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Accurate and objective performance assessment is essential for both trainees and certified surgeons. However, existing methods can be time consuming, labor intensive, and subject to bias. Machine learning (ML) has the potential to provide rapid, automated, and reproducible feedback without the need for expert reviewers. We aimed to systematically review the literature and determine the ML techniques used for technical surgical skill assessment and identify challenges and barriers in the field. A systematic literature search, in accordance with the PRISMA statement, was performed to identify studies detailing the use of ML for technical skill assessment in surgery. Of the 1896 studies that were retrieved, 66 studies were included. The most common ML methods used were Hidden Markov Models (HMM, 14/66), Support Vector Machines (SVM, 17/66), and Artificial Neural Networks (ANN, 17/66). 40/66 studies used kinematic data, 19/66 used video or image data, and 7/66 used both. Studies assessed the performance of benchtop tasks (48/66), simulator tasks (10/66), and real-life surgery (8/66). Accuracy rates of over 80% were achieved, although tasks and participants varied between studies. Barriers to progress in the field included a focus on basic tasks, lack of standardization between studies, and lack of datasets. ML has the potential to produce accurate and objective surgical skill assessment through the use of methods including HMM, SVM, and ANN. Future ML-based assessment tools should move beyond the assessment of basic tasks and towards real-life surgery and provide interpretable feedback with clinical value for the surgeon. PROSPERO: CRD42020226071

Remote monitoring technologies have transformed healthcare delivery by enabling the in-home management of chronic conditions, improving patient autonomy, and supporting clinical oversight. Passive sensing, a subset of remote monitoring, facilitates unobtrusive, real-time data collection without active user engagement. Leveraging devices such as smartphones, wearables, and smart home sensors, these technologies offer advantages over traditional self-reports and intermittent evaluations by capturing behavioural, physiological, and environmental metrics. This systematic review evaluates the clinical utility of passive sensing technologies used in remote monitoring, with a specific emphasis on their impact on clinical outcomes and feasibility in real-world healthcare settings. A PRISMA-guided search identified 26 studies addressing conditions such as Parkinson’s disease, dementia, cancer, cardiopulmonary disorders, and musculoskeletal issues. Findings demonstrated significant correlations between sensor-derived metrics and clinical assessments, validating their potential as digital biomarkers. These technologies demonstrated feasibility and ecological validity in capturing continuous, real-world health data and offer a unified framework for enhancing patient care through three main applications: monitoring chronic disease progression, detecting acute health deterioration, and supporting therapeutic interventions. For example, these technologies successfully identified gait speed changes in Parkinson’s disease, tracked symptom fluctuations in cancer patients, and provided real-time alerts for acute events such as heart failure decompensation. Challenges included long-term adherence, scalability, data integration, security, and ownership. Future research should prioritise validation across diverse settings, long-term impact assessment, and integration into clinical workflows to maximise their utility.

Successful implementation of any digital intervention in a health care setting requires adoption by all stakeholders. Appropriate consideration of behavioral change is a key driver that is often overlooked during implementation. The nonadoption, abandonment, scale-up, spread, and systems (NASSS) behavioral framework offers a broad evaluation of success for digital health solutions, and the theoretical domains framework (TDF) focuses particularly on adopters, identifying determinants of behavior and potential reasons for implementation issues. The aim of this study was to describe and characterize barriers and facilitators to the adoption of digital solutions within health care using behavioral frameworks: the NASSS and TDF. A systematic search was performed in 4 databases (ie, Ovid in MEDLINE, Embase, Health Management Information Consortium, and PsycINFO). Included studies reported a behavioral change by health care professionals following digital interventions or the practicality of delivering such interventions. Barriers and facilitators were identified, extracted, and classified using the NASSS framework and TDF. Risk of bias was assessed using the Mixed Methods Appraisal Tool. The initial search result included 2704 unique studies, 12 of which met the inclusion criteria and from which data were extracted. All 12 scored ≥3 out of 5 stars on the Mixed Methods Appraisal Tool risk of bias assessment. Out of the 12 studies, 67% (n=8) were conducted in the United States, and 8% (n=1) each in India, Australia, the Netherlands, and Tanzania. The NASSS framework identified facilitators and barriers in 4 domains: the condition or illness, technology, value proposition, and adopter system. The TDF framework identified 8 relevant domains, including knowledge, skills, and beliefs about capabilities. Key facilitators included intuitive technology design aligned with existing workflows, clear communication of value propositions to adopters, adequate provision of training resources tailored to adopters' knowledge levels, and ensuring organizational readiness for technological change. Conversely, significant barriers involved disruptions to clinical workflow, inadequate adopter training or confidence levels, unclear value propositions leading to disengagement, insufficient consideration of cognitive load impacts, such as alert fatigue, and limited organizational preparedness. Notably, psychological factors such as optimism, intentions, and social influences were underreported. This study delineated and analyzed various critical behavioral factors impacting the adoption and implementation of digital interventions in health care. Based on these findings, future research must consider the key factors reported and alternative approaches to assess behaviors influencing adoption that are not presented in the current scientific literature. PROSPERO CRD42022264937; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022264937.

