceptability, however, these terms are often undefined, or definitions vary across studies. Perceived effectiveness, opportunity costs, and attitude towards use remain unexplored areas of UX. Overall, patients and clinicians view the pulse oximeter positively and find it user-friendly. A high level of learnability was found for the device and additional benefits included increasing patient self-efficacy and clinician motivation to work. However, issues getting an accurate reading due to device usability are still experienced by some patients and clinicians. CONCLUSION: This scoping review is the first to summarise user perceptions of the pulse oximeter in a healthcare context. It showed that both patients and clinicians hold positive perceptions of the pulse oximeter and important factors to consider in designing user-focused services include ease-of-use and wearability of devices; context of use including user's prior health and IT knowledge; attitude towards use and perceived effectiveness; impact on user motivation and self-efficacy; and finally, potential user costs like inconvenience or increased anxiety. With the rapid increase in research studies examining pulse oximeter use for RPM since COVID-19, a systematic review is warranted as the next step to consolidate evidence and investigate the impact of these factors on pulse oximeter acceptance and effectiveness.

Process mining for healthcare: Characteristics and challenges.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

Developing a maturity model for cancer multidisciplinary teams.

BACKGROUND: Multidisciplinary teams (MDTs) are considered the "gold standard" of care for patients with cancer but how well they function and the role they play in decision making varies widely. Although several observational tools have been developed to evaluate MDT performance, they are resource intensive and only assess MDT performance at a static point in time. The aim of this study was to develop a validated maturity model as a self-assessment instrument for MDTs to evaluate their performance and monitor improvement over time. METHODS: The authors used a three-phase methodology to develop a maturity model. In the first phase, using a modified Delphi technique, we identified 20 indicators (within five components), each having five levels of maturity [1]. In the second phase, further Delphi iterations were undertaken to refine the content and structure of the model. By the end of the second phase six components and 17 indicators had been established. In the third phase, the refined model was distributed to members from 11 MDTs to test for validity and reliability. 101 valid responses were received. Principal Component Analysis was used to determine the optimal number of components that fit the indicators. Factors with eigenvalue greater than one were extracted. Cronbach's alpha (α) was used to measure the internal consistency of components. Bivariate correlation analysis, measuring pair-wise relationships between indicators (r), was undertaken to assess convergent and discriminant validity. RESULTS: Five factors were extracted from Principal Component Analysis. For the factors extracted, 16 out of 17 indicators showed loadings greater than the 0.4 threshold. All components demonstrated good levels of internal consistency (α > 0.8) and convergent validity (r > 0.6). Discriminant validity cannot be established. Ratings for ease of use (3.6/5) and usefulness (3.4/5) were considered acceptable. CONCLUSIONS: Further work is required to establish discriminant validity and refine the components and indicators. Once further refinement and validation are completed, the maturity model should be a simple tool for MDTs to measure their performance and monitor improvement over time.

Implementation of an Electronic Medication Management System in a large tertiary hospital: a case of qualitative inquiry.

BACKGROUND: Hospitals across Australia are implementing Clinical Information Systems, e.g. Electronic Medication Management Systems (EMMS) at a rapid pace to moderate health services. The benefits of the EMMS depend on the acceptance of the system by the clinicians. The study hospital used a unique patient-centric implementation strategy that was based on the guiding principle of "one patient, one chart" to avoid a patient being on a hybrid medication chart. This paper aims to study the factors facilitating or hindering the adoption of the EMMS as viewed by clinicians and the implementation team. METHODS: Four focus groups (FG), one each for (1) doctors, (2) nurses, (3) pharmacists, and (4) implementation team, were conducted. A guide for the FG was based on the Unified Theory of Acceptance and Use of Technology (UTAUT). RESULTS: A total of 23 unique subthemes were identified and were grouped into five main themes (1) implementation strategy, (2) organisational outcome of EMMS, (3) individual impact of EMMS, (4) IT product, and (5) organisational culture. Clinicians reported improvement in their workflow efficiency post-EMMS implementation. They also reported some challenges in using the EMMS that centered around the area of infrastructure, technical and design issues. Additionally, the implementation team highlighted two crucial factors influencing the success of EMMS implementation, namely: (1) the patient-centric implementation strategy, and (2) the organisation readiness. CONCLUSION: Overall, this study outlines the implementation process of the EMMS in a large healthcare facility from the clinicians' and the implementation team's perspectives using UTAUT model. The result suggests that clinicians' acceptance of the EMMS was highly influenced by the unique implementation strategy (namely, patient-centric approach and clinical leadership in the implementation team). Whereas the level of adoption of EMMS by clinicians was determined by their level of perceived and realised benefits. On the other hand, a number of barriers to the adoption of EMMS were discovered, namely, general training instead of customised training based on local needs, technical and design issues and lack of availability of computer systems. It is suggested that promptly resolving these issues can improve the adoption of the EMMS.

