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Background: Uncertainty is a common challenge for nurses in clinical decision-making, which can compromise patient care quality and safety. To address this issue, it is essential to understand how nurses perceive and cope with uncertainty in their practice. Aim: This study aimed to explore nurses' perceptions of uncertainty in clinical decision-making using a qualitative approach. Methods: This study was conducted with a qualitative approach and conventional content analysis in 2020. Participants consisted of 17 nurses from different wards of teaching hospitals in Northwestern Iran, recruited using the purposive sampling method. Data were collected through semi-structured interviews and analyzed simultaneously with data collection (June to December 2020). The data were analyzed using the content analysis approach suggested by Wildemuth. Data were managed with MAXQDA10 software. The analysis revealed four main themes and ten subthemes that described the nurses' experiences of uncertainty in clinical decision-making. Results: The main themes were: difficult choice, difficult situation, insufficient judgment, and emotional burden. Conclusions: The study participants defined uncertainty in clinical decision-making as a difficult choice that occurs in difficult situations, which influenced their clinical judgment and emotional well-being. These findings provide valuable insights for developing interventions to help nurses manage uncertainty and improve their decision-making skills and safety.

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BACKGROUND: Accurate diagnosis of bedaquiline (BDQ) resistance remains challenging. A Bayesian approach expresses this uncertainty as a probability of BDQ resistance (prBDQR) with a 95% credible interval. We investigated how prBDQR information influences BDQ prescribing decisions. METHOD: We performed a discrete choice experiment with 55 international rifampicin-resistant tuberculosis physicians. We employed mixed-effects multinomial logistic regression to quantify the effect of prBDQR, patient attributes, and contextual factors on the decision to continue BDQ or not when sequencing results become available. RESULTS: PrBDQR was the most influential factor for BDQ decision-making, three times greater than treatment response. Each percentage point increase in prBDQR resulted in 8.2% lower odds (OR 0.92, 95% CI 0.90-0.93) of continuing BDQ as a fully effective drug and 5.0% lower odds (OR 0.95, 95% CI 0.94-0.96) of continuing it but not counting it as an effective drug. The most favourable patient profile for prescribing BDQ as a fully effective drug was a patient receiving the BPaLM regimen (BDQ, pretomanid, linezolid and moxifloxacin) with low prBDQR, good 1-month treatment response, fluoroquinolone-susceptible TB, and no prior BDQ treatment. Physicians with higher discomfort with uncertainty and more years of experience with BDQ were more inclined to stop BDQ. CONCLUSION: Given the uncertainty of genotype-phenotype associations, physicians valued prBDQR for BDQ decision-making in rifampicin-resistant TB treatment.

CONTEXTE: Le diagnostic précis de la résistance à la bédaquiline (BDQ) reste difficile. Une approche bayésienne exprime cette incertitude sous la forme d'une probabilité de résistance au BDQ (prBDQR) avec un intervalle de crédibilité à 95%. Nous avons étudié comment les informations prBDQR influencent les décisions de prescription de BDQ. MÉTHODE: Nous avons réalisé une expérience à choix discret auprès de 55 médecins internationaux spécialisés dans la TB résistante à la rifampicine. Nous avons utilisé une régression logistique multinomiale à effets mixtes pour quantifier l'effet du prBDQR, des attributs du patient et des facteurs contextuels sur la décision de poursuivre ou non le BDQ lorsque les résultats du séquençage sont disponibles. RÉSULTATS: La prBDQR a été le facteur le plus influent dans la prise de décision en matière de BDQ, trois fois plus importante que la réponse au traitement. Chaque augmentation d'un point de pourcentage du prBDQR a entraîné une baisse de 8,2% des probabilités (RC 0,92 ; IC à 95% 0,90­0,93) de poursuivre le BDQ en tant que médicament pleinement efficace et une baisse de 5,0% des probabilités (RC 0,95 ; IC à 95% 0,94­0,96) de le poursuivre mais de ne pas le compter comme un médicament efficace. Le profil de patient le plus favorable à la prescription de BDQ en tant que médicament pleinement efficace était celui d'un patient recevant le régime BPaLM (BDQ, pretomanid, linezolid et moxifloxacin) avec un faible prBDQR, une bonne réponse au traitement à 1 mois, une tuberculose sensible aux fluoroquinolones et aucun traitement antérieur par BDQ. Les médecins présentant un plus grand malaise face à l'incertitude et plus d'années d'expérience avec la BDQ étaient plus enclins à arrêter la BDQ. CONCLUSION: Compte tenu de l'incertitude des associations génotype-phénotype, les médecins ont valorisé prBDQR pour la prise de décision BDQ dans le traitement de la TB RR.