Background Health systems are increasingly adopting Integrated Neighbourhoods (INs) to deliver hyper-local, community-based care that integrates health, social care, and public sector resources to address healthcare costs, improve outcomes, and reduce health inequalities. However, IN models lack a unified definition and standard framework for development and evaluation, limiting their scalability and effectiveness. This systematic review aims to establish a foundational framework for INs, identifying key domains to guide their implementation (including barriers of implementation, evaluation, and potential for future research. Methods A systematic literature search, restricted to the English language, was performed to identify relevant studies with expert librarian support. Study quality was assessed with the Mixed-Methods Appraisal Tool (MMAT). A Braun and Clarke thematic analysis was conducted to identify recurring themes and extract key domains. Results A total of 29 studies met the inclusion criteria, encompassing a diverse range of IN models with varying focus areas and methodologies. Seven key domains emerged as central to effective IN models: integrator host, integrator enablers, integrator partnership principles, core integrated workforce, core areas of work, and services provided. These domains support multidisciplinary collaboration, enhance resource utilisation, and promote community engagement. However, barriers such as funding limitations, digital exclusion, and inconsistent evaluation frameworks present challenges to IN scalability and sustainability. Conclusion This proposed framework provides a starting point for a standardised structure for implementing and evaluating INs, guiding clinicians, academics, and policymakers in developing sustainable, equitable, and adaptable community-based care solutions with the potential to improve access to patients from low-socioeconomic and underserved communities. Trial Registration PROSPERO ID: CRD42024597197; available: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=597197 .

Background: Continuous vital sign monitoring using wearable sensors has gained traction for the early detection of patient deterioration, particularly with the advent of virtual wards. Objective: The objective was to evaluate the reliability of a wearable sensor for monitoring heart rate (HR), respiratory rate (RR), and temperature in acutely unwell hospital patients and to identify the optimal time window for alert generation. Methods: A prospective cohort study recruited 500 patients in a single hospital. Sensor readings were compared to standard intermittent nurse observations using Bland–Altman plots to assess the limits of agreement. Results: HR demonstrated good agreement with nurse observations (intraclass correlation coefficient [ICC] = 0.66, r = 0.86, p < 0.001), with a mean difference of 3.63 bpm (95% LoA: −10.87 to 18.14 bpm). RR exhibited weaker agreement (ICC = 0.20, r = 0.18, p < 0.001), with a mean difference of −2.72 breaths per minute (95% LoA: −10.91 to 5.47 bpm). Temperature showed poor to fair agreement (ICC = 0.30, r = 0.39, p < 0.001), with a mean difference of −0.57 °C (95% LoA: −1.72 to 0.58 °C). A 10 min averaging window was identified as optimal, balancing data retention and real-time alerting. Conclusions: Wearable sensors demonstrate potential for reliable continuous monitoring of vital signs, supporting their future integration into real-world clinical practice for improved patient safety.

Advances in digital technologies have allowed remote monitoring and digital alerting systems to gain popularity. Despite this, limited evidence exists to substantiate claims that digital alerting can improve clinical outcomes. The aim of this study was to appraise the evidence on the clinical outcomes of digital alerting systems in remote monitoring through a systematic review and meta-analysis. A systematic literature search, with no language restrictions, was performed to identify studies evaluating healthcare outcomes of digital sensor alerting systems used in remote monitoring across all (medical and surgical) cohorts. The primary outcome was hospitalisation; secondary outcomes included hospital length of stay (LOS), mortality, emergency department and outpatient visits. Standard, pooled hazard ratio and proportion of means meta-analyses were performed. A total of 33 studies met the eligibility criteria; of which, 23 allowed for a meta-analysis. A 9.6% mean decrease in hospitalisation favouring digital alerting systems from a pooled random effects analysis was noted. However, pooled weighted mean differences and hazard ratios did not reproduce this finding. Digital alerting reduced hospital LOS by a mean difference of 1.043 days. A 3% mean decrease in all-cause mortality from digital alerting systems was noted. There was no benefit of digital alerting with respect to emergency department or outpatient visits. Digital alerts can considerably reduce hospitalisation and length of stay for certain cohorts in remote monitoring. Further research is required to confirm these findings and trial different alerting protocols to understand optimal alerting to guide future widespread implementation.

