Integrated diabetes care: The Association of British Clinical Diabetologists (ABCD) national survey report

Executive Summary A national survey on integrated diabetes services was carried out by the Association of British Clinical Diabetologists (ABCD) during the COVID-19 pandemic and has provided some very useful insights into the current state of integration to deliver a joined-up diabetes service in the UK. This survey was carried out during the second half of 2020 and explored three main areas: (1) current state of clinical integration between primary and secondary (specialist) diabetes services; (2) the state of IT integration among the diabetes IT systems and hospital-based electronic patient records (EPR) and between hospital and primary care; (3) to ascertain the membership of their views on a ‘one-stop service’ for collecting annual review data for diabetes and the potential barriers to achieve this. The results presented are a summary of the survey, while the full unedited survey report, especially on the qualitative aspects, is available to ABCD members. The survey was mailed to 518 individuals, of which 431 (83.2%) were consultants and 53 (10.2%) were specialist registrars. Of the 83 replies received, 98% were from consultants and the responses represented a total of 73 hospital diabetes services. The findings of this survey revealed that full integration of clinical services among primary care and specialist diabetes teams is uncommon, although there are good examples of clinical integration in different formats. In a number of areas, primary care and specialist diabetes services continue to work in silos despite a universal recognition that integrated services are desirable and are likely to improve quality of care. Clinical leadership, resources and buy-in from those who commission services were deemed important factors to help improve the development of integrated care systems. In hospitals with dedicated diabetes IT systems the information flow from these diabetes systems to the EPR was not universal, raising concerns that vital information about an individual’s diabetes may not be available to other hospital clinical specialities at the time of delivery of care, posing a significant clinical risk. IT integration among primary and specialist diabetes teams in England was only available in certain areas and was mostly based around the use of SystmOne. The survey also identified a diversity of opinions regarding the current arrangements of the Quality Outcome Framework (QOF), where GPs are incentivised to collect data for annual review of routine diabetes care. Many were of the opinion that annual review processes should be performed by clinical teams who are tasked to deliver diabetes care to the individual, while others felt that the status quo should continue with primary care GPs being responsible. A one-stop service for eye screening for diabetes and other annual measurements nearer to people’s homes was identified as an improvement, but several logistic barriers were identified. We recognise the limitations of any survey which expresses opinions of participants. However, we believe the present survey represents a significant proportion of diabetes units in the UK and provides insights into the current state of integrated services in diabetes. There are significant learnings for diabetes communities, and the information can be used to improve and galvanise delivery of integrated diabetes care in the UK.

Beauty Is in the AI of the Beholder: Are We Ready for the Clinical Integration of Artificial Intelligence in Radiography? An Exploratory Analysis of Perceived AI Knowledge, Skills, Confidence, and Education Perspectives of UK Radiographers

Introduction: The use of artificial intelligence (AI) in medical imaging and radiotherapy has been met with both scepticism and excitement. However, clinical integration of AI is already well-underway. Many authors have recently reported on the AI knowledge and perceptions of radiologists/medical staff and students however there is a paucity of information regarding radiographers. Published literature agrees that AI is likely to have significant impact on radiology practice. As radiographers are at the forefront of radiology service delivery, an awareness of the current level of their perceived knowledge, skills, and confidence in AI is essential to identify any educational needs necessary for successful adoption into practice.Aim: The aim of this survey was to determine the perceived knowledge, skills, and confidence in AI amongst UK radiographers and highlight priorities for educational provisions to support a digital healthcare ecosystem.Methods: A survey was created on Qualtrics® and promoted via social media (Twitter®/LinkedIn®). This survey was open to all UK radiographers, including students and retired radiographers. Participants were recruited by convenience, snowball sampling. Demographic information was gathered as well as data on the perceived, self-reported, knowledge, skills, and confidence in AI of respondents. Insight into what the participants understand by the term “AI” was gained by means of a free text response. Quantitative analysis was performed using SPSS® and qualitative thematic analysis was performed on NVivo®.Results: Four hundred and eleven responses were collected (80% from diagnostic radiography and 20% from a radiotherapy background), broadly representative of the workforce distribution in the UK. Although many respondents stated that they understood the concept of AI in general (78.7% for diagnostic and 52.1% for therapeutic radiography respondents, respectively) there was a notable lack of sufficient knowledge of AI principles, understanding of AI terminology, skills, and confidence in the use of AI technology. Many participants, 57% of diagnostic and 49% radiotherapy respondents, do not feel adequately trained to implement AI in the clinical setting. Furthermore 52% and 64%, respectively, said they have not developed any skill in AI whilst 62% and 55%, respectively, stated that there is not enough AI training for radiographers. The majority of the respondents indicate that there is an urgent need for further education (77.4% of diagnostic and 73.9% of therapeutic radiographers feeling they have not had adequate training in AI), with many respondents stating that they had to educate themselves to gain some basic AI skills. Notable correlations between confidence in working with AI and gender, age, and highest qualification were reported.Conclusion: Knowledge of AI terminology, principles, and applications by healthcare practitioners is necessary for adoption and integration of AI applications. The results of this survey highlight the perceived lack of knowledge, skills, and confidence for radiographers in applying AI solutions but also underline the need for formalised education on AI to prepare the current and prospective workforce for the upcoming clinical integration of AI in healthcare, to safely and efficiently navigate a digital future. Focus should be given on different needs of learners depending on age, gender, and highest qualification to ensure optimal integration.