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This study aims to introduce collaborative learning as a method in the Mental Health and Diseases Nursing course for actively enrolled fourth-year nursing students. The objective is to assess the impact of collaborative learning on critical thinking and clinical decision-making among nursing students. Collaborative learning involves the use of small groups to help students identify effective ways of working together to enhance their learning outcomes. A quantitative research methodology was employed in this study. To evaluate the effectiveness of the collaborative learning program, a within-subject repeated-measures design was implemented. This study was conducted in the nursing department of a university in Turkey. The study included 96 female students (92.3 %) and 8 male students (7.7 %). Data collection involved the sociodemographic data form, the California Critical Thinking Disposition Inventory (CCTDI), and the Clinical Decision-Making in Nursing Scale (CDMNS). The students' pre-program CDMNS median score was 146.00 (117.00-177.00), and the post-program CDMNS median score was 147.50 (115.00-175.00). While there was an increase in the students' clinical decision-making scores after the program, this increase was not statistically significant (p > 0.05). The pre-program median score for CCTDI was 223.26 (176.87-296.02), and the post-program median score was 227.88 (188.87-359.00). The students' critical thinking disposition scores showed a statistically significant increase after the program (p < 0.05). The study results revealed notable enhancements among students who participated in courses utilizing the collaborative learning method. These enhancements included heightened academic performance, elevated levels of critical thinking, increased self-confidence, and improved clinical decision-making abilities.

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PURPOSE: As the number of clinical trials in China continues to grow, the assessment of competency of Clinical Research Coordinators (CRCs), who play a crucial role in clinical trials, has become an important and challenging topic. This study aims to construct a competency model for CRCs tailored to the Chinese context, in order to promote the standardisation and regulated development of the CRC industry. STUDY DESIGN AND SETTING: This study was conducted in China, engaging CRCs as the primary subjects. A competency evaluation model for CRCs was constructed through literature review, semi-structured interviews, Delphi expert consultation and the analytic hierarchy process. A questionnaire survey was distributed to a broad sample of CRCs across China to evaluate the model's reliability and validity. RESULTS: The final model encompasses 4 core competency dimensions and 37 indicators, tailored to assess the competencies of CRCs in China. The questionnaire yielded an effective response rate of 81.83%, with high internal consistency(Cronbach's α>0.7). Factor analysis confirmed the model's structure, indicating good reliability and validity. CONCLUSION: This study represents a pioneering effort in constructing a competency model specifically designed for Chinese CRCs, complemented by a robust and valid assessment scale. The findings bear significant implications for the recruitment, training, development and management of CRCs.

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OBJECTIVES: This study aims to develop an innovative approach for monitoring and assessing labor pain through ECG waveform analysis, utilizing machine learning techniques to monitor pain resulting from uterine contractions. METHODS: The study was conducted at National Taiwan University Hospital between January and July 2020. We collected a dataset of 6010 ECG samples from women preparing for natural spontaneous delivery (NSD). The ECG data was used to develop an ECG waveform-based Nociception Monitoring Index (NoM). The dataset was divided into training (80%) and validation (20%) sets. Multiple machine learning models, including LightGBM, XGBoost, SnapLogisticRegression, and SnapDecisionTree, were developed and evaluated. Hyperparameter optimization was performed using grid search and five-fold cross-validation to enhance model performance. RESULTS: The LightGBM model demonstrated superior performance with an AUC of 0.96 and an accuracy of 90%, making it the optimal model for monitoring labor pain based on ECG data. Other models, such as XGBoost and SnapLogisticRegression, also showed strong performance, with AUC values ranging from 0.88 to 0.95. CONCLUSIONS: This study demonstrates that the integration of machine learning algorithms with ECG data significantly enhances the accuracy and reliability of labor pain monitoring. Specifically, the LightGBM model exhibits exceptional precision and robustness in continuous pain monitoring during labor, with potential applicability extending to broader healthcare settings. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04461704.

