Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies Using AI (QUADAS-AI): Protocol for a Qualitative Study.

BACKGROUND: Quality assessment of diagnostic accuracy studies (QUADAS), and more recently QUADAS-2, were developed to aid the evaluation of methodological quality within primary diagnostic accuracy studies. However, its current form, QUADAS-2 does not address the unique considerations raised by artificial intelligence (AI)-centered diagnostic systems. The rapid progression of the AI diagnostics field mandates suitable quality assessment tools to determine the risk of bias and applicability, and subsequently evaluate translational potential for clinical practice. OBJECTIVE: We aim to develop an AI-specific QUADAS (QUADAS-AI) tool that addresses the specific challenges associated with the appraisal of AI diagnostic accuracy studies. This paper describes the processes and methods that will be used to develop QUADAS-AI. METHODS: The development of QUADAS-AI can be distilled into 3 broad stages. Stage 1-a project organization phase had been undertaken, during which a project team and a steering committee were established. The steering committee consists of a panel of international experts representing diverse stakeholder groups. Following this, the scope of the project was finalized. Stage 2-an item generation process will be completed following (1) a mapping review, (2) a meta-research study, (3) a scoping survey of international experts, and (4) a patient and public involvement and engagement exercise. Candidate items will then be put forward to the international Delphi panel to achieve consensus for inclusion in the revised tool. A modified Delphi consensus methodology involving multiple online rounds and a final consensus meeting will be carried out to refine the tool, following which the initial QUADAS-AI tool will be drafted. A piloting phase will be carried out to identify components that are considered to be either ambiguous or missing. Stage 3-once the steering committee has finalized the QUADAS-AI tool, specific dissemination strategies will be aimed toward academic, policy, regulatory, industry, and public stakeholders, respectively. RESULTS: As of July 2024, the project organization phase, as well as the mapping review and meta-research study, have been completed. We aim to complete the item generation, including the Delphi consensus, and finalize the tool by the end of 2024. Therefore, QUADAS-AI will be able to provide a consensus-derived platform upon which stakeholders may systematically appraise the methodological quality associated with AI diagnostic accuracy studies by the beginning of 2025. CONCLUSIONS: AI-driven systems comprise an increasingly significant proportion of research in clinical diagnostics. Through this process, QUADAS-AI will aid the evaluation of studies in this domain in order to identify bias and applicability concerns. As such, QUADAS-AI may form a key part of clinical, governmental, and regulatory evaluation frameworks for AI diagnostic systems globally. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58202.

Evaluating the diagnostic and triage performance of digital and online symptom checkers for the presentation of myocardial infarction; A retrospective cross-sectional study.

Online symptom checkers are increasingly popular health technologies that enable patients to input their symptoms to produce diagnoses and triage advice. However, there is concern regarding the performance and safety of symptom checkers in diagnosing and triaging patients with life-threatening conditions. This retrospective cross-sectional study aimed to evaluate and compare commercially available symptom checkers for performance in diagnosing and triaging myocardial infarctions (MI). Symptoms and biodata of MI patients were inputted into 8 symptom checkers identified through a systematic search. Anonymised clinical data of 100 consecutive MI patients were collected from a tertiary coronary intervention centre between 1st January 2020 to 31st December 2020. Outcomes included (1) diagnostic sensitivity as defined by symptom checkers outputting MI as the primary diagnosis (D1), or one of the top three (D3), or top five diagnoses (D5); and (2) triage sensitivity as defined by symptom checkers outputting urgent treatment recommendations. Overall D1 sensitivity was 48±31% and varied between symptom checkers (range: 6-85%). Overall D3 and D5 sensitivity were 73±20% (34-92%) and 79±14% (63-94%), respectively. Overall triage sensitivity was 83±13% (55-91%). 24±16% of atypical cases had a correct D1 though for female atypical cases D1 sensitivity was only 10%. Atypical MI D3 and D5 sensitivity were 44±21% and 48±24% respectively and were significantly lower than typical MI cases (p<0.01). Atypical MI triage sensitivity was significantly lower than typical cases (53±20% versus 84±15%, p<0.01). Female atypical cases had significantly lower diagnostic and triage sensitivity than typical female MI cases (p<0.01).Given the severity of the pathology, the diagnostic performance of symptom checkers for correctly diagnosing an MI is concerningly low. Moreover, there is considerable inter-symptom checker performance variation. Patients presenting with atypical symptoms were under-diagnosed and under-triaged, especially if female. This study highlights the need for improved clinical performance, equity and transparency associated with these technologies.

