Understanding the enablers and barriers to implementing a patient-led escalation system: a qualitative study

Background: The management of acute deterioration following surgery remains highly variable. Patients and families can play an important role in identifying early signs of deterioration but effective contribution to escalation of care can be practically difficult to achieve. This paper reports the enablers and barriers to the implementation of patient-led escalation systems found during a process evaluation of a quality improvement programme Rescue for Emergency Surgery Patients Observed to uNdergo acute Deterioration (RESPOND). Methods: The research used ethnographic methods, including over 100 hours of observations on surgical units in three English hospitals in order to understand the everyday context of care. Observations focused on the coordination of activities such as handovers and how rescue featured as part of this. We also conducted 27 interviews with a range of clinical and managerial staff and patients. We employed a thematic analysis approach, combined with a theoretically focused implementation coding framework, based on Normalisation Process Theory. Results: We found that organisational infrastructural support in the form of a leadership support and clinical care outreach teams with capacity were enablers in implementing the patient-led escalation system. Barriers to implementation included making changes to professional practice without discussing the value and legitimacy of operationalising patient concerns, and ensuring equity of use. We found that organisational work is needed to overcome patient fears about disrupting social and cultural norms. Conclusions: This paper reveals the need for infrastructural support to facilitate the implementation of a patient-led escalation system, and leadership support to normalise the everyday process of involving patients and families in escalation. This type of system may not achieve its goals without properly understanding and addressing the concerns of both nurses and patients

A systematic literature review of skyline query processing over data stream

Recently, skyline query processing over data stream has gained a lot of attention especially from the database community owing to its own unique challenges. Skyline queries aims at pruning a search space of a potential large multi-dimensional set of objects by keeping only those objects that are not worse than any other. Although an abundance of skyline query processing techniques have been proposed, there is a lack of a Systematic Literature Review (SLR) on current research works pertinent to skyline query processing over data stream. In regard to this, this paper provides a comparative study on the state-of-the-art approaches over the period between 2000 and 2022 with the main aim to help readers understand the key issues which are essential to consider in relation to processing skyline queries over streaming data. Seven digital databases were reviewed in accordance with the Preferred Reporting Items for Systematic Reviews (PRISMA) procedures. After applying both the inclusion and exclusion criteria, 23 primary papers were further examined. The results show that the identified skyline approaches are driven by the need to expedite the skyline query processing mainly due to the fact that data streams are time varying (time sensitive), continuous, real time, volatile, and unrepeatable. Although, these skyline approaches are tailored made for data stream with a common aim, their solutions vary to suit with the various aspects being considered, which include the type of skyline query, type of streaming data, type of sliding window, query processing technique, indexing technique as well as the data stream environment employed. In this paper, a comprehensive taxonomy is developed along with the key aspects of each reported approach, while several open issues and challenges related to the topic being reviewed are highlighted as recommendation for future research direction

Thorax support vest to prevent sternal wound infections in cardiac surgery patients—a systematic review and meta-analysis

OBJECTIVES: Midline sternotomy is the main surgical access for cardiac surgeries. The most prominent complication of sternotomy is sternal wound infection (SWI). The use of a thorax support vest (TSV) that limits thorax movement and ensures sternal stability has been suggested to prevent postoperative SWI. METHODS: We performed a meta-analysis to evaluate differences in clinical outcomes with and without the use of TSV after cardiac surgery in randomized trials. The primary outcome was deep SWI (DSWI). Secondary outcomes were superficial SWI, sternal wound dehiscence, and hospital length of stay (LOS). A trial sequential analysis was performed. Fixed (F) and random effects (R) models were calculated. RESULTS: A total of 4 studies (3820 patients) were included. Patients who wore the TSV had lower incidence of DSWI [odds ratio (OR) = F: 0.24, 95% confidence interval (CI), 0.13–0.43, P < 0.01; R: 0.24, 0.04–1.59, P = 0.08], sternal wound dehiscence (OR = F: 0.08, 95% CI, 0.02–0.27, P < 0.01; R: 0.10, 0.00–2.20, P = 0.08) and shorter hospital LOS (standardized mean difference = F: −0.30, −0.37 to −0.24, P < 0.01; R: −0.63, −1.29 to 0.02, P = 0.15). There was no difference regarding the incidence of superficial SWI (OR = F: 0.71, 95% CI, 0.34–1.47, P = 0.35; R: 0.64, 0.10, 4.26, P = 0.42). The trial sequential analysis, however, showed that the observed decrease in DSWI in the TSV arm cannot be considered conclusive based on the existing evidence. CONCLUSIONS: This meta-analysis suggests that the use of a TSV after cardiac surgery could potentially be associated with a reduction in sternal wound complications. However, despite the significant treatment effect in the available studies, the evidence is not solid enough to provide strong practice recommendations

Publisher Correction: Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI (Nature Medicine, (2022), 28, 5, (924-933), 10.1038/s41591-022-01772-9)

In the version of this article initially published, a list of the DECIDE-AI expert group members and their affiliations was omitted and has now been included in the HTML and PDF versions of the article. *A list of authors and their affiliations appears online

The effects of preceding prospective collaborative studies (IDEAL Stage 2b) on the quality and impact of subsequent randomized controlled trials (IDEAL Stage 3) evaluating surgical innovations: a protocol for a case-control systematic review of the literature