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Introduction: Closed hand fractures represent a significant proportion of emergency department attendances, result in substantial health service utilisation and have a detrimental effect on quality of life. Increasingly, hand therapists in the United Kingdom provide first line fracture treatment. However, the knowledge and skills required to work in such an extended scope capacity have not been elucidated or standardised. This literature review synthesises and reports evidence for the knowledge requisite of clinicians to make evidence-based treatment decisions for patients with hand fractures. Methods: A systematic search was undertaken, using Embase, MEDLINE, PsychInfo and CINAHL electronic databases. Inclusion criteria were English language, full research reports of studies assessing of the reliability or validity of the decision-making process in hand fracture treatment published between 2013 and 2023. Data were summarised narratively. Results: 15 studies met inclusion criteria; most assessed decision making for metacarpal fractures. Studies on imaging (n = 4) suggested the reliability of plain radiograph interpretation of hand fracture characteristics such as angulation is good and similar across various levels of experience. Agreement between surgeons and therapists in choosing surgical or nonsurgical treatment was generally good, but factors influencing decision making remained unclear. No evidence was identified that explored clinical assessment knowledge (subjective or objective patient factors) or the specific competencies required to treat hand fractures. Conclusions: There is limited evidence for the knowledge and skills required of clinicians for the competent assessment and treatment of hand fractures. Stakeholder consensus work is required to develop robust competencies and standardise practice.

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Background: Despite ongoing interventions, SARS-CoV-2 continues to cause significant global morbidity and mortality. Early diagnosis and intervention are crucial for effective clinical management. However, prognostic features based on transcriptional data have shown limited effectiveness, highlighting the need for more precise biomarkers to improve COVID-19 treatment outcomes. Methods: We retrospectively analyzed 149 clinical features from 189 COVID-19 patients, identifying prognostic features via univariate Cox regression. The cohort was split into training and validation sets, and 77 prognostic models were developed using seven machine learning algorithms. Among these, the least absolute shrinkage and selection operator (Lasso) method was employed to refine the selection of prognostic variables by ten-fold cross-validation strategy, which were then integrated with random survival forests (RSF) to build a robust COVID-19-related prognostic model (CRM). Model accuracy was evaluated across training, validation, and entire cohorts. The diagnostic relevance of interleukin-10 (IL-10) was confirmed in bulk transcriptional data and validated at the single-cell level, where we also examined changes in cellular communication between mononuclear cells with differing IL-10 expression and other immune cells. Results: Univariate Cox regression identified 43 prognostic features. Among the 77 machine learning models, the combination of Lasso and RSF produced the most robust CRM. This model consistently performed well across training, validation, and entire cohorts. IL-10 emerged as a key prognostic feature within the CRM, validated by single-cell transcriptional data. Transcriptome analysis confirmed the stable diagnostic value of IL-10, with mononuclear cells identified as the primary IL-10 source. Moreover, differential IL-10 expression in these cells was linked to altered cellular communication in the COVID-19 immune microenvironment. Conclusion: The CRM provides accurate prognostic predictions for COVID-19 patients. Additionally, the study underscores the importance of early IL-10 level testing upon hospital admission, which could inform therapeutic strategies.