The cost of living crisis - how does it impact the health and life of individuals? A survey exploring perceptions in Italy, Germany, Sweden and the United Kingdom.

BACKGROUND: The Cost of Living Crisis (CoLC), a real term reduction in basic income, risks individuals being unable to afford essentials such as heat, food and clothing. The impact of the CoLC is disproportionate - with different population sub-groups more likely to be negatively affected. The objective of this survey was to evaluate the perceived impact of the CoLC on the life and health of participants across four European countries. METHODS: A survey housing two questions to investigate the relationship between the CoLC and its perceived impact on life and health was developed. Four European countries (U.K., Sweden, Italy and Germany) took part via the YouGov platform. Logistic regression models were created for each country and question to evaluate which population characteristics were associated with a negative reported impact of the CoLC. RESULTS: A total of 8,152 unique individuals responded between 17th March and 30th March 2023. Each country was equally represented. Those aged 36-64 were more likely to report a negative impact of the CoLC on their life and health than younger participants (p < 0.001, p = 0.02 respectively). Across all countries, females were significantly more likely to report a negative impact on their life and health, however, when analysed according to country, in Sweden females were less likely to report a negative impact (p < 0.001). Those in lower income families or who reported poor health in the preceding 12 months were significantly more likely to report a negative impact of the CoLC on their life and health. There was no difference within the participant group on the reported impact of the CoLC based on location (rural vs. urban). CONCLUSIONS: We demonstrate the disproportionate negative impact of the CoLC on both life and health in different population subgroups. Germany and Sweden appeared to be more resilient to the effects of the CoLC, particularly for certain population subgroups. It is important to understand the differing effects of a CoLC, and to learn from successful health and economic strategies in order to create targeted policy and create a population resilient to economic shocks.

Clinical Simulation in the Regulation of Software as a Medical Device: An eDelphi Study.

BACKGROUND: Accelerated digitalization in the health sector requires the development of appropriate evaluation methods to ensure that digital health technologies (DHTs) are safe and effective. Software as a medical device (SaMD) is a commonly used DHT by clinicians to provide care to patients. Traditional research methods for evaluating health care products, such as randomized clinical trials, may not be suitable for DHTs, such as SaMD. However, evidence to show their safety and efficacy is needed by regulators before they can be used in practice. Clinical simulation can be used by researchers to test SaMD in an agile and low-cost way; yet, there is limited research on criteria to assess the robustness of simulations and, subsequently, their relevance for a regulatory decision. OBJECTIVE: The objective of this study was to gain consensus on the criteria that should be used to assess clinical simulation from a regulatory perspective when it is used to generate evidence for SaMD. METHODS: An eDelphi study approach was chosen to develop a set of criteria to assess clinical simulation when used to evaluate SaMD. Participants were recruited through purposive and snowball sampling based on their experience and knowledge in relevant sectors. They were guided through an initial scoping questionnaire with key themes identified from the literature to obtain a comprehensive list of criteria. Participants voted upon these criteria in 2 Delphi rounds, with criteria being excluded if consensus was not met. Participants were invited to add qualitative comments during rounds and qualitative analysis was performed on the comments gathered during the first round. Consensus was predefined by 2 criteria: if <10% of the panelists deemed the criteria as "not important" or "not important at all" and >60% "important" or "very important." RESULTS: In total, 33 international experts in the digital health field, including academics, regulators, policy makers, and industry representatives, completed both Delphi rounds, and 43 criteria gained consensus from the participants. The research team grouped these criteria into 7 domains-background and context, overall study design, study population, delivery of the simulation, fidelity, software and artificial intelligence, and study analysis. These 7 domains were formulated into the simulation for regulation of SaMD framework. There were key areas of concern identified by participants regarding the framework criteria, such as the importance of how simulation fidelity is achieved and reported and the avoidance of bias throughout all stages. CONCLUSIONS: This study proposes the simulation for regulation of SaMD framework, developed through an eDelphi consensus process, to evaluate clinical simulation when used to assess SaMD. Future research should prioritize the development of safe and effective SaMD, while implementing and refining the framework criteria to adapt to new challenges.

The Portuguese NHS 2024 reform: transformation through vertical integration.

