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BackgroundImplementation of remote monitoring solutions and digital alerting tools in health care has historically been challenging, despite the impetus provided by the COVID-19 pandemic. To date, a health systems–based approach to systematically describe barriers and facilitators across multiple domains has not been undertaken. ObjectiveWe aimed to undertake a comprehensive mixed methods analysis of barriers and facilitators for successful implementation of remote monitoring and digital alerting tools in complex health organizations. MethodsA mixed methods approach using a modified Technology Acceptance Model questionnaire and semistructured interviews mapped to the validated fit among humans, organizations, and technology (HOT-fit) framework was undertaken. Likert frequency responses and deductive thematic analyses were performed. ResultsA total of 11 participants responded to the questionnaire and 18 participants to the interviews. Key barriers and facilitators could be mapped onto 6 dimensions, which incorporated aspects of digitization: system use (human), user satisfaction (human), environment (organization), structure (organization), information and service quality (technology), and system quality (technology). ConclusionsThe recommendations proposed can enhance the potential for future remote sensing solutions to be more successfully integrated in health care practice, resulting in more successful use of “virtual wards.” Trial RegistrationClinicalTrials.gov NCT05321004; https://www.clinicaltrials.gov/study/NCT05321004

Abstract Background Basic surgical skills teaching is often delivered with didactic audio-visual content, and new digital technologies may allow more engaging and effective ways of teaching to be developed. The Microsoft HoloLens 2 (HL2) is a multi-functional mixed reality headset. This prospective feasibility study sought to assess the device as a tool for enhancing technical surgical skills training. Methods A prospective randomised feasibility study was conducted. 36 novice medical students were trained to perform a basic arteriotomy and closure using a synthetic model. Participants were randomised to receive a structured surgical skills tutorial via a bespoke mixed reality HL2 tutorial (n = 18), or via a standard video-based tutorial (n = 18). Proficiency scores were assessed by blinded examiners using a validated objective scoring system and participant feedback collected. Results The HL2 group showed significantly greater improvement in overall technical proficiency compared to the video group (10.1 vs. 6.89, p = 0.0076), and a greater consistency in skill progression with a significantly narrower range of scores (SD 2.48 vs. 4.03, p = 0.026). Participant feedback showed the HL2 technology to be more interactive and engaging with minimal device related problems experienced. Conclusions This study has demonstrated that mixed reality technology may provide a higher quality educational experience, improved skill progression and greater consistency in learning when compared to traditional teaching methodologies for basic surgical skills. Further work is required to refine, translate, and evaluate the scalability and applicability of the technology across a broad range of skills-based disciplines.

Abstract Background Mixed Reality technology may provide many advantages over traditional teaching methods. Despite its potential, the technology has yet to be used for the formal assessment of clinical competency. This study sought to collect validity evidence and assess the feasibility of using the HoloLens 2 mixed reality headset for the conduct and augmentation of Objective Structured Clinical Examinations (OSCEs). Methods A prospective cohort study was conducted to compare the assessment of undergraduate medical students undertaking OSCEs via HoloLens 2 live (HLL) and recorded (HLR), and gold-standard in-person (IP) methods. An augmented mixed reality scenario was also assessed. Results Thirteen undergraduate participants completed a total of 65 OSCE stations. Overall inter-modality correlation was 0.81 (p = 0.01), 0.98 (p = 0.01) and 0.82 (p = 0.01) for IP vs. HLL, HLL vs. HLR and IP vs. HLR respectively. Skill based correlations for IP vs. HLR were assessed for history taking (0.82, p = 0.01), clinical examination (0.81, p = 0.01), procedural (0.88, p = 0.01) and clinical skills (0.92, p = 0.01), and assessment of a virtual mixed reality patient (0.74, p = 0.01). The HoloLens device was deemed to be usable and practical (Standard Usability Scale (SUS) score = 51.5), and the technology was thought to deliver greater flexibility and convenience, and have the potential to expand and enhance assessment opportunities. Conclusions HoloLens 2 is comparable to traditional in-person examination of undergraduate medical students for both live and recorded assessments, and therefore is a valid and robust method for objectively assessing performance. The technology is in its infancy, and users need to develop confidence in its usability and reliability as an assessment tool. However, the potential to integrate additional functionality including holographic content, automated tracking and data analysis, and to facilitate remote assessment may allow the technology to enhance, expand and standardise examinations across a range of educational contexts.