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BACKGROUND: Successful deployment of clinical prediction models for clinical deterioration relates not only to predictive performance but to integration into the decision making process. Models may demonstrate good discrimination and calibration, but fail to match the needs of practising acute care clinicians who receive, interpret, and act upon model outputs or alerts. We sought to understand how prediction models for clinical deterioration, also known as early warning scores (EWS), influence the decision-making of clinicians who regularly use them and elicit their perspectives on model design to guide future deterioration model development and implementation. METHODS: Nurses and doctors who regularly receive or respond to EWS alerts in two digital metropolitan hospitals were interviewed for up to one hour between February 2022 and March 2023 using semi-structured formats. We grouped interview data into sub-themes and then into general themes using reflexive thematic analysis. Themes were then mapped to a model of clinical decision making using deductive framework mapping to develop a set of practical recommendations for future deterioration model development and deployment. RESULTS: Fifteen nurses (n = 8) and doctors (n = 7) were interviewed for a mean duration of 42 min. Participants emphasised the importance of using predictive tools for supporting rather than supplanting critical thinking, avoiding over-protocolising care, incorporating important contextual information and focusing on how clinicians generate, test, and select diagnostic hypotheses when managing deteriorating patients. These themes were incorporated into a conceptual model which informed recommendations that clinical deterioration prediction models demonstrate transparency and interactivity, generate outputs tailored to the tasks and responsibilities of end-users, avoid priming clinicians with potential diagnoses before patients were physically assessed, and support the process of deciding upon subsequent management. CONCLUSIONS: Prediction models for deteriorating inpatients may be more impactful if they are designed in accordance with the decision-making processes of acute care clinicians. Models should produce actionable outputs that assist with, rather than supplant, critical thinking.

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In the high-stakes realm of critical care, where daily decisions are crucial and clear communication is paramount, comprehending the rationale behind Artificial Intelligence (AI)-driven decisions appears essential. While AI has the potential to improve decision-making, its complexity can hinder comprehension and adherence to its recommendations. "Explainable AI" (XAI) aims to bridge this gap, enhancing confidence among patients and doctors. It also helps to meet regulatory transparency requirements, offers actionable insights, and promotes fairness and safety. Yet, defining explainability and standardising assessments are ongoing challenges and balancing performance and explainability can be needed, even if XAI is a growing field.

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Background Artificial intelligence (AI) and machine learning (ML) are currently used in the clinical field to improve the outcome predictions on disease diagnosis and prognosis. However, to date, few AI/ML applications have been reported in rare diseases, such as hemophilia. In this study, taking advantage of the ATHNdataset, an extensive repository of hemostasis and thrombosis data, we aimed to demonstrate the application of AI/ML approaches to build predictive models to identify persons with hemophilia (PwH) who are at risk of poor outcome and to inform providers in clinical decision-making towards helping patients prevent long-term complications. Materials and methods This project was carried out in two steps. First, the data were mined from ATHN 7, a subset study of the ATHNdataset, to determine markers that defined "poor outcome." Second, we applied multiple AI/ML approaches on the ATHNdataset to validate our findings and to develop predictive models to identify PwH at risk of poor outcomes. The classical regression-based predictive model was used as a reference to evaluate the performance of various AI/ML models. Results Our models included features similarly distributed to response variables of interest, resulting in a limited ability to distinguish poor outcomes. Low recall (<53%) resulted in no single model reliably predicting poor outcomes out of all actual positive cases. Our results suggest that, to build a more useful AI/ML model, we may need a larger dataset size along with additional features. Furthermore, our results showed that most of the AI/ML models outperformed the classical logistic regression model in both model accuracy and precision. Conclusions Our AI and ML model showed limited ability to predict poor outcomes in people with hemophilia.

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Objective: Bipolar Disorder (BD) is a severe mental illness associated with high rates of general medical comorbidity, reduced life expectancy, and premature mortality. Although BD has been associated with high medical hospitalization, the factors that contribute to this risk remain largely unexplored. We used baseline medical and psychiatric records to develop a supervised machine learning model to predict general medical admissions after discharge from psychiatric hospitalization. Methods: In this retrospective three-year cohort study of 71 patients diagnosed with BD (mean age=52.19 years, females=56.33%), lasso regression models combining medical and psychiatric records, as well as those using them separately, were fitted and their predictive power was estimated using a leave-one-out cross-validation procedure. Results: The proportion of medical admissions in patients with BD was higher compared with age- and sex-matched hospitalizations in the same region (25.4% vs. 8.48%). The lasso model fairly accurately predicted the outcome (area under the curve [AUC]=69.5%, 95%C.I.=55-84.1; sensitivity=61.1%, specificity=75.5%, balanced accuracy=68.3%). Notably, pre-existing cardiovascular, neurological, or osteomuscular diseases collectively accounted for more than 90% of the influence on the model. The accuracy of the model based on medical records was slightly inferior (AUC=68.7%, 95%C.I. = 54.6-82.9), while that of the model based on psychiatric records only was below chance (AUC=61.8%, 95%C.I.=46.2-77.4). Conclusion: Our findings support the need to monitor medical comorbidities during clinical decision-making to tailor and implement effective preventive measures in people with BD. Further research with larger sample sizes and prospective cohorts is warranted to replicate these findings and validate the predictive model.

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BACKGROUND: Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients. METHODS: We performed a retrospective cohort study of patients with PNs who underwent lung biopsy. We collected clinical data and used a commercially available AI radiomics-based CAD tool to calculate a Lung Cancer Prediction (LCP) score. We developed logistic regression models to evaluate a well-validated clinical risk prediction model (the Mayo Clinic model) with and without the LCP score (Mayo vs Mayo + LCP) using area under the curve (AUC), risk stratification table, and standardized net benefit analyses. RESULTS: Among the 134 patients undergoing PN biopsy, cancer prevalence was 61%. Addition of the radiomics-based LCP score to the Mayo model was associated with increased predictive accuracy (likelihood ratio test, P = .012). The AUCs for the Mayo and Mayo + LCP models were 0.58 (95% CI, 0.48-0.69) and 0.65 (95% CI, 0.56-0.75), respectively. At the 65% risk threshold, the Mayo + LCP model was associated with increased sensitivity (56% vs 38%; P = .019), similar false positive rate (33% vs 35%; P = .8), and increased standardized net benefit (18% vs -3.3%) compared to the Mayo model. CONCLUSIONS: Use of a commercially available AI radiomics-based CAD tool as a supplement to clinical information improved PN cancer risk prediction and may result in clinically meaningful changes in risk stratification.

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AIM: To describe undergraduate nursing students' clinical decision-making in post-procedural bleeding scenarios and explore the changes from the first to the final year of their program. BACKGROUND: Bleeding is a common complication following invasive procedures and its effective management requires nurses to develop strong clinical decision-making competencies. Although nursing education programs typically address bleeding complications, there is a gap in understanding how nursing students make clinical decisions regarding these scenarios. Additionally, little is known about how their approach to bleeding management evolves over the course of their education. DESIGN: Longitudinal mixed-methods study based on the Recognition-Primed Decision Model. METHODS: A total of 59 undergraduate students recorded their responses to two clinical decision-making vignettes depicting patients with signs of bleeding post-hip surgery (first year) and cardiac catheterization (final year). Their responses were analyzed using content analysis. The resulting categories capture the cues students noticed, the goals they aimed to achieve, the actions they proposed and their expectations for how the bleeding situations might unfold. Code frequencies showing the most variation between the first and final years were analyzed to explore changes in students' clinical decision-making. RESULTS: Nearly all students focused on two primary categories: 'Bleeding' and 'Instability and Shock.' Fewer students addressed six secondary categories: 'Stress and Concern,' 'Pain,' 'Lifestyle and Social History,' 'Wound Infection,' 'Arrhythmia,' and 'Generalities in Surgery.' Students often concentrated on actions to manage bleeding without further assessing its causes. Changes from the first to the final year included a more focused assessment of instability and shifts in preferred actions. CONCLUSIONS: This study reveals that nursing students often prioritize immediate actions to stop bleeding while sometimes overlooking the assessment of underlying causes or broader care goals. It suggests that concept-based learning and reflection on long-term outcomes could improve clinical decision-making in post-procedural care.

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Aim: Characterize the logistical challenges faced by healthcare professionals (HCPs), patients and caregivers during the chimeric antigen receptor T-cell (CAR T) treatment process for non-Hodgkin lymphoma patients.Materials & methods: HCPs in the US and UK experienced with CAR T administration participated in interviews and completed a web-based survey.Results: A total of 133 (80 US, 53 UK) HCPs participated. Two or more logistical challenges were identified by ≥60% of respondents across all stages of the CAR T process. Commonly reported challenges were lengthy waiting periods, administrative and payer-related barriers, limited healthcare capacity, caregiver support and (particularly in the US) patient out-of-pocket costs.Conclusion: The CAR T treatment process presents numerous challenges, highlighting an unmet need for more convenient therapies.