Vertical integration models aim for the integration of services from different levels of care (e.g., primary, and secondary care) with the objective of increasing coordination and continuity of care as well as improving efficiency, quality, and access outcomes. This paper provides a view of the Portuguese National Health Service (NHS) healthcare providers' vertical integration, operationalized by the Portuguese NHS Executive Board during 2023 and 2024. This paper also aims to contribute to the discussion regarding the opportunities and constraints posed by public healthcare organizations vertical integration reforms. The Portuguese NHS operationalized the development and generalization of Local Health Units management model throughout the country. The same institutions are now responsible for both the primary care and the hospital care provided by public services in each geographic area, in an integrated manner. This 2024 reform also changed the NHS organic and organizational structures, opening paths to streamline the continuum of care. However, it will be important to ensure adequate monitoring and support, with the participation of healthcare services as well as community structures and other stakeholders, to promote an effective integration of care.

Eye-controlled endoscopy - a benchtop trial of a novel robotic steering platform - iGAZE2.

The endoscopic control system has remained similar in design for many decades The remit of advanced therapeutic endoscopy continues to expand requiring precision control and high cognitive workloads. Robotic systems are emerging, but all still require bimanual control and expensive and large new systems. Eye tracking is an exciting area that can be used as an endoscope control system. This is a study to establish the feasibility of an eye-controlled endoscope and compare its performance and cognitive demand to use of a conventional endoscope. An eye gaze-control system consisting of eye-tracking glasses, customised software and a small motor unit was built and attached to a conventional endoscope. Twelve non-endoscopists used both the eye gaze system and a conventional endoscope to complete a benchtop task in a simulated oesophagus and stomach. Completion of tasks was timed. Subjective feedback was collected from each participant on task load using the NASA Task Load Index. Participants were significantly quicker completing the task using iGAZE2 vs a conventional endoscope (65.02 ± 16.34s vs 104.21 ± 51.31s, p = 0.013) Participants were also significantly quicker completing retroflexion using iGAZE2 vs a conventional endoscope (8.48 ± 3.08 vs 11.38 ± 5.36s, p = 0.036). Participants reported a significantly lower workload (raw NASA-TLX score) when using iGAZE2 vs the conventional endoscope (152.1 ± 63.4 vs 319.6 ± 81.6, p = 0.0001) (Fig. 7). Users found iGAZE2 to have a significantly lower temporal demand, mental demand, effort, mental demand, physical demand, and frustration level. The eye gaze system is an exciting, small, and retrofittable system to any endoscope. The system shows exciting potential as a novel endoscopic control system with a significantly lower workload and better performance in novices suggesting a more intuitive control system.

Objective Assessment of Cognitive Workload in Surgery: A Systematic Review.

OBJECTIVE: To systematically review technologies that objectively measure CWL in surgery, assessing their psychometric and methodological characteristics. SUMMARY BACKGROUND DATA: Surgical tasks involving concurrent clinical decision-making and the safe application of technical and non-technical skills require a substantial cognitive demand and resource utilization. Cognitive overload leads to impaired clinical decision-making and performance decline. Assessing cognitive workload (CWL) could enable interventions to alleviate burden and improve patient safety. METHODS: Ovid MEDLINE, OVID Embase, the Cochrane Library and IEEE Xplore databases were searched from inception to August 2023. Full-text, peer-reviewed original studies in a population of surgeons, anesthesiologists or interventional radiologists were considered, with no publication date constraints. Study population, task paradigm, stressor, Cognitive Load Theory (CLT) domain, objective and subjective parameters, statistical analysis and results were extracted. Studies were assessed for a) definition of CWL, b) details of the clinical task paradigm, and c) objective CWL assessment tool. Assessment tools were evaluated using psychometric and methodological characteristics. RESULTS: 10790 studies were identified; 9004 were screened; 269 full studies were assessed for eligibility, of which 67 met inclusion criteria. The most widely used assessment modalities were autonomic (32 eye studies and 24 cardiac). Intrinsic workload (e.g. task complexity) and germane workload (effect of training or expertize) were the most prevalent designs investigated. CWL was not defined in 30 of 67 studies (44.8%). Sensitivity was greatest for neurophysiological instruments (100% EEG, 80% fNIRS); and across modalities accuracy increased with multi-sensor recordings. Specificity was limited to cardiac and ocular metrics, and was found to be sub-optimal (50% and 66.67%). Cardiac sensors were the least intrusive, with 54.2% of studies conducted in naturalistic clinical environments (higher ecological validity). CONCLUSION: Physiological metrics provide an accessible, objective assessment of CWL, but dependence on autonomic function negates selectivity and diagnosticity. Neurophysiological measures demonstrate favorable sensitivity, directly measuring brain activation as a correlate of cognitive state. Lacking an objective gold standard at present, we recommend the concurrent use of multimodal objective sensors and subjective tools for cross-validation. A theoretical and technical framework for objective assessment of CWL is required to overcome the heterogeneity of methodological reporting, data processing, and analysis.

Raising the barcode: improving medication safety behaviours through a behavioural science-informed feedback intervention. A quality improvement project and difference-in-difference analysis.

Barcode medication administration (BCMA) technology can improve patient safety by using scanning technology to ensure the right drug and dose are given to the right patient. Implementation can be challenging, requiring adoption of different workflows by nursing staff. In one London National Health Service trust scanning rates were lower than desired at around 0-20% of doses per ward. Our objective was to encourage patient safety behaviours in the form of medication scanning through implementation of a feedback intervention. This was informed by behavioural science, codesigned with nurses and informed by known barriers to use. Five wards were selected to trial the intervention over an 18-week period beginning August 2021. The remaining 14 hospital wards acted as controls. Intervention wards had varying uptake of BCMA at baseline and represented a range of specialties. A bespoke feedback intervention comprising three behavioural science constructs (gamification, the messenger effect and framing) was delivered to each intervention ward each week. A linear difference-in-difference analysis was used to evaluate the impact of our intervention on scan rates, both for the overall 18-week period and at two weekly intervals within this timeframe. We identified a 23.1 percentage point increase in medication scan rates (from an average baseline of 15.0% to 38.1%) on the intervention wards compared with control (p<0.001) following implementation of the intervention. Feedback had most impact in the first 6 weeks, with an initial percentage point increase of 26.3 (p<0.001), which subsequently plateaued. Neither clinical specialty nor number of beds on each ward were significant factors in our models. Our study demonstrated that a feedback intervention, codesigned with end users and incorporating behavioural science constructs, can lead to a significant increase in the adoption of BCMA scanning.

Identifying barriers to outpatient appointment attendance in patient groups at risk of inequity: a mixed methods study in a London NHS trust.

BACKGROUND: There is significant health inequity in the United Kingdom (U.K.), with different populations facing challenges accessing health services, which can impact health outcomes. At one London National Health Service (NHS) Trust, data showed that patients from deprived areas and minority ethnic groups had a higher likelihood of missing their first outpatient appointment. This study's objectives were to understand barriers to specific patient populations attending first outpatient appointments, explore systemic factors and assess appointment awareness. METHODS: Five high-volume specialties identified as having inequitable access based on ethnicity and deprivation were selected as the study setting. Mixed methods were employed to understand barriers to outpatient attendance, including qualitative semi-structured interviews with patients and staff, observations of staff workflows and interrogation of quantitative data on appointment communication. To identify barriers, semi-structured interviews were conducted with patients who missed their appointment and were from a minority ethnic group or deprived area. Staff interviews and observations were carried out to further understand attendance barriers. Patient interview data were analysed using inductive thematic analysis to create a thematic framework and triangulated with staff data. Subthemes were mapped onto a behavioural science framework highlighting behaviours that could be targeted. Quantitative data from patient interviews were analysed to assess appointment awareness and communication. RESULTS: Twenty-six patients and 11 staff were interviewed, with four staff observed. Seven themes were identified as barriers - communication factors, communication methods, healthcare system, system errors, transport, appointment, and personal factors. Knowledge about appointments was an important identified behaviour, supported by eight out of 26 patients answering that they were unaware of their missed appointment. Environmental context and resources were other strongly represented behavioural factors, highlighting systemic barriers that prevent attendance. CONCLUSION: This study showed the barriers preventing patients from minority ethnic groups or living in deprived areas from attending their outpatient appointment. These barriers included communication factors, communication methods, healthcare the system, system errors, transport, appointment, and personal factors. Healthcare services should acknowledge this and work with public members from these communities to co-design solutions supporting attendance. Our work provides a basis for future intervention design, informed by behavioural science and community involvement.

Impact of the cost-of-living crisis on patient preferences towards virtual consultations.