A novel method for clinical risk prediction with low-quality data.

In real-world data, predictive models for clinical risks (such as adverse drug reactions, hospital readmission, and chronic disease onset) are constantly struggling with low-quality issues, namely redundant and highly correlated features, extreme category imbalances, and most importantly, a large number of missing values. In most existing work, each patient is represented as a value vector with the fixed-length from some feature space, and missing values are forced to be imputed, which introduces much noise for prediction if the data set is highly incomplete. Besides, other challenges are either remaining unresolved or only partially solved when modeling, but without a systematic approach. In this paper, we propose a novel framework to address these low-quality problems, that we first treat patients as bags with the various number of feature-value pairs, called instances, and map them to an embedding space through our proposed feature embedding method to learn from it directly. In this way, predictive models can avoid the negative impact of missing data naturally. A novel multi-instance neural network is then connected, using two computational modules to deal with the problems of correlated and redundant features: multi-head attention and attention-based multi-instance pooling. They are capable of capturing the instance correlations and locating valuable information in each instance or bag. The feature embedding and multi-instance neural network are parameterized and optimized jointly in an end-to-end manner. Moreover, the training process is under both main and auxiliary supervision with focal loss functions to avoid the caveat of a highly imbalanced label set. This proposed framework is named AMI-Net3. We evaluate it on three suitable data sets from real-world settings with different clinical risk prediction tasks: adverse drug reaction of risperidone, schizophrenia relapse, and invasive fungi infection, respectively. The comprehensive experimental results demonstrate the effectiveness and superiority of our proposed method over competitive baselines.

Compliance with Standard Precautions and Its Relationship with Views on Infection Control and Prevention Policy among Healthcare Workers during COVID-19 Pandemic.

Background: Standard precautions prevent the spread of infections in healthcare settings. Incompliance with infection control guidelines of healthcare workers (HCWs) may increase their risk of exposure to infectious disease, especially under pandemics. The purpose of this study was to assess the level of compliance with the infection prevention and control practices among HCWs in different healthcare settings and its relationship with their views on workplace infection control measures during the COVID-19 pandemic. Methods: Nurses in Hong Kong were invited to respond to a cross-sectional online survey, in which their views on workplace infection and prevention policy, compliance with standard precautions and self-reported health during pandemics were collected. Results: The respondents were dissatisfied with workplace infection and prevention policy in terms of comprehensiveness (62%), clarity (64%), timeliness (63%), and transparency (60%). For the protective behavior, the respondents did not fully comply with the standard precautions when they were involved in medical care. Their compliance was relatively low when having proper patient handling (54%) and performing invasive procedures (46%). A multivariate analysis model proved that the level of compliance of the standard precautions was positively associated with the satisfaction on infection control and prevention policy among high risk group (0.020; 95% CI: 0.005-0.036), while older respondents had higher level of compliance among the inpatient and outpatient groups (coefficient range: 0.065-0.076). The higher level of compliance was also significantly associated with working in designated team and having chronic condition of the respondents among high-risk and inpatient groups. Conclusions: Standard precautions are the most important elements to reduce cross-transmission among HCWs and patients while the satisfaction on infection control and prevention policy would increase the compliance among the high-risk group. An overall suboptimal compliance and poor views on the infection prevention and control guidelines is a warning signal to healthcare system especially during pandemics.

Usability evaluation of an integrated electronic medication management system implemented in an oncology setting using the unified theory of the acceptance and use of technology.