Chimeric antigen receptor T-cell (CAR T) therapy is a new treatment for patients with non-Hodgkin lymphoma that have not responded to other types of treatment. CAR T therapy uses a person's own immune cells (T cells), which are modified in a laboratory to attack cancer cells. While CAR T therapy has the potential to be effective, there are challenges associated with the treatment process. In this study, we surveyed 133 healthcare professionals (HCPs) in the United States and United Kingdom to understand their experiences with logistical challenges involved in navigating the CAR T process. More than 60% of participants identified two or more logistical challenges at every stage of the CAR T treatment process. The most commonly reported challenges included long waiting periods, limited room at hospitals, availability of caregivers to support patients and issues related to out-of-pocket costs, travel and lodging for patients who are treated at specialized centers. In the United States, challenges related to insurance coverage and out-of-pocket costs for patients were highlighted. More than half of HCPs reported that patients' cancer getting worse while waiting to receive CAR T was a reason why patients may not proceed to treatment. While operational improvements might address some challenges in the CAR T treatment process, these findings highlight the need for more convenient, readily available and easily administered therapies for patients with non-Hodgkin lymphoma.

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OBJECTIVES: To assess whether genotype-guided selection of oral antiplatelet drugs using a clinical decision support (CDS) algorithm reduces the rate of major adverse cardiovascular and cerebrovascular events (MACCEs) among Caribbean Hispanic patients, after 6 months. DESIGN: An open-label, multicentre, non-randomised clinical trial. SETTING: Eight secondary and tertiary care hospitals (public and private) in Puerto Rico. PARTICIPANTS: 300 Caribbean Hispanic patients on clopidogrel, both genders, underwent percutaneous coronary intervention (PCI) for acute coronary syndromes, stable ischaemic heart disease and documented extracardiac vascular diseases. INTERVENTIONS: Patients were separated into standard-of-care (SoC) and genotype-guided (pharmacogenetic (PGx)-CDS) groups (150 each) and stratified by risk scores. Risk scores were calculated based on a previously developed CDS risk prediction algorithm designed to make actionable treatment recommendations for each patient. Individual platelet function, genotypes, clinical and demographic data were included. Ticagrelor was recommended for patients with a high-risk score ≥2 in the PGx-CDS group only, the rest were kept or de-escalated to clopidogrel. The intervention took place within 3-5 days after PCI. Adherence medication score was also measured. PRIMARY AND SECONDARY OUTCOMES: The occurrence rate of MACCEs (primary) and bleeding episodes (secondary). Statistical associations between patient time free of events and predictor variables (ie, treatment groups, risk scores) were tested using Kaplan-Meier survival analyses and Cox proportional-hazards regression models. RESULTS: The genotype-guided group had a clinically lower but not significantly different risk of MACCEs compared with the SoC group (8.7% vs 10.7%, p=0.56; HR=0.56). Among high-risk score patients, genotype-driven guidance of antiplatelet therapy showed superiority over SoC in reducing MACCE incidence 6 months postcoronary stenting (adjusted HR=0.104; p< 0.0001). CONCLUSIONS: The potential benefit of implementing our PGx-CDS algorithm to significantly reduce the incidence rate of MACCEs in post-PCI Caribbean Hispanic patients on clopidogrel was observed exclusively among high-risk patients, with apparently no evident effect in other patient groups. TRIAL REGISTRATION NUMBER: NCT03419325.