INTRODUCTION: Since 2021, the world has been facing a cost-of-living crisis which has negatively affected population health. Meanwhile, little is known about its impact on patients' preferences to access care. We aimed to analyse public preference for the modality of consultation (virtual vs face-to-face) before and after the onset of crisis and factors associated with these preferences. METHODS: An online cross-sectional survey was administered to the public in the United Kingdom, Germany, Italy and Sweden. McNemar tests were conducted to analyse pre- and post-crisis differences in preferences; logistic regression was used to examine the demographic factors associated with public preferences. RESULTS: Since the onset of crisis, the number of people choosing virtual consultations has increased in the United Kingdom (7.0% vs 9.5% P < 0.001), Germany (6.6% vs 8.6%, P < 0.008) and Italy (6.0% vs 9.8%, P < 0.001). Before the crisis, a stronger preference for virtual consultations was observed in people from urban areas (OR 1.28, 95% CI 1.05-1.56), while increasing age was associated with a lower preference for virtual care (OR 0.966, 95% CI 0.961-0.972). Younger people were more likely to switch to virtual care, while change to face-to-face was associated with younger age and lower income (OR 1.34, 95% CI 1.12-1.62). Older adults were less likely to change preference. CONCLUSIONS: Since the onset of the cost-of-living crisis, public preference for virtual consultations has increased, particularly in younger population. This contrasts with older adults and people with lower-than-average incomes. The rationale behind patients' preferences should be investigated to ensure patients can access their preferred modality of care.

Patients' subjective well-being: Determinants and its usage as a metric of healthcare service quality.

It is commonly suggested that patients' subjective well-being (SWB) can be affected by pre-treatment conditions and treatment experiences, and hence SWB can be used to measure and improve healthcare quality. With data collected in a hospital in the UK (N = 446), we investigated the determinants of patients' SWB and evaluated its use in healthcare research. Our findings showed strong relationships between pre-treatment conditions and patients' SWB: anxiety and depression negatively predicted SWB across all three domains, mobility positively predicted the life satisfaction and happiness domains, while the ability to self care and pain and discomfort also predicted SWB in some domains. In contrast, patients' satisfaction with the treatment only played minor roles in determining SWB, much less so the characteristics of their nurses. The general lack of associations between treatment experiences and patient's SWB highlighted the challenges of using SWB to measure healthcare quality and inform policy making.

ROAD2H: Development and evaluation of an open-source explainable artificial intelligence approach for managing co-morbidity and clinical guidelines.

Introduction: Clinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. In this work, we develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle-income country in West Balkans. Methods: We used an ontological framework, the Transition-based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International global initiative for chronic obstructive lung disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR-based implementation of the Assumptions-Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed-methods evaluation was conducted with 20 simulated cases and five pulmonologists. Results: Pulmonologists agreed 97% of the time with the GOLD-based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co-morbidities was suggested in the future along with customisation of the level of explanation with expertise. Conclusion: An ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Extension to other guidelines and multiple co-morbidities is needed to test the approach further.

Usability and Feasibility Evaluation of a Web-Based and Offline Cybersecurity Resource for Health Care Organizations (The Essentials of Cybersecurity in Health Care Organizations Framework Resource): Mixed Methods Study.

BACKGROUND: Cybersecurity is a growing challenge for health systems worldwide as the rapid adoption of digital technologies has led to increased cyber vulnerabilities with implications for patients and health providers. It is critical to develop workforce awareness and training as part of a safety culture and continuous improvement within health care organizations. However, there are limited open-access, health care-specific resources to help organizations at different levels of maturity develop their cybersecurity practices. OBJECTIVE: This study aims to assess the usability and feasibility of the Essentials of Cybersecurity in Health Care Organizations (ECHO) framework resource and evaluate the strengths, weaknesses, opportunities, and threats associated with implementing the resource at the organizational level. METHODS: A mixed methods, cross-sectional study of the acceptability and usability of the ECHO framework resource was undertaken. The research model was developed based on the technology acceptance model. Members of the Imperial College Leading Health Systems Network and other health care organizations identified through the research teams' networks were invited to participate. Study data were collected through web-based surveys 1 month and 3 months from the date the ECHO framework resource was received by the participants. Quantitative data were analyzed using R software (version 4.2.1). Descriptive statistics were calculated using the mean and 95% CIs. To determine significant differences between the distribution of answers by comparing results from the 2 survey time points, 2-tailed t tests were used. Qualitative data were analyzed using Microsoft Excel. Thematic analysis used deductive and inductive approaches to capture themes and concepts. RESULTS: A total of 16 health care organizations participated in the study. The ECHO framework resource was well accepted and useful for health care organizations, improving their understanding of cybersecurity as a priority area, reducing threats, and enabling organizational planning. Although not all participants were able to implement the resource as part of information computing technology (ICT) cybersecurity activities, those who did were positive about the process of change. Learnings from the implementation process included the usefulness of the resource for raising awareness and ease of use based on familiarity with other standards, guidelines, and tools. Participants noted that several sections of the framework were difficult to operationalize due to costs or budget constraints, human resource limitations, leadership support, stakeholder engagement, and limited time. CONCLUSIONS: The research identified the acceptability and usability of the ECHO framework resource as a health-focused cybersecurity resource for health care organizations. As cybersecurity in health care organizations is everyone's responsibility, there is potential for the framework resource to be used by staff with varied job roles. Future research needs to explore how it can be updated for ICT staff and implemented in practice and how educational materials on different aspects of the framework could be developed.