BACKGROUND: Medication management processes in an Oncology setting are complex and difficult to examine in isolation from interrelated processes and contextual factors. This qualitative study aims to evaluate the usability of an Electronic Medication Management System (EMMS) implemented in a specialised oncology unit using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. METHODS: The study was conducted in a 12-bed outpatient Oncology unit of a major teaching hospital 6 months following implementation of a commercial EMMS. In-depth semi-structured interviews were conducted with doctors, nurses and pharmacists using the system to assess usability. The UTAUT framework was used to analyse the results, which facilitated evaluation of interrelated aspects and provided a structured summary of user experience and usability factors. RESULTS: Direct cross-comparison between user groups illustrated that doctors and pharmacists were generally satisfied with the facilitating conditions (hardware and training), but had divergent perceptions of performance (automation, standardised protocols and communication and documented) and effort (mental and temporal demand) expectancy. In counterpoint, nurses were generally satisfied across all constructs. Prior experience using an alternative EMMS influenced performance and effort expectancy and was related to early dissatisfaction with the EMMS. Furthermore, whilst not originally designed for the healthcare setting, the flexibility of the UTAUT allowed for translation to the hospital environment. CONCLUSION: Nurses demonstrated overall satisfaction with the EMMS, whilst doctors and pharmacists perceived usability problems, particularly related to restricted automaticity and system complexity, which hindered perceived EMMS success. The study demonstrates the feasibility and utility of the UTAUT framework to evaluate usability of an EMMS for multiple user groups in the Oncology setting.

Assessment of Upper Limb Lymphedema: A Qualitative Study Exploring Clinicians' Clinical Reasoning.

Background: A variety of objective and subjective assessments are available for clinical assessment of lymphedema. The aim of this study was to explore the clinical reasoning underpinning the assessment of upper limb lymphedema by experienced lymphedema clinicians. Methods and Results: Semistructured, individual, interviews were conducted with lymphedema therapists (n = 14) from a variety of treatment settings. These interviews were conducted after observations of these therapists assessing patients with lymphedema and focused on: (1) the therapists' rationale for the assessments selected, (2) how the data were analyzed, and (3) how the information was then used. Assessment selection was guided by the purpose of the visit, patient preference, resources, and time available. Subjective measures of visible and palpated tissue changes were used to target treatment, and objective measures of circumference and bioimpedance spectroscopy and patient report of symptoms informed treatment evaluation and disease progression. Objective data collected were primarily analyzed for interlimb difference and change between appointments. Conclusions: A range of clinical assessments were used in the evaluation of lymphedema to detect the presence of lymphedema, estimate the extent of soft tissue change, understand the patient experience of lymphedema, and evaluate treatment response. A primary determinant for the collection of objective measures was the appointment duration. Current methods of data analysis and reporting do not facilitate the review of change over time.

Medication safety improvements during care transitions in an Australian intensive care unit following implementation of an electronic medication management system.

BACKGROUND: For patients requiring admission to the Intensive Care Unit (ICU), transfers of care (TOC) during admission to and discharge from the ICU are particularly high-risk periods for medication errors. In the Australian setting, commonly general wards and the ICU do not share an integrated Electronic Medical ecord (EMR) and specifically an Electronic Medication Management System (EMMS) as part of the EMR. PURPOSE: To evaluate the effect of a hospital wide integrated EMMS on medication error rates during ICU admission and at TOC. METHOD: A 6-month historical control study was performed before and after implementation of the EMMS in the ICU of a tertiary hospital. Prescribing errors detected by pharmacists in the study period were divided into phase 1, (pre-EMMS, 6months), phase 2 (3 months post implementation after shakedown stage) and phase 3 (next 3 months of post implementation). They were categorized as prescribing error types under system or clinical intervention. Chi square statistics and interrupted time series analysis were used to determine if there was significant change in the proportion of patients who had an error at TOC during each phase. Logistics regression was used to determine the relationship between the dependent (error type) and the independent variable (study phase) for errors that occurred during TOC. RESULTS: Errors occurred during TOC in 42 %, 64 % and 19 % of patients in phase 1, 2 and 3 respectively. There was a significant decline in the proportion of patients with an error between phase 1 and 3 (p < 0.01). During a patient's ICU admission, at least one medication error occurred in 28.3 %, 62.6 % and 25.1 % in phase 1, 2 and 3 respectively. Besides procedural errors, the likelihood of an error occurring was greatest in phase 1, compared to phase 2 and 3 across system-related error categories. CONCLUSION: Medication errors during TOC reduced following implementation of the integrated ICU EMMS. EMMS safety features facilitated reduced system related prescribing errors as well as the severity of errors made.