BackgroundCOVID-19 has had a catastrophic impact in terms of human lives lost. Medical education has also been impacted as appropriately stringent infection control policies precluded medical trainees from attending clinical teaching. Lecture-based education has been easily transferred to a digital platform, but bedside teaching has not. ObjectiveThis study aims to assess the feasibility of using a mixed reality (MR) headset to deliver remote bedside teaching. MethodsTwo MR sessions were led by senior doctors wearing the HoloLens headset. The trainers selected patients requiring their specialist input. The headset allowed bidirectional audiovisual communication between the trainer and trainee doctors. Trainee doctor conceptions of bedside teaching, impact of the COVID-19 pandemic on bedside teaching, and the MR sessions were evaluated using pre- and postround questionnaires, using Likert scales. Data related to clinician exposure to at-risk patients and use of personal protective equipment (PPE) were collected. ResultsPrequestionnaire respondents (n=24) strongly agreed that bedside teaching is key to educating clinicians (median 7, IQR 6-7). Postsession questionnaires showed that, overall, users subjectively agreed the MR session was helpful to their learning (median 6, IQR 5.25-7) and that it was worthwhile (median 6, IQR 5.25-7). Mixed reality versus in-person teaching led to a 79.5% reduction in cumulative clinician exposure time and 83.3% reduction in PPE use. ConclusionsThis study is proof of principle that HoloLens can be used effectively to deliver clinical bedside teaching. This novel format confers significant advantages in terms of minimizing exposure of trainees to COVID-19, reducing PPE use, enabling larger attendance, and delivering convenient and accessible real-time clinical training.

Abstract Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020. We identified 11,921 studies, of which 503 were included in the systematic review. Eighty-two studies in ophthalmology, 82 in breast disease and 115 in respiratory disease were included for meta-analysis. Two hundred twenty-four studies in other specialities were included for qualitative review. Peer-reviewed studies that reported on the diagnostic accuracy of DL algorithms to identify pathology using medical imaging were included. Primary outcomes were measures of diagnostic accuracy, study design and reporting standards in the literature. Estimates were pooled using random-effects meta-analysis. In ophthalmology, AUC’s ranged between 0.933 and 1 for diagnosing diabetic retinopathy, age-related macular degeneration and glaucoma on retinal fundus photographs and optical coherence tomography. In respiratory imaging, AUC’s ranged between 0.864 and 0.937 for diagnosing lung nodules or lung cancer on chest X-ray or CT scan. For breast imaging, AUC’s ranged between 0.868 and 0.909 for diagnosing breast cancer on mammogram, ultrasound, MRI and digital breast tomosynthesis. Heterogeneity was high between studies and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an overestimation of the diagnostic accuracy of DL algorithms on medical imaging. There is an immediate need for the development of artificial intelligence-specific EQUATOR guidelines, particularly STARD, in order to provide guidance around key issues in this field.

BackgroundThe successful implementation of clinical smartphone apps in hospital settings requires close collaboration with industry partners. A large-scale, hospital-wide implementation of a clinical mobile app for health care professionals developed in partnership with Google Health and academic partners was deployed on a bring-your-own-device basis using mobile device management at our UK academic hospital. As this was the first large-scale implementation of this type of innovation in the UK health system, important insights and lessons learned from the deployment may be useful to other organizations considering implementing similar technology in partnership with commercial companies. ObjectiveThe aims of this study are to define the key enablers and barriers and to propose a road map for the implementation of a hospital-wide clinical mobile app developed in collaboration with an industry partner as a data processor and an academic partner for independent evaluation. MethodsSemistructured interviews were conducted with high-level stakeholders from industry, academia, and health care providers who had instrumental roles in the implementation of the app at our hospital. The interviews explored the participants’ views on the enablers and barriers to the implementation process. The interviews were analyzed using a broadly deductive approach to thematic analysis. ResultsIn total, 14 participants were interviewed. Key enablers identified were the establishment of a steering committee with high-level clinical involvement, well-defined roles and responsibilities between partners, effective communication strategies with end users, safe information governance precautions, and increased patient engagement and transparency. Barriers identified were the lack of dedicated resources for mobile change at our hospital, risk aversion, unclear strategy and regulation, and the implications of bring-your-own-device and mobile device management policies. The key lessons learned from the deployment process were highlighted, and a road map for the implementation of large-scale clinical mobile apps in hospital settings was proposed. ConclusionsDespite partnering with one of the world’s biggest technology companies, the cultural and technological change required for mobile working and implementation in health care was found to be a significant challenge. With an increasing requirement for health care organizations to partner with industry for advanced mobile technologies, the lessons learned from our implementation can influence how other health care organizations undertake a similar mobile change and improve the chances of successful widespread mobile transformation.