Testing the impact of differing behavioural science informed text message content in COVID-19 vaccination invitations on vaccine uptake: A randomised clinical trial.

Behavioural science constructs can be incorporated into messaging strategies to enhance the effectiveness of public health campaigns by increasing the occurrence of desired behaviours. This study investigated the impact of behavioural science-informed text message strategies on COVID-19 vaccination rates in 18-39-year-olds in an area of low uptake in London during the first vaccination offer round in the United Kingdom. This three-armed randomised trial recruited unvaccinated residents of an urban Central London suburb being offered their first vaccination between May and June 2021. Participants were randomised to receive the control (current practice) text message or one of two different behavioural science-informed COVID-19 vaccine invitation strategies. Both intervention strategies contained the phrase "your vaccine is ready and waiting for you", aiming to evoke a sense of ownership, with one strategy also including a pre-alert message. The main outcome measures were vaccination rates at 3 and 8 weeks after message delivery. A total of 88,820 residents were randomly assigned to one of the three trial arms. Each arm had a vaccine uptake rate of 27.2 %, 27.4 % and 27.3 % respectively. The mean age of participants was 28.2 years (SD ± 5.7), the mean index of multiple deprivation was 4.3 (SD ± 2.0) and 50.4 % were women. Vaccine uptake varied by demographics, however there was no significant difference between trial arms (p = 0.872). Delivery was successful for 53.6 % of text messages. Our choice of behavioural science informed messaging strategies did not improve vaccination rates above the rate seen for the current practice message. This likely reflects the wide exposure to public health campaigns during the pandemic, as such text messages nudges were unlikely to alter existing informed decision-making processes. Text message delivery was relatively low, indicating a need for accurate mobile phone number records and multi-modal approaches to reach eligible patients for vaccination. The protocol was registered at clinicaltrials.gov (NCT04895683) on 20/05/2021.

Transcutaneous fluorescence spectroscopy: development and characterization of a compact, portable, and fiber-optic sensor.

Significance: The integrity of the intestinal barrier is gaining recognition as a significant contributor to various pathophysiological conditions, including inflammatory bowel disease, celiac disease, environmental enteric dysfunction (EED), and malnutrition. EED, for example, manifests as complex structural and functional changes in the small intestine leading to increased intestinal permeability, inflammation, and reduced absorption of nutrients. Despite the importance of gut function, current techniques to assess intestinal permeability (such as endoscopic biopsies or dual sugar assays) are either highly invasive, unreliable, and/or difficult to perform in certain patient populations (e.g., infants). Aim: We present a portable, optical sensor based on transcutaneous fluorescence spectroscopy to assess gut function (in particular, intestinal permeability) in a fast and noninvasive manner. Approach: Participants receive an oral dose of a fluorescent contrast agent, and a wearable fiber-optic probe detects the permeation of the contrast agent from the gut into the blood stream by measuring the fluorescence intensity noninvasively at the fingertip. We characterized the performance of our compact optical sensor by comparing it against an existing benchtop spectroscopic system. In addition, we report results from a human study in healthy volunteers investigating the impact of skin tone and contrast agent dose on transcutaneous fluorescence signals. Results: The first study with eight healthy participants showed good correlation between our compact sensor and the existing benchtop spectroscopic system [correlation coefficient (r)>0.919, p<0.001]. Further experiments in 14 healthy participants revealed an approximately linear relationship between the ingested contrast agent dose and the collected signal intensity. Finally, a parallel study on the impact of different skin tones showed no significant differences in signal levels between participants with different skin tones (p>0.05). Conclusions: In this paper, we demonstrate the potential of our compact transcutaneous fluorescence sensor for noninvasive monitoring of intestinal health.