An Unsupervised Machine Learning Paradigm for Artifact Removal from Electrodermal Activity in an Uncontrolled Clinical Setting

Objective: Electrodermal activity (EDA) reflects sympathetic nervous system activity through sweating-related changes in skin conductance. To enable EDA data to be used robustly in clinical settings, we need to develop artifact detection and removal frameworks that can handle the types of interference experienced in clinical settings. Methods: We collected EDA data from 69 subjects while they were undergoing surgery in the operating room. We then built an artifact removal framework using unsupervised learning methods and informed features to remove the heavy artifact that resulted from the use of surgical electrocautery during the surgery and compared it to other existing methods for artifact removal from EDA data. Results: Our framework was able to remove the vast majority of artifact from the EDA data across all subjects with high sensitivity (94%) and specificity (90%). In contrast, existing methods used for comparison struggled to be sufficiently sensitive and specific, and none effectively removed artifact even if it was identifiable. In addition, the use of unsupervised learning methods in our framework removes the need for manually labeled datasets for training. Conclusion: Our framework allows for robust removal of heavy artifact from EDA data in clinical settings such as surgery. Since this framework only relies on a small set of informed features, it can be expanded to other modalities such as ECG and EEG. Significance: Robust artifact removal from EDA data is the first step to enable clinical integration of EDA as part of standard monitoring in settings such as the operating room.

Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiology

Artificial intelligence (AI) is a branch of computer science with a variety of subfields and techniques, exploited to serve as a deductive tool that performs tasks originally requiring human cognition. AI tools and its subdomains are being incorporated into healthcare delivery for the improvement of medical data interpretation encompassing clinical management, diagnostics, and prognostic outcomes. In the field of neuroradiology, AI manifested through deep machine learning and connected neural networks (CNNs) has demonstrated incredible accuracy in identifying pathology and aiding in diagnosis and prognostication in several areas of neurology and neurosurgery. In this literature review, we survey the available clinical data highlighting the utilization of AI in the field of neuroradiology across multiple neurological and neurosurgical subspecialties. In addition, we discuss the emerging role of AI in neuroradiology, its strengths and limitations, as well as future needs in strengthening its role in clinical practice. Our review evaluated data across several subspecialties of neurology and neurosurgery including vascular neurology, spinal pathology, traumatic brain injury (TBI), neuro-oncology, multiple sclerosis, Alzheimer’s disease, and epilepsy. AI has established a strong presence within the realm of neuroradiology as a successful and largely supportive technology aiding in the interpretation, diagnosis, and even prognostication of various pathologies. More research is warranted to establish its full scientific validity and determine its maximum potential to aid in optimizing and providing the most accurate imaging interpretation.

Fostering intrinsic motivation in remote undergraduate histopathology education

AimsThe levels of abstraction, vast vocabulary and high cognitive load present significant challenges in undergraduate histopathology education. Self-determination theory describes three psychological needs which promote intrinsic motivation. This paper describes, evaluates and justifies a remotely conducted, post-COVID-19 histopathology placement designed to foster intrinsic motivation.Methods90 fourth-year medical students took part in combined synchronous and asynchronous remote placements integrating virtual microscopy into complete patient narratives through Google Classroom, culminating in remote, simulated multidisciplinary team meeting sessions allowing participants to vote on ‘red flag’ signs and symptoms, investigations, histological diagnoses, staging and management of simulated virtual patients. The placement was designed to foster autonomy, competence and relatedness, generating authenticity, transdisciplinary integration and clinical relevance. A postpositivistic evaluation was undertaken with a validated preplacement and postplacement questionnaire capturing quantitative and qualitative data.ResultsThere was a significant (p<0.001) improvement in interest, confidence and competence in histopathology. Clinical integration and relevance, access to interactive resources and collaborative learning promoted engagement and sustainability post-COVID-19. Barriers to online engagement included participant lack of confidence and self-awareness in front of peers.ConclusionsFostering autonomy, competence and relatedness in post-COVID-19, remote educational designs can promote intrinsic motivation and authentic educational experiences. Ensuring transdisciplinary clinical integration, the appropriate use of novel technology and a focus on patient narratives can underpin the relevance of undergraduate histopathology education. The presentation of normal and diseased tissue in this way can serve as an important mode for the acquisition and application of clinically relevant knowledge expected of graduates.

Social Features of Integration in Health Systems and Their Relationship to Provider Experience, Care Quality and Clinical Integration

More is known about the structural features of health system integration than the social features—elements of normative integration (alignment of norms) and interpersonal integration (collaboration among professionals and with patients). We surveyed practice managers and 1,360 staff and physicians at 59 practice sites within 17 health systems (828 responses; 61%). Building on prior theory, we developed and established the psychometric properties of survey measures describing normative and interpersonal integration. Normative and interpersonal integration were both consistently related to better provider experience, perceived care quality, and clinical integration (e.g., a 1-point increase in a practice’s normative integration was associated with 0.53-point higher job satisfaction and 0.77-point higher perceived care quality in the practice, measured on 1 to 5 scales, p < .01). Variation in social features of integration may help explain why some health systems better integrate care, pointing to normative and interpersonal integration as potential resources for improvement.