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OBJECTIVES: To identify the barriers and facilitators to the successful implementation of imaging-based diagnostic artificial intelligence (AI)-assisted decision-making software in China, using the updated Consolidated Framework for Implementation Research (CFIR) as a theoretical basis to develop strategies that promote effective implementation. DESIGN: This qualitative study involved semistructured interviews with key stakeholders from both clinical settings and industry. Interview guide development, coding, analysis and reporting of findings were thoroughly informed by the updated CFIR. SETTING: Four healthcare institutions in Beijing and Shanghai and two vendors of AI-assisted decision-making software for lung nodules detection and diabetic retinopathy screening were selected based on purposive sampling. PARTICIPANTS: A total of 23 healthcare practitioners, 6 hospital informatics specialists, 4 hospital administrators and 7 vendors of the selected AI-assisted decision-making software were included in the study. RESULTS: Within the 5 CFIR domains, 10 constructs were identified as barriers, 8 as facilitators and 3 as both barriers and facilitators. Major barriers included unsatisfactory clinical performance (Innovation); lack of collaborative network between primary and tertiary hospitals, lack of information security measures and certification (outer setting); suboptimal data quality, misalignment between software functions and goals of healthcare institutions (inner setting); unmet clinical needs (individuals). Key facilitators were strong empirical evidence of effectiveness, improved clinical efficiency (innovation); national guidelines related to AI, deployment of AI software in peer hospitals (outer setting); integration of AI software into existing hospital systems (inner setting) and involvement of clinicians (implementation process). CONCLUSIONS: The study findings contributed to the ongoing exploration of AI integration in healthcare from the perspective of China, emphasising the need for a comprehensive approach considering both innovation-specific factors and the broader organisational and contextual dynamics. As China and other developing countries continue to advance in adopting AI technologies, the derived insights could further inform healthcare practitioners, industry stakeholders and policy-makers, guiding policies and practices that promote the successful implementation of imaging-based diagnostic AI-assisted decision-making software in healthcare for optimal patient care.

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OBJECTIVE: To undertake a review of systematic reviews on the clinical outcomes of robotic-assisted surgery across a mix of intracavity procedures, using evidence mapping to inform the decision makers on the best utilisation of robotic-assisted surgery. ELIGIBILITY CRITERIA: We included systematic reviews with randomised controlled trials and non-randomised controlled trials describing any clinical outcomes. DATA SOURCES: Ovid Medline, Embase and Cochrane Library from 2017 to 2023. DATA EXTRACTION AND SYNTHESIS: We first presented the number of systematic reviews distributed in different specialties. We then mapped the body of evidence across selected procedures and synthesised major findings of clinical outcomes. We used a measurement tool to assess systematic reviews to evaluate the quality of systematic reviews. The overlap of primary studies was managed by the corrected covered area method. RESULTS: Our search identified 165 systematic reviews published addressing clinical evidence of robotic-assisted surgery. We found that for all outcomes except operative time, the evidence was largely positive or neutral for robotic-assisted surgery versus both open and laparoscopic alternatives. Evidence was more positive versus open. The evidence for the operative time was mostly negative. We found that most systematic reviews were of low quality due to a failure to deal with the inherent bias in observational evidence. CONCLUSION: Robotic surgery has a strong clinical effectiveness evidence base to support the expanded use of robotic-assisted surgery in six common intracavity procedures, which may provide an opportunity to increase the proportion of minimally invasive surgeries. Given the high incremental cost of robotic-assisted surgery and longer operative time, future economic studies are required to determine the optimal use of robotic-assisted surgery capacity.

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Several barriers exist in Alberta, Canada to providing accurate and accessible diagnoses for patients presenting with acute knee injuries and chronic knee problems. In efforts to improve quality of care for these patients, an evidence-informed clinical decision-making tool was developed. Forty-five expert panelists were purposively chosen to represent stakeholder groups, various expertise, and each of Alberta Health Services' 5 geographical health regions. A systematic rapid review and modified Delphi approach were executed with the intention of developing standardized clinical decision-making processes for acute knee injuries, atraumatic/overuse conditions, knee arthritis, and degenerative meniscus. Standardized criteria for screening, history-taking, physical examination, diagnostic imaging, timelines, and treatment were developed. This tool standardizes and optimizes assessment and diagnosis of acute knee injuries and chronic knee problems in Alberta. This project was a highly collaborative, province-wide effort led by Alberta Health Services' Bone and Joint Health Strategic Clinical Network (BJH SCN) and the Alberta Bone and Joint Health Institute (ABJHI).

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