Evaluating the Impact of the National Health Service Digital Academy on Participants' Perceptions of Their Identity as Leaders of Digital Health Change: Mixed Methods Study.

BACKGROUND: The key to the digital leveling-up strategy of the National Health Service is the development of a digitally proficient leadership. The National Health Service Digital Academy (NHSDA) Digital Health Leadership program was designed to support emerging digital leaders to acquire the necessary skills to facilitate transformation. This study examined the influence of the program on professional identity formation as a means of creating a more proficient digital health leadership. OBJECTIVE: This study aims to examine the impact of the NHSDA program on participants' perceptions of themselves as digital health leaders. METHODS: We recruited 41 participants from 2 cohorts of the 2-year NHSDA program in this mixed methods study, all of whom had completed it >6 months before the study. The participants were initially invited to complete a web-based scoping questionnaire. This involved both quantitative and qualitative responses to prompts. Frequencies of responses were aggregated, while free-text comments from the questionnaire were analyzed inductively. The content of the 30 highest-scoring dissertations was also reviewed by 2 independent authors. A total of 14 semistructured interviews were then conducted with a subset of the cohort. These focused on individuals' perceptions of digital leadership and the influence of the course on the attainment of skills. In total, 3 in-depth focus groups were then conducted with participants to examine shared perceptions of professional identity as digital health leaders. The transcripts from the interviews and focus groups were aligned with a previously published examination of leadership as a framework. RESULTS: Of the 41 participants, 42% (17/41) were in clinical roles, 34% (14/41) were in program delivery or management roles, 20% (8/41) were in data science roles, and 5% (2/41) were in "other" roles. Interviews and focus groups highlighted that the course influenced 8 domains of professional identity: commitment to the profession, critical thinking, goal orientation, mentoring, perception of the profession, socialization, reflection, and self-efficacy. The dissertation of the practice model, in which candidates undertake digital projects within their organizations supported by faculty, largely impacted metacognitive skill acquisition and goal orientation. However, the program also affected participants' values and direction within the wider digital health community. According to the questionnaire, after graduation, 59% (24/41) of the participants changed roles in search of more prominence within digital leadership, with 46% (11/24) reporting that the course was a strong determinant of this change. CONCLUSIONS: A digital leadership course aimed at providing attendees with the necessary attributes to guide transformation can have a significant impact on professional identity formation. This can create a sense of belonging to a wider health leadership structure and facilitate the attainment of organizational and national digital targets. This effect is diminished by a lack of locoregional support for professional development.

Real-time classification of tumour and non-tumour tissue in colorectal cancer using diffuse reflectance spectroscopy and neural networks to aid margin assessment.

BACKGROUND: Colorectal cancer is the third most commonly diagnosed malignancy and the second leading cause of mortality worldwide. A positive resection margin following surgery for colorectal cancer is linked with higher rates of local recurrence and poorer survival. The authors investigated diffuse reflectance spectroscopy (DRS) to distinguish tumour and non-tumour tissue in ex-vivo colorectal specimens, to aid margin assessment and provide augmented visual maps to the surgeon in real-time. METHODS: Patients undergoing elective colorectal cancer resection surgery at a London-based hospital were prospectively recruited. A hand-held DRS probe was used on the surface of freshly resected ex-vivo colorectal tissue. Spectral data were acquired for tumour and non-tumour tissue. Binary classification was achieved using conventional machine learning classifiers and a convolutional neural network (CNN), which were evaluated in terms of sensitivity, specificity, accuracy and the area under the curve. RESULTS: A total of 7692 mean spectra were obtained for tumour and non-tumour colorectal tissue. The CNN-based classifier was the best performing machine learning algorithm, when compared to contrastive approaches, for differentiating tumour and non-tumour colorectal tissue, with an overall diagnostic accuracy of 90.8% and area under the curve of 96.8%. Live on-screen classification of tissue type was achieved using a graduated colourmap. CONCLUSION: A high diagnostic accuracy for a DRS probe and tracking system to differentiate ex-vivo tumour and non-tumour colorectal tissue in real-time with on-screen visual feedback was highlighted by this study. Further in-vivo studies are needed to ensure integration into a surgical workflow.