Clinician Assessment of Upper Limb Lymphedema: An Observational Study.

Background: Clinical management of lymphedema requires assessment, initially for detection, and then for determining treatment response and informing the treatment plan. It is unknown how the components of a lymphedema assessment are used in a clinical environment. Methods and Results: Experienced lymphedema therapists were observed assessing patients presenting with new or existing upper body lymphedema. Occupational and physiotherapists specializing in lymphedema management (n = 14) from public and private, rural and urban settings in Australia were visited at their work sites and observed with a minimum of two patients. In total, 37 upper limb assessments were observed. Reasons for attendance included: initial assessment with new swelling (n = 4); screening/detection for possible lymphedema (n = 3); bandaging as part of an intensive treatment program (n = 2); and review (n = 28). Clinicians were observed, in order of frequency, using (1) patient-reported outcomes, (2) palpation, (3) visual assessment, (4) assessment of limb size using circumference measurements, and (5) assessment of extracellular fluid using bioimpedance spectroscopy. Although clinicians selected similar assessments, differences were observed in the measurement protocols and informed reported. Objective assessment was commonly absent when the time available for an appointment was 30 minutes. Conclusions: While clinicians spent a significant portion of an appointment time assessing the limb, a standardized approach to the assessment of lymphedema was not observed. In the absence of a standardized assessment set, therapists have developed bespoke assessment routines.

Improving the Digestibility of Plant Defensins to Meet Regulatory Requirements for Transgene Products in Crop Protection.

Despite the use of chemical fungicides, fungal diseases have a major impact on the yield and quality of plant produce globally and hence there is a need for new approaches for disease control. Several groups have examined the potential use of antifungal plant defensins for plant protection and have produced transgenic plants expressing plant defensins with enhanced resistance to fungal disease. However, before they can be developed commercially, transgenic plants must pass a series of strict regulations to ensure that they are safe for human and animal consumption as well as the environment. One of the requirements is rapid digestion of the transgene protein in the gastrointestinal tract to minimize the risk of any potential allergic response. Here, we examine the digestibility of two plant defensins, NaD1 from Nicotiana alata and SBI6 from soybean, which have potent antifungal activity against major cereal pathogens. The native defensins were not digestible in simulated gastrointestinal fluid assays. Several modifications to the sequences enhanced the digestibility of the two small proteins without severely impacting their antifungal activity. However, these modified proteins did not accumulate as well as the native proteins when transiently expressed in planta, suggesting that the protease-resistant structure of plant defensins facilitates their stability in planta.

Machine Learning Clustering for Blood Pressure Variability Applied to Systolic Blood Pressure Intervention Trial (SPRINT) and the Hong Kong Community Cohort.

Visit-to-visit blood pressure variability (BPV) has been shown to be a predictor of cardiovascular disease. We aimed to classify the BPV levels using different machine learning algorithms. Visit-to-visit blood pressure readings were extracted from the SPRINT study in the United States and eHealth cohort in Hong Kong (HK cohort). Patients were clustered into low, medium, and high BPV levels with the traditional quantile clustering and 5 machine learning algorithms including K-means. Clustering methods were assessed by Stability Index. Similarities were assessed by Davies-Bouldin Index and Silhouette Index. Cox proportional hazard regression models were fitted to compare the risk of myocardial infarction, stroke, and heart failure. A total of 8133 participants had average blood pressure measurement 14.7 times in 3.28 years in SPRINT and 1094 participants who had average blood pressure measurement 165.4 times in 1.37 years in HK cohort. Quantile clustering assigned one-third participants as high BPV level, but machine learning methods only assigned 10% to 27%. Quantile clustering is the most stable method (stability index: 0.982 in the SPRINT and 0.948 in the HK cohort) with some levels of clustering similarities (Davies-Bouldin Index: 0.752 and 0.764, respectively). K-means clustering is the most stable across the machine learning algorithms (stability index: 0.975 and 0.911, respectively) with the lowest clustering similarities (Davies-Bouldin Index: 0.653 and 0.680, respectively). One out of 7 in the population was classified with high BPV level, who showed to have higher risk of stroke and heart failure. Machine learning methods can improve BPV classification for better prediction of cardiovascular diseases.