Previous studies have shown that patients with normal vasodilator myocardial perfusion imaging (MPI) findings remain at a greater risk of future cardiac events than patients with normal exercise MPI findings. The aim was to assess improvement in risk classification provided by the heart rate response (HRR) in patients with normal vasodilator MPI findings when added to traditional risk stratification. We retrospectively studied 2,000 patients with normal regadenoson or adenosine MPI findings. Risk stratification was performed using Adult Treatment Panel III framework. Patients were stratified by HRR (percentage of increase from baseline) into tertiles specific to each vasodilator. All-cause mortality and cardiac death/nonfatal myocardial infarction (MI) ≤2 years from the index MPI were recorded. During follow-up, 11.8% patients died and 2.7% patients experienced cardiac death/nonfatal MI in the adenosine and regadenoson groups, respectively. The patients who died had a greater Framingham risk score (12 ± 4 vs 11 ± 4, p = 0.009) and lower HRR (22 ± 16 vs 32 ± 21, p <0.0001). In an adjusted Cox model, the lowest tertile HRR was associated with an increased risk of mortality (hazard ratio 2.1) and cardiac death/nonfatal MI (hazard ratio 2.9; p <0.01). Patients in the highest HRR tertile, irrespective of the Adult Treatment Panel III category, were at low risk. When added to the Adult Treatment Panel III categories, the HRR resulted in net reclassification improvement in mortality of 18% and cardiac death/nonfatal MI of 22%. In conclusion, a blunted HRR to vasodilator stress was independently associated with an increased risk of cardiac events and overall mortality in patients with normal vasodilator MPI findings. The HRR correctly reclassified a substantial proportion of these patients in addition to the traditional risk classification models and identified patients with normal vasodilator MPI findings, who had a truly low risk of events.

Laparoscopic Roux-en-Y gastric bypass (RYGB) is crucial for significant weight reduction and treating obesity-related issues. However, the impact of gastrojejunostomy (GJ) anastomosis diameter on weight loss remains unclear. We investigate this influence on post-RYGB weight loss outcomes. A systematic search was conducted. Six studies met the inclusion criteria, showing varied GJ diameters and follow-up durations (1–5 years). Smaller GJ diameters generally correlated with greater short-to-medium-term weight loss, with a threshold beyond which complications like stenosis increased. Studies had moderate-to-low bias risk, emphasizing the need for precise GJ area quantification post-operation. This review highlights a negative association between smaller GJ diameters and post-RYGB weight loss, advocating for standardized measurement techniques. Future research should explore intra-operative and AI-driven methods for optimizing GJ diameter determination.

The rapid uptake of digital technology into the operating room has the potential to improve patient outcomes, increase efficiency of the use of operating rooms, and allow surgeons to progress quickly up learning curves. These technologies are, however, dependent on huge amounts of data, and the consequences of their mismanagement are significant. While the field of artificial intelligence ethics is able to provide a broad framework for those designing and implementing these technologies into the operating room, there is a need to determine and address the ethical and data governance challenges of using digital technology in this unique environment. The objectives of this study are to define the term digital surgery and gain expert consensus on the key ethical and data governance issues, barriers, and future research goals of the use of artificial intelligence in surgery. Experts from the fields of surgery, ethics and law, policy, artificial intelligence, and industry will be invited to participate in a 4-round consensus Delphi exercise. In the first round, participants will supply free-text responses across 4 key domains: ethics, data governance, barriers, and future research goals. They will also be asked to provide their understanding of the term digital surgery. In subsequent rounds, statements will be grouped, and participants will be asked to rate the importance of each issue on a 9-point Likert scale ranging from 1 (not at all important) to 9 (critically important). Consensus is defined a priori as a score of 7 to 9 by 70% of respondents and 1 to 3 by less than 30% of respondents. A final online meeting round will be held to discuss inclusion of statements and draft a consensus document. Full ethical approval has been obtained for the study by the local research ethics committee at Imperial College, London (20IC6136). We anticipate round 1 to commence in January 2021. The results of this study will define the term digital surgery, identify the key issues and barriers, and shape future research in this area. PRR1-10.2196/26552.

The 2013 Breast Cancer Campaign gap analysis established breast cancer research priorities without a specific focus on surgical research or the role of surgeons on breast cancer research. This Review aims to identify opportunities and priorities for research in breast surgery to complement the 2013 gap analysis. To identify these goals, research-active breast surgeons met and identified areas for breast surgery research that mapped to the patient pathway. Areas included diagnosis, neoadjuvant treatment, surgery, adjuvant therapy, and attention to special groups (eg, those receiving risk-reducing surgery). Section leads were identified based on research interests, with invited input from experts in specific areas, supported by consultation with members of the Association of Breast Surgery and Independent Cancer Patients' Voice groups. The document was iteratively modified until participants were satisfied that key priorities for surgical research were clear. Key research gaps included issues surrounding overdiagnosis and treatment; optimising treatment options and their selection for neoadjuvant therapies and subsequent surgery; reducing rates of re-operations for breast-conserving surgery; generating evidence for clinical effectiveness and cost-effectiveness of breast reconstruction, and mechanisms for assessing novel interventions; establishing optimal axillary management, especially post-neoadjuvant treatment; and defining and standardising indications for risk-reducing surgery. We propose strategies for resolving these knowledge gaps. Surgeons are ideally placed for a central role in breast cancer research and should foster a culture of engagement and participation in research to benefit patients and health-care systems. Development of infrastructure and surgical research capacity, together with appropriate allocation of research funding, is needed to successfully address the key clinical and translational research gaps that are highlighted in this Review within the next two decades.