BackgroundConsiderable research is being conducted as to how artificial intelligence (AI) can be effectively applied to health care. However, for the successful implementation of AI, large amounts of health data are required for training and testing algorithms. As such, there is a need to understand the perspectives and viewpoints of patients regarding the use of their health data in AI research. ObjectiveWe surveyed a large sample of patients for identifying current awareness regarding health data research, and for obtaining their opinions and views on data sharing for AI research purposes, and on the use of AI technology on health care data. MethodsA cross-sectional survey with patients was conducted at a large multisite teaching hospital in the United Kingdom. Data were collected on patient and public views about sharing health data for research and the use of AI on health data. ResultsA total of 408 participants completed the survey. The respondents had generally low levels of prior knowledge about AI. Most were comfortable with sharing health data with the National Health Service (NHS) (318/408, 77.9%) or universities (268/408, 65.7%), but far fewer with commercial organizations such as technology companies (108/408, 26.4%). The majority endorsed AI research on health care data (357/408, 87.4%) and health care imaging (353/408, 86.4%) in a university setting, provided that concerns about privacy, reidentification of anonymized health care data, and consent processes were addressed. ConclusionsThere were significant variations in the patient perceptions, levels of support, and understanding of health data research and AI. Greater public engagement levels and debates are necessary to ensure the acceptability of AI research and its successful integration into clinical practice in future.

The Covid-19 pandemic has placed unprecedented pressure on healthcare systems and workers around the world. Such pressures may impact on working conditions, psychological wellbeing and perception of safety. In spite of this, no study has assessed the relationship between safety attitudes and psychological outcomes. Moreover, only limited studies have examined the relationship between personal characteristics and psychological outcomes during Covid-19. From 22nd March 2020 to 18th June 2020, healthcare workers from the United Kingdom, Poland, and Singapore were invited to participate using a self-administered questionnaire comprising the Safety Attitudes Questionnaire (SAQ), Oldenburg Burnout Inventory (OLBI) and Hospital Anxiety and Depression Scale (HADS) to evaluate safety culture, burnout and anxiety/depression. Multivariate logistic regression was used to determine predictors of burnout, anxiety and depression. Of 3,537 healthcare workers who participated in the study, 2,364 (67%) screened positive for burnout, 701 (20%) for anxiety, and 389 (11%) for depression. Significant predictors of burnout included patient-facing roles: doctor (OR 2.10; 95% CI 1.49-2.95), nurse (OR 1.38; 95% CI 1.04-1.84), and 'other clinical' (OR 2.02; 95% CI 1.45-2.82); being redeployed (OR 1.27; 95% CI 1.02-1.58), bottom quartile SAQ score (OR 2.43; 95% CI 1.98-2.99), anxiety (OR 4.87; 95% CI 3.92-6.06) and depression (OR 4.06; 95% CI 3.04-5.42). Significant factors inversely correlated with burnout included being tested for SARS-CoV-2 (OR 0.64; 95% CI 0.51-0.82) and top quartile SAQ score (OR 0.30; 95% CI 0.22-0.40). Significant factors associated with anxiety and depression, included burnout, gender, safety attitudes and job role. Our findings demonstrate a significant burden of burnout, anxiety, and depression amongst healthcare workers. A strong association was seen between SARS-CoV-2 testing, safety attitudes, gender, job role, redeployment and psychological state. These findings highlight the importance of targeted support services for at risk groups and proactive SARS-CoV-2 testing of healthcare workers.

BackgroundThere is a need to understand the impact of COVID-19 on colorectal cancer care globally and determine drivers of variation.ObjectiveTo evaluate COVID-19 impact on colorectal cancer services globally and identify predictors for behaviour change.DesignAn online survey of colorectal cancer service change globally in May and June 2020.ParticipantsAttending or consultant surgeons involved in the care of patients with colorectal cancer.Main outcome measuresChanges in the delivery of diagnostics (diagnostic endoscopy), imaging for staging, therapeutics and surgical technique in the management of colorectal cancer. Predictors of change included increased hospital bed stress, critical care bed stress, mortality and world region.Results191 responses were included from surgeons in 159 centers across 46 countries, demonstrating widespread service reduction with global variation. Diagnostic endoscopy was reduced in 93% of responses, even with low hospital stress and mortality; whilst rising critical care bed stress triggered complete cessation (p = 0.02). Availability of CT and MRI fell by 40-41%, with MRI significantly reduced with high hospital stress. Neoadjuvant therapy use in rectal cancer changed in 48% of responses, where centers which had ceased surgery increased its use (62 vs 30%, p = 0.04) as did those with extended delays to surgery (pConclusionsThe COVID-19 pandemic was associated with severe restrictions in the availability of colorectal cancer services on a global scale, with significant variation in behaviours which cannot be fully accounted for by hospital burden or mortality.

Abstract not available.

Summary: Background: The use of health information technology (IT) is rapidly increasing to support improvements in the delivery of care. Although health IT is delivering huge benefits, new technology can also introduce unique risks. Despite these risks, evidence on the preventability and effects of health IT failures on patients is scarce. In our study we therefore sought to evaluate the preventability and effects of health IT failures by examining patient safety incidents in England and Wales. Methods: We designed our study as a retrospective analysis of 10 years of incident reporting in England and Wales. We used text mining with the words “computer”, “system”, “workstation”, and “network” to explore free-text incident descriptors to identify incidents related to health IT failures following a previously described approach. We then applied an n-gram model of searching to identify contiguous sequences of words and provide spatial context. We examined incident details, recorded harm, and preventability. Standard descriptive statistics were applied. Degree of harm was identified according to standardised definitions and preventability was assessed by two independent reviewers. Findings: We identified 2627 incidents related to health IT failures. 2557 (97%) of 2627 incidents were assessed for harm (70 incidents were excluded). 2106 (82%) of 2557 health IT failures caused no harm to patients, 331 (13%) caused low harm, 102 (4%) caused moderate harm, 14 (1%) caused severe harm, and four (

Curriculum-based measures (CBMs), which allow educators to monitor progress over time and make instructional decisions based on student performance, represent a fixture of general approaches to reading and data-based instructional frameworks. However, evidence supporting the use of CBM for students with intellectual disabilities is limited. This study evaluated the criterion validity of a reading CBM battery. Multiple CBM and standardized criterion measures were administered to elementary-age children ("N" = 56) with intellectual disabilities. Inferential analyses identified numerous domain-specific correlations between CBM and criterion measures; however, no single CBM emerged as a more effective predictor of reading performance. Findings provide qualified support for the use of CBM with children who have intellectual disabilities.

Data races are common in interrupt-driven programs and have already led to well-known real-world problems. Unfortunately, existing dynamic tools for reporting data races in interrupt-driven programs are not only unsound, but they also fail to verify the existence of data races in such programs. This paper presents an efficient and scalable on-the-fly technique that precisely detects, without false positives, apparent data races in interrupt-driven programs. The technique combines a tailored lightweight labeling scheme to maintain logical concurrency between the main program and every instance of its interrupt handlers with a precise detection protocol that analyzes conflicting shared memory accesses by storing at most two accesses for each shared variable. We implemented a prototype of this technique, called i Race, on top of the Avrora simulation framework. An empirical evaluation of i Race revealed the presence of data races in some existing TinyOS components and applications with a worst-case slowdown of only about 6 times on average and an increased average memory consumption of only about 20% in comparison with the original program execution. The evaluation also proved that the labeling scheme alone generates an average runtime overhead of only about 0.4x while consuming only about 12% more memory than the original program execution.

We present a 74-year-old woman who developed a portal vein thrombosis following an elective total knee replacement. She had atrial fibrillation for which she was taking warfarin for anticoagulation. Seven days prior to surgery, she was instructed to discontinue warfarin and replace it with prophylactic low-molecular-weight heparin. On postoperative day 1, routine blood tests revealed deranged hepatic synthetic function, despite standard anticoagulation management. Doppler ultrasonography confirmed a portal vein thrombosis. She was treated with therapeutic doses of low-molecular-weight heparin until her international normalised ratio reached therapeutic levels. Her liver function results had normalised 2 weeks later. Portal vein thrombosis is a potentially fatal complication that is reversible if identified and treated early.