A systematic review assessing the effect of IDEAL stage 2b on IDEAL stage 3 in surger

Am I safe? An interpretative phenomenological analysis of vulnerability as experienced by patients with complications following surgery

Abdominal surgery carries with it risks of complications. Little is known about patients' experiences of post-surgical deterioration. There is a real need to understand the psychosocial as well as the biological aspects of deterioration in order to improve care and outcomes for patients. Drawing on in-depth interviews with seven abdominal surgery survivors, we present an idiographic account of participants' experiences, situating their contribution to safety within their personal lived experiences and meaning-making of these episodes of deterioration. Our analysis reveals an overarching group experiential theme of vulnerability in relation to participants' experiences of complications after abdominal surgery. This encapsulates the uncertainty of the situation all the participants found themselves in, and the nature and seriousness of their health conditions. The extent of participants' vulnerability is revealed by detailing how they made sense of their experience, how they negotiated feelings of (un)safety drawing on their relationships with family and staff and the legacy of feelings they were left with when their expectations of care (care as imagined) did not meet the reality of their experiences (care as received). The participants' experiences highlight the power imbalance between patients and professionals in terms of whose knowledge counts within the hospital context. The study reveals the potential for epistemic injustice to arise when patients' concerns are ignored or dismissed. Our data has implications for designing strategies to enable escalation of care, both in terms of supporting staff to deliver compassionate care, and in strengthening patient and family involvement in rescue processes. </p

Intraoperative Applications of Artificial Intelligence in Robotic Surgery: A Scoping Review of Current Development Stages and Levels of Autonomy

OBJECTIVE: A scoping review of the literature was conducted to identify intraoperative AI applications for robotic surgery under development and categorise them by 1) purpose of the applications, 2) level of autonomy, 3) stage of development, and 4) type of measured outcome. BACKGROUND: In robotic surgery, artificial intelligence (AI) based applications have the potential to disrupt a field so far based on a master-slave paradigm. However, there is no available overview about this technology’s current stage of development and level of autonomy. METHODS: MEDLINE and EMBASE were searched between January 1st 2010 and May 21st 2022. Abstract screening, full text review and data extraction were performed independently by two reviewers. Level of autonomy was defined according to the Yang et al classification and stage of development according to the IDEAL framework. RESULTS: 129 studies were included in the review. 97 studies (75%) described applications providing Robot Assistance (autonomy level 1), 30 studies (23%) application enabling Task Autonomy (autonomy level 2), and two studies (2%) application achieving Conditional autonomy (autonomy level 3). All studies were at IDEAL stage 0 and no clinical investigations on humans were found. 116 (90%) conducted in silico or ex-vivo experiments on inorganic material, 9 (7%) ex-vivo experiments on organic material, and 4 (3%) performed in vivo experiments in porcine models. CONCLUSION: Clinical evaluation of intraoperative AI applications for robotic surgery is still in its infancy and most applications have a low level of autonomy. With increasing levels of autonomy, the evaluation focus seems to shift from AI-specific metrics to process outcomes, although common standards are needed to allow comparison between systems

Comparative effectiveness of cervical vs thoracic spinal-thrust manipulation for care of cervicogenic headache: A randomized controlled trial.

BackgroundThere is ample evidence supporting the use of different manipulative therapy techniques for Cervicogenic Headache (CgH). However, no technique can be singled as the best available treatment for patients with CgH. Therefore, the objective of the study is to find and compare the clinical effects of cervical spine over thoracic spine manipulation and conventional physiotherapy in patients with CgH.Design, setting, and participantsIt is a prospective, randomized controlled study conducted between July 2020 and January 2023 at the University hospital. N = 96 eligible patients with CgH were selected based on selection criteria and they were divided into cervical spine manipulation (CSM; n = 32), thoracic spine manipulation (TSM; n = 32) and conventional physiotherapy (CPT; n = 32) groups, and received the respective treatment for four weeks. Primary (CgH frequency) and secondary CgH pain intensity, CgH disability, neck pain frequency, neck pain intensity, neck pain threshold, cervical flexion rotation test (CFRT), neck disability index (NDI) and quality of life (QoL) scores were measured. The effects of treatment at various intervals were analyzed using a 3 × 4 linear mixed model analysis (LMM), with treatment group (cervical spine manipulation, thoracic spine manipulation, and conventional physiotherapy) and time intervals (baseline, 4 weeks, 8 weeks, and 6 months), and the statistical significance level was set at P ResultsThe reports of the CSM, TSM and CPT groups were compared between the groups. Four weeks following treatment CSM group showed more significant changes in primary (CgH frequency) and secondary (CgH pain intensity, CgH disability, neck pain frequency, pain intensity, pain threshold, CFRT, NDI and QoL) than the TSM and CPT groups (p = 0.001). The same gradual improvement was seen in the CSM group when compared to TSM and CPT groups (p = 0.001) in the above variables at 8 weeks and 6 months follow-up.ConclusionThe reports of the current randomized clinical study found that CSM resulted in significantly better improvements in pain parameters (intensity, frequency and threshold) functional disability and quality of life in patients with CgH than thoracic spine manipulation and conventional physiotherapy.Trial registrationClinical trial registration: CTRI/2020/06/026092 trial was registered prospectively on 24/06/2020

Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings