Provision of ankle foot orthoses for children with cerebral palsy in Norway.

Introduction: Practice of ankle-foot orthoses (AFO) provision for ambulatory children with cerebral palsy is underreported and the literature is not consistent on choice of AFO-design. This study describes clinical practice of AFO provision for children with cerebral palsy and evaluates how clinical practice aligns with existing recommendations. Methods: An online, cross-sectional survey was conducted, inviting all Norwegian orthotists working with children with cerebral palsy. Orthotic practice was investigated using a self-reported survey design. Results: From all eligible orthotists, 54% responded, revealing that AFO provision involves patients, physicians, and physiotherapists at different stages. Patient preference directly influenced the ultimate AFO-design. Shank vertical angle was evaluated by 79%. For children with crouch gait and those with short gastrocnemius, a majority preferred a combination of rigid and articulated/flexible AFO-designs. Instrumented gait analysis was conducted by 51% at AFO delivery stage. Conclusions: The findings show that AFO provision in Norway is collaborative, involving clinical team members and consideration of patient preferences. A discrepancy between clinical practice and existing recommendations for children with crouch gait and those with short gastrocnemius is observed.

Comparative Study to Evaluate the Accuracy of Differential Diagnosis Lists Generated by Gemini Advanced, Gemini, and Bard for a Case Report Series Analysis: Cross-Sectional Study.

BACKGROUND: Generative artificial intelligence (GAI) systems by Google have recently been updated from Bard to Gemini and Gemini Advanced as of December 2023. Gemini is a basic, free-to-use model after a user's login, while Gemini Advanced operates on a more advanced model requiring a fee-based subscription. These systems have the potential to enhance medical diagnostics. However, the impact of these updates on comprehensive diagnostic accuracy remains unknown. OBJECTIVE: This study aimed to compare the accuracy of the differential diagnosis lists generated by Gemini Advanced, Gemini, and Bard across comprehensive medical fields using case report series. METHODS: We identified a case report series with relevant final diagnoses published in the American Journal Case Reports from January 2022 to March 2023. After excluding nondiagnostic cases and patients aged 10 years and younger, we included the remaining case reports. After refining the case parts as case descriptions, we input the same case descriptions into Gemini Advanced, Gemini, and Bard to generate the top 10 differential diagnosis lists. In total, 2 expert physicians independently evaluated whether the final diagnosis was included in the lists and its ranking. Any discrepancies were resolved by another expert physician. Bonferroni correction was applied to adjust the P values for the number of comparisons among 3 GAI systems, setting the corrected significance level at P value <.02. RESULTS: In total, 392 case reports were included. The inclusion rates of the final diagnosis within the top 10 differential diagnosis lists were 73% (286/392) for Gemini Advanced, 76.5% (300/392) for Gemini, and 68.6% (269/392) for Bard. The top diagnoses matched the final diagnoses in 31.6% (124/392) for Gemini Advanced, 42.6% (167/392) for Gemini, and 31.4% (123/392) for Bard. Gemini demonstrated higher diagnostic accuracy than Bard both within the top 10 differential diagnosis lists (P=.02) and as the top diagnosis (P=.001). In addition, Gemini Advanced achieved significantly lower accuracy than Gemini in identifying the most probable diagnosis (P=.002). CONCLUSIONS: The results of this study suggest that Gemini outperformed Bard in diagnostic accuracy following the model update. However, Gemini Advanced requires further refinement to optimize its performance for future artificial intelligence-enhanced diagnostics. These findings should be interpreted cautiously and considered primarily for research purposes, as these GAI systems have not been adjusted for medical diagnostics nor approved for clinical use.

Predicting the severity of mycoplasma pneumoniae pneumonia in pediatric and adult patients: a multicenter study.

The purpose of this study is to develop a nomogram model for early prediction of the severe mycoplasma pneumoniae pneumonia (SMPP) in Pediatric and Adult Patients. A retrospective analysis was conducted on patients with MPP, classifying them into SMPP and non-severe MPP (NSMPP) groups. A total of 550 patients (NSMPP 374 and SMPP 176) were enrolled in the study and allocated to training, validation cohorts. 278 patients (NSMPP 224 and SMPP 54) were retrospectively collected from two institutions and allocated to testing cohort. The risk factors for SMPP were identified using univariate analysis. For radiomic feature selection, Spearman's correlation and the least absolute shrinkage and selection operator (LASSO) were utilized. Logistic regression was used to build different models, including clinical, imaging, radiomics, and integrated models (combining clinical, imaging, and radiomics features selected). The model's discrimination was evaluated using a receiver operating characteristic curve, its calibration with a calibration curve, and the results were visualized using the Hosmer-Lemeshow goodness-of-fit test. Thirteen clinical features and fourteen imaging features were selected for constructing the clinical and imaging models. Simultaneously, a set of twenty-five radiomics features were utilized to build the radiomics model. The integrated model demonstrated good calibration and discrimination in the training cohorts (AUC, 0.922; 95% CI: 0.900, 0.942), validation cohorts (AUC, 0.879; 95% CI: 0.806, 0.920), and testing cohorts (AUC, 0.877; 95% CI: 0.836, 0.916). The discriminatory and predictive efficacy of the clinical model in testing cohorts increased further after clinical and radiological features were incorporated (AUC, 0.849 vs. 0.922, P = 0.002). The model demonstrated exemplary predictive efficacy for SMPP by leveraging a comprehensive set of inputs, encompassing clinical data, quantitative and qualitative radiological features, along with radiomics features. The integration of these three aspects in the predictive model further enhanced the performance of the clinical model, indicating the potential for extensive clinical applications.

eConsent administered by Community Health Workers in a study using the Trials within Cohorts (TwiCs) design-Experiences from the Community-Based chronic Care Lesotho (ComBaCaL) project.

Improving access to essential health services requires the development of innovative health service delivery models and their scientific assessment in often large-scale pragmatic trials. In many low- and middle-income countries, lay Community Health Workers (CHWs) play an important role in delivering essential health services. As trusted members of their communities with basic medical training, they may also contribute to health data collection. Digital clinical decision support applications may facilitate the involvement of CHWs in service delivery and data collection. Electronic consent (eConsent) can streamline the consent process that is required if the collected data is used for the scientific purposes. Here, we describe the experiences of using eConsent in the Community-Based chronic Care Lesotho (ComBaCaL) cohort study and multiple nested pragmatic cluster-randomized trials assessing CHW-led care delivery models for type 2 diabetes and arterial hypertension using the Trials within Cohorts (TwiCs) design. More than a hundred CHWs, acting both as service providers and data collectors in remote villages of Lesotho utilize an eConsent application that is linked to a tailored clinical decision support and data collection application. The eConsent application presents simplified consent information and generates personalized consent forms that are signed electronically on a tablet and then uploaded to the database of the clinical decision support application. This significantly streamlines the consent process and allows for quality consent documentation through timely central monitoring, facilitating the CHW-led management of a large-scale population-based cohort in a remote low-resource area with continuous enrollment-currently at more than 16,000 participants.

Co-creation and evaluation of an algorithm for the development of a mobile application for wound care among new graduate nurses: A mixed methods study.

Chronic wounds are a growing concern due to aging populations, sedentary lifestyles and increasing rates of obesity and chronic diseases. The impact of such wounds is felt worldwide, posing a considerable clinical, environmental and socioeconomic challenge and impacting the quality of life. The increasing complexity of care requires a holistic approach, along with extensive knowledge and skills. The challenge experienced by health-care professionals is particularly significant for newly graduate nurses, who face a gap between theory and practice. Digital tools, such as mobile applications, can support wound care by facilitating more precise assessments, early treatment, complication prevention and better outcomes. They also aid in clinical decision-making and improve healthcare delivery in remote areas. Several mobile applications have emerged to enhance wound care. However, there are no applications dedicated to newly graduate nurses. The aim of this study was to co-create and evaluate an algorithm for the development of a wound care mobile application supporting clinical decisions for new graduate nurses. The development of this mobile application is envisioned to improve knowledge application and facilitate evidence-based practice. This study is part of a multiphase project that adopted a pragmatic epistemological approach, using the 'Knowledge-to-Action' conceptual model and Duchscher's Stages of Transition Theory. Following a scoping review, an expert consensus, and stakeholder meetings, this study was pursued through a sequential exploratory mixed methods design carried out in two phases. In the initial phase, 21 participants engaged in semi-structured focus groups to explore their needs regarding clinical decision support in wound care, explore their perceptions of the future mobile application's content and identify and categorize essential components. Through descriptive analysis, five overarching themes emerged, serving as guiding principles for conceptual data model development and refinement. These findings confirmed the significance of integrating a comprehensive glossary complemented by photos, ensuring compatibility between the mobile application and existing documentation systems, and providing quick access to information to avoid burdening work routines. Subsequently, the algorithm was created from the qualitative data collected. The second phase involved presenting an online SurveyMonkey® questionnaire to 34 participants who were not part of the initial phase to quantitatively measure the usability of this algorithm among future users. This phase revealed very positive feedback regarding the usability [score of 6.33 (±0.19) on a scale of 1-7], which reinforces its quality. The technology maturation process can now continue with the development of a prototype and subsequent validation in a laboratory setting.

Prospectively investigating the impact of AI onshared decision-making in post kidney transplant care (PRIMA-AI): protocol for a longitudinal qualitative study among patients, their support persons and treating physicians at a tertiary care centre.

INTRODUCTION: As healthcare is shifting from a paternalistic to a patient-centred approach, medical decision making becomes more collaborative involving patients, their support persons (SPs) and physicians. Implementing shared decision-making (SDM) into clinical practice can be challenging and becomes even more complex with the introduction of artificial intelligence (AI) as a potential actant in the communicative network. Although there is more empirical research on patients' and physicians' perceptions of AI, little is known about the impact of AI on SDM. This study will help to fill this gap. To the best of our knowledge, this is the first systematic empirical investigation to prospectively assess the views of patients, their SPs and physicians on how AI affects SDM in physician-patient communication after kidney transplantation. Using a transdisciplinary approach, this study will explore the role and impact of an AI-decision support system (DSS) designed to assist with medical decision making in the clinical encounter. METHODS AND ANALYSIS: This is a plan to roll out a 2 year, longitudinal qualitative interview study in a German kidney transplant centre. Semi-structured interviews with patients, SPs and physicians will be conducted at baseline and in 3-, 6-, 12- and 24-month follow-up. A total of 50 patient-SP dyads and their treating physicians will be recruited at baseline. Assuming a dropout rate of 20% per year, it is anticipated that 30 patient-SP dyads will be included in the last follow-up with the aim of achieving data saturation. Interviews will be audio-recorded and transcribed verbatim. Transcripts will be analysed using framework analysis. Participants will be asked to report on their (a) communication experiences and preferences, (b) views on the influence of the AI-based DSS on the normative foundations of the use of AI in medical decision-making, focusing on agency along with trustworthiness, transparency and responsibility and (c) perceptions of the use of the AI-based DSS, as well as barriers and facilitators to its implementation into routine care. ETHICS AND DISSEMINATION: Approval has been granted by the local ethics committee of Charité-Universitätsmedizin Berlin (EA1/177/23 on 08 August 2023). This research will be conducted in accordance with the principles of the Declaration of Helsinki (1996). The study findings will be used to develop communication guidance for physicians on how to introduce and sustainably implement AI-assisted SDM. The study results will also be used to develop lay language patient information on AI-assisted SDM. A broad dissemination strategy will help communicate the results of this research to a variety of target groups, including scientific and non-scientific audiences, to allow for a more informed discourse among different actors from policy, science and society on the role and impact of AI in physician-patient communication.

Establishing Triage Competencies and Verification Processes: A Survey Study.

INTRODUCTION: Although the ED triage function is a critical means of ensuring patient safety, core competencies for ED triage are not well defined in the literature. The purpose of the study was to identify and validate emergency triage nursing competencies and to develop a competency verification process. METHODS: A sample of 1181 emergency nurses evenly divided between roles with oversight of triage training and competency assessment (manager-level and staff nurses performing triage) completed an online survey evaluating competency elements that comprised the following in terms of frequency and importance, training modalities, and evaluation methods: expert assessment, clinical judgment, management of medical resources, communication, and timely decisions. RESULTS: Both manager-level and triage nurses agreed on the importance of the identified competencies. Gaps in training and evaluation were reported by both staff nurses and manager-level nurses. Triage nurses reported less training offered and less competency evaluation compared with manager-level nurses. Triage nurses reported performing all competencies more frequently and at higher level of competency than manager-level nurses reporting on triage nurse performance. DISCUSSION: This study provides both a standard set of triage competencies and a method by which to evaluate them. Managers and educators might consider this standard to establish initial triage role competency and periodic competency assessment per institutional guidelines. The gap in perceived education and evaluation suggests that standard education and evaluation processes be adopted across emergency departments.

Hybrid FDG-PET/MRI for Diagnosis and Clinical Management of Patients with Suspected Perihilar Cholangiocarcinoma: A Feasibility Pilot Study.

Purpose: Recently introduced hybrid 2-[18 F]-fluoro-2-deoxy-D-glucose (18 F-FDG) Positron Emission Tomography (PET) combined with Magnetic Resonance Imaging (MRI) may aid in proper diagnosis and staging of perihilar cholangiocarcinoma (pCCA). The aim of this study is to assess the effect of 18 F-FDG PET/MRI on diagnosis and clinical decision making in the pre-operative work up of pCCA. Methods: In this single-centre pilot study patients with presumed resectable pCCA underwent state-of-the-art 18 F-FDG hybrid PET/MRI using digital silicone photomultiplier detectors integrated within a 3-Tesla bore. Data were collected on several baseline and imaging characteristics. The primary outcome measure was the added diagnostic information and the effect on clinical decision making. Secondary aim was to correlate quantitative PET signal intensity to patient- and tumour characteristics. High and low SUVmax subgroups related to the mean value were made. Significance of lesion- and patient characteristics with the high and low SUVmax subgroups, as well as TLR and TBR, was evaluated with Fisher's exact test or Mann-Whitney-U test. Results: In total 14 patients were included (mean age 62.4 years, 64% male). Final diagnosis was pCCA in 10 patients (71.4%), follicular lymphoma in one patient (7.1%) and benign disease in the remaining three patients. FDG-PET/MRI added valuable diagnostic information in six (43%) patients and affected clinical decision making in two of these patients (14%) by increasing confidence for malignancy which lead to the decision for surgery on short term. High SUVmax values were seen in half of cases with pCCA and half of cases with non-cancerous lesions. In addition, high SUVmax values were directly associated with primary sclerosing cholangitis when present (p = 0.03). Conclusion: Simultaneous 18 F-FDG-PET/MRI added diagnostic information in six of fourteen patients and influenced clinical decision making in two patients (14%) with presumed resectable pCCA.

Robotics and AI into healthcare from the perspective of European regulation: who is responsible for medical malpractice?

The integration of robotics and artificial intelligence into medical practice is radically revolutionising patient care. This fusion of advanced technologies with healthcare offers a number of significant benefits, including more precise diagnoses, personalised treatments and improved health data management. However, it is critical to address very carefully the medico-legal challenges associated with this progress. The responsibilities between the different players concerned in medical liability cases are not yet clearly defined, especially when artificial intelligence is involved in the decision-making process. Complexity increases when technology intervenes between a person's action and the result, making it difficult for the patient to prove harm or negligence. In addition, there is the risk of an unfair distribution of blame between physicians and healthcare institutions. The analysis of European legislation highlights the critical issues related to the attribution of legal personality to autonomous robots and the recognition of strict liability for medical doctors and healthcare institutions. Although European legislation has helped to standardise the rules on this issue, some questions remain unresolved. We argue that specific laws are needed to address the issue of medical liability in cases where robotics and artificial intelligence are used in healthcare.

Clinical decision making under uncertainty: a bootstrapped counterfactual inference approach.

BACKGROUND: Learning policies for decision-making, such as recommending treatments in clinical settings, is important for enhancing clinical decision-support systems. However, the challenge lies in accurately evaluating and optimizing these policies for maximum efficacy. This paper addresses this gap by focusing on two key aspects of policy learning: evaluation and optimization. METHOD: We develop counterfactual policy learning algorithms for practical clinical applications to suggest viable treatment for patients. We first design a bootstrap method for counterfactual assessment and enhancement of policies, aiming to diminish uncertainty in clinical decisions. Building on this, we introduce an innovative adversarial learning algorithm, inspired by bootstrap principles, to further advance policy optimization. RESULTS: The efficacy of our algorithms was validated using both semi-synthetic and real-world clinical datasets. Our method outperforms baseline algorithms, reducing the variance in policy evaluation by 30% and the error rate by 25%. In policy optimization, it enhances the reward by 1% to 3%, highlighting the practical value of our approach in clinical decision-making. CONCLUSION: This study demonstrates the effectiveness of combining bootstrap and adversarial learning techniques in policy learning for clinical decision support. It not only enhances the accuracy and reliability of policy evaluation and optimization but also paves avenues for leveraging advanced counterfactual machine learning in healthcare.